what is the difference between hypothesis and research questions

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Research Questions vs Hypothesis: What’s The Difference?

by  Antony W

August 1, 2024

You’ll need to come up with a research question or a hypothesis to guide your next research project. But what is a hypothesis in the first place? What is the perfect definition for a research question? And, what’s the difference between the two?

In this guide to research questions vs hypothesis, we’ll look at the definition of each component and the difference between the two.

We’ll also look at when a research question and a hypothesis may be useful and provide you with some tips that you can use to come up with hypothesis and research questions that will suit your research topic . 

Let’s get to it.

What’s a Research Question?

We define a research question as the exact question you want to answer on a given topic or research project. Good research questions should be clear and easy to understand, allow for the collection of necessary data, and be specific and relevant to your field of study.

Research questions are part of heuristic research methods, where researchers use personal experiences and observations to understand a research subject. By using such approaches to explore the question, you should be able to provide an analytical justification of why and how you should respond to the question. 

While it’s common for researchers to focus on one question at a time, more complex topics may require two or more questions to cover in-depth.

When is a Research Question Useful? 

A research question may be useful when and if: 

• There isn’t enough previous research on the topic
• You want to report a wider range out of outcome when doing your research project
• You want to conduct a more open ended inquiries 

Perhaps the biggest drawback with research questions is that they tend to researchers in a position to “fish expectations” or excessively manipulate their findings.

Again, research questions sometimes tend to be less specific, and the reason is that there often no sufficient previous research on the questions.

What’s a Hypothesis? 

A hypothesis is a statement you can approve or disapprove. You develop a hypothesis from a research question by changing the question into a statement.

Primarily applied in deductive research, it involves the use of scientific, mathematical, and sociological findings to agree to or write off an assumption.

Researchers use the null approach for statements they can disapprove. They take a hypothesis and add a “not” to it to make it a working null hypothesis.

A null hypothesis is quite common in scientific methods. In this case, you have to formulate a hypothesis, and then conduct an investigation to disapprove the statement.

If you can disapprove the statement, you develop another hypothesis and then repeat the process until you can’t disapprove the statement.

In other words, if a hypothesis is true, then it must have been repeatedly tested and verified.

The consensus among researchers is that, like research questions, a hypothesis should not only be clear and easy to understand but also have a definite focus, answerable, and relevant to your field of study. 

When is a Hypothesis Useful?

A hypothesis may be useful when or if:

• There’s enough previous research on the topic
• You want to test a specific model or a particular theory
• You anticipate a likely outcome in advance 

The drawback to hypothesis as a scientific method is that it can hinder flexibility, or possibly blind a researcher not to see unanticipated results.

Research Question vs Hypothesis: Which One Should Come First 

Researchers use scientific methods to hone on different theories. So if the purpose of the research project were to analyze a concept, a scientific method would be necessary.

Such a case requires coming up with a research question first, followed by a scientific method.

Since a hypothesis is part of a research method, it will come after the research question.

Research Question vs Hypothesis: What’s the Difference? 

The following are the differences between a research question and a hypothesis.

We look at the differences in purpose and structure, writing, as well as conclusion. 

Research Questions vs Hypothesis: Some Useful Advice 

As much as there are differences between hypothesis and research questions, you have to state either one in the introduction and then repeat the same in the conclusion of your research paper.

Whichever element you opt to use, you should clearly demonstrate that you understand your topic, have achieved the goal of your research project, and not swayed a bit in your research process.

If it helps, start and conclude every chapter of your research project by providing additional information on how you’ve or will address the hypothesis or research question.

You should also include the aims and objectives of coming up with the research question or formulating the hypothesis. Doing so will go a long way to demonstrate that you have a strong focus on the research issue at hand. 

Research Questions vs Hypothesis: Conclusion 

If you need help with coming up with research questions, formulating a hypothesis, and completing your research paper writing , feel free to talk to us. 

About the author 

Antony W is a professional writer and coach at Help for Assessment. He spends countless hours every day researching and writing great content filled with expert advice on how to write engaging essays, research papers, and assignments.

Hypothesis vs. Research Question

What's the difference.

Hypothesis and research question are both essential components of the scientific research process, but they serve different purposes. A research question is a broad inquiry that aims to explore a specific topic or problem. It is often open-ended and seeks to gather information and generate knowledge. On the other hand, a hypothesis is a specific statement that predicts the relationship between variables or the outcome of a study. It is based on existing knowledge or theories and is testable through empirical research. While a research question guides the overall direction of a study, a hypothesis provides a more focused and specific prediction that can be tested and validated.

Further Detail

Introduction.

When conducting research, whether in the scientific or social sciences realm, it is essential to have a clear direction and purpose. Two key components that help guide the research process are the hypothesis and research question. While both serve as important tools in formulating and conducting research, they differ in their nature, purpose, and structure. In this article, we will explore the attributes of hypothesis and research question, highlighting their similarities and differences.

A hypothesis is a statement that proposes a relationship between variables or predicts an outcome based on existing knowledge or theories. It is an educated guess or assumption that guides the research process and helps researchers test their theories. Hypotheses are typically formulated in a declarative form, stating the expected relationship or outcome.

One of the key attributes of a hypothesis is that it is testable. This means that it can be empirically examined and either supported or rejected through data analysis. A hypothesis should be specific and precise, clearly defining the variables involved and the expected relationship between them. It should also be falsifiable, meaning that it can be proven wrong if the data does not support it.

Hypotheses are often used in quantitative research, where researchers aim to measure and analyze data to draw conclusions. They provide a framework for designing experiments or surveys and guide the collection and analysis of data. Hypotheses can be either directional, predicting the direction of the relationship between variables, or non-directional, suggesting that a relationship exists without specifying its direction.

For example, in a study examining the effects of exercise on mental health, a directional hypothesis could be: "Increased physical exercise will lead to a decrease in symptoms of anxiety." On the other hand, a non-directional hypothesis could be: "There is a relationship between physical exercise and symptoms of anxiety."

Research Question

A research question, on the other hand, is an inquiry that guides the research process and seeks to explore a specific topic or phenomenon. It is a broad, open-ended question that does not propose a specific relationship or outcome. Research questions are often used in qualitative research, where the focus is on understanding and interpreting social phenomena.

Unlike hypotheses, research questions do not require a definitive answer or prediction. Instead, they aim to explore and generate knowledge about a particular subject. Research questions are often formulated using words like "how," "what," "why," or "in what way," indicating the intention to investigate and understand a phenomenon.

Research questions are typically more flexible and adaptable than hypotheses. They allow researchers to explore multiple perspectives and gather rich, in-depth data. Research questions can evolve and change throughout the research process as new insights emerge. They provide a framework for qualitative data collection methods such as interviews, observations, or focus groups.

For example, in a study exploring the experiences of individuals living with chronic pain, a research question could be: "How do individuals with chronic pain cope with their condition on a daily basis?" This question allows for a comprehensive exploration of coping mechanisms, experiences, and challenges faced by individuals living with chronic pain.

Similarities and Differences

While hypotheses and research questions serve distinct purposes, they also share some similarities. Both hypotheses and research questions are essential components of the research process, providing a clear focus and direction. They both aim to guide the research and generate new knowledge. Additionally, both hypotheses and research questions should be grounded in existing literature and theories, ensuring that the research contributes to the existing body of knowledge.

However, the main difference between hypotheses and research questions lies in their structure and purpose. Hypotheses are more specific and focused, proposing a relationship or outcome that can be tested and analyzed quantitatively. They are often used in experimental or survey-based research designs. On the other hand, research questions are broader and open-ended, allowing for qualitative exploration and interpretation of social phenomena. They are commonly used in qualitative research designs.

Another difference is that hypotheses are often derived from research questions. Researchers start with a research question and then formulate a hypothesis based on existing knowledge and theories. The hypothesis provides a more specific and testable statement that can be examined through data analysis. In this way, hypotheses can be seen as a more refined version of research questions, focusing on a specific aspect of the research topic.

Furthermore, hypotheses are more commonly used in deductive research, where researchers start with a theory and test it through empirical evidence. Research questions, on the other hand, are often used in inductive research, where researchers gather data and generate theories or explanations based on the observed patterns or themes.

It is important to note that both hypotheses and research questions can coexist in a research study. In some cases, a study may have both a research question and a hypothesis. The research question provides a broader context and exploration of the topic, while the hypothesis allows for a more focused investigation of a specific relationship or outcome.

In conclusion, hypotheses and research questions are vital components of the research process, providing guidance and direction. While they share similarities in terms of their contribution to knowledge generation and grounding in existing literature, they differ in their structure, purpose, and application. Hypotheses are specific, testable statements that propose a relationship or outcome, often used in quantitative research. Research questions, on the other hand, are broad, open-ended inquiries that guide qualitative research and aim to explore and understand social phenomena. Both hypotheses and research questions play crucial roles in advancing research and expanding our understanding of the world.

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Home » Education » Difference Between Hypothesis and Research Question

Difference Between Hypothesis and Research Question

Main difference – hypothesis vs research question.

Research question and hypothesis are the foundations of a research study. Formulating the research question or developing the hypothesis can help you to decide on the approach of the research. A research question is the question the research study sets out to answer. Hypothesis is the statement the research study sets out to prove or disprove. The main difference between hypothesis and research question is that hypothesis is predictive in nature whereas research question is inquisitive in nature.

In this article, we’ll discuss,

1. What is a Hypothesis? – Meaning, Features, Characteristics, and Usage

2. What is a Research Question? – Meaning, Features, Characteristics, and Usage

What is a Hypothesis

A hypothesis is a prediction about the relationship between two or more variables. It can be described as an educated guess about what happens in an experiment. Researchers usually tend to use hypotheses when significant knowledge is already available on the subject. The hypothesis is based on this existing knowledge. After the hypothesis is developed, the researcher can develop data, analyze and use them to support or negate the hypothesis.

Not all studies have hypotheses. They are usually used in experimental quantitative research studies. They are useful in testing a specific theory or model.  A complete hypothesis always includes the variables, population and the predicted relationship between the variables. The main disadvantage of hypotheses is that their tendency to blind a researcher to unexpected results. 

What is a Research Question

A research question is the question a research study sets to answer. However, a research study can have more than one research question. The research methodologies , tools used to collect data, etc. all depend on the research question.

Research questions are often used in qualitative research, which seek to answer open-ended questions . But they can also be used in quantitative studies. Research questions can be used instead of hypotheses when there is little previous research on the subject. Research questions allow the researcher to conduct more open-ended queries, and a wide range of results can be reported.

A properly constructed research question should always be clear and concise. It should include the variables, population and the topic being studied.

Hypothesis is a tentative prediction about the relationship between two or more variables.

Research Question is the question a research study sets to answer.

Hypothesis is predictive in nature.

Research Question is inquisitive in nature.

Existing Research

Hypothesis can be used if there is significant knowledge or previous research on this subject.

Research Question can be used if there is little previous research on the subject.

Quantitative vs Qualitative

Hypothesis is mainly used in experimental quantitative studies.

Research Question can be used in both quantitative and qualitative studies.

Hypothesis doesn’t allow a wide range of outcomes.

Research Question allows a wide range of outcomes.

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“Research: Mediterranean Center of Medical Sciences” by  McmScience Mediterranean Center of Medical Sciences   (CC BY 2.0)  via Flickr

About the Author: Hasa

Hasanthi is a seasoned content writer and editor with over 8 years of experience. Armed with a BA degree in English and a knack for digital marketing, she explores her passions for literature, history, culture, and food through her engaging and informative writing.

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The Difference Between Research Questions & Hypothesis

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Research questions and hypothesis are tools used in similar ways for different research methods. Both hypothesis and research questions are written before research begins and are used to help guide the research. Hypothesis are used in deductive research, where researchers use logic and scientific findings to either prove or disprove assumptions. Heuristic research is based on experience, where researchers use observations to learn about the research subject.

Definitions

A hypothesis is defined as an educated guess, while a research question is simply the researcher wondering about the world. Hypothesis are part of the scientific research method. They are employed in research in science, sociology, mathematics and more. Research questions are part of heuristic research methods, and are also used in many fields including literature, and sociology.

As its name suggests, research questions are always written as questions. Hypothesis are written as statements preceded with the words "I predict." For example, a research question would ask, "What is the effect of heat on the effectiveness of bleach?" A hypothesis would state, "I predict heat will diminish the effectiveness of bleach."

Before Writing

Before writing a hypothesis, the researcher must determine what others have discovered about this subject. On the other hand, a research question requires less preparation, but focus and structure is critical.

For example, a researcher using a hypothesis would look up studies about bleach, information on the chemical properties of the chemical when heated and data about its effectiveness before writing the hypothesis. When using a research question, the researcher would think about how to phrase the question to ensure its scope is not too broad, too narrow or impossible to answer.

Writing Conclusions

When writing the conclusion for research conducted using a hypothesis, the researcher will write whether the hypothesis was correct or incorrect, followed by an explanation of the results of the research. The researcher using only a research question will write the answer to the question, followed by the findings of the research.

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Research Questions & Hypotheses

Generally, in quantitative studies, reviewers expect hypotheses rather than research questions. However, both research questions and hypotheses serve different purposes and can be beneficial when used together.

Research Questions

Clarify the research’s aim (farrugia et al., 2010).

• Research often begins with an interest in a topic, but a deep understanding of the subject is crucial to formulate an appropriate research question.
• Descriptive: “What factors most influence the academic achievement of senior high school students?”
• Comparative: “What is the performance difference between teaching methods A and B?”
• Relationship-based: “What is the relationship between self-efficacy and academic achievement?”
• Increasing knowledge about a subject can be achieved through systematic literature reviews, in-depth interviews with patients (and proxies), focus groups, and consultations with field experts.
• Some funding bodies, like the Canadian Institute for Health Research, recommend conducting a systematic review or a pilot study before seeking grants for full trials.
• The presence of multiple research questions in a study can complicate the design, statistical analysis, and feasibility.
• It’s advisable to focus on a single primary research question for the study.
• The primary question, clearly stated at the end of a grant proposal’s introduction, usually specifies the study population, intervention, and other relevant factors.
• The FINER criteria underscore aspects that can enhance the chances of a successful research project, including specifying the population of interest, aligning with scientific and public interest, clinical relevance, and contribution to the field, while complying with ethical and national research standards.

• The P ICOT approach is crucial in developing the study’s framework and protocol, influencing inclusion and exclusion criteria and identifying patient groups for inclusion.

• Defining the specific population, intervention, comparator, and outcome helps in selecting the right outcome measurement tool.
• The more precise the population definition and stricter the inclusion and exclusion criteria, the more significant the impact on the interpretation, applicability, and generalizability of the research findings.
• A restricted study population enhances internal validity but may limit the study’s external validity and generalizability to clinical practice.
• A broadly defined study population may better reflect clinical practice but could increase bias and reduce internal validity.
• An inadequately formulated research question can negatively impact study design, potentially leading to ineffective outcomes and affecting publication prospects.

Checklist: Good research questions for social science projects (Panke, 2018)

Research Hypotheses

Present the researcher’s predictions based on specific statements.

• These statements define the research problem or issue and indicate the direction of the researcher’s predictions.
• Formulating the research question and hypothesis from existing data (e.g., a database) can lead to multiple statistical comparisons and potentially spurious findings due to chance.
• The research or clinical hypothesis, derived from the research question, shapes the study’s key elements: sampling strategy, intervention, comparison, and outcome variables.
• Hypotheses can express a single outcome or multiple outcomes.
• After statistical testing, the null hypothesis is either rejected or not rejected based on whether the study’s findings are statistically significant.
• Hypothesis testing helps determine if observed findings are due to true differences and not chance.
• Hypotheses can be 1-sided (specific direction of difference) or 2-sided (presence of a difference without specifying direction).
• 2-sided hypotheses are generally preferred unless there’s a strong justification for a 1-sided hypothesis.
• A solid research hypothesis, informed by a good research question, influences the research design and paves the way for defining clear research objectives.

Types of Research Hypothesis

• In a Y-centered research design, the focus is on the dependent variable (DV) which is specified in the research question. Theories are then used to identify independent variables (IV) and explain their causal relationship with the DV.
• Example: “An increase in teacher-led instructional time (IV) is likely to improve student reading comprehension scores (DV), because extensive guided practice under expert supervision enhances learning retention and skill mastery.”
• Hypothesis Explanation: The dependent variable (student reading comprehension scores) is the focus, and the hypothesis explores how changes in the independent variable (teacher-led instructional time) affect it.
• In X-centered research designs, the independent variable is specified in the research question. Theories are used to determine potential dependent variables and the causal mechanisms at play.
• Example: “Implementing technology-based learning tools (IV) is likely to enhance student engagement in the classroom (DV), because interactive and multimedia content increases student interest and participation.”
• Hypothesis Explanation: The independent variable (technology-based learning tools) is the focus, with the hypothesis exploring its impact on a potential dependent variable (student engagement).
• Probabilistic hypotheses suggest that changes in the independent variable are likely to lead to changes in the dependent variable in a predictable manner, but not with absolute certainty.
• Example: “The more teachers engage in professional development programs (IV), the more their teaching effectiveness (DV) is likely to improve, because continuous training updates pedagogical skills and knowledge.”
• Hypothesis Explanation: This hypothesis implies a probable relationship between the extent of professional development (IV) and teaching effectiveness (DV).
• Deterministic hypotheses state that a specific change in the independent variable will lead to a specific change in the dependent variable, implying a more direct and certain relationship.
• Example: “If the school curriculum changes from traditional lecture-based methods to project-based learning (IV), then student collaboration skills (DV) are expected to improve because project-based learning inherently requires teamwork and peer interaction.”
• Hypothesis Explanation: This hypothesis presumes a direct and definite outcome (improvement in collaboration skills) resulting from a specific change in the teaching method.
• Example : “Students who identify as visual learners will score higher on tests that are presented in a visually rich format compared to tests presented in a text-only format.”
• Explanation : This hypothesis aims to describe the potential difference in test scores between visual learners taking visually rich tests and text-only tests, without implying a direct cause-and-effect relationship.
• Example : “Teaching method A will improve student performance more than method B.”
• Explanation : This hypothesis compares the effectiveness of two different teaching methods, suggesting that one will lead to better student performance than the other. It implies a direct comparison but does not necessarily establish a causal mechanism.
• Example : “Students with higher self-efficacy will show higher levels of academic achievement.”
• Explanation : This hypothesis predicts a relationship between the variable of self-efficacy and academic achievement. Unlike a causal hypothesis, it does not necessarily suggest that one variable causes changes in the other, but rather that they are related in some way.

Tips for developing research questions and hypotheses for research studies

• Perform a systematic literature review (if one has not been done) to increase knowledge and familiarity with the topic and to assist with research development.
• Learn about current trends and technological advances on the topic.
• Seek careful input from experts, mentors, colleagues, and collaborators to refine your research question as this will aid in developing the research question and guide the research study.
• Use the FINER criteria in the development of the research question.
• Ensure that the research question follows PICOT format.
• Develop a research hypothesis from the research question.
• Ensure that the research question and objectives are answerable, feasible, and clinically relevant.

If your research hypotheses are derived from your research questions, particularly when multiple hypotheses address a single question, it’s recommended to use both research questions and hypotheses. However, if this isn’t the case, using hypotheses over research questions is advised. It’s important to note these are general guidelines, not strict rules. If you opt not to use hypotheses, consult with your supervisor for the best approach.

Farrugia, P., Petrisor, B. A., Farrokhyar, F., & Bhandari, M. (2010). Practical tips for surgical research: Research questions, hypotheses and objectives.  Canadian journal of surgery. Journal canadien de chirurgie ,  53 (4), 278–281.

Hulley, S. B., Cummings, S. R., Browner, W. S., Grady, D., & Newman, T. B. (2007). Designing clinical research. Philadelphia.

Panke, D. (2018). Research design & method selection: Making good choices in the social sciences.  Research Design & Method Selection , 1-368.

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How Does a Hypothesis Differ From a Research Question?

To understand the difference between a hypothesis and a research question , we must first define the exact nature of scientific inquiry . Essentially, scientific inquiry represents a structured and systematic approach to exploration and discovery, grounded in empirical evidence and guided by the principles of logical reasoning and critical analysis. At the heart of scientific inquiry lies a fundamental commitment to unbiased observation and the rigorous assessment of information, a process that seeks to generate verifiable knowledge based on well-founded theories and methodological robustness.

A pivotal facet of successful scientific investigation is the appropriate framing of research, which serves to delineate the scope and direction of the scholarly endeavor. The meticulous articulation of research parameters not only guides investigators in the methodical exploration of a particular phenomenon but also ensures the reliability and validity of the findings derived from it. Correctly framing a research endeavor equips scholars with a clear framework, thereby preventing research ambiguities and facilitating a coherent and purposeful investigative journey.

Central to the framing of research are two interrelated yet distinct elements: the research question and the hypothesis. While the research question generally articulates the primary inquiry or set of inquiries to be addressed in a study, offering a focal point for the exploration, a hypothesis presents a tentative, testable prediction regarding the expected outcomes of the research. It is grounded in the existing literature and theoretical frameworks, serving as a provisional answer to the research question that is subject to empirical verification.

In essence, a research question seeks to identify and explore potential relationships, patterns, or trends, fostering a deep understanding of the underlying phenomena. In contrast, a hypothesis endeavors to affirm or refute predetermined assumptions through methodical testing and validation, aiming to substantiate or discredit specific theoretical postulates.

To correctly formulate and differentiate between research questions and hypotheses, let us investigate each one in further detail.

Understanding hypotheses

Crafting a well-defined hypothesis is a pivotal step in scholarly research. This task necessitates a profound grasp of the subject matter alongside a comprehensive awareness of existing scholarly dialogues and theories relevant to the topic. The hypothesis acts as a foundational pillar that directs the analytical pathways of the investigation, anchoring the exploration with grounded expectations based on existing knowledge.

In the formulation of a hypothesis, researchers must adhere to vital principles to ensure the creation of a substantial and verifiable statement. A robust hypothesis is delineated by several attributes, including precision, testability, and a congruent alignment with established research and theories. Moreover, it is formulated to facilitate empirical substantiation, aiming to either confirm or refute the established propositions through systematic investigation.

To deepen our comprehension of a hypothesis, let us examine some examples in different research contexts, illustrating how a hypothesis can shape and steer a study:

• Individuals between the ages of 40 and 60 who engage in regular physical activity are less likely to develop heart diseases than those who do not.
• Adolescents who experience traumatic events during the COVID-19 pandemic have a higher prevalence of mental health issues than those who do not.
• Remote learning hampers the development of social skills in elementary school students more than traditional classroom learning does.
• Implementing multicultural education strategies diminishes the achievement gap in multicultural classrooms.
• Marine ecosystems that experience high levels of plastic pollution exhibit a substantial reduction in biodiversity.
• Urbanization leads to a significant decrease in biodiversity in metropolitan areas due to habitat loss.
• Voting behavior in urban communities is significantly influenced by the socioeconomic status of the individuals.
• The prevalent use of social media significantly influences the formation of societal norms and behaviors in contemporary society.
• The integration of artificial intelligence in manufacturing elevates efficiency and productivity.
• An increased dependence on digital platforms compromises personal privacy and heightens the risk of data security breaches.

Each of these hypothesis examples is constructed to offer focused and testable propositions, rooted in contemporary concerns, creating a pathway for empirical verification and the generation of data-driven insights.

Understanding research questions

A critical first step in any research endeavor is the formulation of a research question, a task that requires a deep understanding of both the topic at hand and the existing scholarly landscape surrounding it. The research question serves as the beacon that guides the trajectory of the investigation, providing a focal point that centers the research activities and objectives.

In constructing a research question, scholars must be guided by certain key principles to ensure that their inquiry is both meaningful and fruitful. A well-framed research question is characterized by clarity, specificity, and a sensible alignment with existing research, which aids in building upon established foundations to foster novel insights within its scholarly domain.

To further understand the concept of research questions, let us consider some concrete examples from various fields that illustrate how a well-articulated research question can guide a research project:

• How does lifestyle affect the risk of heart disease in adults aged 40-60?
• What impact has the COVID-19 pandemic had on mental health outcomes in adolescents?
• How does remote learning impact the academic performance and social skills of elementary school students?
• What strategies can be employed to reduce the achievement gap in multicultural classrooms?
• What are the effects of plastic waste on marine ecosystems?
• How does urbanization impact biodiversity in metropolitan regions?
• How do socioeconomic factors influence voting behavior in urban communities?
• What role does social media play in shaping contemporary societal norms and behaviors?
• How does the implementation of artificial intelligence in manufacturing enhance efficiency and productivity?
• What are the implications of increasing reliance on digital platforms for personal privacy and data security?

Each of these research question examples not only maintains a clear focus on a specific topic but also stands grounded in current concerns, thereby paving the way for empirical exploration and data-driven conclusions.

Key differences between a hypothesis and a research question

In scholarly research, it is imperative to differentiate clearly between a hypothesis and a research question. The following table delineates the comparative aspects of both concepts:

When to use which

The decision to use a hypothesis or a research question largely hinges on the nature and objectives of the study. Essentially, researchers delineate between exploratory and confirmatory research . The former seeks to explore new phenomena and generate new insights, while the latter aims to verify existing theories and hypotheses. Understanding the correct circumstance for employing either a research question or a hypothesis can significantly streamline the research process, directing it towards more targeted conclusions. Let's delve into the specific situations where one may be more appropriate over the other.

Situations where a hypothesis is more appropriate

• Confirmatory Research: When the research is grounded in existing theories and seeks to validate or invalidate a specific claim or relationship.
• Quantitative Studies: In research designs that predominantly involve statistical analysis of numerical data to address the research problem.
• Experimental Research: Where controlled experiments are conducted to explore the causal relationships between different variables.
• Deductive Approaches: When the research follows a deductive approach , deriving a specific prediction from a general theory.

Situations where a research question is more appropriate

• Exploratory Research: In studies aiming to explore a new field or topic without much existing literature or established theories.
• Qualitative Research: When the study involves analyzing non-numerical data such as texts, interviews, or observational data to garner insights.
• Pilot Studies: Preliminary studies that aim to identify potential issues and refine research tools before a large-scale study.
• Inductive Approaches: Research approaches that work from specific observations to broader generalizations, aiming to develop new theories.

The interrelation between hypotheses and research questions

Understanding how a research question can give rise to hypotheses.

In scholarly inquiries, the formation of a hypothesis often finds its genesis in a well-articulated research question. This dynamic represents a pivotal juncture in research methodology, facilitating a transition from questioning to hypothesizing and setting the stage for focused analytical scrutiny. Leveraging the exploratory nature of research questions can foster the formulation of grounded hypotheses, guiding the investigative trajectory towards evidence-based conclusions.

Indeed, a well-structured research question can give rise to a series of hypotheses, each presenting a plausible answer to the research question and serving as a focal point for systematic investigation. This correlation facilitates a scaffolded approach to exploration, where researchers can build a layered understanding through a structured inquiry process.

Can a hypothesis transform into a research question?

This iterative process we have described can be envisioned as a cyclic pathway rather than a linear trajectory, wherein hypotheses, once tested and analyzed, can refine or even reformulate the initial research questions. This reflexive relationship fosters a deepened understanding and a more nuanced exploration of the research topic at hand.

To illustrate, consider a research question in the field of healthcare: "What are the primary factors influencing sleep quality in adults?" From this question, a researcher might derive several hypotheses, such as "Adults who engage in regular physical activity experience better sleep quality than those who do not." Once this hypothesis is tested, the findings could lead to further questions, fine-tuning the initial research query to delve into specific age groups, lifestyle factors, or physiological aspects, thereby perpetuating a cycle of inquiry that propels the research into deeper and more focused directions.

Research questions serve as the launchpad for scientific exploration, fostering a direction and scope that steer investigations towards relevant and focused pathways. Conversely, hypotheses act as tentative answers to these research questions, laying a grounded foundation for systematic investigations and guiding the trajectory towards evidence-based conclusions.

Selecting the right approach—whether formulating a hypothesis or crafting a research question—is not merely a procedural choice; it is a strategic decision that significantly influences the outcome of the investigation. Recognizing the interdependent and reflexive relationship between the two can foster a more robust and nuanced approach to scientific inquiry.

By embracing the cyclic pathway that intertwines questioning with hypothesizing, researchers can unlock deeper levels of understanding, paving the way for profound discoveries enriched with insight. Remember, the quality of the answers we obtain is invariably linked to the quality of the questions we ask and the hypotheses we formulate.

Header image by Luke Tanis .

Clarifying the Research Questions or Hypotheses

• First Online: 28 March 2017

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• Kenan Dikilitaş 3 &
• Carol Griffiths 4  

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This chapter deals with the important, but often neglected, issue of establishing research questions or hypotheses, whether this is done before or (in the “real world”) often after the study has been conducted. The point is made that, in fact, research questions tend to be more common than hypotheses in action research, and guidelines are suggested for delineating such questions and deciding on appropriate question types according to the research purpose. Some example questions are provided to stimulate ideas, and an example action research study which will proceed in stages throughout the book is begun here.

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Dikilitaş, K., Griffiths, C. (2017). Clarifying the Research Questions or Hypotheses. In: Developing Language Teacher Autonomy through Action Research. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-319-50739-2_2

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What is a Research Hypothesis: How to Write it, Types, and Examples

Any research begins with a research question and a research hypothesis . A research question alone may not suffice to design the experiment(s) needed to answer it. A hypothesis is central to the scientific method. But what is a hypothesis ? A hypothesis is a testable statement that proposes a possible explanation to a phenomenon, and it may include a prediction. Next, you may ask what is a research hypothesis ? Simply put, a research hypothesis is a prediction or educated guess about the relationship between the variables that you want to investigate.  

It is important to be thorough when developing your research hypothesis. Shortcomings in the framing of a hypothesis can affect the study design and the results. A better understanding of the research hypothesis definition and characteristics of a good hypothesis will make it easier for you to develop your own hypothesis for your research. Let’s dive in to know more about the types of research hypothesis , how to write a research hypothesis , and some research hypothesis examples .  

Table of Contents

What is a hypothesis ?  

A hypothesis is based on the existing body of knowledge in a study area. Framed before the data are collected, a hypothesis states the tentative relationship between independent and dependent variables, along with a prediction of the outcome.  

What is a research hypothesis ?  

Young researchers starting out their journey are usually brimming with questions like “ What is a hypothesis ?” “ What is a research hypothesis ?” “How can I write a good research hypothesis ?”   

A research hypothesis is a statement that proposes a possible explanation for an observable phenomenon or pattern. It guides the direction of a study and predicts the outcome of the investigation. A research hypothesis is testable, i.e., it can be supported or disproven through experimentation or observation.     

Characteristics of a good hypothesis  

Here are the characteristics of a good hypothesis :  

• Clearly formulated and free of language errors and ambiguity  
• Concise and not unnecessarily verbose  
• Has clearly defined variables  
• Testable and stated in a way that allows for it to be disproven  
• Can be tested using a research design that is feasible, ethical, and practical   
• Specific and relevant to the research problem  
• Rooted in a thorough literature search  
• Can generate new knowledge or understanding.  

How to create an effective research hypothesis  

A study begins with the formulation of a research question. A researcher then performs background research. This background information forms the basis for building a good research hypothesis . The researcher then performs experiments, collects, and analyzes the data, interprets the findings, and ultimately, determines if the findings support or negate the original hypothesis.  

Let’s look at each step for creating an effective, testable, and good research hypothesis :  

• Identify a research problem or question: Start by identifying a specific research problem.   
• Review the literature: Conduct an in-depth review of the existing literature related to the research problem to grasp the current knowledge and gaps in the field.   
• Formulate a clear and testable hypothesis : Based on the research question, use existing knowledge to form a clear and testable hypothesis . The hypothesis should state a predicted relationship between two or more variables that can be measured and manipulated. Improve the original draft till it is clear and meaningful.  
• State the null hypothesis: The null hypothesis is a statement that there is no relationship between the variables you are studying.   
• Define the population and sample: Clearly define the population you are studying and the sample you will be using for your research.  
• Select appropriate methods for testing the hypothesis: Select appropriate research methods, such as experiments, surveys, or observational studies, which will allow you to test your research hypothesis .  

Remember that creating a research hypothesis is an iterative process, i.e., you might have to revise it based on the data you collect. You may need to test and reject several hypotheses before answering the research problem.  

How to write a research hypothesis  

When you start writing a research hypothesis , you use an “if–then” statement format, which states the predicted relationship between two or more variables. Clearly identify the independent variables (the variables being changed) and the dependent variables (the variables being measured), as well as the population you are studying. Review and revise your hypothesis as needed.  

An example of a research hypothesis in this format is as follows:  

“ If [athletes] follow [cold water showers daily], then their [endurance] increases.”  

Population: athletes  

Independent variable: daily cold water showers  

Dependent variable: endurance  

You may have understood the characteristics of a good hypothesis . But note that a research hypothesis is not always confirmed; a researcher should be prepared to accept or reject the hypothesis based on the study findings.  

Research hypothesis checklist  

Following from above, here is a 10-point checklist for a good research hypothesis :  

• Testable: A research hypothesis should be able to be tested via experimentation or observation.  
• Specific: A research hypothesis should clearly state the relationship between the variables being studied.  
• Based on prior research: A research hypothesis should be based on existing knowledge and previous research in the field.  
• Falsifiable: A research hypothesis should be able to be disproven through testing.  
• Clear and concise: A research hypothesis should be stated in a clear and concise manner.  
• Logical: A research hypothesis should be logical and consistent with current understanding of the subject.  
• Relevant: A research hypothesis should be relevant to the research question and objectives.  
• Feasible: A research hypothesis should be feasible to test within the scope of the study.  
• Reflects the population: A research hypothesis should consider the population or sample being studied.  
• Uncomplicated: A good research hypothesis is written in a way that is easy for the target audience to understand.  

By following this research hypothesis checklist , you will be able to create a research hypothesis that is strong, well-constructed, and more likely to yield meaningful results.  

Types of research hypothesis  

Different types of research hypothesis are used in scientific research:  

1. Null hypothesis:

A null hypothesis states that there is no change in the dependent variable due to changes to the independent variable. This means that the results are due to chance and are not significant. A null hypothesis is denoted as H0 and is stated as the opposite of what the alternative hypothesis states.   

Example: “ The newly identified virus is not zoonotic .”  

2. Alternative hypothesis:

This states that there is a significant difference or relationship between the variables being studied. It is denoted as H1 or Ha and is usually accepted or rejected in favor of the null hypothesis.  

Example: “ The newly identified virus is zoonotic .”  

3. Directional hypothesis :

This specifies the direction of the relationship or difference between variables; therefore, it tends to use terms like increase, decrease, positive, negative, more, or less.   

Example: “ The inclusion of intervention X decreases infant mortality compared to the original treatment .”   

4. Non-directional hypothesis:

While it does not predict the exact direction or nature of the relationship between the two variables, a non-directional hypothesis states the existence of a relationship or difference between variables but not the direction, nature, or magnitude of the relationship. A non-directional hypothesis may be used when there is no underlying theory or when findings contradict previous research.  

Example, “ Cats and dogs differ in the amount of affection they express .”  

5. Simple hypothesis :

A simple hypothesis only predicts the relationship between one independent and another independent variable.  

Example: “ Applying sunscreen every day slows skin aging .”  

6 . Complex hypothesis :

A complex hypothesis states the relationship or difference between two or more independent and dependent variables.   

Example: “ Applying sunscreen every day slows skin aging, reduces sun burn, and reduces the chances of skin cancer .” (Here, the three dependent variables are slowing skin aging, reducing sun burn, and reducing the chances of skin cancer.)  

7. Associative hypothesis:  

An associative hypothesis states that a change in one variable results in the change of the other variable. The associative hypothesis defines interdependency between variables.  

Example: “ There is a positive association between physical activity levels and overall health .”  

8 . Causal hypothesis:

A causal hypothesis proposes a cause-and-effect interaction between variables.  

Example: “ Long-term alcohol use causes liver damage .”  

Note that some of the types of research hypothesis mentioned above might overlap. The types of hypothesis chosen will depend on the research question and the objective of the study.  

Research hypothesis examples  

Here are some good research hypothesis examples :  

“The use of a specific type of therapy will lead to a reduction in symptoms of depression in individuals with a history of major depressive disorder.”  

“Providing educational interventions on healthy eating habits will result in weight loss in overweight individuals.”  

“Plants that are exposed to certain types of music will grow taller than those that are not exposed to music.”  

“The use of the plant growth regulator X will lead to an increase in the number of flowers produced by plants.”  

Characteristics that make a research hypothesis weak are unclear variables, unoriginality, being too general or too vague, and being untestable. A weak hypothesis leads to weak research and improper methods.   

Some bad research hypothesis examples (and the reasons why they are “bad”) are as follows:  

“This study will show that treatment X is better than any other treatment . ” (This statement is not testable, too broad, and does not consider other treatments that may be effective.)  

“This study will prove that this type of therapy is effective for all mental disorders . ” (This statement is too broad and not testable as mental disorders are complex and different disorders may respond differently to different types of therapy.)  

“Plants can communicate with each other through telepathy . ” (This statement is not testable and lacks a scientific basis.)  

Importance of testable hypothesis  

If a research hypothesis is not testable, the results will not prove or disprove anything meaningful. The conclusions will be vague at best. A testable hypothesis helps a researcher focus on the study outcome and understand the implication of the question and the different variables involved. A testable hypothesis helps a researcher make precise predictions based on prior research.  

To be considered testable, there must be a way to prove that the hypothesis is true or false; further, the results of the hypothesis must be reproducible.  

Frequently Asked Questions (FAQs) on research hypothesis  

1. What is the difference between research question and research hypothesis ?  

A research question defines the problem and helps outline the study objective(s). It is an open-ended statement that is exploratory or probing in nature. Therefore, it does not make predictions or assumptions. It helps a researcher identify what information to collect. A research hypothesis , however, is a specific, testable prediction about the relationship between variables. Accordingly, it guides the study design and data analysis approach.

2. When to reject null hypothesis ?

A null hypothesis should be rejected when the evidence from a statistical test shows that it is unlikely to be true. This happens when the test statistic (e.g., p -value) is less than the defined significance level (e.g., 0.05). Rejecting the null hypothesis does not necessarily mean that the alternative hypothesis is true; it simply means that the evidence found is not compatible with the null hypothesis.  

3. How can I be sure my hypothesis is testable?  

A testable hypothesis should be specific and measurable, and it should state a clear relationship between variables that can be tested with data. To ensure that your hypothesis is testable, consider the following:  

• Clearly define the key variables in your hypothesis. You should be able to measure and manipulate these variables in a way that allows you to test the hypothesis.  
• The hypothesis should predict a specific outcome or relationship between variables that can be measured or quantified.   
• You should be able to collect the necessary data within the constraints of your study.  
• It should be possible for other researchers to replicate your study, using the same methods and variables.   
• Your hypothesis should be testable by using appropriate statistical analysis techniques, so you can draw conclusions, and make inferences about the population from the sample data.  
• The hypothesis should be able to be disproven or rejected through the collection of data.  

4. How do I revise my research hypothesis if my data does not support it?  

If your data does not support your research hypothesis , you will need to revise it or develop a new one. You should examine your data carefully and identify any patterns or anomalies, re-examine your research question, and/or revisit your theory to look for any alternative explanations for your results. Based on your review of the data, literature, and theories, modify your research hypothesis to better align it with the results you obtained. Use your revised hypothesis to guide your research design and data collection. It is important to remain objective throughout the process.  

5. I am performing exploratory research. Do I need to formulate a research hypothesis?  

As opposed to “confirmatory” research, where a researcher has some idea about the relationship between the variables under investigation, exploratory research (or hypothesis-generating research) looks into a completely new topic about which limited information is available. Therefore, the researcher will not have any prior hypotheses. In such cases, a researcher will need to develop a post-hoc hypothesis. A post-hoc research hypothesis is generated after these results are known.  

6. How is a research hypothesis different from a research question?

A research question is an inquiry about a specific topic or phenomenon, typically expressed as a question. It seeks to explore and understand a particular aspect of the research subject. In contrast, a research hypothesis is a specific statement or prediction that suggests an expected relationship between variables. It is formulated based on existing knowledge or theories and guides the research design and data analysis.

7. Can a research hypothesis change during the research process?

Yes, research hypotheses can change during the research process. As researchers collect and analyze data, new insights and information may emerge that require modification or refinement of the initial hypotheses. This can be due to unexpected findings, limitations in the original hypotheses, or the need to explore additional dimensions of the research topic. Flexibility is crucial in research, allowing for adaptation and adjustment of hypotheses to align with the evolving understanding of the subject matter.

8. How many hypotheses should be included in a research study?

The number of research hypotheses in a research study varies depending on the nature and scope of the research. It is not necessary to have multiple hypotheses in every study. Some studies may have only one primary hypothesis, while others may have several related hypotheses. The number of hypotheses should be determined based on the research objectives, research questions, and the complexity of the research topic. It is important to ensure that the hypotheses are focused, testable, and directly related to the research aims.

9. Can research hypotheses be used in qualitative research?

Yes, research hypotheses can be used in qualitative research, although they are more commonly associated with quantitative research. In qualitative research, hypotheses may be formulated as tentative or exploratory statements that guide the investigation. Instead of testing hypotheses through statistical analysis, qualitative researchers may use the hypotheses to guide data collection and analysis, seeking to uncover patterns, themes, or relationships within the qualitative data. The emphasis in qualitative research is often on generating insights and understanding rather than confirming or rejecting specific research hypotheses through statistical testing.

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10 Significant Differences Between Research Question vs Research Hypothesis

Stating, developing and addressing a research question and developing & justifying the research hypothesis has vital significance in the research process. Both help researchers to approach PhD/research/ projects. 

“Research” is a word important for PhD which includes complex processes of finding new knowledge. A PhD candidate has to prepare a project & research proposal, identify a research gap , state a question, prepare a hypothesis and then do research. 

It includes tedious pre-preparation, lucrative research and frustrating post-preparations phases. So overall the research process though is inquisitive but can be managed by discipline and zero date planning. 

So to prepare for PhD, do it with ease and complete it joyfully; one has to understand every element correctly before starting their research. And for that tons and tons of articles and previous research must be read first. 

In addition, as we talked about, precisely identified research problem helps in stating an excellent research question or research hypothesis. Notwithstanding, students usually don’t understand what a research question or hypothesis is! 

The present blog content will focus on differences between research question and hypothesis and may let you understand what each term is. I hope this article will help you learn the PhD research process more accurately. 

Stay tuned,

Research question:  Does this article explain some common differences between a research question and a research hypothesis?

Research hypothesis:  This article explains the major differences between the research question and the research hypothesis. 

Example of research question: 

Some other examples of research questions are: , example of research hypothesis: , some of the examples of research hypotheses: , summary: research question vs hypothesis: , wrapping up: , what is a research question- simple explanation.

Put simply, a research question is a clear and concise question of the study that must be answered at the end. The answer usually is Yes/No type but clearly fills the gap. 

Let’s take an example, 

What are some common problems the LGBT community faces globally? 

Suppose, this one is one research question around which the researcher has to prepare its study. What can he or she do with this topic? 

• Conduct gatherings of the community. 
• Conduct one on one interviews. 
• Conduct News sessions 
• Study previous literature. 
• Organize some Games and invite LGBT community people to take part. 

That’s it, Nothing else he or she can do.  

No statistical analysis is required and performed for this study so the outcome of this study possibly is “problems”. And it can be solved, perhaps. Note that in-depth mathematical models, statistical analysis and other scientific studies aren’t required here. 

• What are the side effects of social media addiction on youth? 
• What are the factors that negatively impact the mental health of US people? 
• How effective carbon emission control strategies are? 

Now let’s understand the research hypothesis. 

What is a research hypothesis?- Simpler explanation

A research hypothesis is postulated in order to predict the results either negative or positive. Notedly is used so often in scientific, experimental and quantitative research.

The research hypothesis is a predictive model for getting results.  

Let’s take an example, 

The effect of time and temperature on biological sample transportation.

This study includes exclusive statistical analysis and data-driven studies to investigate the effect of various temperatures and times on biological specimen transportation. 

 Outcomes of the study will prove that at which temperature a biological sample can be safely transported. 

The outcomes are, 

• Statistics 
• The temperature which isn’t good 
• The temperature which has is best
• The tolerable zone for transportation

To interpret these kinds of results in-depth mathematical models, statistical analysis, scientific experiments and other biological studies are needed. 

• The effect of time and temperature on biological sample transportation. 
• Effect of various doses of antiviral Oseltamivir drug against viral pathogenicity. 
• Various global warming agents and their impact.  

I think you get a brief idea about how each term is different. Some of the technical differences between the research question and hypothesis are explained here. 

Differences between research question and research hypothesis: 

A research question is developed depending upon the problem or gap identified while the hypothesis is prepared based on the existing knowledge. 

More than one research question is present in a single study, while the entire research is developed around a single hypothesis that is either proven or disproven at last. 

In-depth knowledge of the subject and huge data or research studies are required to state a research hypothesis; whereas the research question can be stated using a small group of research data or knowledge. 

This indicates that the relatedness among different variables is pretty uncertain for the research question while is highly related in the case of hypothesis. 

A research question is “brief” yet includes all the important information and is open to debate which typically gives an excellent varied degree of output. 

On the other hand, the research hypothesis is a kind of formal statement- (will be proved or disproved) which assumes the relatedness between two or more variables selected for the study. 

For example,

The number of patients, population size, sample type or method selected for the study. 

Both- qualitative and quantitative studies rely on the research question, however, the hypothesis can be postulated mostly for the quantitative or experimental studies. 

Depending upon the nature of the study, the research questions are of three various types which are casual, descriptive and comparative questions while the hypotheses are causal, null, directional or non-directional. 

A thesis question must be answered; A hypothesis must be tested. 

The research question is more an elaborative research term while the hypothesis is more scientific and predictive in nature. 

Henceforth, research questions are usually used in elaborate studies in subjects such as language, arts and literature. And as we said, that’s pretty straightforward. 

The impact of the “Macbeth play” on European people. 

On the other hand, the research hypothesis is based on possibilities and probabilities whose final results either or neither prove the study and therefore include a purely scientific explanation, mathematics, equations and statistical analysis. 

Studies in science, biology and sociology rely on hypotheses (that must be tested first). 

For example, the impact of temperature and time duration on sample transportation and storage. 

If you are designing scientific research for your PhD, perhaps stating a hypothesis may help you more, although you can raise a question as well to investigate the knowledge. 

Research, as I said, is a complex process, needs the experience to design. 

Early learning may pretty helpful for students to understand the thing well. And hence this article and series of articles on this blog are meant for PhD students. 

Dr. Tushar Chauhan is a Scientist, Blogger and Scientific-writer. He has completed PhD in Genetics. Dr. Chauhan is a PhD coach and tutor.

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Research questions, hypotheses and objectives

Patricia farrugia.

* Michael G. DeGroote School of Medicine, the

Bradley A. Petrisor

† Division of Orthopaedic Surgery and the

Forough Farrokhyar

‡ Departments of Surgery and

§ Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ont

Mohit Bhandari

There is an increasing familiarity with the principles of evidence-based medicine in the surgical community. As surgeons become more aware of the hierarchy of evidence, grades of recommendations and the principles of critical appraisal, they develop an increasing familiarity with research design. Surgeons and clinicians are looking more and more to the literature and clinical trials to guide their practice; as such, it is becoming a responsibility of the clinical research community to attempt to answer questions that are not only well thought out but also clinically relevant. The development of the research question, including a supportive hypothesis and objectives, is a necessary key step in producing clinically relevant results to be used in evidence-based practice. A well-defined and specific research question is more likely to help guide us in making decisions about study design and population and subsequently what data will be collected and analyzed. 1

Objectives of this article

In this article, we discuss important considerations in the development of a research question and hypothesis and in defining objectives for research. By the end of this article, the reader will be able to appreciate the significance of constructing a good research question and developing hypotheses and research objectives for the successful design of a research study. The following article is divided into 3 sections: research question, research hypothesis and research objectives.

Research question

Interest in a particular topic usually begins the research process, but it is the familiarity with the subject that helps define an appropriate research question for a study. 1 Questions then arise out of a perceived knowledge deficit within a subject area or field of study. 2 Indeed, Haynes suggests that it is important to know “where the boundary between current knowledge and ignorance lies.” 1 The challenge in developing an appropriate research question is in determining which clinical uncertainties could or should be studied and also rationalizing the need for their investigation.

Increasing one’s knowledge about the subject of interest can be accomplished in many ways. Appropriate methods include systematically searching the literature, in-depth interviews and focus groups with patients (and proxies) and interviews with experts in the field. In addition, awareness of current trends and technological advances can assist with the development of research questions. 2 It is imperative to understand what has been studied about a topic to date in order to further the knowledge that has been previously gathered on a topic. Indeed, some granting institutions (e.g., Canadian Institute for Health Research) encourage applicants to conduct a systematic review of the available evidence if a recent review does not already exist and preferably a pilot or feasibility study before applying for a grant for a full trial.

In-depth knowledge about a subject may generate a number of questions. It then becomes necessary to ask whether these questions can be answered through one study or if more than one study needed. 1 Additional research questions can be developed, but several basic principles should be taken into consideration. 1 All questions, primary and secondary, should be developed at the beginning and planning stages of a study. Any additional questions should never compromise the primary question because it is the primary research question that forms the basis of the hypothesis and study objectives. It must be kept in mind that within the scope of one study, the presence of a number of research questions will affect and potentially increase the complexity of both the study design and subsequent statistical analyses, not to mention the actual feasibility of answering every question. 1 A sensible strategy is to establish a single primary research question around which to focus the study plan. 3 In a study, the primary research question should be clearly stated at the end of the introduction of the grant proposal, and it usually specifies the population to be studied, the intervention to be implemented and other circumstantial factors. 4

Hulley and colleagues 2 have suggested the use of the FINER criteria in the development of a good research question ( Box 1 ). The FINER criteria highlight useful points that may increase the chances of developing a successful research project. A good research question should specify the population of interest, be of interest to the scientific community and potentially to the public, have clinical relevance and further current knowledge in the field (and of course be compliant with the standards of ethical boards and national research standards).

FINER criteria for a good research question

Adapted with permission from Wolters Kluwer Health. 2

Whereas the FINER criteria outline the important aspects of the question in general, a useful format to use in the development of a specific research question is the PICO format — consider the population (P) of interest, the intervention (I) being studied, the comparison (C) group (or to what is the intervention being compared) and the outcome of interest (O). 3 , 5 , 6 Often timing (T) is added to PICO ( Box 2 ) — that is, “Over what time frame will the study take place?” 1 The PICOT approach helps generate a question that aids in constructing the framework of the study and subsequently in protocol development by alluding to the inclusion and exclusion criteria and identifying the groups of patients to be included. Knowing the specific population of interest, intervention (and comparator) and outcome of interest may also help the researcher identify an appropriate outcome measurement tool. 7 The more defined the population of interest, and thus the more stringent the inclusion and exclusion criteria, the greater the effect on the interpretation and subsequent applicability and generalizability of the research findings. 1 , 2 A restricted study population (and exclusion criteria) may limit bias and increase the internal validity of the study; however, this approach will limit external validity of the study and, thus, the generalizability of the findings to the practical clinical setting. Conversely, a broadly defined study population and inclusion criteria may be representative of practical clinical practice but may increase bias and reduce the internal validity of the study.

PICOT criteria 1

A poorly devised research question may affect the choice of study design, potentially lead to futile situations and, thus, hamper the chance of determining anything of clinical significance, which will then affect the potential for publication. Without devoting appropriate resources to developing the research question, the quality of the study and subsequent results may be compromised. During the initial stages of any research study, it is therefore imperative to formulate a research question that is both clinically relevant and answerable.

Research hypothesis

The primary research question should be driven by the hypothesis rather than the data. 1 , 2 That is, the research question and hypothesis should be developed before the start of the study. This sounds intuitive; however, if we take, for example, a database of information, it is potentially possible to perform multiple statistical comparisons of groups within the database to find a statistically significant association. This could then lead one to work backward from the data and develop the “question.” This is counterintuitive to the process because the question is asked specifically to then find the answer, thus collecting data along the way (i.e., in a prospective manner). Multiple statistical testing of associations from data previously collected could potentially lead to spuriously positive findings of association through chance alone. 2 Therefore, a good hypothesis must be based on a good research question at the start of a trial and, indeed, drive data collection for the study.

The research or clinical hypothesis is developed from the research question and then the main elements of the study — sampling strategy, intervention (if applicable), comparison and outcome variables — are summarized in a form that establishes the basis for testing, statistical and ultimately clinical significance. 3 For example, in a research study comparing computer-assisted acetabular component insertion versus freehand acetabular component placement in patients in need of total hip arthroplasty, the experimental group would be computer-assisted insertion and the control/conventional group would be free-hand placement. The investigative team would first state a research hypothesis. This could be expressed as a single outcome (e.g., computer-assisted acetabular component placement leads to improved functional outcome) or potentially as a complex/composite outcome; that is, more than one outcome (e.g., computer-assisted acetabular component placement leads to both improved radiographic cup placement and improved functional outcome).

However, when formally testing statistical significance, the hypothesis should be stated as a “null” hypothesis. 2 The purpose of hypothesis testing is to make an inference about the population of interest on the basis of a random sample taken from that population. The null hypothesis for the preceding research hypothesis then would be that there is no difference in mean functional outcome between the computer-assisted insertion and free-hand placement techniques. After forming the null hypothesis, the researchers would form an alternate hypothesis stating the nature of the difference, if it should appear. The alternate hypothesis would be that there is a difference in mean functional outcome between these techniques. At the end of the study, the null hypothesis is then tested statistically. If the findings of the study are not statistically significant (i.e., there is no difference in functional outcome between the groups in a statistical sense), we cannot reject the null hypothesis, whereas if the findings were significant, we can reject the null hypothesis and accept the alternate hypothesis (i.e., there is a difference in mean functional outcome between the study groups), errors in testing notwithstanding. In other words, hypothesis testing confirms or refutes the statement that the observed findings did not occur by chance alone but rather occurred because there was a true difference in outcomes between these surgical procedures. The concept of statistical hypothesis testing is complex, and the details are beyond the scope of this article.

Another important concept inherent in hypothesis testing is whether the hypotheses will be 1-sided or 2-sided. A 2-sided hypothesis states that there is a difference between the experimental group and the control group, but it does not specify in advance the expected direction of the difference. For example, we asked whether there is there an improvement in outcomes with computer-assisted surgery or whether the outcomes worse with computer-assisted surgery. We presented a 2-sided test in the above example because we did not specify the direction of the difference. A 1-sided hypothesis states a specific direction (e.g., there is an improvement in outcomes with computer-assisted surgery). A 2-sided hypothesis should be used unless there is a good justification for using a 1-sided hypothesis. As Bland and Atlman 8 stated, “One-sided hypothesis testing should never be used as a device to make a conventionally nonsignificant difference significant.”

The research hypothesis should be stated at the beginning of the study to guide the objectives for research. Whereas the investigators may state the hypothesis as being 1-sided (there is an improvement with treatment), the study and investigators must adhere to the concept of clinical equipoise. According to this principle, a clinical (or surgical) trial is ethical only if the expert community is uncertain about the relative therapeutic merits of the experimental and control groups being evaluated. 9 It means there must exist an honest and professional disagreement among expert clinicians about the preferred treatment. 9

Designing a research hypothesis is supported by a good research question and will influence the type of research design for the study. Acting on the principles of appropriate hypothesis development, the study can then confidently proceed to the development of the research objective.

Research objective

The primary objective should be coupled with the hypothesis of the study. Study objectives define the specific aims of the study and should be clearly stated in the introduction of the research protocol. 7 From our previous example and using the investigative hypothesis that there is a difference in functional outcomes between computer-assisted acetabular component placement and free-hand placement, the primary objective can be stated as follows: this study will compare the functional outcomes of computer-assisted acetabular component insertion versus free-hand placement in patients undergoing total hip arthroplasty. Note that the study objective is an active statement about how the study is going to answer the specific research question. Objectives can (and often do) state exactly which outcome measures are going to be used within their statements. They are important because they not only help guide the development of the protocol and design of study but also play a role in sample size calculations and determining the power of the study. 7 These concepts will be discussed in other articles in this series.

From the surgeon’s point of view, it is important for the study objectives to be focused on outcomes that are important to patients and clinically relevant. For example, the most methodologically sound randomized controlled trial comparing 2 techniques of distal radial fixation would have little or no clinical impact if the primary objective was to determine the effect of treatment A as compared to treatment B on intraoperative fluoroscopy time. However, if the objective was to determine the effect of treatment A as compared to treatment B on patient functional outcome at 1 year, this would have a much more significant impact on clinical decision-making. Second, more meaningful surgeon–patient discussions could ensue, incorporating patient values and preferences with the results from this study. 6 , 7 It is the precise objective and what the investigator is trying to measure that is of clinical relevance in the practical setting.

The following is an example from the literature about the relation between the research question, hypothesis and study objectives:

Study: Warden SJ, Metcalf BR, Kiss ZS, et al. Low-intensity pulsed ultrasound for chronic patellar tendinopathy: a randomized, double-blind, placebo-controlled trial. Rheumatology 2008;47:467–71.

Research question: How does low-intensity pulsed ultrasound (LIPUS) compare with a placebo device in managing the symptoms of skeletally mature patients with patellar tendinopathy?

Research hypothesis: Pain levels are reduced in patients who receive daily active-LIPUS (treatment) for 12 weeks compared with individuals who receive inactive-LIPUS (placebo).

Objective: To investigate the clinical efficacy of LIPUS in the management of patellar tendinopathy symptoms.

The development of the research question is the most important aspect of a research project. A research project can fail if the objectives and hypothesis are poorly focused and underdeveloped. Useful tips for surgical researchers are provided in Box 3 . Designing and developing an appropriate and relevant research question, hypothesis and objectives can be a difficult task. The critical appraisal of the research question used in a study is vital to the application of the findings to clinical practice. Focusing resources, time and dedication to these 3 very important tasks will help to guide a successful research project, influence interpretation of the results and affect future publication efforts.

Tips for developing research questions, hypotheses and objectives for research studies

• Perform a systematic literature review (if one has not been done) to increase knowledge and familiarity with the topic and to assist with research development.
• Learn about current trends and technological advances on the topic.
• Seek careful input from experts, mentors, colleagues and collaborators to refine your research question as this will aid in developing the research question and guide the research study.
• Use the FINER criteria in the development of the research question.
• Ensure that the research question follows PICOT format.
• Develop a research hypothesis from the research question.
• Develop clear and well-defined primary and secondary (if needed) objectives.
• Ensure that the research question and objectives are answerable, feasible and clinically relevant.

FINER = feasible, interesting, novel, ethical, relevant; PICOT = population (patients), intervention (for intervention studies only), comparison group, outcome of interest, time.

Competing interests: No funding was received in preparation of this paper. Dr. Bhandari was funded, in part, by a Canada Research Chair, McMaster University.

Research Aims, Objectives & Questions

The “Golden Thread” Explained Simply (+ Examples)

By: David Phair (PhD) and Alexandra Shaeffer (PhD) | June 2022

The research aims , objectives and research questions (collectively called the “golden thread”) are arguably the most important thing you need to get right when you’re crafting a research proposal , dissertation or thesis . We receive questions almost every day about this “holy trinity” of research and there’s certainly a lot of confusion out there, so we’ve crafted this post to help you navigate your way through the fog.

Overview: The Golden Thread

• What is the golden thread
• What are research aims ( examples )
• What are research objectives ( examples )
• What are research questions ( examples )
• The importance of alignment in the golden thread

What is the “golden thread”?  

The golden thread simply refers to the collective research aims , research objectives , and research questions for any given project (i.e., a dissertation, thesis, or research paper ). These three elements are bundled together because it’s extremely important that they align with each other, and that the entire research project aligns with them.

Importantly, the golden thread needs to weave its way through the entirety of any research project , from start to end. In other words, it needs to be very clearly defined right at the beginning of the project (the topic ideation and proposal stage) and it needs to inform almost every decision throughout the rest of the project. For example, your research design and methodology will be heavily influenced by the golden thread (we’ll explain this in more detail later), as well as your literature review.

The research aims, objectives and research questions (the golden thread) define the focus and scope ( the delimitations ) of your research project. In other words, they help ringfence your dissertation or thesis to a relatively narrow domain, so that you can “go deep” and really dig into a specific problem or opportunity. They also help keep you on track , as they act as a litmus test for relevance. In other words, if you’re ever unsure whether to include something in your document, simply ask yourself the question, “does this contribute toward my research aims, objectives or questions?”. If it doesn’t, chances are you can drop it.

Alright, enough of the fluffy, conceptual stuff. Let’s get down to business and look at what exactly the research aims, objectives and questions are and outline a few examples to bring these concepts to life.

Research Aims: What are they?

Simply put, the research aim(s) is a statement that reflects the broad overarching goal (s) of the research project. Research aims are fairly high-level (low resolution) as they outline the general direction of the research and what it’s trying to achieve .

Research Aims: Examples  

True to the name, research aims usually start with the wording “this research aims to…”, “this research seeks to…”, and so on. For example:

“This research aims to explore employee experiences of digital transformation in retail HR.”   “This study sets out to assess the interaction between student support and self-care on well-being in engineering graduate students”  

As you can see, these research aims provide a high-level description of what the study is about and what it seeks to achieve. They’re not hyper-specific or action-oriented, but they’re clear about what the study’s focus is and what is being investigated.

Need a helping hand?

Research Objectives: What are they?

The research objectives take the research aims and make them more practical and actionable . In other words, the research objectives showcase the steps that the researcher will take to achieve the research aims.

The research objectives need to be far more specific (higher resolution) and actionable than the research aims. In fact, it’s always a good idea to craft your research objectives using the “SMART” criteria. In other words, they should be specific, measurable, achievable, relevant and time-bound”.

Research Objectives: Examples  

Let’s look at two examples of research objectives. We’ll stick with the topic and research aims we mentioned previously.  

For the digital transformation topic:

To observe the retail HR employees throughout the digital transformation. To assess employee perceptions of digital transformation in retail HR. To identify the barriers and facilitators of digital transformation in retail HR.

And for the student wellness topic:

To determine whether student self-care predicts the well-being score of engineering graduate students. To determine whether student support predicts the well-being score of engineering students. To assess the interaction between student self-care and student support when predicting well-being in engineering graduate students.

  As you can see, these research objectives clearly align with the previously mentioned research aims and effectively translate the low-resolution aims into (comparatively) higher-resolution objectives and action points . They give the research project a clear focus and present something that resembles a research-based “to-do” list.

Research Questions: What are they?

Finally, we arrive at the all-important research questions. The research questions are, as the name suggests, the key questions that your study will seek to answer . Simply put, they are the core purpose of your dissertation, thesis, or research project. You’ll present them at the beginning of your document (either in the introduction chapter or literature review chapter) and you’ll answer them at the end of your document (typically in the discussion and conclusion chapters).  

The research questions will be the driving force throughout the research process. For example, in the literature review chapter, you’ll assess the relevance of any given resource based on whether it helps you move towards answering your research questions. Similarly, your methodology and research design will be heavily influenced by the nature of your research questions. For instance, research questions that are exploratory in nature will usually make use of a qualitative approach, whereas questions that relate to measurement or relationship testing will make use of a quantitative approach.  

Let’s look at some examples of research questions to make this more tangible.

Research Questions: Examples  

Again, we’ll stick with the research aims and research objectives we mentioned previously.  

For the digital transformation topic (which would be qualitative in nature):

How do employees perceive digital transformation in retail HR? What are the barriers and facilitators of digital transformation in retail HR?  

And for the student wellness topic (which would be quantitative in nature):

Does student self-care predict the well-being scores of engineering graduate students? Does student support predict the well-being scores of engineering students? Do student self-care and student support interact when predicting well-being in engineering graduate students?  

You’ll probably notice that there’s quite a formulaic approach to this. In other words, the research questions are basically the research objectives “converted” into question format. While that is true most of the time, it’s not always the case. For example, the first research objective for the digital transformation topic was more or less a step on the path toward the other objectives, and as such, it didn’t warrant its own research question.  

So, don’t rush your research questions and sloppily reword your objectives as questions. Carefully think about what exactly you’re trying to achieve (i.e. your research aim) and the objectives you’ve set out, then craft a set of well-aligned research questions . Also, keep in mind that this can be a somewhat iterative process , where you go back and tweak research objectives and aims to ensure tight alignment throughout the golden thread.

The importance of strong alignment 

Alignment is the keyword here and we have to stress its importance . Simply put, you need to make sure that there is a very tight alignment between all three pieces of the golden thread. If your research aims and research questions don’t align, for example, your project will be pulling in different directions and will lack focus . This is a common problem students face and can cause many headaches (and tears), so be warned.

Take the time to carefully craft your research aims, objectives and research questions before you run off down the research path. Ideally, get your research supervisor/advisor to review and comment on your golden thread before you invest significant time into your project, and certainly before you start collecting data .  

Recap: The golden thread

In this post, we unpacked the golden thread of research, consisting of the research aims , research objectives and research questions . You can jump back to any section using the links below.

As always, feel free to leave a comment below – we always love to hear from you. Also, if you’re interested in 1-on-1 support, take a look at our private coaching service here.

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41 Comments

Thank you very much for your great effort put. As an Undergraduate taking Demographic Research & Methodology, I’ve been trying so hard to understand clearly what is a Research Question, Research Aim and the Objectives in a research and the relationship between them etc. But as for now I’m thankful that you’ve solved my problem.

Well appreciated. This has helped me greatly in doing my dissertation.

An so delighted with this wonderful information thank you a lot.

so impressive i have benefited a lot looking forward to learn more on research.

I am very happy to have carefully gone through this well researched article.

Infact,I used to be phobia about anything research, because of my poor understanding of the concepts.

Now,I get to know that my research question is the same as my research objective(s) rephrased in question format.

I please I would need a follow up on the subject,as I intends to join the team of researchers. Thanks once again.

Thanks so much. This was really helpful.

I know you pepole have tried to break things into more understandable and easy format. And God bless you. Keep it up

i found this document so useful towards my study in research methods. thanks so much.

This is my 2nd read topic in your course and I should commend the simplified explanations of each part. I’m beginning to understand and absorb the use of each part of a dissertation/thesis. I’ll keep on reading your free course and might be able to avail the training course! Kudos!

Thank you! Better put that my lecture and helped to easily understand the basics which I feel often get brushed over when beginning dissertation work.

This is quite helpful. I like how the Golden thread has been explained and the needed alignment.

This is quite helpful. I really appreciate!

The article made it simple for researcher students to differentiate between three concepts.

Very innovative and educational in approach to conducting research.

I am very impressed with all these terminology, as I am a fresh student for post graduate, I am highly guided and I promised to continue making consultation when the need arise. Thanks a lot.

A very helpful piece. thanks, I really appreciate it .

Very well explained, and it might be helpful to many people like me.

Wish i had found this (and other) resource(s) at the beginning of my PhD journey… not in my writing up year… 😩 Anyways… just a quick question as i’m having some issues ordering my “golden thread”…. does it matter in what order you mention them? i.e., is it always first aims, then objectives, and finally the questions? or can you first mention the research questions and then the aims and objectives?

Thank you for a very simple explanation that builds upon the concepts in a very logical manner. Just prior to this, I read the research hypothesis article, which was equally very good. This met my primary objective.

My secondary objective was to understand the difference between research questions and research hypothesis, and in which context to use which one. However, I am still not clear on this. Can you kindly please guide?

In research, a research question is a clear and specific inquiry that the researcher wants to answer, while a research hypothesis is a tentative statement or prediction about the relationship between variables or the expected outcome of the study. Research questions are broader and guide the overall study, while hypotheses are specific and testable statements used in quantitative research. Research questions identify the problem, while hypotheses provide a focus for testing in the study.

Exactly what I need in this research journey, I look forward to more of your coaching videos.

This helped a lot. Thanks so much for the effort put into explaining it.

What data source in writing dissertation/Thesis requires?

What is data source covers when writing dessertation/thesis

This is quite useful thanks

I’m excited and thankful. I got so much value which will help me progress in my thesis.

where are the locations of the reserch statement, research objective and research question in a reserach paper? Can you write an ouline that defines their places in the researh paper?

Very helpful and important tips on Aims, Objectives and Questions.

Thank you so much for making research aim, research objectives and research question so clear. This will be helpful to me as i continue with my thesis.

Thanks much for this content. I learned a lot. And I am inspired to learn more. I am still struggling with my preparation for dissertation outline/proposal. But I consistently follow contents and tutorials and the new FB of GRAD Coach. Hope to really become confident in writing my dissertation and successfully defend it.

As a researcher and lecturer, I find splitting research goals into research aims, objectives, and questions is unnecessarily bureaucratic and confusing for students. For most biomedical research projects, including ‘real research’, 1-3 research questions will suffice (numbers may differ by discipline).

Awesome! Very important resources and presented in an informative way to easily understand the golden thread. Indeed, thank you so much.

Well explained

The blog article on research aims, objectives, and questions by Grad Coach is a clear and insightful guide that aligns with my experiences in academic research. The article effectively breaks down the often complex concepts of research aims and objectives, providing a straightforward and accessible explanation. Drawing from my own research endeavors, I appreciate the practical tips offered, such as the need for specificity and clarity when formulating research questions. The article serves as a valuable resource for students and researchers, offering a concise roadmap for crafting well-defined research goals and objectives. Whether you’re a novice or an experienced researcher, this article provides practical insights that contribute to the foundational aspects of a successful research endeavor.

A great thanks for you. it is really amazing explanation. I grasp a lot and one step up to research knowledge.

I really found these tips helpful. Thank you very much Grad Coach.

I found this article helpful. Thanks for sharing this.

thank you so much, the explanation and examples are really helpful

This is a well researched and superbly written article for learners of research methods at all levels in the research topic from conceptualization to research findings and conclusions. I highly recommend this material to university graduate students. As an instructor of advanced research methods for PhD students, I have confirmed that I was giving the right guidelines for the degree they are undertaking.

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Matthew DeCarlo

Chapter Outline

• Empirical vs. ethical questions (4 minute read)
• Characteristics of a good research question (4 minute read)
• Quantitative research questions (7 minute read)
• Qualitative research questions (3 minute read)
• Evaluating and updating your research questions (4 minute read)

Content warning: examples in this chapter include references to sexual violence, sexism, substance use disorders, homelessness, domestic violence, the child welfare system, cissexism and heterosexism, and truancy and school discipline.

9.1 Empirical vs. ethical questions

Learning objectives.

Learners will be able to…

• Define empirical questions and provide an example
• Define ethical questions and provide an example

Writing a good research question is an art and a science. It is a science because you have to make sure it is clear, concise, and well-developed. It is an art because often your language needs “wordsmithing” to perfect and clarify the meaning. This is an exciting part of the research process; however, it can also be one of the most stressful.

Creating a good research question begins by identifying a topic you are interested in studying. At this point, you already have a working question. You’ve been applying it to the exercises in each chapter, and after reading more about your topic in the scholarly literature, you’ve probably gone back and revised your working question a few times. We’re going to continue that process in more detail in this chapter. Keep in mind that writing research questions is an iterative process, with revisions happening week after week until you are ready to start your project.

Empirical vs. ethical questions

When it comes to research questions, social science is best equipped to answer empirical questions —those that can be answered by real experience in the real world—as opposed to  ethical questions —questions about which people have moral opinions and that may not be answerable in reference to the real world. While social workers have explicit ethical obligations (e.g., service, social justice), research projects ask empirical questions to help actualize and support the work of upholding those ethical principles.

In order to help you better understand the difference between ethical and empirical questions, let’s consider a topic about which people have moral opinions. How about SpongeBob SquarePants? [1] In early 2005, members of the conservative Christian group Focus on the Family (2005) [2] denounced this seemingly innocuous cartoon character as “morally offensive” because they perceived his character to be one that promotes a “pro-gay agenda.” Focus on the Family supported their claim that SpongeBob is immoral by citing his appearance in a children’s video designed to promote tolerance of all family forms (BBC News, 2005). [3] They also cited SpongeBob’s regular hand-holding with his male sidekick Patrick as further evidence of his immorality.

So, can we now conclude that SpongeBob SquarePants is immoral? Not so fast. While your mother or a newspaper or television reporter may provide an answer, a social science researcher cannot. Questions of morality are ethical, not empirical. Of course, this doesn’t mean that social science researchers cannot study opinions about or social meanings surrounding SpongeBob SquarePants (Carter, 2010). [4] We study humans after all, and as you will discover in the following chapters of this textbook, we are trained to utilize a variety of scientific data-collection techniques to understand patterns of human beliefs and behaviors. Using these techniques, we could find out how many people in the United States find SpongeBob morally reprehensible, but we could never learn, empirically, whether SpongeBob is in fact morally reprehensible.

Let’s consider an example from a recent MSW research class I taught. A student group wanted to research the penalties for sexual assault. Their original research question was: “How can prison sentences for sexual assault be so much lower than the penalty for drug possession?” Outside of the research context, that is a darn good question! It speaks to how the War on Drugs and the patriarchy have distorted the criminal justice system towards policing of drug crimes over gender-based violence.

Unfortunately, it is an ethical question, not an empirical one. To answer that question, you would have to draw on philosophy and morality, answering what it is about human nature and society that allows such unjust outcomes. However, you could not answer that question by gathering data about people in the real world. If I asked people that question, they would likely give me their opinions about drugs, gender-based violence, and the criminal justice system. But I wouldn’t get the real answer about why our society tolerates such an imbalance in punishment.

As the students worked on the project through the semester, they continued to focus on the topic of sexual assault in the criminal justice system. Their research question became more empirical because they read more empirical articles about their topic. One option that they considered was to evaluate intervention programs for perpetrators of sexual assault to see if they reduced the likelihood of committing sexual assault again. Another option they considered was seeing if counties or states with higher than average jail sentences for sexual assault perpetrators had lower rates of re-offense for sexual assault. These projects addressed the ethical question of punishing perpetrators of sexual violence but did so in a way that gathered and analyzed empirical real-world data. Our job as social work researchers is to gather social facts about social work issues, not to judge or determine morality.

Key Takeaways

• Empirical questions are distinct from ethical questions.
• There are usually a number of ethical questions and a number of empirical questions that could be asked about any single topic.
• While social workers may research topics about which people have moral opinions, a researcher’s job is to gather and analyze empirical data.
• Take a look at your working question. Make sure you have an empirical question, not an ethical one. To perform this check, describe how you could find an answer to your question by conducting a study, like a survey or focus group, with real people.

9.2 Characteristics of a good research question

• Identify and explain the key features of a good research question
• Explain why it is important for social workers to be focused and clear with the language they use in their research questions

Now that you’ve made sure your working question is empirical, you need to revise that working question into a formal research question. So, what makes a good research question? First, it is generally written in the form of a question. To say that your research question is “the opioid epidemic” or “animal assisted therapy” or “oppression” would not be correct. You need to frame your topic as a question, not a statement. A good research question is also one that is well-focused. A well-focused question helps you tune out irrelevant information and not try to answer everything about the world all at once. You could be the most eloquent writer in your class, or even in the world, but if the research question about which you are writing is unclear, your work will ultimately lack direction.

In addition to being written in the form of a question and being well-focused, a good research question is one that cannot be answered with a simple yes or no. For example, if your interest is in gender norms, you could ask, “Does gender affect a person’s performance of household tasks?” but you will have nothing left to say once you discover your yes or no answer. Instead, why not ask, about the relationship between gender and household tasks. Alternatively, maybe we are interested in how or to what extent gender affects a person’s contributions to housework in a marriage? By tweaking your question in this small way, you suddenly have a much more fascinating question and more to say as you attempt to answer it.

A good research question should also have more than one plausible answer. In the example above, the student who studied the relationship between gender and household tasks had a specific interest in the impact of gender, but she also knew that preferences might be impacted by other factors. For example, she knew from her own experience that her more traditional and socially conservative friends were more likely to see household tasks as part of the female domain, and were less likely to expect their male partners to contribute to those tasks. Thinking through the possible relationships between gender, culture, and household tasks led that student to realize that there were many plausible answers to her questions about how  gender affects a person’s contribution to household tasks. Because gender doesn’t exist in a vacuum, she wisely felt that she needed to consider other characteristics that work together with gender to shape people’s behaviors, likes, and dislikes. By doing this, the student considered the third feature of a good research question–she thought about relationships between several concepts. While she began with an interest in a single concept—household tasks—by asking herself what other concepts (such as gender or political orientation) might be related to her original interest, she was able to form a question that considered the relationships  among  those concepts.

This student had one final component to consider. Social work research questions must contain a target population. Her study would be very different if she were to conduct it on older adults or immigrants who just arrived in a new country. The target population is the group of people whose needs your study addresses. Maybe the student noticed issues with household tasks as part of her social work practice with first-generation immigrants, and so she made it her target population. Maybe she wants to address the needs of another community. Whatever the case, the target population should be chosen while keeping in mind social work’s responsibility to work on behalf of marginalized and oppressed groups.

In sum, a good research question generally has the following features:

• It is written in the form of a question
• It is clearly written
• It cannot be answered with “yes” or “no”
• It has more than one plausible answer
• It considers relationships among multiple variables
• It is specific and clear about the concepts it addresses
• It includes a target population
• A poorly focused research question can lead to the demise of an otherwise well-executed study.
• Research questions should be clearly worded, consider relationships between multiple variables, have more than one plausible answer, and address the needs of a target population.

Okay, it’s time to write out your first draft of a research question.

• Once you’ve done so, take a look at the checklist in this chapter and see if your research question meets the criteria to be a good one.

Brainstorm whether your research question might be better suited to quantitative or qualitative methods.

• Describe why your question fits better with quantitative or qualitative methods.
• Provide an alternative research question that fits with the other type of research method.

9.3 Quantitative research questions

• Describe how research questions for exploratory, descriptive, and explanatory quantitative questions differ and how to phrase them
• Identify the differences between and provide examples of strong and weak explanatory research questions

Quantitative descriptive questions

The type of research you are conducting will impact the research question that you ask. Probably the easiest questions to think of are quantitative descriptive questions. For example, “What is the average student debt load of MSW students?” is a descriptive question—and an important one. We aren’t trying to build a causal relationship here. We’re simply trying to describe how much debt MSW students carry. Quantitative descriptive questions like this one are helpful in social work practice as part of community scans, in which human service agencies survey the various needs of the community they serve. If the scan reveals that the community requires more services related to housing, child care, or day treatment for people with disabilities, a nonprofit office can use the community scan to create new programs that meet a defined community need.

Quantitative descriptive questions will often ask for percentage, count the number of instances of a phenomenon, or determine an average. Descriptive questions may only include one variable, such as ours about student debt load, or they may include multiple variables. Because these are descriptive questions, our purpose is not to investigate causal relationships between variables. To do that, we need to use a quantitative explanatory question.

Quantitative explanatory questions

Most studies you read in the academic literature will be quantitative and explanatory. Why is that? If you recall from Chapter 2 , explanatory research tries to build nomothetic causal relationships. They are generalizable across space and time, so they are applicable to a wide audience. The editorial board of a journal wants to make sure their content will be useful to as many people as possible, so it’s not surprising that quantitative research dominates the academic literature.

Structurally, quantitative explanatory questions must contain an independent variable and dependent variable. Questions should ask about the relationship between these variables. The standard format I was taught in graduate school for an explanatory quantitative research question is: “What is the relationship between [independent variable] and [dependent variable] for [target population]?” You should play with the wording for your research question, revising that standard format to match what you really want to know about your topic.

Let’s take a look at a few more examples of possible research questions and consider the relative strengths and weaknesses of each. Table 9.1 does just that. While reading the table, keep in mind that I have only noted what I view to be the most relevant strengths and weaknesses of each question. Certainly each question may have additional strengths and weaknesses not noted in the table. Each of these questions is drawn from student projects in my research methods classes and reflects the work of many students on their research question over many weeks.

Making it more specific

A good research question should also be specific and clear about the concepts it addresses. A student investigating gender and household tasks knows what they mean by “household tasks.” You likely also have an impression of what “household tasks” means. But are your definition and the student’s definition the same? A participant in their study may think that managing finances and performing home maintenance are household tasks, but the researcher may be interested in other tasks like childcare or cleaning. The only way to ensure your study stays focused and clear is to be specific about what you mean by a concept. The student in our example could pick a specific household task that was interesting to them or that the literature indicated was important—for example, childcare. Or, the student could have a broader view of household tasks, one that encompasses childcare, food preparation, financial management, home repair, and care for relatives. Any option is probably okay, as long as the researcher is clear on what they mean by “household tasks.” Clarifying these distinctions is important as we look ahead to specifying how your variables will be measured in Chapter 11 .

Table 9.2 contains some “watch words” that indicate you may need to be more specific about the concepts in your research question.

It can be challenging to be this specific in social work research, particularly when you are just starting out your project and still reading the literature. If you’ve only read one or two articles on your topic, it can be hard to know what you are interested in studying. Broad questions like “What are the causes of chronic homelessness, and what can be done to prevent it?” are common at the beginning stages of a research project as working questions. However, moving from working questions to research questions in your research proposal requires that you examine the literature on the topic and refine your question over time to be more specific and clear. Perhaps you want to study the effect of a specific anti-homelessness program that you found in the literature. Maybe there is a particular model to fighting homelessness, like Housing First or transitional housing, that you want to investigate further. You may want to focus on a potential cause of homelessness such as LGBTQ+ discrimination that you find interesting or relevant to your practice. As you can see, the possibilities for making your question more specific are almost infinite.

Quantitative exploratory questions

In exploratory research, the researcher doesn’t quite know the lay of the land yet. If someone is proposing to conduct an exploratory quantitative project, the watch words highlighted in Table 9.2 are not problematic at all. In fact, questions such as “What factors influence the removal of children in child welfare cases?” are good because they will explore a variety of factors or causes. In this question, the independent variable is less clearly written, but the dependent variable, family preservation outcomes, is quite clearly written. The inverse can also be true. If we were to ask, “What outcomes are associated with family preservation services in child welfare?”, we would have a clear independent variable, family preservation services, but an unclear dependent variable, outcomes. Because we are only conducting exploratory research on a topic, we may not have an idea of what concepts may comprise our “outcomes” or “factors.” Only after interacting with our participants will we be able to understand which concepts are important.

Remember that exploratory research is appropriate only when the researcher does not know much about topic because there is very little scholarly research. In our examples above, there is extensive literature on the outcomes in family reunification programs and risk factors for child removal in child welfare. Make sure you’ve done a thorough literature review to ensure there is little relevant research to guide you towards a more explanatory question.

• Descriptive quantitative research questions are helpful for community scans but cannot investigate causal relationships between variables.
• Explanatory quantitative research questions must include an independent and dependent variable.
• Exploratory quantitative research questions should only be considered when there is very little previous research on your topic.
• Identify the type of research you are engaged in (descriptive, explanatory, or exploratory).
• Create a quantitative research question for your project that matches with the type of research you are engaged in.

Preferably, you should be creating an explanatory research question for quantitative research.

9.4 Qualitative research questions

• List the key terms associated with qualitative research questions
• Distinguish between qualitative and quantitative research questions

Qualitative research questions differ from quantitative research questions. Because qualitative research questions seek to explore or describe phenomena, not provide a neat nomothetic explanation, they are often more general and openly worded. They may include only one concept, though many include more than one. Instead of asking how one variable causes changes in another, we are instead trying to understand the experiences ,  understandings , and  meanings that people have about the concepts in our research question. These keywords often make an appearance in qualitative research questions.

Let’s work through an example from our last section. In Table 9.1, a student asked, “What is the relationship between sexual orientation or gender identity and homelessness for late adolescents in foster care?” In this question, it is pretty clear that the student believes that adolescents in foster care who identify as LGBTQ+ may be at greater risk for homelessness. This is a nomothetic causal relationship—LGBTQ+ status causes changes in homelessness.

However, what if the student were less interested in  predicting  homelessness based on LGBTQ+ status and more interested in  understanding  the stories of foster care youth who identify as LGBTQ+ and may be at risk for homelessness? In that case, the researcher would be building an idiographic causal explanation . The youths whom the researcher interviews may share stories of how their foster families, caseworkers, and others treated them. They may share stories about how they thought of their own sexuality or gender identity and how it changed over time. They may have different ideas about what it means to transition out of foster care.

Because qualitative questions usually center on idiographic causal relationships, they look different than quantitative questions. Table 9.3 below takes the final research questions from Table 9.1 and adapts them for qualitative research. The guidelines for research questions previously described in this chapter still apply, but there are some new elements to qualitative research questions that are not present in quantitative questions.

• Qualitative research questions often ask about lived experience, personal experience, understanding, meaning, and stories.
• Qualitative research questions may be more general and less specific.
• Qualitative research questions may also contain only one variable, rather than asking about relationships between multiple variables.

Qualitative research questions have one final feature that distinguishes them from quantitative research questions: they can change over the course of a study. Qualitative research is a reflexive process, one in which the researcher adapts their approach based on what participants say and do. The researcher must constantly evaluate whether their question is important and relevant to the participants. As the researcher gains information from participants, it is normal for the focus of the inquiry to shift.

For example, a qualitative researcher may want to study how a new truancy rule impacts youth at risk of expulsion. However, after interviewing some of the youth in their community, a researcher might find that the rule is actually irrelevant to their behavior and thoughts. Instead, their participants will direct the discussion to their frustration with the school administrators or the lack of job opportunities in the area. This is a natural part of qualitative research, and it is normal for research questions and hypothesis to evolve based on information gleaned from participants.

However, this reflexivity and openness unacceptable in quantitative research for good reasons. Researchers using quantitative methods are testing a hypothesis, and if they could revise that hypothesis to match what they found, they could never be wrong! Indeed, an important component of open science and reproducability is the preregistration of a researcher’s hypotheses and data analysis plan in a central repository that can be verified and replicated by reviewers and other researchers. This interactive graphic from 538 shows how an unscrupulous research could come up with a hypothesis and theoretical explanation  after collecting data by hunting for a combination of factors that results in a statistically significant relationship. This is an excellent example of how the positivist assumptions behind quantitative research and intepretivist assumptions behind qualitative research result in different approaches to social science.

• Qualitative research questions often contain words or phrases like “lived experience,” “personal experience,” “understanding,” “meaning,” and “stories.”
• Qualitative research questions can change and evolve over the course of the study.
• Using the guidance in this chapter, write a qualitative research question. You may want to use some of the keywords mentioned above.

9.5 Evaluating and updating your research questions

• Evaluate the feasibility and importance of your research questions
• Begin to match your research questions to specific designs that determine what the participants in your study will do

Feasibility and importance

As you are getting ready to finalize your research question and move into designing your research study, it is important to check whether your research question is feasible for you to answer and what importance your results will have in the community, among your participants, and in the scientific literature

Key questions to consider when evaluating your question’s feasibility include:

• Do you have access to the data you need?
• Will you be able to get consent from stakeholders, gatekeepers, and others?
• Does your project pose risk to individuals through direct harm, dual relationships, or breaches in confidentiality? (see Chapter 6 for more ethical considerations)
• Are you competent enough to complete the study?
• Do you have the resources and time needed to carry out the project?

Key questions to consider when evaluating the importance of your question include:

• Can we answer your research question simply by looking at the literature on your topic?
• How does your question add something new to the scholarly literature? (raises a new issue, addresses a controversy, studies a new population, etc.)
• How will your target population benefit, once you answer your research question?
• How will the community, social work practice, and the broader social world benefit, once you answer your research question?
• Using the questions above, check whether you think your project is feasible for you to complete, given the constrains that student projects face.
• Realistically, explore the potential impact of your project on the community and in the scientific literature. Make sure your question cannot be answered by simply reading more about your topic.

Matching your research question and study design

This chapter described how to create a good quantitative and qualitative research question. In Parts 3 and 4 of this textbook, we will detail some of the basic designs like surveys and interviews that social scientists use to answer their research questions. But which design should you choose?

As with most things, it all depends on your research question. If your research question involves, for example, testing a new intervention, you will likely want to use an experimental design. On the other hand, if you want to know the lived experience of people in a public housing building, you probably want to use an interview or focus group design.

We will learn more about each one of these designs in the remainder of this textbook. We will also learn about using data that already exists, studying an individual client inside clinical practice, and evaluating programs, which are other examples of designs. Below is a list of designs we will cover in this textbook:

• Surveys: online, phone, mail, in-person
• Experiments: classic, pre-experiments, quasi-experiments
• Interviews: in-person or via phone or videoconference
• Focus groups: in-person or via videoconference
• Content analysis of existing data
• Secondary data analysis of another researcher’s data
• Program evaluation

The design of your research study determines what you and your participants will do. In an experiment, for example, the researcher will introduce a stimulus or treatment to participants and measure their responses. In contrast, a content analysis may not have participants at all, and the researcher may simply read the marketing materials for a corporation or look at a politician’s speeches to conduct the data analysis for the study.

I imagine that a content analysis probably seems easier to accomplish than an experiment. However, as a researcher, you have to choose a research design that makes sense for your question and that is feasible to complete with the resources you have. All research projects require some resources to accomplish. Make sure your design is one you can carry out with the resources (time, money, staff, etc.) that you have.

There are so many different designs that exist in the social science literature that it would be impossible to include them all in this textbook. The purpose of the subsequent chapters is to help you understand the basic designs upon which these more advanced designs are built. As you learn more about research design, you will likely find yourself revising your research question to make sure it fits with the design. At the same time, your research question as it exists now should influence the design you end up choosing. There is no set order in which these should happen. Instead, your research project should be guided by whether you can feasibly carry it out and contribute new and important knowledge to the world.

• Research questions must be feasible and important.
• Research questions must match study design.
• Based on what you know about designs like surveys, experiments, and interviews, describe how you might use one of them to answer your research question.
• You may want to refer back to Chapter 2 which discusses how to get raw data about your topic and the common designs used in student research projects.
• Not familiar with SpongeBob SquarePants? You can learn more about him on Nickelodeon’s site dedicated to all things SpongeBob:  http://www.nick.com/spongebob-squarepants/ ↵
• Focus on the Family. (2005, January 26). Focus on SpongeBob.  Christianity Today . Retrieved from  http://www.christianitytoday.com/ct/2005/januaryweb-only/34.0c.html ↵
• BBC News. (2005, January 20). US right attacks SpongeBob video. Retrieved from:  http://news.bbc.co.uk/2/hi/americas/4190699.stm ↵
• In fact, an MA thesis examines representations of gender and relationships in the cartoon: Carter, A. C. (2010).  Constructing gender and   relationships in “SpongeBob SquarePants”: Who lives in a pineapple under the sea . MA thesis, Department of Communication, University of South Alabama, Mobile, AL. ↵
• Writing from an outline (10 minute read plus an 8 minute video, and then a 15 minute video)
• Writing your literature review (30 minute read)

Content warning: TBA

6.1: Writing from an outline

Learners will be able to...

• Integrate facts from the literature into scholarly writing
• Experiment with different approaches to integrating information that do not involve direct quotations from other authors

Congratulations! By now, you should have discovered, retrieved, evaluated, synthesized, and organized the information you need for your literature review. It’s now time to turn that stack of articles, papers, and notes into a literature review–it’s time to start writing!

The first step in research writing is outlining. In Chapter 4, we reviewed how to build a topical outline using quotations and facts from other authors. Use that outline (or one you write now) as a way to organize your thoughts.

Watch this video from Nicholas Cifuentes-Goodbody on Outlining . As he highlights, outlining is like building a mise en place before a meal--arranging your ingredients in an orderly way so you can create your masterpiece.

From quotations to original writing

Much like combining ingredients on a kitchen countertop, you will need to mix your ingredients together. That means you will not be relying extensively on quotations from other authors in your literature review. In moving from an outline to a literature review, the key intellectual move is relying on your own ideas about the literature, rather than quoting extensively from other sources.

Integrating ideas from other authors

Watch this video from Nicholas Cifuentes-Goodbody on using quotations in academic writing . In the video, he reviews a few different techniques to integrate quotations or ideas from other authors into your writing. All literature reviews use the ideas from other authors, but it's important not to overuse others' words. Your literature review is evaluated by your professor based on how well it shows  you are able to make connections between different facts in the scientific literature. The examples in this section should highlight how to get other people's words out of the way of your own. Use these strategies to diversify your writing and show your readers how your sources contributed to your work.

1. Make a claim without a quote

Claim ( Citation )

Some view cities as the storehouse of culture and creativity, and propose that urbanization is a consequence of the attractiveness of these social benefits ( Mumford, 1961 ).

More information

Oftentimes you do not need to directly quote a source to convey its conclusions or arguments – and some disciplines discourage quoting directly! Rather you can paraphrase the main point of a paper in your own words and provide an in-text citation. A benefit of using this strategy is that you can offer support for a claim without using a whole paragraph to introduce and frame a quote. You should make sure that you fully understand the paper's argument and that you are following university citation guidelines before attempting to paraphrase something from a paper.

2. Make a claim that is supported by two or more sources:

Claim ( Citation 1 ; Citation 2 ).

Reviews of this literature concede difficulty in making direct comparisons of emission levels across different sets of analysis ( Bader and Bleischwitz, 2009 ; Kennedy et al., 2009 ; Ramaswami et al., 2012 ).

Sometimes multiple sources support your claim, or there are two major publications that deserve credit for providing evidence on a topic. This is a perfect time to use multiple citations. You can cite two, three, or more sources in a single sentence!

Make a claim that has been supported in multiple contexts:

Context 1 ( Citation ), Context 2 ( Citation ), Context 3 ( Citation ).

These results are supported by more recent research on transportation energy consumption ( Liddle, 2014 ), electricity consumption in buildings ( Lariviere and Lafrance, 1999 ), and overall urban GHG emissions ( Marcotullio et al., 2013b ).

More information:

Use this citation strategy when you want to show that a body of research has found support for some claim across several different contexts. This can show the robustness of an effect or phenomenon and can give your claim some added validity

3. Quote important or unique terms

Claim " Term " ( Citation ).

The spatial implications of this thinking are manifest in the " concentric ring model " of urban expansion and its variants ( Harris and Ullman, 1945 ).

While block or even whole-sentence citations are rare in most research papers in the science and social science disciplines, there is often a need to quote specific terms or phrases that were first coined by a certain source or that were well-explained in a specific paper.

4. Quoting definitions

Contextualize quote , " important word or phrase ."

Role conflict is defined as "A situation in which contradictory, competing, or incompatible expectations are placed on an individual by two or more roles held at the same time" (Open Sociology Dictionary, 2023); whereas, role strain is defined as "a situation caused by higher-than-expected demands placed on an individual performing a specific role that leads to difficulty or stress" (Open Sociology Dictionary, 2023). In our study, we hypothesize that caregivers who reenter higher education experience role conflict between school work, paid work, and care work. Further, we hypothesize that this conflict is greater in individuals who had experienced role strain in employment or caregiving prior to entering college.

A direct quotation can bring attention to specific language in your source. When someone puts something perfectly, you can use a quotation to convey the identical meaning in your work. Definitions are an excellent example of when to use a quotation. In other cases, there may be quotations from important thinkers, clients or community members, and others whose specific wording is important.

I encourage you to use few, if any, direct quotations in your literature review. Personally, I think most students are scared of looking stupid and would rather use a good quotation than risk not getting it right. If you are a student who considers themselves a strong writer, this may not sound relevant to you. However, I'm willing to bet that there are many of your peers for whom this describes a particular bit of research anxiety.

When using quotations, make sure to only include the parts of the quotation that are necessary. You do not need to use quotation marks for statistics you use. And I encourage you to find ways to put others' statistics in  your sentences.

Why share information from other sources?

Now that you know some different sentence structures using APA citations, let's examine the purpose behind why you are sharing information from another source. Cited evidence can serve a wide range of purposes in academic papers. These examples will give you an idea of the different ways that you can use citations in your paper.

1. Summarize your source

The studies of Newman and Kenworthy ( 1989, 1999 ) demonstrate a negative relationship between population density and transportation fuel use .

You will help your reader understand your points better if you summarize the key points of a study. Describe the strengths or weaknesses a specific source that has been pivotal in your field. Describe the source's specific methodology, theory, or approach. Be sure to still include a citation. If you mention the name of the author in your text, you still need to provide the date of the study in a parenthetical citation.

2. Cite a method

Despite the popularity of the WUP indicators , they have been routinely criticized because the methodology relies on local- and country-specific definitions of bounding urban areas, resulting in of ten incomparable and widely divergent definitions of the population, density thresholds, or administrative/political units designated ( Satterthwaite, 2007 ).

This is an easy way to give credit to a source that has provided some evidence for the validity of a method or questionnaire. Readers can reference your citation if they are interested in knowing more about the method and its standing in the current literature.

3. Compare sources

Some evidence for this scaling relationship suggests that urban areas with larger population sizes have proportionally smaller energy infrastructures than smaller cities ( Bettencourt et al., 2007 ; Fragkias et al., 2013 ). Other evidence suggests that GHG emissions may increase more than proportionally to population size, such that larger cities exhibit proportionally higher energy demand as they grow than do smaller cities ( Marcotullio et al., 2013 ).

This is one of the most important techniques for creating an effective literature review. This allows you and your readers to consider controversies and discrepancies among the current literature, revealing gaps in the literature or points of contention for further study.

The examples in this guide come from:

Marcotullio, P. J., Hughes, S., Sarzynski, A., Pincetl, S., Sanchez Peña, L., Romero-Lankao, P., Runfola, D. and Seto, K. C. (2014), Urbanization and the carbon cycle: Contributions from social science. Earth's Future, 2: 496–514. doi:10.1002/2014EF000257

Avoiding plagiarism

The most difficult thing about avoiding plagiarism is that reading so much of other people's ideas can make them seem like your own after a while. We recommend you work through this interactive activity on determining how and when to cite other authors.

• Research writing requires outlining, which helps you arrange your facts neatly before writing. It's similar to arranging all of your ingredients before you start cooking.
• Eliminate quotations from your writing as much as possible. Your literature review needs to be your analysis of the literature, not just a summary of other people's good ideas.
• Experiment with the prompts in this chapter as you begin to write your research question. 

6.2 Writing your literature review

• Describe the components of a literature review
• Begin to write your literature review
• Identify the purpose of a problem statement
• Apply the components of a formal argument to your topic
• Use elements of formal writing style, including signposting and transitions
• Recognize commons errors in literature reviews

Writing about research is different than other types of writing. Research writing is not like a journal entry or opinion paper. The goal here is not to apply your research question to your life or growth as a practitioner. Research writing is about the provision and interpretation of facts. The tone should be objective and unbiased, and personal experiences and opinions are excluded. Particularly for students who are used to writing case notes, research writing can be a challenge. That's why its important to normalize getting help! If your professor has not built in peer review, consider setting up a peer review group among your peers. You should also reach out to your academic advisor to see if there are writing services on your campus available to graduate students. No one should feel bad for needing help with something they haven't done before, haven't done in a while, or were never taught how to do. 

If you’ve followed the steps in this chapter, you likely have an outline, summary table, and concept map from which you can begin the writing process. But what do you need to include in your literature review? We’ve mentioned it before, but to summarize, a literature review should:

• Introduce the topic and define its key terms.
• Establish the importance of the topic.
• Provide an overview of the important literature related to the concepts found in the research question.
• Identify gaps or controversies in the literature.
• Point out consistent findings across studies.
• Synthesize that which is known about a topic, rather than just provide a summary of the articles you read.
• Discuss possible implications and directions for future research.

Do you have enough facts and sources to accomplish these tasks? It’s a good time to consult your outlines and notes on each article you plan to include in your literature review. You may also want to consult with your professor on what is expected of you. If there is something you are missing, you may want to jump back to section 2.3 where we discussed how to search for literature. While you can always fill in material, there is the danger that you will start writing without really knowing what you are talking about or what you want to say. For example, if you don’t have a solid definition of your key concepts or a sense of how the literature has developed over time, it will be difficult to make coherent scholarly claims about your topic.

There is no magical point at which one is ready to write. As you consider whether you are ready, it may be useful to ask yourself these questions:

• How will my literature review be organized?
• What section headings will I be using?
• How do the various studies relate to each other?
• What contributions do they make to the field?
• Where are the gaps or limitations in existing research?
• And finally, but most importantly, how does my own research fit into what has already been done?

The problem statement

Scholarly works often begin with a problem statement, which serves two functions. First, it establishes why your topic is a social problem worth studying. Second, it pulls your reader into the literature review. Who would want to read about something unimportant?

A problem statement generally answers the following questions, though these are far from exhaustive:

• Why is this an important problem to study?
• How many people are affected by this problem?
• How does this problem impact other social issues relevant to social work?
• Why is your target population an important one to study?

A strong problem statement, like the rest of your literature review, should be filled with empirical results, theory, and arguments based on the extant literature. A research proposal differs significantly from other more reflective essays you’ve likely completed during your social work studies. If your topic were domestic violence in rural Appalachia, I’m sure you could come up with answers to the above questions without looking at a single source. However, the purpose of the literature review is not to test your intuition, personal experience, or empathy. Instead, research methods are about gaining specific and articulable knowledge to inform action. With a problem statement, you can take a “boring” topic like the color of rooms used in an inpatient psychiatric facility, transportation patterns in major cities, or the materials used to manufacture baby bottles, and help others see the topic as you see it—an important part of the social world that impacts social work practice.

The structure of a literature review

In general, the problem statement belongs at the beginning of the literature review. We usually advise students to spend no more than a paragraph or two for a problem statement. For the rest of your literature review, there is no set formula by which it needs to be organized. However, a literature review generally follows the format of any other essay—Introduction, Body, and Conclusion.

The introduction to the literature review contains a statement or statements about the overall topic. At a minimum, the introduction should define or identify the general topic, issue, or area of concern. You might consider presenting historical background, mentioning the results of a seminal study, and providing definitions of important terms. The introduction may also point to overall trends in what has been previously published on the topic or on conflicts in theory, methodology, evidence, conclusions, or gaps in research and scholarship. We also suggest putting in a few sentences that walk the reader through the rest of the literature review. Highlight your main arguments from the body of the literature review and preview your conclusion. An introduction should let the reader know what to expect from the rest of your review.

The body of your literature review is where you demonstrate your synthesis and analysis of the literature. Again, do not just summarize the literature. We would also caution against organizing your literature review by source—that is, one paragraph for source A, one paragraph for source B, etc. That structure will likely provide an adequate summary of the literature you’ve found, but it would give you almost no synthesis of the literature. That approach doesn’t tell your reader how to put those facts together, it doesn't highlight points of agreement or contention, or how each study builds on the work of others. In short, it does not demonstrate critical thinking.

Organize your review by argument

Instead, use your outlines and notes as a guide what you have to say about the important topics you need to cover. Literature reviews are written from the perspective of an expert in that field. After an exhaustive literature review, you should feel as though you are able to make strong claims about what is true—so make them! There is no need to hide behind “I believe” or “I think.” Put your voice out in front, loud and proud! But make sure you have facts and sources that back up your claims.

I’ve used the term “ argument ” here in a specific way. An argument in writing means more than simply disagreeing with what someone else said, as this classic Monty Python sketch demonstrates. Toulman, Rieke, and Janik (1984) identify six elements of an argument:

• Claim: the thesis statement—what you are trying to prove
• Grounds: theoretical or empirical evidence that supports your claim
• Warrant: your reasoning (rule or principle) connecting the claim and its grounds
• Backing: further facts used to support or legitimize the warrant
• Qualifier: acknowledging that the argument may not be true for all cases
• Rebuttal: considering both sides (as cited in Burnette, 2012) [1]

Let’s walk through an example. If I were writing a literature review on a negative income tax, a policy in which people in poverty receive an unconditional cash stipend from the government each month equal to the federal poverty level, I would want to lay out the following:

• Claim: the negative income tax is superior to other forms of anti-poverty assistance.
• Grounds: data comparing negative income tax recipients to people receiving anti-poverty assistance in existing programs, theory supporting a negative income tax, data from evaluations of existing anti-poverty programs, etc.
• Warrant: cash-based programs like the negative income tax are superior to existing anti-poverty programs because they allow the recipient greater self-determination over how to spend their money.
• Backing: data demonstrating the beneficial effects of self-determination on people in poverty.
• Qualifier: the negative income tax does not provide taxpayers and voters with enough control to make sure people in poverty are not wasting financial assistance on frivolous items.
• Rebuttal: policy should be about empowering the oppressed, not protecting the taxpayer, and there are ways of addressing taxpayer spending concerns through policy design.

Like any effective argument, your literature review must have some kind of structure. For example, it might begin by describing a phenomenon in a general way along with several studies that provide some detail, then describing two or more competing theories of the phenomenon, and finally presenting a hypothesis to test one or more of the theories. Or, it might describe one phenomenon, then describe another that seems inconsistent with the first, then propose a theory that resolves the inconsistency, and finally present a hypothesis to test that theory. In applied research, it might describe a phenomenon or theory, then describe how that phenomenon or theory applies to some important real-world situation, and finally, may suggest a way to test whether it does, in fact, apply to that situation.

Use signposts

Another important issue is  signposting . It may not be a term you are familiar with, but you are likely familiar with the concept. Signposting refers to the words used to identify the organization and structure of your literature review to your reader. The most basic form of signposting is using a topic sentence at the beginning of each paragraph. A topic sentence introduces the argument you plan to make in that paragraph. For example, you might start a paragraph stating, “There is strong disagreement in the literature as to whether psychedelic drugs cause people to develop psychotic disorders, or whether psychotic disorders cause people to use psychedelic drugs.” Within that paragraph, your reader would likely assume you will present evidence for both arguments. The concluding sentence of your paragraph should address the topic sentence, discussing how the facts and arguments from the paragraph you've written support a specific conclusion. To continue with our example, I might say, “There is likely a reciprocal effect in which both the use of psychedelic drugs worsens pre-psychotic symptoms and worsening psychosis increases the desire to use psychedelic drugs.”

Signposting also involves using headings and subheadings. Your literature review will use APA formatting, which means you need to follow their rules for bolding, capitalization, italicization, and indentation of headings. Headings help your reader understand the structure of your literature review. They can also help if the reader gets lost and needs to re-orient themselves within the document. We often tell our students to assume we know nothing (they don’t mind) and need to be shown exactly where they are addressing each part of the literature review. It’s like walking a small child around, telling them “First we’ll do this, then we’ll do that, and when we’re done, we’ll know this!”

Another way to use signposting is to open each paragraph with a sentence that links the topic of the paragraph with the one before it. Alternatively, one could end each paragraph with a sentence that links it with the next paragraph. For example, imagine we wanted to link a paragraph about barriers to accessing healthcare with one about the relationship between the patient and physician. We could use a transition sentence like this: “Even if patients overcome these barriers to accessing care, the physician-patient relationship can create new barriers to positive health outcomes.” A transition sentence like this builds a connection between two distinct topics. Transition sentences are also useful within paragraphs. They tell the reader how to consider one piece of information in light of previous information. Even simple transitional words like 'however' and 'similarly' can help demonstrate critical thinking and link each building block of your argument together.

Many beginning researchers have difficulty incorporating transitions into their writing. Let’s look at an example. Instead of beginning a sentence or paragraph by launching into a description of a study, such as “Williams (2004) found that…,” it is better to start by indicating something about why you are describing this particular study. Here are some simple examples:

• Another example of this phenomenon comes from the work of Williams (2004)...
• Williams (2004) offers one explanation of this phenomenon...
• An alternative perspective has been provided by Williams (2004)...

Now that we know to use signposts, the natural question is “What goes on the signposts?” First, it is important to start with an outline of the main points that you want to make, organized in the order you want to make them. The basic structure of your argument should then be apparent from the outline itself. Unfortunately, there is no formula we can give you that will work for everyone, but we can provide some general pointers on structuring your literature review.

The literature review tends to move from general to more specific ideas. You can build a review by identifying areas of consensus and areas of disagreement. You may choose to present historical studies—preferably seminal studies that are of significant importance—and close with the most recent research. Another approach is to start with the most distantly related facts and literature and then report on those most closely related to your research question. You could also compare and contrast valid approaches, features, characteristics, theories – that is, one approach, then a second approach, followed by a third approach.

Here are some additional tips for writing the body of your literature review:

• Start broad and then narrow down to more specific information.
• When appropriate, cite two or more sources for a single point, but avoid long strings of references for a single idea.
• Use quotes sparingly. Quotations for definitions are okay, but reserve quotes for when something is said so well you couldn’t possible phrase it differently. Never use quotes for statistics.
• Paraphrase when you need to relay the specific details within an article
• Include only the aspects of the study that are relevant to your literature review. Don’t insert extra facts about a study just to take up space.
• Avoid reflective, personal writing. It is traditional to avoid using first-person language (I, we, us, etc.).
• Avoid informal language like contractions, idioms, and rhetorical questions.
• Note any sections of your review that lack citations from the literature. Your arguments need to be based in empirical or theoretical facts. Do not approach this like a reflective journal entry.
• Point out consistent findings and emphasize stronger studies over weaker ones.
• Point out important strengths and weaknesses of research studies, as well as contradictions and inconsistent findings.
• Implications and suggestions for further research (where there are gaps in the current literature) should be specific.

The conclusion should summarize your literature review, discuss implications, and create a space for further research needed in this area. Your conclusion, like the rest of your literature review, should make a point. What are the important implications of your literature review? How do they inform the question you are trying to answer?

You should consult with your professor and the course syllabus about the final structure your literature review should take. Here is an example of one possible structure:

• Establish the importance of the topic
• Number and type of people affected
• Seriousness of the impact
• Physical, psychological, economic, social, or spiritual consequences of the problem
• Definitions of key terms
• Supporting evidence
• Common findings across studies, gaps in the literature
• Research question(s) and hypothesis(es)

Editing your literature review

Literature reviews are more than a summary of the publications you find on a topic. As you have seen in this brief introduction, literature reviews represent a very specific type of research, analysis, and writing. We will explore these topics further in upcoming chapters. As you begin your literature review, here are some common errors to avoid:

• Accepting a researcher’s finding as valid without evaluating methodology and data
• Ignoring contrary findings and alternative interpretations
• Using findings that are not clearly related to your own study or using findings that are too general
• Dedicating insufficient time to literature searching
• Reporting statistical results from a single study, rather than synthesizing the results of multiple studies to provide a comprehensive view of the literature on a topic
• Relying too heavily on secondary sources
• Overusing quotations
• Not justifying arguments using specific facts or theories from the literature

For your literature review, remember that your goal is to construct an argument for the importance of your research question. As you start editing your literature review, make sure it is balanced. Accurately report common findings, areas where studies contradict each other, new theories or perspectives, and how studies cause us to reaffirm or challenge our understanding of your topic.

It is acceptable to argue that the balance of the research supports the existence of a phenomenon or is consistent with a theory (and that is usually the best that researchers in social work can hope for), but it is not acceptable to ignore contradictory evidence. A large part of what makes a research question interesting is uncertainty about its answer (University of Minnesota, 2016). [2]

In addition to subjectivity and bias, writer's block can obstruct the completion of your literature review. Often times, writer’s block can stem from confusing the creating and editing parts of the writing process. Many writers often start by simply trying to type out what they want to say, regardless of how good it is. Author Anne Lamott (1995) [3] terms these “shitty first drafts,” and we all write them. They are a natural and important part of the writing process.

Even if you have a detailed outline from which to work, the words are not going to fall into place perfectly the first time you start writing. You should consider turning off the editing and critiquing part of your brain for a while and allow your thoughts to flow. Don’t worry about putting a correctly formatted internal citation (as long as  you know which source you used there) when you first write. Just get the information out. Only after you’ve reached a natural stopping point might you go back and edit your draft for grammar, APA style, organization, flow, and more. Divorcing the writing and editing process can go a long way to addressing writer’s block—as can picking a topic about which you have something to say!

As you are editing, keep in mind these questions adapted from Green (2012): [4]

• Content: Have I clearly stated the main idea or purpose of the paper? Is the thesis or focus clearly presented and appropriate for the reader?
• Organization: How well is it structured? Is the organization spelled out and easy to follow for the reader ?
• Flow: Is there a logical flow from section to section, paragraph to paragraph, sentence to sentence? Are there transitions between and within paragraphs that link ideas together?
• Development: Have I validated the main idea with supporting material? Are supporting data sufficient? Does the conclusion match the introduction?
• Form: Are there any APA style issues, redundancy, problematic wording and terminology (always know the definition of any word you use!), flawed sentence constructions and selection, spelling, and punctuation?

Social workers use the APA style guide to format and structure their literature reviews. Most students know APA style only as it relates to internal and external citations. If you are confused about them, consult this amazing APA style guide from the University of Texas-Arlington library. Your university's library likely has resources they created to help you with APA style, and you can meet with a librarian or your professor to talk about formatting questions you have. Make sure you budget in a few hours at the end of each project to build a correctly formatted references page and check your internal citations. The highest quality online source of information on APA style is the APA style blog, where you can search questions and answers from the

Of course, APA style is about much more than knowing there is a period after "et al." or citing the location a book was published. APA style is also about what the profession considers to be good writing. If you haven't picked up an APA publication manual because you use citation generators, know that I did the same thing when I was in school. Purchasing the APA manual can help you with a common problem we hear about from students. Every professor (and every website about APA style) seems to have their own peculiar idea of "correct" APA style that you can, if needed, demonstrate is not accurate.

• A literature review is not a book report. Do not organize it by article, with one paragraph for each source in your references. Instead, organize it based on the key ideas and arguments.
• The problem statement draws the reader into your topic by highlighting the importance of the topic to social work and to society overall.
• Signposting is an important component of academic writing that helps your reader follow the structure of your argument and of your literature review.
• Transitions demonstrate critical thinking and help guide your reader through your arguments.
• Editing and writing are separate processes.
• Consult with an APA style guide or a librarian to help you format your paper.

Look at your professor's prompt for the literature review component of your research proposal (or if you don't have one, use the example question provided in this section).

• Write 2-3 facts you would use to address each question or component in the prompt.
• Reflect on which questions you have a lot of information about and which you need to gather more information about in order to answer adequately.

Outline the structure of your literature review using your concept map from Section 5.2 as a guide.

• Identify the key arguments you will make and how they are related to each other.
• Reflect on topic sentences and concluding sentences you would use for each argument.
• Human subjects research (19 minute read)
• Specific ethical issues to consider (12 minute read)
• Benefits and harms of research across the ecosystem (7 minute read)
• Being an ethical researcher (8 minute read)

Content warning: examples in this chapter contain references to numerous incidents of unethical medical experimentation (e.g. intentionally injecting diseases into unknowing participants, withholding proven treatments), social experimentation under extreme conditions (e.g. being directed to deliver electric shocks to test obedience), violations of privacy, gender and racial inequality, research with people who are incarcerated or on parole, experimentation on animals, abuse of people with Autism, community interactions with law enforcement, WWII, the Holocaust, and Nazi activities (especially related to research on humans).

With your literature review underway, you are ready to begin thinking in more concrete terms about your research topic. Recall our discussion in Chapter 2 on practical and ethical considerations that emerge as part of the research process. In this chapter, we will expand on the ethical boundaries that social scientists must abide by when conducting human subjects research. As a result of reading this chapter, you should have a better sense of what is possible and ethical for the research project you create.

6.1 Human subjects research

• Understand what we mean by ethical research and why it is important
• Understand some of the egregious ethical violations that have occurred throughout history

While all research comes with its own set of ethical concerns, those associated with research conducted on human subjects vary dramatically from those of research conducted on nonliving entities. The US Department of Health and Human Services (USDHHS) defines a human subject as “a living individual about whom an investigator (whether professional or student) conducting research obtains (1) data through intervention or interaction with the individual, or (2) identifiable private information” (USDHHS, 1993, para. 1). [5] Some researchers prefer the term "participants" to "subjects'" as it acknowledges the agency of people who participate in the study. For our purposes, we will use the two terms interchangeably.

In some states, human subjects also include deceased individuals and human fetal materials. Nonhuman research subjects, on the other hand, are objects or entities that investigators manipulate or analyze in the process of conducting research. Nonhuman research subjects typically include sources such as newspapers, historical documents, pieces of clothing, television shows, buildings, and even garbage (to name just a few), that are analyzed for unobtrusive research projects. Unsurprisingly, research on human subjects is regulated much more heavily than research on nonhuman subjects. This is why many student research projects use data that is publicly available, rather than recruiting their own study participants. However, there are ethical considerations that all researchers must take into account, regardless of their research subject. We’ll discuss those considerations in addition to concerns that are unique to human subject research.

Why do research participants need protection?

First and foremost, we are professionally bound to engage in the ethical practice of research. This chapter discusses ethical research and will show you how to engage in research that is consistent with the NASW Code of Ethics as well as national and international ethical standards all researchers are accountable to. Before we begin, we need to understand the historical occurrences that were the catalyst for the formation of the current ethical standards . This chapter will enable you to view ethics from a micro, mezzo, and macro perspective.

The research process has led to many life-changing discoveries; these have improved life expectancy, improved living conditions, and helped us understand what contributes to certain social problems. That said, not all research has been conducted in respectful, responsible, or humane ways. Unfortunately, some research projects have dramatically marginalized, oppressed, and harmed participants and whole communities.

Would you believe that the following actions have been carried out in the name of research? I realize there was a content warning at the beginning of the chapter, but it is worth mentioning that the list below of research atrocities may be particularly upsetting or triggering.

• intentionally froze healthy body parts of prisoners to see if they could develop a treatment for freezing [6]
• scaled the body parts of prisoners to how best to treat soldiers who had injuries from being exposed to high temperatures [7]
• intentionally infected healthy individuals to see if they could design effective methods of treatment for infections [8]
• gave healthy people TB to see if they could treat it [9]
• attempted to transplant limbs, bones, and muscles to another person to see if this was possible [10]
• castrated and irradiated genitals to see if they could develop a faster method of sterilization [11]
• starved people and only allowed them to drink seawater to see if they could make saline water drinkable [12]
• artificially inseminated women with animal sperm to see what would happen [13]
• gassed living people to document how they would die [14]
• conducted cruel experiments on people and if they did not die, would kill them so they could undergo an autopsy [15]
• refused to treat syphilis in African American men (when treatment was available) because they wanted to track the progression of the illness [16]
• vivisected humans without anesthesia to see how illnesses that researches gave prisoners impacted their bodies [17]
• intentionally tried to infect prisoners with the Bubonic Plague [18]
• intentionally infected prisoners, prostitutes, soldiers, and children with syphilis to study the disease's progression [19]
• performed gynecological experiments on female slaves without anesthesia to investigate new surgical methods [20]

The sad fact is that not only did all of these occur, in many instances, these travesties continued for years until exposed and halted. Additionally, these examples have contributed to the formation of a legacy of distrust toward research. Specifically, many underrepresented groups have a deep distrust of agencies that implement research and are often skeptical of research findings. This has made it difficult for groups to support and have confidence in medical treatments, advances in social service programs, and evidence-informed policy changes. While the aforementioned unethical examples may have ended, this deep and painful wound on the public's trust remains. Consequently, we must be vigilant in our commitment to ethical research.

Many of the situations described may seem like extreme historical cases of misuse of power as researchers. However, ethical problems in research don't just happen in these extreme occurrences. None of us are immune to making unethical choices and the ethical practice of research requires conscientious mindful attention to what we are asking of our research participants. A few examples of less noticeable ethical issues might include: failing to fully explain to someone in advance what their participation might involve because you are in a rush to recruit a large enough sample; or only presenting findings that support your ideas to help secure a grant that is relevant to your research area. Remember, any time research is conducted with human beings, there is the chance that ethical violations may occur that pose social, emotional, and even physical risks for groups, and this is especially true when vulnerable or oppressed groups are involved.

A brief history of unethical social science research

Research on humans hasn’t always been regulated in the way it is today. The earliest documented cases of research using human subjects are of medical vaccination trials (Rothman, 1987). [21] One such case took place in the late 1700s, when scientist Edward Jenner exposed an 8-year-old boy to smallpox in order to identify a vaccine for the devastating disease. Medical research on human subjects continued without much law or policy intervention until the mid-1900s when, at the end of World War II, a number of Nazi doctors and scientists were put on trial for conducting human experimentation during the course of which they tortured and murdered many concentration camp inmates (Faden & Beauchamp, 1986). [22] The trials, conducted in Nuremberg, Germany, resulted in the creation of the Nuremberg Code , a 10-point set of research principles designed to guide doctors and scientists who conduct research on human subjects. Today, the Nuremberg Code guides medical and other research conducted on human subjects, including social scientific research.

Medical scientists are not the only researchers who have conducted questionable research on humans. In the 1960s, psychologist Stanley Milgram (1974) [23] conducted a series of experiments designed to understand obedience to authority in which he tricked subjects into believing they were administering an electric shock to other subjects. In fact, the shocks weren’t real at all, but some, though not many, of Milgram’s research participants experienced extreme emotional distress after the experiment (Ogden, 2008). [24] A reaction of emotional distress is understandable. The realization that one is willing to administer painful shocks to another human being just because someone who looks authoritative has told you to do so might indeed be traumatizing—even if you later learn that the shocks weren’t real.

Around the same time that Milgram conducted his experiments, sociology graduate student Laud Humphreys (1970) [25] was collecting data for his dissertation on the tearoom trade, which was the practice of men engaging in anonymous sexual encounters in public restrooms. Humphreys wished to understand who these men were and why they participated in the trade. To conduct his research, Humphreys offered to serve as a “watch queen,” in a local park restroom where the tearoom trade was known to occur. His role would be to keep an eye out for police while also getting the benefit of being able to watch the sexual encounters. What Humphreys did not do was identify himself as a researcher to his research subjects. Instead, he watched his subjects for several months, getting to know several of them, learning more about the tearoom trade practice and, without the knowledge of his research subjects, jotting down their license plate numbers as they pulled into or out of the parking lot near the restroom.

Sometime after participating as a watch queen, with the help of several insiders who had access to motor vehicle registration information, Humphreys used those license plate numbers to obtain the names and home addresses of his research subjects. Then, disguised as a public health researcher, Humphreys visited his subjects in their homes and interviewed them about their lives and their health. Humphreys’ research dispelled a good number of myths and stereotypes about the tearoom trade and its participants. He learned, for example, that over half of his subjects were married to women and many of them did not identify as gay or bisexual. [26]

Once Humphreys’ work became public, there was some major controversy at his home university (e.g., the chancellor tried to have his degree revoked), among scientists in general, and among members of the public, as it raised public concerns about the purpose and conduct of social science research. In addition, the Washington   Post  journalist Nicholas von Hoffman wrote the following warning about “sociological snoopers”:

We’re so preoccupied with defending our privacy against insurance investigators, dope sleuths, counterespionage men, divorce detectives and credit checkers, that we overlook the social scientists behind the hunting blinds who’re also peeping into what we thought were our most private and secret lives. But they are there, studying us, taking notes, getting to know us, as indifferent as everybody else to the feeling that to be a complete human involves having an aspect of ourselves that’s unknown (von Hoffman, 1970). [27]

In the original version of his report, Humphreys defended the ethics of his actions. In 2008 [28] , years after Humphreys’ death, his book was reprinted with the addition of a retrospect on the ethical implications of his work. In his written reflections on his research and the fallout from it, Humphreys maintained that his tearoom observations constituted ethical research on the grounds that those interactions occurred in public places. But Humphreys added that he would conduct the second part of his research differently. Rather than trace license numbers and interview unwitting tearoom participants in their homes under the guise of public health research, Humphreys instead would spend more time in the field and work to cultivate a pool of informants. Those informants would know that he was a researcher and would be able to fully consent to being interviewed. In the end, Humphreys concluded “there is no reason to believe that any research subjects have suffered because of my efforts, or that the resultant demystification of impersonal sex has harmed society” (Humphreys, 2008, p. 231). [29]

Today, given increasing regulation of social scientific research, chances are slim that a researcher would be allowed to conduct a project similar to Humphreys’. Some argue that Humphreys’ research was deceptive, put his subjects at risk of losing their families and their positions in society, and was therefore unethical (Warwick, 1973; Warwick, 1982). [30] Others suggest that Humphreys’ research “did not violate any premise of either beneficence or the sociological interest in social justice” and that the benefits of Humphreys’ research, namely the dissolution of myths about the tearoom trade specifically and human sexual practice more generally, outweigh the potential risks associated with the work (Lenza, 2004, p. 23). [31] What do you think, and why?

These and other studies (Reverby, 2009) [32] led to increasing public awareness of and concern about research on human subjects. In 1974, the US Congress enacted the National Research Act , which created the National Commission for the Protection of Human Subjects in Biomedical and Behavioral Research. The commission produced  The Belmont Report , a document outlining basic ethical principles for research on human subjects (National Commission for the Protection of Human Subjects in Biomedical and Behavioral Research, 1979). [33] The National Research Act (1974) [34] also required that all institutions receiving federal support establish institutional review boards (IRBs) to protect the rights of human research subjects. Since that time, many organizations that do not receive federal support but where research is conducted have also established review boards to evaluate the ethics of the research that they conduct. IRBs are overseen by the federal Office of Human Research Protections .

The Belmont Report

As mentioned above, The Belmont Report is a federal document that outlines the foundational principles that guide the ethical practice of research in the United States. These ethical principles include: respect for persons, beneficence, and justice. Each of these terms has specific implications as they are applied to the practice of research. These three principles arose as a response to many of the mistreatment and abuses that have been previously discussed and provide important guidance as researchers consider how they will construct and conduct their research studies. As you are crafting your research proposal, makes sure you are mindful of these important ethical guidelines.

Respect for Persons

As social workers, our professional code of ethics requires that we recognize and respect the "inherent dignity and worth of the person." [35] This is very similar to the ethical research principle of r espect for persons . According to this principle, as researchers, we need to treat all research participants with respect, dignity and inherent autonomy. This is reflected by ensuring that participants have self-determination to make informed decisions about their participation in research, that they have a clear understanding of what they will be asked to do and any risks involved, and that their participation is voluntary and can be stopped at any time. Furthermore, for those persons who may have diminished autonomy (e.g. children, people who are incarcerated), extra protections must be built in to these research studies to ensure that respect for persons continues to be demonstrated towards these groups, as they may be especially vulnerable to exploitation and coercion through the research process. A critical tool in establishing respect for persons in your research is the informed consent process, which will be discussed in more detail below.

Beneficence

You may not be familiar with this word yet, but the concept is pretty straightforward. The main idea with beneficence is that the intent of research is to do good. As researchers, to accomplish this, we seek to maximize benefits and minimize risks. Benefits may be something good or advantageous directly received by the research participant, or they may represent a broader good to a wider group of people or the scientific community at large (such as increasing knowledge about the topic or social problem that you are studying). Risks are potential physical, social, or emotional harm that may come about as a response to participation in a study. These risks may be more immediate (e.g. risk of identifying information about a participant being shared, or a participant being upset or triggered by a particular question), or long-term (e.g. some aspect about the person could be shared that could lead to long-term stigmatization). As researchers, we need to think about risk that might be experienced by the individual, but also risks that might be directed towards the community or population(s) the individual may represent. For instance, if our study is specifically focused on surveying single parents, we need to consider how the sharing of our findings might impact this group and how they are perceived. It is a very rare study in which there is no risk to participants. However, a well-designed and ethically sound study will seek to minimize these risks, provide resources to anticipate and address them, and maximize the benefits that are gained through the study.

The final ethical principle we need to cover is justice. While you likely have some idea what justice is, for the purposes of research, justice is the idea that the benefits and the burdens of research are distributed fairly across populations and groups. To help illustrate the concept of justice in research, research in the area of mental health and psychology has historically been critiqued as failing to adequately represent women and people of diverse racial and ethnic groups in their samples (Cundiff, 2012). [36] This has created a body of knowledge that is overly representative of the white male experience, further reinforcing systems of power and privilege. In addition, consider the influence of language as it relates to research justice. If we create studies that only recruit participants fluent in English, which many studies do, we are often failing to satisfy the ethical principle of justice as it applies to people who don't speak English. It is unrealistic to think that we can represent all people in all studies. However, we do need to thoughtfully acknowledge voices that might not be reflected in our samples and attempt to recruit diverse and representative samples whenever possible.

These three principles provide the foundation for the oversight work that is carried out by Institutional Review Boards, our next topic.

Institutional review boards

Institutional review boards, or IRBs, are tasked with ensuring that the rights and welfare of human research subjects will be protected at all institutions, including universities, hospitals, nonprofit research institutions, and other organizations, that receive federal support for research. IRBs typically consist of members from a variety of disciplines, such as sociology, economics, education, social work, and communications (to name a few). Most IRBs also include representatives from the community in which they reside. For example, representatives from nearby prisons, hospitals, or treatment centers might sit on the IRBs of university campuses near them. The diversity of membership helps to ensure that the many and complex ethical issues that may arise from human subjects research will be considered fully and by a knowledgeable and experienced panel. Investigators conducting research on human subjects are required to submit proposals outlining their research plans to IRBs for review and approval prior to beginning their research. Even students who conduct research on human subjects must have their proposed work reviewed and approved by the IRB before beginning any research (though, on some campuses, exceptions are made for student projects that will not be shared outside of the classroom).

The IRB has three levels of review, defined in statute by the USDHHS.

Exempt review is the lowest level of review. Studies that are considered exempt expose participants to the least potential for harm and often involve little participation by human subjects. In social work, exempt studies often examine data that is publicly available or secondary data from another researcher that has been de-identified by the person who collected it.

Expedited review is the middle level of review. Studies considered under expedited review do not have to go before the full IRB board because they expose participants to minimal risk. However, the studies must be thoroughly reviewed by a member of the IRB committee. While there are many types of studies that qualify for expedited review, the most relevant to social workers include the use of existing medical records, recordings (such as interviews) gathered for research purposes, and research on individual group characteristics or behavior.

Finally, the highest level of review is called a  full board review . A full board review will involve multiple members of the IRB evaluating your proposal. When researchers submit a proposal under full board review, the full IRB board will meet, discuss any questions or concerns with the study, invite the researcher to answer questions and defend their proposal, and vote to approve the study or send it back for revision. Full board proposals pose greater than minimal risk to participants. They may also involve the participation of  vulnerable populations , or people who need additional protection from the IRB. Vulnerable populations include prisoners, children, people with cognitive impairments, people with physical disabilities, employees, and students. While some of these populations can fall under expedited review in some cases, they will often require the full IRB to approve their study.

It may surprise you to hear that IRBs are not always popular or appreciated by researchers. Who wouldn’t want to conduct ethical research, you ask? In some cases, the concern is that IRBs are most well-versed in reviewing biomedical and experimental research, neither of which is particularly common within social work. Much social work research, especially qualitative research, is open-ended in nature, a fact that can be problematic for IRBs. The members of IRBs often want to know in advance exactly who will be observed, where, when, and for how long, whether and how they will be approached, exactly what questions they will be asked, and what predictions the researcher has for their findings. Providing this level of detail for a year-long participant observation within an activist group of 200-plus members, for example, would be extraordinarily frustrating for the researcher in the best case and most likely would prove to be impossible. Of course, IRBs do not intend to have researchers avoid studying controversial topics or avoid using certain methodologically sound data collection techniques, but unfortunately, that is sometimes the result. The solution is not to eradicate review boards, which serve a necessary and important function, but instead to help educate IRB members about the variety of social scientific research methods and topics covered by social workers and other social scientists.

What we have provided here is only a short summary of federal regulations and international agreements that provide the boundaries between ethical and unethical research.

Here are a few more detailed guides for continued learning about research ethics and human research protections.

• University of California, San Francisco: Levels of IRB Review
• United States Department of Health and Human Services: The Belmont Report
• NIH, National Institute of Environmental Health Sciences: What is Ethics in Research & Why is it important 
• NIH: Guiding Principles for Ethical Research
• Council on Social Work Education: National Statement on Research Integrity in Social Work
• Butler, I. (2002). A code of ethics for social work and social care research.  British Journal of Social Work ,  32 (2), 239-248
• Research on human subjects presents a unique set of challenges and opportunities when it comes to conducting ethical research.
• Research on human subjects has not always been regulated to the extent that it is today.
• All institutions receiving federal support for research must have an IRB. Organizations that do not receive federal support but where research is conducted also often include IRBs as part of their organizational structure.
• Researchers submit studies for IRB review at one of three different levels, depending on the level of harm the study may cause.
• Recall whether your project will gather data from human subjects and sketch out what the data collection process might look like.
• Identify which level of IRB review is most appropriate for your project.
• For many students, your professors may have existing agreements with your university's IRB that allow students to conduct research projects outside the supervision of the IRB. Make sure that your project falls squarely within those parameters. If you feel you may be outside of such an agreement, consult with your professor to see if you will need to submit your study for IRB review before starting your project.

6.2 Specific ethical issues to consider

• Define informed consent, and describe how it works
• Identify the unique concerns related to the study of vulnerable populations
• Differentiate between anonymity and confidentiality
• Explain the ethical responsibilities of social workers conducting research

As should be clear by now, conducting research on humans presents a number of unique ethical considerations. Human research subjects must be given the opportunity to consent to their participation in research, and be fully informed of the study’s risks, benefits, and purpose. Further, subjects’ identities and the information they share should be protected by researchers. Of course, how consent and identity protection are defined may vary by individual researcher, institution, or academic discipline. In this section, we’ll take a look at a few specific topics that individual researchers must consider before embarking on research with human subjects.

Informed consent

An expectation of voluntary participation is presumed in all social work research projects. In other words, we cannot force anyone to participate in our research without that person’s knowledge or consent. Researchers must therefore design procedures to obtain subjects’ informed consent to participate in their research. This specifically relates back to the ethical principle of respect for persons outlined in The Belmont Report . Informed consent  is defined as a subject’s voluntary agreement to participate in research based on a full understanding of the research and of the possible risks and benefits involved. Although it sounds simple, ensuring that one has actually obtained informed consent is a much more complex process than you might initially presume.

The first requirement is that, in giving their informed consent, subjects may neither waive nor even  appear  to waive any of their legal rights. Subjects also cannot release a researcher, her sponsor, or institution from any legal liability should something go wrong during the course of their participation in the research (USDHHS,2009). [37] Because social work research does not typically involve asking subjects to place themselves at risk of physical harm by, for example, taking untested drugs or consenting to new medical procedures, social work researchers do not often worry about potential liability associated with their research projects. However, their research may involve other types of risks.

For example, what if a social work researcher fails to sufficiently conceal the identity of a subject who admits to participating in a local swinger’s club? In this case, a violation of confidentiality may negatively affect the participant’s social standing, marriage, custody rights, or employment. Social work research may also involve asking about intimately personal topics that may be difficult for participants to discuss, such as trauma or suicide. Participants may re-experience traumatic events and symptoms when they participate in your study. Even if you are careful to fully inform your participants of all risks before they consent to the research process, I’m sure you can empathize with thinking you could bear talking about a difficult topic and then finding it too overwhelming once you start. In cases like these, it is important for a social work researcher to have a plan to provide supports. This may mean providing referrals to counseling supports in the community or even calling the police if the participant is an imminent danger to himself or others.

It is vital that social work researchers explain their mandatory reporting duties in the consent form and ensure participants understand them before they participate. Researchers should also emphasize to participants that they can stop the research process at any time or decide to withdraw from the research study for any reason. Importantly, it is not the job of the social work researcher to act as a clinician to the participant. While a supportive role is certainly appropriate for someone experiencing a mental health crisis, social workers must ethically avoid dual roles. Referring a participant in crisis to other mental health professionals who may be better able to help them is the expectation.

Beyond the legal issues, most IRBs require researchers to share some details about the purpose of the research, possible benefits of participation, and, most importantly, possible risks associated with participating in that research with their subjects. In addition, researchers must describe how they will protect subjects’ identities, how, where, and for how long any data collected will be stored, how findings may be shared, and whom to contact for additional information about the study or about subjects’ rights. All this information is typically shared in an informed consent form that researchers provide to subjects. In some cases, subjects are asked to sign the consent form indicating that they have read it and fully understand its contents. In other cases, subjects are simply provided a copy of the consent form and researchers are responsible for making sure that subjects have read and understand the form before proceeding with any kind of data collection. Your IRB will often provide guidance or even templates for what they expect to see included in an informed consent form. This is a document that they will inspect very closely. Table 6.1 outlines elements to include in your informed consent. While these offer a guideline for you, you should always visit your schools, IRB website to see what guidance they offer. They often provide a template that they prefer researchers to use. Using these templates ensures that you are using the language that the IRB reviewers expect to see and this can also save you time.

One last point to consider when preparing to obtain informed consent is that not all potential research subjects are considered equally competent or legally allowed to consent to participate in research. Subjects from vulnerable populations may be at risk of experiencing undue influence or coercion (USDHHS, 2009). [38] The rules for consent are more stringent for vulnerable populations. For example, minors must have the consent of a legal guardian in order to participate in research. In some cases, the minors themselves are also asked to participate in the consent process by signing special, age-appropriate assent forms designed specifically for them. Prisoners and parolees also qualify as vulnerable populations. Concern about the vulnerability of these subjects comes from the very real possibility that prisoners and parolees could perceive that they will receive some highly desired reward, such as early release, if they participate in research or that there could be punitive consequences if they choose not to participate. When a participant faces undue or excess pressure to participate by either favorable or unfavorable means, this is known as coercion and must be avoided by researchers.

Another potential concern regarding vulnerable populations is that they may be underrepresented or left out of research opportunities, specifically because of concerns about their ability to consent. So, on the one hand, researchers must take extra care to ensure that their procedures for obtaining consent from vulnerable populations are not coercive. The procedures for receiving approval to conduct research with these groups may be more rigorous than that for non-vulnerable populations. On the other hand, researchers must work to avoid excluding members of vulnerable populations from participation simply on the grounds that they are vulnerable or that obtaining their consent may be more complex. While there is no easy solution to this ethical research dilemma, an awareness of the potential concerns associated with research on vulnerable populations is important for identifying whatever solution is most appropriate for a specific case.

Protection of identities

As mentioned earlier, the informed consent process includes the requirement that researchers outline how they will protect the identities of subjects. This aspect of the research process, however, is one of the most commonly misunderstood. Furthermore, failing to protect identities is one of the greatest risks to participants in social work research studies.

In protecting subjects’ identities, researchers typically promise to maintain either the anonymity or confidentiality of their research subjects. These are two distinctly different terms and they are NOT interchangeable. Anonymity is the more stringent of the two and is very hard to guarantee in most research studies. When a researcher promises anonymity to participants, not even the researcher is able to link participants’ data with their identities. Anonymity may be impossible for some social work researchers to promise due to the modes of data collection many social workers employ. Face-to-face interviewing means that subjects will be visible to researchers and will hold a conversation, making anonymity impossible. In other cases, the researcher may have a signed consent form or obtain personal information on a survey and will therefore know the identities of their research participants. In these cases, a researcher should be able to at least promise confidentiality to participants.

Offering  confidentiality means that some identifying information is known at some time by the research team, but only the research team has access to this identifying information and this information will not be linked with their data in any publicly accessible way. Confidentiality in research is quite similar to confidentiality in clinical practice. You know who your clients are, but others do not. You agree to keep their information and identity private. As you can see under the “Risks” section of the consent form in Figure 5.1, sometimes it is not even possible to promise that a subject’s confidentiality will be maintained. This is the case if data are collected in public or in the presence of other research participants in the course of a focus group, for example. Participants who social work researchers deem to be of imminent danger to self or others or those that disclose abuse of children and other vulnerable populations fall under a social worker’s duty to report. Researchers must then violate confidentiality to fulfill their legal obligations.

There are a number of steps that researchers can take to protect the identities of research participants. These include, but are not limited to:

• Collecting data in private spaces
• Not requesting information that will uniquely identify or "out" that person as a participant
• Assigning study identification codes or pseudonyms
• Keeping signed informed consent forms separate from other data provided by the participant
• Making sure that physical data is kept in a locked and secured location, and the virtual data is encrypted or password-protected
• Reporting data in aggregate (only discussing the data collectively, not by individual responses)

Protecting research participants’ identities is not always a simple prospect, especially for those conducting research on stigmatized groups or illegal behaviors. Sociologist Scott DeMuth learned that all too well when conducting his dissertation research on a group of animal rights activists. As a participant observer, DeMuth knew the identities of his research subjects. So when some of his research subjects vandalized facilities and removed animals from several research labs at the University of Iowa, a grand jury called on Mr. DeMuth to reveal the identities of the participants in the raid. When DeMuth refused to do so, he was jailed briefly and then charged with conspiracy to commit animal enterprise terrorism and cause damage to the animal enterprise (Jaschik, 2009). [39]

Publicly, DeMuth’s case raised many of the same questions as Laud Humphreys’ work 40 years earlier. What do social scientists owe the public? Is DeMuth, by protecting his research subjects, harming those whose labs were vandalized? Is he harming the taxpayers who funded those labs? Or is it more important that DeMuth emphasize what he owes his research subjects, who were told their identities would be protected? DeMuth’s case also sparked controversy among academics, some of whom thought that as an academic himself, DeMuth should have been more sympathetic to the plight of the faculty and students who lost years of research as a result of the attack on their labs. Many others stood by DeMuth, arguing that the personal and academic freedom of scholars must be protected whether we support their research topics and subjects or not. DeMuth’s academic adviser even created a new group, Scholars for Academic Justice , to support DeMuth and other academics who face persecution or prosecution as a result of the research they conduct. What do you think? Should DeMuth have revealed the identities of his research subjects? Why or why not?

Discipline-specific considerations

Often times, specific disciplines will provide their own set of guidelines for protecting research subjects and, more generally, for conducting ethical research. For social workers, the National Association of Social Workers (NASW) Code of Ethics section 5.02 describes the responsibilities of social workers in conducting research. Summarized below, these responsibilities are framed as part of a social worker’s responsibility to the profession. As representative of the social work profession, it is your responsibility to conduct and use research in an ethical manner.

A social worker should:

• Monitor and evaluate policies, programs, and practice interventions
• Contribute to the development of knowledge through research
• Keep current with the best available research evidence to inform practice
• Ensure voluntary and fully informed consent of all participants
• Not engage in any deception in the research process
• Allow participants to withdraw from the study at any time
• Provide access to appropriate supportive services for participants
• Protect research participants from harm
• Maintain confidentiality
• Report findings accurately
• Disclose any conflicts of interest
• Researchers must obtain the informed consent of research participants.
• Social workers must take steps to minimize the harms that could arise during the research process.
• If anonymity is promised, individual participants cannot be linked with their data.
• If confidentiality is promised, the identities of research participants cannot be revealed, even if individual participants can be linked with their data.
• The NASW Code of Ethics includes specific responsibilities for social work researchers.
• Talk with your professor to see if an informed consent form is required for your research project. If documentation is required, customize the template provided by your professor or the IRB at your school, using the details of your study. If documentation on consent is not required, for example if consent is given verbally, use the templates as guides to create a guide for what you will say to participants regarding informed consent.
• Identify whether your data will be confidential or anonymous. Describe the measures you will take to protect the identities of individuals in your study. How will you store the data? How will you ensure that no one can identify participants based on what you report in papers and presentations? Be sure to think carefully. People can be identified by characteristics such as age, gender, disability status, location, etc.

6.3 Benefits and harms of research across the ecosystem

• Identify and distinguish between micro-, mezzo-, and macro-level considerations with respect to the ethical conduct of social scientific research

This chapter began with a long list of harmful acts that researchers engaged in while conducting studies on human subjects. Indeed, even the last section on informed consent and protection of confidential information can be seen in light of minimizing harm and maximizing benefits. The benefits of your study should be greater than the harms. But who benefits from your research study, and who might be harmed? The first person who benefits is, most clearly, you as the researcher. You need a project to complete, be it for a grade, a grant, an academic responsibility, etc. However you need to make sure that your benefit does not come at the expense of harming others. Furthermore, research requires resources, including resources from the communities we work with. Part of being good stewards of these resources as social work researchers means that we need to engage in research that benefits the people we serve in meaningful and relevant ways. We need to consider how others are impacted by our research.

Micro-, mezzo-, and macro-level concerns

One useful way to think about the breadth of ethical questions that might arise out of any research project is to think about potential issues from the perspective of different analytical levels that are familiar to us as social workers. In Chapter 1 , you learned about the micro-, mezzo-, and macro-levels of inquiry and how a researcher’s specific point of focus might vary depending on her level of inquiry. Here we’ll apply this ecological framework to a discussion of research ethics. Within most research projects, there are specific questions that arise for researchers at each of these three levels.

At the micro-level, researchers must consider their own conduct and the impact on individual research participants. For example, did Stanley Milgram behave ethically when he allowed research participants to think that they were administering electric shocks to fellow participants? Did Laud Humphreys behave ethically when he deceived his research subjects about his own identity? Were the rights of individuals in these studies protected? How did these participants benefit themselves from the research that was conducted? While not social workers by trade, would the actions of these two researchers hold up against our professional NASW Code of Ethics? The questions posed here are the sort that you will want to ask yourself as a researcher when considering ethics at the micro-level.

At the mezzo-level, researchers should think about their duty to the community. How will the results of your study impact your target population? Ideally, your results will benefit your target population by identifying important areas for social workers to intervene and to better understand the experiences of the communities they serve. However, it is possible that your study may perpetuate negative stereotypes about your target population or damage its reputation. Indigenous people in particular have highlighted how historically social science has furthered marginalization of indigenous peoples (Smith, 2013). [40] Mezzo-level concerns should also address other groups or organizations that are connected to your target population. This may include the human service agencies with whom you've partnered for your study as well as the communities and peoples they serve. If your study reflected negatively on a particular housing project in your area, for example, will community members seek to remove it from their community? Or might it draw increased law enforcement presence that is unwanted by participants or community members? Research is a powerful tool and can be used for many purposes, not all of them altruistic. In addition, research findings can have many implications, intended and unintended. As responsible researchers, we need to do our best to thoughtfully anticipate these consequences.

Finally, at the macro-level, a researcher should consider duty to, and the expectations of, society. Perhaps the most high-profile case involving macro-level questions of research ethics comes from debates over whether to use data gathered by, or cite published studies based on data gathered from, the Nazis in the course of their unethical and horrendous experiments on humans during World War II (Moe, 1984). [41] Some argue that because the data were gathered in such an unquestionably unethical manner, they should never be used. The data, say these people, are neither valid nor reliable and should therefore not be used in any current scientific investigation (Berger, 1990). [42]

On the other hand, some people argue that data themselves are neutral; that “information gathered is independent of the ethics of the methods and that the two are not linked together” (Pozos, 1992, p. 104). [43] Others point out that not using the data could inadvertently strengthen the claims of those who deny that the Holocaust ever happened. In his striking statement in support of publishing the data, medical ethics professor Velvl Greene (1992) says,

Instead of banning the Nazi data or assigning it to some archivist or custodial committee, I maintain that it be exhumed, printed, and disseminated to every medical school in the world along with the details of methodology and the names of the doctors who did it, whether or not they were indicted, acquitted, or hanged.…Let the students and the residents and the young doctors know that this was not ancient history or an episode from a horror movie where the actors get up after filming and prepare for another role. It was real. It happened yesterday (p. 169–170). [44]

While debates about the use of data collected by the Nazis are typically centered on medical scientists’ use of them, there are conceivable circumstances under which these data might be used by social scientists. Perhaps, for example, a social scientist might wish to examine contemporary reactions to the experiments. Or perhaps the data could be used in a study of the sociology of science. What do you think? Should data gathered by the Nazis be used or cited today? What arguments can you make in support of your position, and how would you respond to those who disagree?

Additionally at the macro-level, you must also consider your responsibilities to the profession of social work. When you engage in social work research, you stand on the reputation the profession has built for over a century. Since research is public-facing, meaning that research findings are intended to be shared publicly, you are an ambassador for the profession. How you conduct yourself as a social work researcher has potential implications for how the public perceives both social work and research. As a social worker, you have a responsibility to work towards greater social, environmental, and economic justice and human rights. Your research should reflect this responsibility. Attending to research ethics helps to fulfill your responsibilities to the profession, in addition to your target population.

Table 6.2 summarizes the key questions that researchers might ask themselves about the ethics of their research at each level of inquiry.

• At the micro-level, researchers should consider their own conduct and the rights of individual research participants.
• At the mezzo-level, researchers should consider the expectations of their profession, any organizations that may have funded their research, and the communities affected by their research.
• At the macro-level, researchers should consider their duty to and the expectations of society with respect to social science research.
• Summarize the benefits and harms at the micro-, mezzo-, and macro-level of inquiry. At which level of inquiry is your research project?
• In a few sentences, identify whether the benefits of your study outweigh the potential harms.

6.4 Being an ethical researcher

• Identify why researchers must provide a detailed description of methodology
• Describe what it means to use science in an ethical way

Research ethics has to do with both how research is conducted and how findings from that research are used. In this section, we’ll consider research ethics from both angles.

Doing science the ethical way

As you should now be aware, researchers must consider their own personal ethical principles in addition to following those of their institution, their discipline, and their community. We’ve already considered many of the ways that social workers strive to ensure the ethical practice of research, such as informing and protecting subjects. But the practice of ethical research doesn’t end once subjects have been identified and data have been collected. Social workers must also fully disclose their research procedures and findings. This means being honest about how research subjects were identified and recruited, how exactly data were collected and analyzed, and ultimately, what findings were reached.

If researchers fully disclose how they conducted their research, then those who use their work to build research projects, create social policies, or make treatment decisions can have greater confidence in the work. By sharing how research was conducted, a researcher helps assure readers they have conducted legitimate research and didn’t simply come to whatever conclusions they wanted   to find. A description or presentation of research findings that is not accompanied by information about research methodology is missing relevant information. Sometimes methodological details are left out because there isn’t time or space to share them. This is often the case with news reports of research findings. Other times, there may be a more insidious reason that important information is missing. This may be the case if sharing methodological details would call the legitimacy of a study into question. As researchers, it is our ethical responsibility to fully disclose our research procedures. As consumers of research, it is our ethical responsibility to pay attention to such details. We’ll discuss this more in the next section.

There’s a New Yorker cartoon that depicts a set of filing cabinets that aptly demonstrates what we don’t want to see happen with research. Each filing cabinet drawer in the cartoon is labeled differently. The labels include such headings as, “Our Facts,” “Their Facts,” “Neutral Facts,” “Disputable Facts,” “Absolute Facts,” “Bare Facts,” “Unsubstantiated Facts,” and “Indisputable Facts.” The implication of this cartoon is that one might just choose to open the file drawer of her choice and pick whichever facts one likes best. While this may occur if we use some of the unscientific ways of knowing described in Chapter 1 , it is fortunately not how the discovery of knowledge in social work, or in any other science for that matter, takes place. There actually is a method to this madness we call research. At its best, research reflects a systematic, transparent, informative process.

Honesty in research is facilitated by the scientific principle of replication . Ideally, this means that one scientist could repeat another’s study with relative ease. By replicating a study, we may become more (or less) confident in the original study’s findings. Replication is far more difficult (perhaps impossible) to achieve in the case of many qualitative studies, as our purpose is often a deep understanding of very specific circumstances, rather than the broad, generalizable knowledge we traditionally seek in quantitative studies. Nevertheless, transparency in the research process is an important standard for all social scientific researchers—that we provide as much detail as possible about the processes by which we reach our conclusions. This allows the quality of our research to be evaluated. Along with replication, peer review is another important principle of the scientific process. Peer review involves other knowledgeable researchers in our field of study to evaluate our research and to determine if it is of sufficient quality to share with the public. There are valid critiques of the peer review process: that it is biased towards studies with positive findings, that it may reinforce systemic barriers to oppressed groups accessing and leveraging knowledge, that it is far more subjective and/or unreliable than it claims to be. Despite these critiques, peer review remains a foundational concept for how scientific knowledge is generated.

Full disclosure also includes the need to be honest about a study’s strengths and weaknesses, both with oneself and with others. Being aware of the strengths and weaknesses of your own work can help a researcher make reasonable recommendations about the next steps other researchers might consider taking in their inquiries. Awareness and disclosure of a study’s strengths and weaknesses can also help highlight the theoretical or policy implications of one’s work. In addition, openness about strengths and weaknesses helps those reading the research better evaluate the work and decide for themselves how or whether to rely on its findings. Finally, openness about a study’s sponsors is crucial. How can we effectively evaluate research without knowing who paid the bills? This allows us to assess for potential conflicts of interest that may compromise the integrity of the research.

The standard of replicability, the peer-review process, and openness about a study’s strengths, weaknesses, and funding sources enables those who read the research to evaluate it fairly and completely. Knowledge of funding sources is often raised as an issue in medical research. Understandably, independent studies of new drugs may be more compelling to the Food and Drug Administration (FDA) than studies touting the virtues of a new drug that happen to have been funded by the company who created that drug. But medical researchers aren’t the only ones who need to be honest about their funding. If we know, for example, that a political think tank with ties to a particular party has funded some research, we can take that knowledge into consideration when reviewing the study’s findings and stated policy implications. Lastly, and related to this point, we must consider how, by whom, and for what purpose research may be used.

Using science the ethical way

Science has many uses. By “use” I mean the ways that science is understood and applied (as opposed to the way it is conducted). Some use science to create laws and social policies; others use it to understand themselves and those around them. Some people rely on science to improve their life conditions or those of other people, while still others use it to improve their businesses or other undertakings. In each case, the most ethical way for us to use science is to educate ourselves about the design and purpose of any studies we may wish to use. This helps us to more adequately critique the value of this research, to recognize its strengths and limitations.

As part of my research course, students are asked to critique a research article. I often find in this assignment that students often have very lofty expectations for everything that 'should' be included in the journal article they are reviewing. While I appreciate the high standards, I often give them feedback that it is perhaps unrealistic (even unattainable) for a research study to be perfectly designed and described for public consumption. All research has limitations; this may be a consequence of limited resources, issues related to feasibility, and unanticipated roadblocks or problems as we are carrying out our research. Furthermore, the ways we disseminate or share our research often has restrictions on what and how we can share our findings. This doesn't mean that a study with limitations has no value—every study has limitations! However, as we are reviewing research, we should look for an open discussion about methodology , strengths, and weaknesses of the study that helps us to interpret what took place and in what ways it may be important.

For instance, this can be especially important to think about in terms of a study's sample. It can be challenging to recruit a diverse and representative sample for your study (however, that doesn't mean we shouldn't try!). The next time you are reading research studies that were used to help establish an evidence based practice (EBP), make sure to look at the description of the sample. We cannot assume that what works for one group of people will uniformly work with all groups of people with very different life experiences; however, historically much of our intervention repertoire has been both created by and evaluated on white men. If research studies don't obtain a diverse sample, for whatever reason, we would expect that the authors would identify this as a limitation and an area requiring further study. We need to challenge our profession to provide practices, strategies, models, interventions, and policies that have been evaluated and tested for their efficacy with the diverse range of people that we work with as social workers.

Social scientists who conduct research on behalf of organizations and agencies may face additional ethical questions about the use of their research, particularly when the organization for which a study is conducted controls the final report and the publicity it receives. There is a potential conflict of interest for evaluation researchers who are employees of the agency being evaluated. A similar conflict of interest might exist between independent researchers whose work is being funded by some government agency or private foundation.

So who decides what constitutes ethical conduct or use of research? Perhaps we all do. What qualifies as ethical research may shift over time and across cultures as individual researchers, disciplinary organizations, members of society, and regulatory entities, such as institutional review boards, courts, and lawmakers, all work to define the boundaries between ethical and unethical research.

• Conducting research ethically requires that researchers be ethical not only in their data collection procedures but also in reporting their methods and findings.
• The ethical use of research requires an effort to understand research, an awareness of your own limitations in terms of knowledge and understanding, and the honest application of research findings.
• Think about your research hypothesis at this point. What would happen if your results revealed information that could harm the population you are studying? What are your ethical responsibilities as far as reporting about your research?
• Ultimately, we cannot control how others will use the results of our research. What are the implications of this for how you report on your research?
• Reading the results of empirical studies (16 minute read)
• Annotating empirical journal articles (15 minute read)
• Generalizability and transferability of empirical results (15 minute read)

Content warning: examples in this chapter contain references to domestic violence and details on types of abuse, drug use, poverty, mental health, sexual harassment and details on harassing behaviors, children’s mental health, LGBTQ+ oppression and suicide, obesity, anti-poverty stigma, and psychotic disorders.

5.1 Reading the results of empirical studies

• Describe how statistical significance and confidence intervals demonstrate which results are most important
• Differentiate between qualitative and quantitative results in an empirical journal article

If you recall from section 3.1 , empirical journal articles are those that report the results of quantitative or qualitative data analyzed by the author. They follow a set structure—introduction, methods, results, discussion/conclusions. This section is about reading the most challenging section: results.

I want to normalize not understanding statistics terms and symbols. However, a basic understanding of a results section goes a very long way to understanding the key results in an article. This will take you beyond the two or three sentences in the abstract that summarize the study's results and into the nitty-gritty of what they found for each concept they studied.

Read beyond the abstract

At this point, I have read hundreds of literature reviews written by students. One of the challenges I have noted is that students will report the results as summarized in the abstract, rather than the detailed findings laid out in the results section of the article. This poses a problem when you are writing a literature review because you need to provide specific and clear facts that support your reading of the literature. The abstract may say something like: “we found that poverty is associated with mental health status.” For your literature review, you want the details, not the summary. In the results section of the article, you may find a sentence that states: “children living in households experiencing poverty are three times more likely to have a mental health diagnosis.” This more specific statistical information provides a stronger basis on which to build the arguments in your literature review.

Using the summarized results in an abstract is an understandable mistake to make. The results section often contains figures and tables that may be challenging to understand. Often, without having completed more advanced coursework on statistical or qualitative analysis, some of the terminology, symbols, or diagrams may be difficult to comprehend. This section is all about how to read and interpret the results of an empirical (quantitative or qualitative) journal article. Our discussion here will be basic, and in parts three and four of the textbook, you will learn more about how to interpret results from statistical tests and qualitative data analysis.

Remember, this section only addresses empirical articles. Non-empirical articles (e.g., theoretical articles, literature reviews) don't have results. They cite the analysis of raw data completed by other authors, not the person writing the journal article who is merely summarizing others' work.

Quantitative results

Quantitative articles often contain tables, and scanning them is a good way to begin reading the results. A table usually provides a quick, condensed summary of the report’s key findings. Tables are a concise way to report large amounts of data. Some tables present descriptive information about a researcher’s sample (often the first table in a results section). These tables will likely contain frequencies (N) and percentages (%). For example, if gender happened to be an important variable for the researcher’s analysis, a descriptive table would show how many and what percent of all study participants are of a particular gender. Frequencies or “how many” will probably be listed as N, while the percent symbol (%) might be used to indicate percentages.

In a table presenting a causal relationship, two sets of variables are represented. The independent variable , or cause, and the dependent variable , the effect. We will discuss these further when we review quantitative conceptualization and measurement. Independent variable attributes are typically presented in the table’s columns, while dependent variable attributes are presented in rows. This allows the reader to scan a table’s rows to see how values on the dependent variable change as the independent variable values change (i.e., changes in the dependent variable depend on changes in the independent variable). Tables displaying results of quantitative analysis will also likely include some information about which relationships are significant or not. We will discuss the details of significance and p-values later in this section.

Let’s look at a specific example: Table 5.1. It presents the causal relationship between gender and experiencing harassing behaviors at work. In this example, gender is the independent variable (the cause) and the harassing behaviors listed are the dependent variables (the effects). [46] Therefore, we place gender in the table’s columns and harassing behaviors in the table’s rows.

Reading across the table’s top row, we see that 2.9% of women in the sample reported experiencing subtle or obvious threats to their safety at work, while 4.7% of men in the sample reported the same. We can read across each of the rows of the table in this way. Reading across the bottom row, we see that 9.4% of women in the sample reported experiencing staring or invasion of their personal space at work while just 2.3% of men in the sample reported having the same experience. We’ll discuss  p values later in this section.

While you can certainly scan tables for key results, they are often difficult to understand without reading the text of the article. The article and table were meant to complement each other, and the text should provide information on how the authors interpret their findings. The table is not redundant with the text of the results section. Additionally, the first table in most results sections is a summary of the study's sample, which provides more background information on the study than information about hypotheses and findings. It is also a good idea to look back at the methods section of the article as the data analysis plan the authors outline should walk you through the steps they took to analyze their data which will inform how they report them in the results section.

Statistical significance

The statistics reported in Table 5.1 represent what the researchers found in their sample. The purpose of statistical analysis is usually to generalize from a the small number of people in a study's sample to a larger population of people. Thus, the researchers intend to make causal arguments about harassing behaviors at workplaces beyond those covered in the sample.

Generalizing is key to understanding statistical significance . According to Cassidy and colleagues, (2019) [47] 89% of research methods textbooks in psychology define statistical significance incorrectly. This includes an early draft of this textbook which defined statistical significance as "the likelihood that the relationships we observe could be caused by something other than chance." If you have previously had a research methods class, this might sound familiar to you. It certainly did to me!

But statistical significance is less about "random chance" than more about the null hypothesis . Basically, at the beginning of a study a researcher develops a hypothesis about what they expect to find, usually that there is a statistical relationship between two or more variables . The null hypothesis is the opposite. It is the hypothesis that there is no relationship between the variables in a research study. Researchers then can hopefully reject the null hypothesis because they find a relationship between the variables.

For example, in Table 5.1 researchers were examining whether gender impacts harassment. Of course, researchers assumed that women were more likely to experience harassment than men. The null hypothesis, then, would be that gender has no impact on harassment. Once we conduct the study, our results will hopefully lead us to reject the null hypothesis because we find that gender impacts harassment. We would then generalize from our study's sample to the larger population of people in the workplace.

Statistical significance is calculated using a p-value which is obtained by comparing the statistical results with a hypothetical set of results if the researchers re-ran their study a large number of times. Keeping with our example, imagine we re-ran our study with different men and women from different workplaces hundreds and hundred of times and we assume that the null hypothesis is true that gender has no impact on harassment. If results like ours come up pretty often when the null hypothesis is true, our results probably don't mean much. "The smaller the p-value, the greater the statistical incompatibility with the null hypothesis" (Wasserstein & Lazar, 2016, p. 131). [48] Generally, researchers in the social sciences have used 0.05 as the value at which a result is significant (p is less than 0.05) or not significant (p is greater than 0.05). The p-value 0.05 refers to if 5% of those hypothetical results from re-running our study show the same or more extreme relationships when the null hypothesis is true. Researchers, however, may choose a stricter standard such as 0.01 in which only 1% of those hypothetical results are more extreme or a more lenient standard like 0.1 in which 10% of those hypothetical results are more extreme than what was found in the study.

Let's look back at Table 5.1. Which one of the relationships between gender and harassing behaviors is statistically significant? It's the last one in the table, "staring or invasion of personal space," whose p-value is 0.039 (under the p<0.05 standard to establish statistical significance). Again, this indicates that if we re-ran our study over and over again and gender did not  impact staring/invasion of space (i.e., the null hypothesis was true), only 3.9% of the time would we find similar or more extreme differences between men and women than what we observed in our study. Thus, we conclude that for staring or invasion of space only , there is a statistically significant relationship.

For contrast, let's look at "being pushed, hit, or grabbed" and run through the same analysis to see if it is statistically significant. If we re-ran our study over and over again and the null hypothesis was true, 48% of the time (p=.48) we would find similar or more extreme differences between men and women. That means these results are not statistically significant.

This discussion should also highlight a point we discussed previously: that it is important to read the full results section, rather than simply relying on the summary in the abstract. If the abstract stated that most tests revealed no statistically significant relationships between gender and harassment, you would have missed the detail on which behaviors were and were not associated with gender. Read the full results section! And don't be afraid to ask for help from a professor in understanding what you are reading, as results sections are often not written to be easily understood.

Statistical significance and p-values have been critiqued recently for a number of reasons, including that they are misused and misinterpreted (Wasserstein & Lazar, 2016) [49] , that researchers deliberately manipulate their analyses to have significant results (Head et al., 2015) [50] , and factor into the difficulty scientists have today in reproducing many of the results of previous social science studies (Peng, 2015). [51] For this reason, we share these principles, adapted from those put forth by the American Statistical Association, [52]  for understanding and using p-values in social science:

• P-values provide evidence against a null hypothesis.
• P-values do not indicate whether the results were produced by random chance alone or if the researcher's hypothesis is true, though both are common misconceptions.
• Statistical significance can be detected in minuscule differences that have very little effect on the real world.
• Nuance is needed to interpret scientific findings, as a conclusion does not become true or false when the p-value passes from p=0.051 to p=0.049.
• Real-world decision-making must use more than reported p-values. It's easy to run analyses of large datasets and only report the significant findings.
• Greater confidence can be placed in studies that pre-register their hypotheses and share their data and methods openly with the public.
• "By itself, a p-value does not provide a good measure of evidence regarding a model or hypothesis. For example, a p-value near 0.05 taken by itself offers only weak evidence against the null hypothesis. Likewise, a relatively large p-value does not imply evidence in favor of the null hypothesis; many other hypotheses may be equally or more consistent with the observed data" (Wasserstein & Lazar, 2016, p. 132).

Confidence intervals

Because of the limitations of p-values, scientists can use other methods to determine whether their models of the world are true. One common approach is to use a confidence interval , or a range of values in which the true value is likely to be found. Confidence intervals are helpful because, as principal #5 above points out, p-values do not measure the size of an effect (Greenland et al., 2016). [53] Remember, something that has very little impact on the world can be statistically significant, and the values in a confidence interval would be helpful. In our example from Table 5.1, imagine our analysis produced a confidence interval that women are 1.2-3.4x more likely to experience "staring or invasion of personal space" than men. As with p-values, calculation for a confidence interval compares what was found in one study with a hypothetical set of results if we repeated the study over and over again. If we calculated 95% confidence intervals for all of the hypothetical set of hundreds and hundreds of studies, that would be our confidence interval. 

Confidence intervals are pretty intuitive. As of this writing, my wife and are expecting our second child. The doctor told us our due date was December 11th. But the doctor also told us that December 11th was only their best estimate. They were actually 95% sure our baby might be born any time in the 30-day period between November 27th and December 25th. Confidence intervals are often listed with a percentage, like 90% or 95%, and a range of values, such as between November 27th and December 25th. You can read that as: "we are 95% sure your baby will be born between November 27th and December 25th because we've studied hundreds of thousands of fetuses and mothers, and we're 95% sure your baby will be within these two dates."

Notice that we're hedging our bets here by using words like "best estimate." When testing hypotheses, social scientists generally phrase their findings in a tentative way, talking about what results "indicate" or "support," rather than making bold statements about what their results "prove." Social scientists have humility because they understand the limitations of their knowledge. In a literature review, using a single study or fact to "prove" an argument right or wrong is often a signal to the person reading your literature review (usually your professor) that you may not have appreciated the limitations of that study or its place in the broader literature on the topic. Strong arguments in a literature review include multiple facts and ideas that span across multiple studies.

You can learn more about creating tables, reading tables, and tests of statistical significance in a class focused exclusively on statistical analysis. We provide links to many free and openly licensed resources on statistics in Chapter 16 . For now, we hope this brief introduction to reading tables will improve your confidence in reading and understanding the results sections in quantitative empirical articles.

Qualitative results

Quantitative articles will contain a lot of numbers and the results of statistical tests demonstrating associations between those numbers. Qualitative articles, on the other hand, will consist mostly of quotations from participants. For most qualitative articles, the authors want to put their results in the words of their participants, as they are the experts. Articles that lack quotations make it difficult to assess whether the researcher interpreted the data in a trustworthy, unbiased manner. These types of articles may also indicate how often particular themes or ideas came up in the data, potentially reflective of how important they were to participants.

Authors often organize qualitative results by themes and subthemes. For example, see this snippet from the results section in Bonanno and Veselak (2019) [54] discussion parents' attitudes towards child mental health information sources.

Data analysis revealed four themes related to participants’ abilities to access mental health help and information for their children, and parents’ levels of trust in these sources. These themes are: others’ firsthand experiences family and friends with professional experience, protecting privacy, and uncertainty about schools as information sources. Trust emerged as an overarching and unifying concept for all of these themes. Others’ firsthand experiences. Several participants reported seeking information from other parents who had experienced mental health struggles similar to their own children. They often referenced friends or family members who had been or would be good sources of information due to their own personal experiences. The following quote from Adrienne demonstrates the importance of firsthand experience: [I would only feel comfortable sharing concerns or asking for advice] if I knew that they had been in the same situation. (Adrienne) Similarly, Michelle said: And I talked to a friend of mine who has kids who have IEPs in the district to see, kind of, how did she go about it. (Michelle) ... Friends/family with professional experience . Several respondents referred to friends or family members who had professional experience with or knowledge of child mental health and suggested that these individuals would be good sources of information. For example, Hannah said: Well, what happened with me was I have an uncle who’s a psychiatrist. Sometimes if he’s up in (a city to the north), he’s retired, I can call him sometimes and get information. (Hannah) Michelle, who was in nursing school, echoed this sentiment: At this point, [if my child’s behavioral difficulties continued], I would probably call one of my [nursing] professors. That’s what I’ve done in the past when I’ve needed help with certain things...I have a professor who I would probably consider a friend who I would probably talk to first. She has a big adolescent practice. (Michelle) (p. 402-403)

The terms in bold above refer to the key themes (i.e., qualitative results) that were present in the data. Researchers will state the process by which they interpret each theme, providing a definition and usually some quotations from research participants. Researchers will also draw connections between themes, note consensus or conflict over themes, and situate the themes within the study context.

Qualitative results are specific to the time, place, and culture in which they arise, so you will have to use your best judgment to determine whether these results are relevant to your study. For example, students in my class at Radford University in Southwest Virginia may be studying rural populations. Would a study on group homes in a large urban city transfer well to group homes in a rural area?

Maybe. But even if you were using data from a qualitative study in another rural area, are all rural areas the same? How is the client population and sociocultural context in the article similar or different to the one in your study? Qualitative studies have tremendous depth, but researchers must be intentional about drawing conclusions about one context based on a study in another context. To make conclusions about how a study applies in another context, researchers need to examine each component of an empirical journal article--they need to annotate!

• The results section of empirical articles are often the most difficult to understand.
• To understand a quantitative results section, look for results that were statistically significant and examine the confidence interval, if provided.
• To understand a qualitative results section, look for definitions of themes or codes and use the quotations provided to understand the participants’ perspective.

Select a quantitative empirical article related to your topic.

• Write down the results the authors identify as statistically significant in the results section.
• How do the authors interpret their results in the discussion section?
• Do the authors provide enough information in the introduction for you to understand their results?

Select a qualitative empirical article relevant to your topic.

• Write down the key themes the authors identify and how they were defined by the participants.

5.2 Annotating empirical journal articles

• Define annotation and describe how to use it to identify, extract, and reflect on the information you need from an article

Annotation refers to the process of writing notes on an article. There are many ways to do this. The most basic technique is to print out the article and build a binder related to your topic. Raul Pacheco-Vega's excellent blog has a post on his approach to taking physical notes. Honestly, while you are there, browse around that website. It is full of amazing tips for students conducting a literature review and graduate research projects. I see a lot of benefits to the paper, pen, and highlighter approach to annotating articles. Personally though, I prefer to use a computer to write notes on an article because my handwriting is terrible and typing notes allows me search for keywords. For other students, electronic notes work best because they cannot afford to print every article that they will use in their paper. No matter what you use, the point is that you need to write notes when you're reading. Reading is research!

There are a number of free software tools you can use to help you annotate a journal article. Most PDF readers like Adobe Acrobat have a commenting and highlighting feature, though the PDF readers included with internet browsers like Google Chrome, Microsoft Edge, and Safari do not have this feature. The best approach may be to use a citation manager like Zotero. Using a citation manager, you can build a library of articles, save your annotations, and link annotations across PDFs using keywords. They also provide integration with word processing programs to help with citations in a reference list

Of course, I don't follow this advice because I have a system that works well for me. I have a PDF open in one computer window and a Word document open in a window next to it. I type notes and copy quotes, listing the page number for each note I take. It's a bit low-tech, but it does make my notes searchable. This way, when I am looking for a concept or quote, I can simply search my notes using the Find feature in Word and get to the information I need.

Annotation and reviewing literature does not have to be a solo project. If are working in a group, you can use the Hypothes.is web browser extension to annotate articles collaboratively. You can also use Google Docs to collaboratively annotate a shared PDF using the commenting feature and write collaborative notes in a shared document. By sharing your highlights and comments, you can split the work of getting the most out of each article you read and build off one another's ideas.

Common annotations

In this section, we present common annotations people make when reading journal articles. These annotations are adapted from Craig Whippo and Raul Pacheco-Vega . If you are annotating on paper, I suggest using different color highlighters for each type of annotation listed below. If you are annotating electronically, you can use the names below as tags to easily find information later. For example, if you are searching for definitions of key concepts, you can either click on the tag for [definitions] in your PDF reader or thumb through a printed copy of article for whatever color or tag you used to indicate definitions of key terms. Most of all, you want to avoid reading through all of your sources again just to find that one thing you know you read somewhere . Time is a graduate student's most valuable resource, so our goal here is to help you spend your time reading the literature wisely.

Personal reflections

Personal reflections are all about you. What do you think? Are there any areas you are confused about? Any new ideas or reflections come to mind while you're reading? Treat these annotations as a means of capturing your first reflections about an article. Write down any questions or thoughts that come to mind as you read. If you think the author says something inaccurate or unsubstantiated, write that down. If you don't understand something, make a note about it and ask your professor. Don't feel bad! Journal articles are hard to understand sometimes, even for professors. Your goal is to critically read the literature, so write down what you think while reading! Table 4.2 contains some questions that might stimulate your thoughts.

Definitions

Note definitions of key terms for your topic. At minimum, you should include a scholarly definition for the concepts represented in your working question. If your working question asks about the process of leaving a relationship with domestic violence, your research proposal will have to explain how you define domestic violence, as well as how you define "leaving" an abusive relationship. While you may already know what you mean by domestic violence, the person reading your research proposal does not.

Annotating definitions also helps you engage with the scholarly debate around your topic. Definitions are often contested among scholars. Some definitions of domestic violence will be more comprehensive, including things such economic abuse or forcing the victim to problematically use substances. Other definitions will be less comprehensive, covering only physical, verbal, and sexual abuse. Often, how someone defines something conceptually is highly related to how they measure it in their study. Since you will have to do both of these things, find a definition that feels right to you or create your own, noting the ways in which it is similar or different from those in the literature.

Definitions are also an important way of dealing with jargon. Becoming familiar with a new content area involves learning the jargon experts use. For example, in the last paragraph I used the term economic abuse, but that's probably not a term you've heard before. If you were conducting a literature review on domestic violence, you would want to search for keywords like economic abuse if they are relevant to your working question. You will also want to know what they mean so you can use them appropriately in designing your study and writing your literature review.

Theoretical perspective

Noting the theoretical perspective of the article can help you interpret the data in the same manner as the author. For example, articles on supervised injection facilities for people who use intravenous drugs most likely come from a harm reduction perspective, and understanding the theory behind harm reduction is important to make sense of empirical results. Articles should be grounded in a theoretical perspective that helps the author conceptualize and understand the data. As we discussed in Chapter 3 , some journal articles are entirely theoretical and help you understand the theories or conceptual models related to your topic. We will help you determine a theoretical perspective for your project in Chapter 7 . For now, it's a good idea to note what theories authors mention when talking about your topic area. Some articles are better about this than others, and many authors make it a bit challenging to find theory (if mentioned at all). In other articles, it may help to note which social work theories are missing  from the literature. For example, a study's findings might address issues of oppression and discrimination, but the authors may not use critical theory to make sense of what happened.

Background knowledge

It's a good idea to note any relevant information the author relies on for background. When an author cites facts or opinions from others, you are subsequently able to get information from multiple articles simultaneously. For example, if we were looking at this meta-analysis about domestic violence , in the introduction section, the authors provide facts from many other sources. These facts will likely be relevant to your inquiry on domestic violence, as well.

As you are looking at background information, you should also note any subtopics or concepts about which there is controversy or consensus. The author may present one viewpoint and then an opposing viewpoint, something you may do in your literature review as well. Similarly, they may present facts that scholars in the field have come to consensus on and describe the ways in which different sources support these conclusions.

Sources of interest

Note any relevant sources the author cites. If there is any background information you plan to use, note the original source of that information. When you write your literature review, cite the original source of a piece of information you are using, which may not be where you initially read it . Remember that you should read and refer to the primary source . If you are reading Article A and the author cites a fact from Article B, you should note Article B in your annotations and use Article B when you cite the fact in your paper. You should also make sure Article A interpreted Article B correctly and scan Article B for any other useful facts.

Research question/Purpose

Authors should be clear about the purpose of their article. Charitable authors will give you a sentence that starts with something like this:

• "The purpose of this research project was..."
• "Our research question was..."
• "The research project was designed to test the following hypothesis..."

Unfortunately, not all authors are so clear, and you may to hunt around for the research question or hypothesis. Generally, in an empirical article, the research question or hypothesis is at the end of the introduction. In non-empirical articles, the author will likely discuss the purpose of the article in the abstract or introduction.

We will discuss in greater detail how to read the results of empirical articles in Chapter 5 . For now, just know that you should highlight any of the key findings of an article. They will be described very briefly in the abstract, and in much more detail in the article itself. In an empirical article, you should look at both the 'Results' and 'Discussion' sections. For a non-empirical article, the key findings will likely be in the conclusion. You can also find them in the topic or concluding sentences in a paragraph within the body of the article.

How do researchers know something when they see it? Found in the 'Methods' section of empirical articles, the measures section is where researchers spell out the tools, or measures, they used to gather data. For quantitative studies, you will want to get familiar with the questions researchers typically use to measure key variables. For example, to measure domestic violence, researchers often use the Conflict Tactics Scale . The more frequently used and cited a measure is, the more we know about how well it works (or not). Qualitative studies will often provide at least some of the interview or focus group questions they used with research participants. They will also include information about how their inquiry and hypotheses may have evolved over time. Keep in mind however, sometimes important information is cut out of an article during editing. If you need more information, consider reaching out to the author directly. Before you do so, check if the author provided an appendix with the information you need or if the article links to a their data and measures as part open data sharing practices.

Who exactly were the study participants and how were they recruited? In quantitative studies, you will want to pay attention to the sample size. Generally, the larger the sample, the greater the study's explanatory power. Additionally, randomly drawn samples are desirable because they leave any variation up to chance. Samples that are conducted out of convenience can be biased and non-representative of the larger population. In qualitative studies, non-random sampling is appropriate but consider this: how well does what we find for this group of people transfer to the people who will be in your study? For qualitative studies and quantitative studies, look for how well the sample is described and whether there are important characteristics missing from the article that you would need to determine the quality of the sample.

Limitations

Honest authors will include these at the end of each article. But you should also note any additional limitations you find with their work as well.

Your annotations

These are just a few suggested annotations, but you can come up with your own. For example, maybe there are annotations you would use for different assignments or for the problem statement in your research proposal. If you have an argument or idea that keeps coming to mind when you read, consider creating an annotation for it so you can remember which part of each article supports your ideas. Whatever works for you. The goal with annotation is to extract as much information from each article while reading, so you don't have to go back through everything again. It's useless to read an article and forget most of what you read. Annotate!

• Begin your search by reading thorough and cohesive literature reviews. Review articles are great sources of information to get a broad perspective of your topic.
• Don’t read an article just to say you’ve read it. Annotate and take notes so you don’t have to re-read it later.
• Use software or paper-and-pencil approaches to write notes on articles.
• Annotation is best used when closely reading an empirical study highly similar to your research project.
• Select an empirical article highly related to the study you would like to conduct.
• Annotate the article using the aforementioned annotations and create some of your own.
• Create the first draft of a summary table with key information from this empirical study that you would like to compare to other empirical studies you closely read.

5.3 Generalizability and transferability of empirical results

• Define generalizability and transferability.
• Assess the generalizability and transferability to how researchers use the results from empirical research studies to make arguments about what is objectively true.
• Relate both concepts to the hierarchy of evidence and the types of articles in the scholarly literature

Now that you have read an empirical article in detail, it's important to put its results in conversation with the broader literature on your topic. In this chapter we discuss two important concepts-- generalizability and   transferability --and the interrelationship between the two. We also explain how these two properties of empirical data impact your literature review and evidence-based practice.

Generalizability

The figure below provides a common approach to assessing empirical evidence. As you move up the pyramid below, you can be more sure that the data contained in those studies generalizes to all people who experience the issue.

As we reviewed in Chapter 1, objective truth is true for everyone, regardless of context. In other words, objective truths generalize beyond the sample of people from whom data were collected to the larger population of people who experience the issue under examination. You can be much more sure that information from a systematic review or meta-analysis will generalize than something from a case study of a single person, pilot projects, and other studies that do not seek to establish generalizability.

The type of article listed here is also related to the types of research methods the authors used. While we cover many of these approaches in this textbook, some of them (like cohort studies) are somewhat less common in social work. Additionally, there is one important research method, survey design, that does not appear in this diagram. Finally, social work research uses many different types of qualitative research--some of which generates more generalizable data than others.

For a refresher on the different types of evidence available in each type of article, refer back to section 4.1. You'll recall the hierarchy of evidence as described by McNeese & Thyer (2004) [55]

• Systematic reviews and meta-analyses
• Randomized controlled trials
• Quasi-experimental studies
• Case-control and cohort studies
• Pre-experimental (or non-experimental) group studies
• Qualitative studies

Because there is further variation in the types of studies used by social work researchers, I expanded the hierarchy of evidence to cover a greater breadth of research methods in Figure 5.3.

Refined information from multiple sources

The top of the hierarchy represents refined scientific information or meta-research . Meta-research uses the scientific method to analyze and improve the scientific production of knowledge. For example, meta-analyses pull together samples of people from all high-quality studies on a given topic area creating a super-study with far more people than any single researcher could feasibly collect data from. Because scientists (and clinical experts) refine data across multiple studies, these represent the most generalizable research findings.

Of course, not all meta-analyses or systematic reviews are of good quality. As a peer reviewer for a scholarly journal, I have seen poor quality systematic reviews that make methodological mistakes—like not including relevant keywords—that lead to incorrect conclusions. Unfortunately, not all errors are caught in the peer review process, and not all limitations are acknowledged by the authors. Just because you are looking at a systematic review does not mean you are looking at THE OBJECTIVE TRUTH. Nevertheless, you can be pretty sure that results from these studies are generalizable to the population in the study’s research question.

A good way to visualize the process of sampling is by examining the procedure used for systematic reviews and meta-analyses to scientifically search for articles. In Figure 5.4 below, you can see how researchers conducting a systematic review identified a large pool of potentially relevant articles, downloaded and analyzed them for relevance, and in the end, analyzed only 71 articles in their systematic review out of a total of 1,589 potentially relevant articles. Because systematic reviews or meta-analyses are intended to make strong, generalizable conclusions, they often exclude studies that still contain good information.

In the process of selecting articles for a meta-analysis and systematic review, researchers may exclude articles with important information for a number of good reasons. No study is perfect, and all research methods decisions come with limitations--including meta-research. Authors conducting a meta-analysis cannot include a study unless researchers provide data for the authors to include in their meta-analysis, and many empirical journal articles do not make their data available. Additionally, a study’s intervention or measures may be a bit different than what researchers want to make conclusions about. This is a key truth applicable across all articles you read—who or what gets selected for analysis in a research project determines how well the project’s results generalize to everyone.

We will talk about this in future chapters as sampling, and in those chapters, we will learn which sampling approaches are intended to support generalizability and which are used for other purposes. For example, availability or convenience sampling is often used to get quick information while random sampling approaches are intended to support generalizability. It is impossible to know everything about your article right now, but by the end of this course, you will have the information you need to critically examine the generalizability of a sample.

Primary sources (empirical studies)

Because refined sources like systematic reviews exclude good studies, they are only a first step in getting to know a topic area. You will need to examine primary sources--the reports of researchers who conducted empirical studies--to make evidence-based conclusions about your topic. Figure 5.3 describes three different types of data and ranks them vertically based on how well you can be sure the information generalizes.

As we will discuss further in our chapter on causal explanations, a key factor in scientifically assessing what happened first. Researchers conducting intervention studies are causing change by providing therapy, housing, or whatever the intervention is and measuring the outcomes of that intervention after they happen. This is unlike survey researchers, who do not introduce an intervention but ask people to self-report information on a questionnaire. Longitudinal surveys are particularly helpful because they can provide a clearer picture of whether the cause came before the effect in a causal relationship, but because they are expensive and time-consuming to conduct, longitudinal studies are relatively rare in the literature and most surveys measure people at only one point in time. Thus, because researchers cannot tightly control the causal variable (an intervention, an experience of abuse, etc.) we can be somewhat less certain of the conclusions of surveys than experiments. At the same time, because surveys measure people in their naturalistic environment rather than in a laboratory or artificial setting, they may do a better job at reducing the potential for the researcher to influence the data a participant provides. Surveys also provide descriptive information--like the number of people with a diagnosis or risk factor--that experiments cannot provide.

Surveys and experiments are commonly used in social work, and we will describe the methods they use in future chapters. When assessing the generalizability of a given survey or experiment, you are looking at whether the methods used by the researchers improve generalizability (or, at least that those methods are intended to improve generalizability). Specifically, there are sampling, measurement, and design decisions that researchers make that can improve generalizability. And once the study is conducted, whether those methods worked as intended also impact generalizability.

We address sampling, measurement, and design in the coming chapters, and you will need more in-depth knowledge of research methods to assess the generalizability of the results you are reading. In the meantime, Figure 5.3 is organized by design, and this is a good starting point for your inquiry since it only requires you to identify the design in each empirical article--which should be included in the abstract and described in detail in the methods section. For more information on how to conduct sampling, measurement, and design in a way that maximizes generalizability, read Part 2 of this textbook.

When searching for design of a study, look for specific keywords that indicate the researcher used methods that do not generalize well like pilot study, pre-experiment, non-experiment, convenience sample, availability sample, and exploratory study. When researchers are seeking to perform a pilot study, they are optimizing for time, not generalizability. Their results may still be useful to you! But, you should not generalize from their study to all people with the issue under analysis without a lot of caution and additional supporting evidence. Instead, you should see whether the lessons from this study might transfer to the context in which you are researching--our next topic.

Qualitative studies use sampling, measures, and designs that do not try to optimize generalizability. Thus, if the results of a qualitative study indicate 10 out of 50 students who participated in the focus group found the mandatory training on harassment to be unhelpful, does that mean 20% of all college students at this university find it unhelpful? Because focus groups and interviews (and other qualitative methods we will discuss) use qualitative methods, they are not concerned with generalizability. It would not make sense to generalize from focus groups to all people in a population. Instead, focus groups methods optimize for trustworthy and authentic research projects that make sure, for example, all themes and quotes in the researcher's report are traceable to quotes from focus group participants. Instead of providing what is generally true, qualitative research provides a thick description of people's experiences so you can understand them. S ubjective inquiry is less generalizable but provides greater depth in understanding people's feelings, beliefs, and decision-making processes within their context. 

In Figure 5.3, you will note that some qualitative studies are ranked higher than others in terms of generalizability. Meta-syntheses are ranked highest because they are meta-research, pooling together the themes and raw data from multiple qualitative studies into a super-study. A meta-synthesis is the qualitative equivalent of a meta-analysis, which analyzes quantitative data. Because the researchers conducting the meta-syntheses aim to make more broad generalizations across research studies, even though generalizability is not strictly the goal. In a similar way, grounded theory studies (a type of qualitative design) aim to produce a testable hypothesis that could generalize. At the bottom of the hierarchy are individual case studies, which report what happens with a single person, organization, or event. It's best not to think too long about the generalizability of qualitative results. When examining qualitative articles, you should be examining their transferability, our topic for the next subsection.

Transferability

Generalizability asks one question: How well does the sample of people in this study represent everyone with this issue? If you read in a study that 50% of people in the sample experienced depression, does that mean 50% of everyone experiences depression? We previewed future discussions in this textbook that will discuss the specific quantitative research methods used to optimize the generalizability of results. By adhering strictly to best practices in sampling, measurement, and design, researchers can provide you with good evidence for the generalizability of their study's results.

Of course, generalizability is not the only question worth asking. Just because a study's sample represents a broader population does not mean it is helpful for making conclusions about your working question. In assessing a study's transferability, you are making a weaker but compelling argument that the conclusions of one study can be applied to understanding the people in your working question and research project. Generalizable results may be applicable because they are broadly transferable across situations, and you can be confident in that when they follow the best practices in this textbook for improving generalizability. However, there may be aspects of a study that make its results difficult to transfer to your topic area.

When evaluating the transferability of a research result to your working question, consider the sample, measures, and design. That is, how data was collected from individuals, who those individuals are, and what researchers did with them. You may find that the samples in generalizable studies do not talk about the specific ethnic, cultural, or geographic group that is in your working question. Similarly, studies that measure the outcomes of substance use treatment by measuring sobriety may not match your working question on moderation, medication adherence, or substitution as an outcome in substance use treatment. Evaluating the transferability of designs may help you identify whether the methods the authors used would be similar to those you might use if you were to conduct a study gathering and collecting your own raw data.

Assessing transferability is more subjective. You are using your knowledge of your topic area and research methods (which are always improving!) to make a reasonable argument about why a given piece of evidence from a primary source helps you understand something. Look back at Table 5.2, your annotations, and the researchers' sampling, data analysis, results, and design. Using your critical thinking (and the knowledge you can in Part 2 and Part 3 of this textbook) you will need to make a reasonable argument that these results transfer to the people, places, and culture that you are talking about in your working question.

In the final chapter of Part 1, we will discuss how to assemble the facts you have taken from journal articles into a literature review that represents what  you think about the topic.

• Developing your theoretical framework
• Conceptual definitions
• Inductive & deductive reasoning

Nomothetic causal explanations

Content warning: examples in this chapter include references to sexual harassment, domestic violence, gender-based violence, the child welfare system, substance use disorders, neonatal abstinence syndrome, child abuse, racism, and sexism.

11.1 Developing your theoretical framework

• Differentiate between theories that explain specific parts of the social world versus those that are more broad and sweeping in their conclusions
• Identify the theoretical perspectives that are relevant to your project and inform your thinking about it
• Define key concepts in your working question and develop a theoretical framework for how you understand your topic.

Theories provide a way of looking at the world and of understanding human interaction. Paradigms are grounded in big assumptions about the world—what is real, how do we create knowledge—whereas theories describe more specific phenomena. Well, we are still oversimplifying a bit. Some theories try to explain the whole world, while others only try to explain a small part. Some theories can be grouped together based on common ideas but retain their own individual and unique features. Our goal is to help you find a theoretical framework that helps you understand your topic more deeply and answer your working question.

Theories: Big and small

In your human behavior and the social environment (HBSE) class, you were introduced to the major theoretical perspectives that are commonly used in social work. These are what we like to call big-T 'T'heories. When you read about systems theory, you are actually reading a synthesis of decades of distinct, overlapping, and conflicting theories that can be broadly classified within systems theory. For example, within systems theory, some approaches focus more on family systems while others focus on environmental systems, though the core concepts remain similar.

Different theorists define concepts in their own way, and as a result, their theories may explore different relationships with those concepts. For example, Deci and Ryan's (1985) [56] self-determination theory discusses motivation and establishes that it is contingent on meeting one's needs for autonomy, competency, and relatedness. By contrast, ecological self-determination theory, as written by Abery & Stancliffe (1996), [57] argues that self-determination is the amount of control exercised by an individual over aspects of their lives they deem important across the micro, meso, and macro levels. If self-determination were an important concept in your study, you would need to figure out which of the many theories related to self-determination helps you address your working question.

Theories can provide a broad perspective on the key concepts and relationships in the world or more specific and applied concepts and perspectives. Table 7.2 summarizes two commonly used lists of big-T Theoretical perspectives in social work. See if you can locate some of the theories that might inform your project.

Competing theoretical explanations

Within each area of specialization in social work, there are many other theories that aim to explain more specific types of interactions. For example, within the study of sexual harassment, different theories posit different explanations for why harassment occurs.

One theory, first developed by criminologists, is called routine activities theory. It posits that sexual harassment is most likely to occur when a workplace lacks unified groups and when potentially vulnerable targets and motivated offenders are both present (DeCoster, Estes, & Mueller, 1999). [60]

Other theories of sexual harassment, called relational theories, suggest that one's existing relationships are the key to understanding why and how workplace sexual harassment occurs and how people will respond when it does occur (Morgan, 1999). [61] Relational theories focus on the power that different social relationships provide (e.g., married people who have supportive partners at home might be more likely than those who lack support at home to report sexual harassment when it occurs).

Finally, feminist theories of sexual harassment take a different stance. These theories posit that the organization of our current gender system, wherein those who are the most masculine have the most power, best explains the occurrence of workplace sexual harassment (MacKinnon, 1979). [62] As you might imagine, which theory a researcher uses to examine the topic of sexual harassment will shape the questions asked about harassment. It will also shape the explanations the researcher provides for why harassment occurs.

For a graduate student beginning their study of a new topic, it may be intimidating to learn that there are so many theories beyond what you’ve learned in your theory classes. What’s worse is that there is no central database of theories on your topic. However, as you review the literature in your area, you will learn more about the theories scientists have created to explain how your topic works in the real world. There are other good sources for theories, in addition to journal articles. Books often contain works of theoretical and philosophical importance that are beyond the scope of an academic journal. Do a search in your university library for books on your topic, and you are likely to find theorists talking about how to make sense of your topic. You don't necessarily have to agree with the prevailing theories about your topic, but you do need to be aware of them so you can apply theoretical ideas to your project.

Applying big-T theories to your topic

The key to applying theories to your topic is learning the key concepts associated with that theory and the relationships between those concepts, or propositions . Again, your HBSE class should have prepared you with some of the most important concepts from the theoretical perspectives listed in Table 7.2. For example, the conflict perspective sees the world as divided into dominant and oppressed groups who engage in conflict over resources. If you were applying these theoretical ideas to your project, you would need to identify which groups in your project are considered dominant or oppressed groups, and which resources they were struggling over. This is a very general example. Challenge yourself to find small-t theories about your topic that will help you understand it in much greater detail and specificity. If you have chosen a topic that is relevant to your life and future practice, you will be doing valuable work shaping your ideas towards social work practice.

Integrating theory into your project can be easy, or it can take a bit more effort. Some people have a strong and explicit theoretical perspective that they carry with them at all times. For me, you'll probably see my work drawing from exchange and choice, social constructionist, and critical theory. Maybe you have theoretical perspectives you naturally employ, like Afrocentric theory or person-centered practice. If so, that's a great place to start since you might already be using that theory (even subconsciously) to inform your understanding of your topic. But if you aren't aware of whether you are using a theoretical perspective when you think about your topic, try writing a paragraph off the top of your head or talking with a friend explaining what you think about that topic. Try matching it with some of the ideas from the broad theoretical perspectives from Table 7.2. This can ground you as you search for more specific theories. Some studies are designed to test whether theories apply the real world while others are designed to create new theories or variations on existing theories. Consider which feels more appropriate for your project and what you want to know.

Another way to easily identify the theories associated with your topic is to look at the concepts in your working question. Are these concepts commonly found in any of the theoretical perspectives in Table 7.2? Take a look at the Payne and Hutchison texts and see if any of those look like the concepts and relationships in your working question or if any of them match with how you think about your topic. Even if they don't possess the exact same wording, similar theories can help serve as a starting point to finding other theories that can inform your project. Remember, HBSE textbooks will give you not only the broad statements of theories but also sources from specific theorists and sub-theories that might be more applicable to your topic. Skim the references and suggestions for further reading once you find something that applies well.

Choose a theoretical perspective from Hutchison, Payne, or another theory textbook that is relevant to your project. Using their textbooks or other reputable sources, identify :

• At least five important concepts from the theory
• What relationships the theory establishes between these important concepts (e.g., as x increases, the y decreases)
• How you can use this theory to better understand the concepts and variables in your project?

Developing your own theoretical framework

Hutchison's and Payne's frameworks are helpful for surveying the whole body of literature relevant to social work, which is why they are so widely used. They are one framework, or way of thinking, about all of the theories social workers will encounter that are relevant to practice. Social work researchers should delve further and develop a theoretical or conceptual framework of their own based on their reading of the literature. In Chapter 8 , we will develop your theoretical framework further, identifying the cause-and-effect relationships that answer your working question. Developing a theoretical framework is also instructive for revising and clarifying your working question and identifying concepts that serve as keywords for additional literature searching. The greater clarity you have with your theoretical perspective, the easier each subsequent step in the research process will be.

Getting acquainted with the important theoretical concepts in a new area can be challenging. While social work education provides a broad overview of social theory, you will find much greater fulfillment out of reading about the theories related to your topic area. We discussed some strategies for finding theoretical information in Chapter 3 as part of literature searching. To extend that conversation a bit, some strategies for searching for theories in the literature include:

• Consider searching for these keywords in the title or abstract, specifically
• Looking at the references and cited by links within theoretical articles and textbooks
• Looking at books, edited volumes, and textbooks that discuss theory
• Talking with a scholar on your topic, or asking a professor if they can help connect you to someone
• Nice authors are clear about how they use theory to inform their research project, usually in the introduction and discussion section.
• For example, from the broad umbrella of systems theory, you might pick out family systems theory if you want to understand the effectiveness of a family counseling program.

It's important to remember that knowledge arises within disciplines, and that disciplines have different theoretical frameworks for explaining the same topic. While it is certainly important for the social work perspective to be a part of your analysis, social workers benefit from searching across disciplines to come to a more comprehensive understanding of the topic. Reaching across disciplines can provide uncommon insights during conceptualization, and once the study is completed, a multidisciplinary researcher will be able to share results in a way that speaks to a variety of audiences. A study by An and colleagues (2015) [63] uses game theory from the discipline of economics to understand problems in the Temporary Assistance for Needy Families (TANF) program. In order to receive TANF benefits, mothers must cooperate with paternity and child support requirements unless they have "good cause," as in cases of domestic violence, in which providing that information would put the mother at greater risk of violence. Game theory can help us understand how TANF recipients and caseworkers respond to the incentives in their environment, and highlight why the design of the "good cause" waiver program may not achieve its intended outcome of increasing access to benefits for survivors of family abuse.

Of course, there are natural limits on the depth with which student researchers can and should engage in a search for theory about their topic. At minimum, you should be able to draw connections across studies and be able to assess the relative importance of each theory within the literature. Just because you found one article applying your theory (like game theory, in our example above) does not mean it is important or often used in the domestic violence literature. Indeed, it would be much more common in the family violence literature to find psychological theories of trauma, feminist theories of power and control, and similar theoretical perspectives used to inform research projects rather than game theory, which is equally applicable to survivors of family violence as workers and bosses at a corporation. Consider using the Cited By feature to identify articles, books, and other sources of theoretical information that are seminal or well-cited in the literature. Similarly, by using the name of a theory in the keywords of a search query (along with keywords related to your topic), you can get a sense of how often the theory is used in your topic area. You should have a sense of what theories are commonly used to analyze your topic, even if you end up choosing a different one to inform your project.

Theories that are not cited or used as often are still immensely valuable. As we saw before with TANF and "good cause" waivers, using theories from other disciplines can produce uncommon insights and help you make a new contribution to the social work literature. Given the privileged position that the social work curriculum places on theories developed by white men, students may want to explore Afrocentricity as a social work practice theory (Pellebon, 2007) [64] or abolitionist social work (Jacobs et al., 2021) [65] when deciding on a theoretical framework for their research project that addresses concepts of racial justice. Start with your working question, and explain how each theory helps you answer your question. Some explanations are going to feel right, and some concepts will feel more salient to you than others. Keep in mind that this is an iterative process. Your theoretical framework will likely change as you continue to conceptualize your research project, revise your research question, and design your study.

By trying on many different theoretical explanations for your topic area, you can better clarify your own theoretical framework. Some of you may be fortunate enough to find theories that match perfectly with how you think about your topic, are used often in the literature, and are therefore relatively straightforward to apply. However, many of you may find that a combination of theoretical perspectives is most helpful for you to investigate your project. For example, maybe the group counseling program for which you are evaluating client outcomes draws from both motivational interviewing and cognitive behavioral therapy. In order to understand the change happening in the client population, you would need to know each theory separately as well as how they work in tandem with one another. Because theoretical explanations and even the definitions of concepts are debated by scientists, it may be helpful to find a specific social scientist or group of scientists whose perspective on the topic you find matches with your understanding of the topic. Of course, it is also perfectly acceptable to develop your own theoretical framework, though you should be able to articulate how your framework fills a gap within the literature.

If you are adapting theoretical perspectives in your study, it is important to clarify the original authors' definitions of each concept. Jabareen (2009) [66] offers that conceptual frameworks are not merely collections of concepts but, rather, constructs in which each concept plays an integral role. [67] A conceptual framework is a network of linked concepts that together provide a comprehensive understanding of a phenomenon. Each concept in a conceptual framework plays an ontological or epistemological role in the framework, and it is important to assess whether the concepts and relationships in your framework make sense together. As your framework takes shape, you will find yourself integrating and grouping together concepts, thinking about the most important or least important concepts, and how each concept is causally related to others.

Much like paradigm, theory plays a supporting role for the conceptualization of your research project. Recall the ice float from Figure 7.1. Theoretical explanations support the design and methods you use to answer your research question. In student projects that lack a theoretical framework, I often see the biases and errors in reasoning that we discussed in Chapter 1 that get in the way of good social science. That's because theories mark which concepts are important, provide a framework for understanding them, and measure their interrelationships. If you are missing this foundation, you will operate on informal observation, messages from authority, and other forms of unsystematic and unscientific thinking we reviewed in Chapter 1 .

Theory-informed inquiry is incredibly helpful for identifying key concepts and how to measure them in your research project, but there is a risk in aligning research too closely with theory. The theory-ladenness of facts and observations produced by social science research means that we may be making our ideas real through research. This is a potential source of confirmation bias in social science. Moreover, as Tan (2016) [68] demonstrates, social science often proceeds by adopting as true the perspective of Western and Global North countries, and cross-cultural research is often when ethnocentric and biased ideas are most visible . In her example, a researcher from the West studying teacher-centric classrooms in China that rely partially on rote memorization may view them as less advanced than student-centered classrooms developed in a Western country simply because of Western philosophical assumptions about the importance of individualism and self-determination. Developing a clear theoretical framework is a way to guard against biased research, and it will establish a firm foundation on which you will develop the design and methods for your study.

• Just as empirical evidence is important for conceptualizing a research project, so too are the key concepts and relationships identified by social work theory.
• Using theory your theory textbook will provide you with a sense of the broad theoretical perspectives in social work that might be relevant to your project.
• Try to find small-t theories that are more specific to your topic area and relevant to your working question.
• In Chapter 2 , you developed a concept map for your proposal. Take a moment to revisit your concept map now as your theoretical framework is taking shape. Make any updates to the key concepts and relationships in your concept map. . If you need a refresher, we have embedded a short how-to video from the University of Guelph Library (CC-BY-NC-SA 4.0) that we also used in Chapter 2 .

11.2 Conceptual definitions

• Define measurement and conceptualization
• Apply Kaplan’s three categories to determine the complexity of measuring a given variable
• Identify the role previous research and theory play in defining concepts
• Distinguish between unidimensional and multidimensional concepts
• Critically apply reification to how you conceptualize the key variables in your research project

In social science, when we use the term  measurement , we mean the process by which we describe and ascribe meaning to the key facts, concepts, or other phenomena that we are investigating. At its core, measurement is about defining one’s terms in as clear and precise a way as possible. Of course, measurement in social science isn’t quite as simple as using a measuring cup or spoon, but there are some basic tenets on which most social scientists agree when it comes to measurement. We’ll explore those, as well as some of the ways that measurement might vary depending on your unique approach to the study of your topic.

An important point here is that measurement does not require any particular instruments or procedures. What it does require is a systematic procedure for assigning scores, meanings, and descriptions to individuals or objects so that those scores represent the characteristic of interest. You can measure phenomena in many different ways, but you must be sure that how you choose to measure gives you information and data that lets you answer your research question. If you're looking for information about a person's income, but your main points of measurement have to do with the money they have in the bank, you're not really going to find the information you're looking for!

The question of what social scientists measure can be answered by asking yourself what social scientists study. Think about the topics you’ve learned about in other social work classes you’ve taken or the topics you’ve considered investigating yourself. Let’s consider Melissa Milkie and Catharine Warner’s study (2011) [69] of first graders’ mental health. In order to conduct that study, Milkie and Warner needed to have some idea about how they were going to measure mental health. What does mental health mean, exactly? And how do we know when we’re observing someone whose mental health is good and when we see someone whose mental health is compromised? Understanding how measurement works in research methods helps us answer these sorts of questions.

As you might have guessed, social scientists will measure just about anything that they have an interest in investigating. For example, those who are interested in learning something about the correlation between social class and levels of happiness must develop some way to measure both social class and happiness. Those who wish to understand how well immigrants cope in their new locations must measure immigrant status and coping. Those who wish to understand how a person’s gender shapes their workplace experiences must measure gender and workplace experiences (and get more specific about which experiences are under examination). You get the idea. Social scientists can and do measure just about anything you can imagine observing or wanting to study. Of course, some things are easier to observe or measure than others.

Observing your variables

In 1964, philosopher Abraham Kaplan (1964) [70] wrote The   Conduct of Inquiry,  which has since become a classic work in research methodology (Babbie, 2010). [71] In his text, Kaplan describes different categories of things that behavioral scientists observe. One of those categories, which Kaplan called “observational terms,” is probably the simplest to measure in social science. Observational terms are the sorts of things that we can see with the naked eye simply by looking at them. Kaplan roughly defines them as conditions that are easy to identify and verify through direct observation. If, for example, we wanted to know how the conditions of playgrounds differ across different neighborhoods, we could directly observe the variety, amount, and condition of equipment at various playgrounds.

Indirect observables , on the other hand, are less straightforward to assess. In Kaplan's framework, they are conditions that are subtle and complex that we must use existing knowledge and intuition to define. If we conducted a study for which we wished to know a person’s income, we’d probably have to ask them their income, perhaps in an interview or a survey. Thus, we have observed income, even if it has only been observed indirectly. Birthplace might be another indirect observable. We can ask study participants where they were born, but chances are good we won’t have directly observed any of those people being born in the locations they report.

Sometimes the measures that we are interested in are more complex and more abstract than observational terms or indirect observables. Think about some of the concepts you’ve learned about in other social work classes—for example, ethnocentrism. What is ethnocentrism? Well, from completing an introduction to social work class you might know that it has something to do with the way a person judges another’s culture. But how would you  measure  it? Here’s another construct: bureaucracy. We know this term has something to do with organizations and how they operate but measuring such a construct is trickier than measuring something like a person’s income. The theoretical concepts of ethnocentrism and bureaucracy represent ideas whose meanings we have come to agree on. Though we may not be able to observe these abstractions directly, we can observe their components.

Kaplan referred to these more abstract things that behavioral scientists measure as constructs.  Constructs  are “not observational either directly or indirectly” (Kaplan, 1964, p. 55), [72] but they can be defined based on observables. For example, the construct of bureaucracy could be measured by counting the number of supervisors that need to approve routine spending by public administrators. The greater the number of administrators that must sign off on routine matters, the greater the degree of bureaucracy. Similarly, we might be able to ask a person the degree to which they trust people from different cultures around the world and then assess the ethnocentrism inherent in their answers. We can measure constructs like bureaucracy and ethnocentrism by defining them in terms of what we can observe. [73]

The idea of coming up with your own measurement tool might sound pretty intimidating at this point. The good news is that if you find something in the literature that works for you, you can use it (with proper attribution, of course). If there are only pieces of it that you like, you can reuse those pieces (with proper attribution and describing/justifying any changes). You don't always have to start from scratch!

Look at the variables in your research question.

• Classify them as direct observables, indirect observables, or constructs.
• Do you think measuring them will be easy or hard?
• What are your first thoughts about how to measure each variable? No wrong answers here, just write down a thought about each variable.

Measurement starts with conceptualization

In order to measure the concepts in your research question, we first have to understand what we think about them. As an aside, the word concept  has come up quite a bit, and it is important to be sure we have a shared understanding of that term. A  concept is the notion or image that we conjure up when we think of some cluster of related observations or ideas. For example, masculinity is a concept. What do you think of when you hear that word? Presumably, you imagine some set of behaviors and perhaps even a particular style of self-presentation. Of course, we can’t necessarily assume that everyone conjures up the same set of ideas or images when they hear the word  masculinity . While there are many possible ways to define the term and some may be more common or have more support than others, there is no universal definition of masculinity. What counts as masculine may shift over time, from culture to culture, and even from individual to individual (Kimmel, 2008). This is why defining our concepts is so important.\

Not all researchers clearly explain their theoretical or conceptual framework for their study, but they should! Without understanding how a researcher has defined their key concepts, it would be nearly impossible to understand the meaning of that researcher’s findings and conclusions. Back in Chapter 7 , you developed a theoretical framework for your study based on a survey of the theoretical literature in your topic area. If you haven't done that yet, consider flipping back to that section to familiarize yourself with some of the techniques for finding and using theories relevant to your research question. Continuing with our example on masculinity, we would need to survey the literature on theories of masculinity. After a few queries on masculinity, I found a wonderful article by Wong (2010) [74] that analyzed eight years of the journal Psychology of Men & Masculinity and analyzed how often different theories of masculinity were used . Not only can I get a sense of which theories are more accepted and which are more marginal in the social science on masculinity, I am able to identify a range of options from which I can find the theory or theories that will inform my project. 

Identify a specific theory (or more than one theory) and how it helps you understand...

• Your independent variable(s).
• Your dependent variable(s).
• The relationship between your independent and dependent variables.

Rather than completing this exercise from scratch, build from your theoretical or conceptual framework developed in previous chapters.

In quantitative methods, conceptualization involves writing out clear, concise definitions for our key concepts. These are the kind of definitions you are used to, like the ones in a dictionary. A conceptual definition involves defining a concept in terms of other concepts, usually by making reference to how other social scientists and theorists have defined those concepts in the past. Of course, new conceptual definitions are created all the time because our conceptual understanding of the world is always evolving.

Conceptualization is deceptively challenging—spelling out exactly what the concepts in your research question mean to you. Following along with our example, think about what comes to mind when you read the term masculinity. How do you know masculinity when you see it? Does it have something to do with men or with social norms? If so, perhaps we could define masculinity as the social norms that men are expected to follow. That seems like a reasonable start, and at this early stage of conceptualization, brainstorming about the images conjured up by concepts and playing around with possible definitions is appropriate. However, this is just the first step. At this point, you should be beyond brainstorming for your key variables because you have read a good amount of research about them

In addition, we should consult previous research and theory to understand the definitions that other scholars have already given for the concepts we are interested in. This doesn’t mean we must use their definitions, but understanding how concepts have been defined in the past will help us to compare our conceptualizations with how other scholars define and relate concepts. Understanding prior definitions of our key concepts will also help us decide whether we plan to challenge those conceptualizations or rely on them for our own work. Finally, working on conceptualization is likely to help in the process of refining your research question to one that is specific and clear in what it asks. Conceptualization and operationalization (next section) are where "the rubber meets the road," so to speak, and you have to specify what you mean by the question you are asking. As your conceptualization deepens, you will often find that your research question becomes more specific and clear.

If we turn to the literature on masculinity, we will surely come across work by Michael Kimmel , one of the preeminent masculinity scholars in the United States. After consulting Kimmel’s prior work (2000; 2008), [75] we might tweak our initial definition of masculinity. Rather than defining masculinity as “the social norms that men are expected to follow,” perhaps instead we’ll define it as “the social roles, behaviors, and meanings prescribed for men in any given society at any one time” (Kimmel & Aronson, 2004, p. 503). [76] Our revised definition is more precise and complex because it goes beyond addressing one aspect of men’s lives (norms), and addresses three aspects: roles, behaviors, and meanings. It also implies that roles, behaviors, and meanings may vary across societies and over time. Using definitions developed by theorists and scholars is a good idea, though you may find that you want to define things your own way.

As you can see, conceptualization isn’t as simple as applying any random definition that we come up with to a term. Defining our terms may involve some brainstorming at the very beginning. But conceptualization must go beyond that, to engage with or critique existing definitions and conceptualizations in the literature. Once we’ve brainstormed about the images associated with a particular word, we should also consult prior work to understand how others define the term in question. After we’ve identified a clear definition that we’re happy with, we should make sure that every term used in our definition will make sense to others. Are there terms used within our definition that also need to be defined? If so, our conceptualization is not yet complete. Our definition includes the concept of "social roles," so we should have a definition for what those mean and become familiar with role theory to help us with our conceptualization. If we don't know what roles are, how can we study them?

Let's say we do all of that. We have a clear definition of the term masculinity with reference to previous literature and we also have a good understanding of the terms in our conceptual definition...then we're done, right? Not so fast. You’ve likely met more than one man in your life, and you’ve probably noticed that they are not the same, even if they live in the same society during the same historical time period. This could mean there are dimensions of masculinity. In terms of social scientific measurement, concepts can be said to have multiple dimensions  when there are multiple elements that make up a single concept. With respect to the term  masculinity , dimensions could based on gender identity, gender performance, sexual orientation, etc.. In any of these cases, the concept of masculinity would be considered to have multiple dimensions.

While you do not need to spell out every possible dimension of the concepts you wish to measure, it is important to identify whether your concepts are unidimensional (and therefore relatively easy to define and measure) or multidimensional (and therefore require multi-part definitions and measures). In this way, how you conceptualize your variables determines how you will measure them in your study. Unidimensional concepts are those that are expected to have a single underlying dimension. These concepts can be measured using a single measure or test. Examples include simple concepts such as a person’s weight, time spent sleeping, and so forth. 

One frustrating this is that there is no clear demarcation between concepts that are inherently unidimensional or multidimensional. Even something as simple as age could be broken down into multiple dimensions including mental age and chronological age, so where does conceptualization stop? How far down the dimensional rabbit hole do we have to go? Researchers should consider two things. First, how important is this variable in your study? If age is not important in your study (maybe it is a control variable), it seems like a waste of time to do a lot of work drawing from developmental theory to conceptualize this variable. A unidimensional measure from zero to dead is all the detail we need. On the other hand, if we were measuring the impact of age on masculinity, conceptualizing our independent variable (age) as multidimensional may provide a richer understanding of its impact on masculinity. Finally, your conceptualization will lead directly to your operationalization of the variable, and once your operationalization is complete, make sure someone reading your study could follow how your conceptual definitions informed the measures you chose for your variables. 

Write a conceptual definition for your independent and dependent variables.

• Cite and attribute definitions to other scholars, if you use their words.
• Describe how your definitions are informed by your theoretical framework.
• Place your definition in conversation with other theories and conceptual definitions commonly used in the literature.
• Are there multiple dimensions of your variables?
• Are any of these dimensions important for you to measure?

Do researchers actually know what we're talking about?

Conceptualization proceeds differently in qualitative research compared to quantitative research. Since qualitative researchers are interested in the understandings and experiences of their participants, it is less important for them to find one fixed definition for a concept before starting to interview or interact with participants. The researcher’s job is to accurately and completely represent how their participants understand a concept, not to test their own definition of that concept.

If you were conducting qualitative research on masculinity, you would likely consult previous literature like Kimmel’s work mentioned above. From your literature review, you may come up with a  working definition  for the terms you plan to use in your study, which can change over the course of the investigation. However, the definition that matters is the definition that your participants share during data collection. A working definition is merely a place to start, and researchers should take care not to think it is the only or best definition out there.

In qualitative inquiry, your participants are the experts (sound familiar, social workers?) on the concepts that arise during the research study. Your job as the researcher is to accurately and reliably collect and interpret their understanding of the concepts they describe while answering your questions. Conceptualization of concepts is likely to change over the course of qualitative inquiry, as you learn more information from your participants. Indeed, getting participants to comment on, extend, or challenge the definitions and understandings of other participants is a hallmark of qualitative research. This is the opposite of quantitative research, in which definitions must be completely set in stone before the inquiry can begin.

The contrast between qualitative and quantitative conceptualization is instructive for understanding how quantitative methods (and positivist research in general) privilege the knowledge of the researcher over the knowledge of study participants and community members. Positivism holds that the researcher is the "expert," and can define concepts based on their expert knowledge of the scientific literature. This knowledge is in contrast to the lived experience that participants possess from experiencing the topic under examination day-in, day-out. For this reason, it would be wise to remind ourselves not to take our definitions too seriously and be critical about the limitations of our knowledge.

Conceptualization must be open to revisions, even radical revisions, as scientific knowledge progresses. While I’ve suggested consulting prior scholarly definitions of our concepts, you should not assume that prior, scholarly definitions are more real than the definitions we create. Likewise, we should not think that our own made-up definitions are any more real than any other definition. It would also be wrong to assume that just because definitions exist for some concept that the concept itself exists beyond some abstract idea in our heads. Building on the paradigmatic ideas behind interpretivism and the critical paradigm, researchers call the assumption that our abstract concepts exist in some concrete, tangible way is known as reification . It explores the power dynamics behind how we can create reality by how we define it.

Returning again to our example of masculinity. Think about our how our notions of masculinity have developed over the past few decades, and how different and yet so similar they are to patriarchal definitions throughout history. Conceptual definitions become more or less popular based on the power arrangements inside of social science the broader world. Western knowledge systems are privileged, while others are viewed as unscientific and marginal. The historical domination of social science by white men from WEIRD countries meant that definitions of masculinity were imbued their cultural biases and were designed explicitly and implicitly to preserve their power. This has inspired movements for cognitive justice as we seek to use social science to achieve global development.

• Measurement is the process by which we describe and ascribe meaning to the key facts, concepts, or other phenomena that we are investigating.
• Kaplan identified three categories of things that social scientists measure including observational terms, indirect observables, and constructs.
• Some concepts have multiple elements or dimensions.
• Researchers often use measures previously developed and studied by other researchers.
• Conceptualization is a process that involves coming up with clear, concise definitions.
• Conceptual definitions are based on the theoretical framework you are using for your study (and the paradigmatic assumptions underlying those theories).
• Whether your conceptual definitions come from your own ideas or the literature, you should be able to situate them in terms of other commonly used conceptual definitions.
• Researchers should acknowledge the limited explanatory power of their definitions for concepts and how oppression can shape what explanations are considered true or scientific.

Think historically about the variables in your research question.

• How has our conceptual definition of your topic changed over time?
• What scholars or social forces were responsible for this change?

Take a critical look at your conceptual definitions.

• How participants might define terms for themselves differently, in terms of their daily experience?
• On what cultural assumptions are your conceptual definitions based?
• Are your conceptual definitions applicable across all cultures that will be represented in your sample?

11.3 Inductive and deductive reasoning

• Describe inductive and deductive reasoning and provide examples of each
• Identify how inductive and deductive reasoning are complementary

Congratulations! You survived the chapter on theories and paradigms. My experience has been that many students have a difficult time thinking about theories and paradigms because they perceive them as "intangible" and thereby hard to connect to social work research. I even had one student who said she got frustrated just reading the word "philosophy."

Rest assured, you do not need to become a theorist or philosopher to be an effective social worker or researcher. However, you should have a good sense of what theory or theories will be relevant to your project, as well as how this theory, along with your working question, fit within the three broad research paradigms we reviewed. If you don't have a good idea about those at this point, it may be a good opportunity to pause and read more about the theories related to your topic area.

Theories structure and inform social work research. The converse is also true: research can structure and inform theory. The reciprocal relationship between theory and research often becomes evident to students when they consider the relationships between theory and research in inductive and deductive approaches to research. In both cases, theory is crucial. But the relationship between theory and research differs for each approach.

While inductive and deductive approaches to research are quite different, they can also be complementary. Let’s start by looking at each one and how they differ from one another. Then we’ll move on to thinking about how they complement one another.

Inductive reasoning

A researcher using inductive reasoning begins by collecting data that is relevant to their topic of interest. Once a substantial amount of data have been collected, the researcher will then step back from data collection to get a bird’s eye view of their data. At this stage, the researcher looks for patterns in the data, working to develop a theory that could explain those patterns. Thus, when researchers take an inductive approach, they start with a particular set of observations and move to a more general set of propositions about those experiences. In other words, they move from data to theory, or from the specific to the general. Figure 8.1 outlines the steps involved with an inductive approach to research.

There are many good examples of inductive research, but we’ll look at just a few here. One fascinating study in which the researchers took an inductive approach is Katherine Allen, Christine Kaestle, and Abbie Goldberg’s (2011) [77] study of how boys and young men learn about menstruation. To understand this process, Allen and her colleagues analyzed the written narratives of 23 young cisgender men in which the men described how they learned about menstruation, what they thought of it when they first learned about it, and what they think of it now. By looking for patterns across all 23 cisgender men’s narratives, the researchers were able to develop a general theory of how boys and young men learn about this aspect of girls’ and women’s biology. They conclude that sisters play an important role in boys’ early understanding of menstruation, that menstruation makes boys feel somewhat separated from girls, and that as they enter young adulthood and form romantic relationships, young men develop more mature attitudes about menstruation. Note how this study began with the data—men’s narratives of learning about menstruation—and worked to develop a theory.

In another inductive study, Kristin Ferguson and colleagues (Ferguson, Kim, & McCoy, 2011) [78] analyzed empirical data to better understand how to meet the needs of young people who are homeless. The authors analyzed focus group data from 20 youth at a homeless shelter. From these data they developed a set of recommendations for those interested in applied interventions that serve homeless youth. The researchers also developed hypotheses for others who might wish to conduct further investigation of the topic. Though Ferguson and her colleagues did not test their hypotheses, their study ends where most deductive investigations begin: with a theory and a hypothesis derived from that theory. Section 8.4 discusses the use of mixed methods research as a way for researchers to test hypotheses created in a previous component of the same research project.

You will notice from both of these examples that inductive reasoning is most commonly found in studies using qualitative methods, such as focus groups and interviews. Because inductive reasoning involves the creation of a new theory, researchers need very nuanced data on how the key concepts in their working question operate in the real world. Qualitative data is often drawn from lengthy interactions and observations with the individuals and phenomena under examination. For this reason, inductive reasoning is most often associated with qualitative methods, though it is used in both quantitative and qualitative research.

Deductive reasoning

If inductive reasoning is about creating theories from raw data, deductive reasoning is about testing theories using data. Researchers using deductive reasoning take the steps described earlier for inductive research and reverse their order. They start with a compelling social theory, create a hypothesis about how the world should work, collect raw data, and analyze whether their hypothesis was confirmed or not. That is, deductive approaches move from a more general level (theory) to a more specific (data); whereas inductive approaches move from the specific (data) to general (theory).

A deductive approach to research is the one that people typically associate with scientific investigation. Students in English-dominant countries that may be confused by inductive vs. deductive research can rest part of the blame on Sir Arthur Conan Doyle, creator of the Sherlock Holmes character. As Craig Vasey points out in his breezy introduction to logic book chapter , Sherlock Holmes more often used inductive rather than deductive reasoning (despite claiming to use the powers of deduction to solve crimes). By noticing subtle details in how people act, behave, and dress, Holmes finds patterns that others miss. Using those patterns, he creates a theory of how the crime occurred, dramatically revealed to the authorities just in time to arrest the suspect. Indeed, it is these flashes of insight into the patterns of data that make Holmes such a keen inductive reasoner. In social work practice, rather than detective work, inductive reasoning is supported by the intuitions and practice wisdom of social workers, just as Holmes' reasoning is sharpened by his experience as a detective.

So, if deductive reasoning isn't Sherlock Holmes' observation and pattern-finding, how does it work? It starts with what you have already done in Chapters 3 and 4, reading and evaluating what others have done to study your topic. It continued with Chapter 5, discovering what theories already try to explain how the concepts in your working question operate in the real world. Tapping into this foundation of knowledge on their topic, the researcher studies what others have done, reads existing theories of whatever phenomenon they are studying, and then tests hypotheses that emerge from those theories. Figure 8.2 outlines the steps involved with a deductive approach to research.

While not all researchers follow a deductive approach, many do. We’ll now take a look at a couple excellent recent examples of deductive research. 

In a study of US law enforcement responses to hate crimes, Ryan King and colleagues (King, Messner, & Baller, 2009) [79] hypothesized that law enforcement’s response would be less vigorous in areas of the country that had a stronger history of racial violence. The authors developed their hypothesis from prior research and theories on the topic. They tested the hypothesis by analyzing data on states’ lynching histories and hate crime responses. Overall, the authors found support for their hypothesis and illustrated an important application of critical race theory.

In another recent deductive study, Melissa Milkie and Catharine Warner (2011) [80] studied the effects of different classroom environments on first graders’ mental health. Based on prior research and theory, Milkie and Warner hypothesized that negative classroom features, such as a lack of basic supplies and heat, would be associated with emotional and behavioral problems in children. One might associate this research with Maslow's hierarchy of needs or systems theory. The researchers found support for their hypothesis, demonstrating that policymakers should be paying more attention to the mental health outcomes of children’s school experiences, just as they track academic outcomes (American Sociological Association, 2011). [81]

Complementary approaches

While inductive and deductive approaches to research seem quite different, they can actually be rather complementary. In some cases, researchers will plan for their study to include multiple components, one inductive and the other deductive. In other cases, a researcher might begin a study with the plan to conduct either inductive or deductive research, but then discovers along the way that the other approach is needed to help illuminate findings. Here is an example of each such case.

Dr. Amy Blackstone (n.d.), author of Principles of sociological inquiry: Qualitative and quantitative methods , relates a story about her mixed methods research on sexual harassment.

We began the study knowing that we would like to take both a deductive and an inductive approach in our work. We therefore administered a quantitative survey, the responses to which we could analyze in order to test hypotheses, and also conducted qualitative interviews with a number of the survey participants. The survey data were well suited to a deductive approach; we could analyze those data to test hypotheses that were generated based on theories of harassment. The interview data were well suited to an inductive approach; we looked for patterns across the interviews and then tried to make sense of those patterns by theorizing about them. For one paper (Uggen & Blackstone, 2004) [82] , we began with a prominent feminist theory of the sexual harassment of adult women and developed a set of hypotheses outlining how we expected the theory to apply in the case of younger women’s and men’s harassment experiences. We then tested our hypotheses by analyzing the survey data. In general, we found support for the theory that posited that the current gender system, in which heteronormative men wield the most power in the workplace, explained workplace sexual harassment—not just of adult women but of younger women and men as well. In a more recent paper (Blackstone, Houle, & Uggen, 2006), [83] we did not hypothesize about what we might find but instead inductively analyzed interview data, looking for patterns that might tell us something about how or whether workers’ perceptions of harassment change as they age and gain workplace experience. From this analysis, we determined that workers’ perceptions of harassment did indeed shift as they gained experience and that their later definitions of harassment were more stringent than those they held during adolescence. Overall, our desire to understand young workers’ harassment experiences fully—in terms of their objective workplace experiences, their perceptions of those experiences, and their stories of their experiences—led us to adopt both deductive and inductive approaches in the work. (Blackstone, n.d., p. 21) [84]

Researchers may not always set out to employ both approaches in their work but sometimes find that their use of one approach leads them to the other. One such example is described eloquently in Russell Schutt’s  Investigating the Social World (2006). [85] As Schutt describes, researchers Sherman and Berk (1984) [86] conducted an experiment to test two competing theories of the effects of punishment on deterring deviance (in this case, domestic violence).Specifically, Sherman and Berk hypothesized that deterrence   theory (see Williams, 2005 [87] for more information on that theory) would provide a better explanation of the effects of arresting accused batterers than labeling theory . Deterrence theory predicts that arresting an accused spouse batterer will  reduce  future incidents of violence. Conversely, labeling theory predicts that arresting accused spouse batterers will  increase  future incidents (see Policastro & Payne, 2013 [88] for more information on that theory). Figure 8.3 summarizes the two competing theories and the hypotheses Sherman and Berk set out to test.

Research from these follow-up studies were mixed. In some cases, arrest deterred future incidents of violence. In other cases, it did not. This left the researchers with new data that they needed to explain. The researchers therefore took an inductive approach in an effort to make sense of their latest empirical observations. The new studies revealed that arrest seemed to have a deterrent effect for those who were married and employed, but that it led to increased offenses for those who were unmarried and unemployed. Researchers thus turned to control theory, which posits that having some stake in conformity through the social ties provided by marriage and employment, as the better explanation (see Davis et al., 2000 [90] for more information on this theory).

What the original Sherman and Berk study, along with the follow-up studies, show us is that we might start with a deductive approach to research, but then, if confronted by new data we must make sense of, we may move to an inductive approach. We will expand on these possibilities in section 8.4 when we discuss mixed methods research.

Ethical and critical considerations

Deductive and inductive reasoning, just like other components of the research process comes with ethical and cultural considerations for researchers. Specifically, deductive research is limited by existing theory. Because scientific inquiry has been shaped by oppressive forces such as sexism, racism, and colonialism, what is considered theory is largely based in Western, white-male-dominant culture. Thus, researchers doing deductive research may artificially limit themselves to ideas that were derived from this context. Non-Western researchers, international social workers, and practitioners working with non-dominant groups may find deductive reasoning of limited help if theories do not adequately describe other cultures.

While these flaws in deductive research may make inductive reasoning seem more appealing, on closer inspection you'll find similar issues apply. A researcher using inductive reasoning applies their intuition and lived experience when analyzing participant data. They will take note of particular themes, conceptualize their definition, and frame the project using their unique psychology. Since everyone's internal world is shaped by their cultural and environmental context, inductive reasoning conducted by Western researchers may unintentionally reinforcing lines of inquiry that derive from cultural oppression.

Inductive reasoning is also shaped by those invited to provide the data to be analyzed. For example, I recently worked with a student who wanted to understand the impact of child welfare supervision on children born dependent on opiates and methamphetamine. Due to the potential harm that could come from interviewing families and children who are in foster care or under child welfare supervision, the researcher decided to use inductive reasoning and to only interview child welfare workers.

Talking to practitioners is a good idea for feasibility, as they are less vulnerable than clients. However, any theory that emerges out of these observations will be substantially limited, as it would be devoid of the perspectives of parents, children, and other community members who could provide a more comprehensive picture of the impact of child welfare involvement on children. Notice that each of these groups has less power than child welfare workers in the service relationship. Attending to which groups were used to inform the creation of a theory and the power of those groups is an important critical consideration for social work researchers.

As you can see, when researchers apply theory to research they must wrestle with the history and hierarchy around knowledge creation in that area. In deductive studies, the researcher is positioned as the expert, similar to the positivist paradigm presented in Chapter 5. We've discussed a few of the limitations on the knowledge of researchers in this subsection, but the position of the "researcher as expert" is inherently problematic. However, it should also not be taken to an extreme. A researcher who approaches inductive inquiry as a naïve learner is also inherently problematic. Just as competence in social work practice requires a baseline of knowledge prior to entering practice, so does competence in social work research. Because a truly naïve intellectual position is impossible—we all have preexisting ways we view the world and are not fully aware of how they may impact our thoughts—researchers should be well-read in the topic area of their research study but humble enough to know that there is always much more to learn.

• Inductive reasoning begins with a set of empirical observations, seeking patterns in those observations, and then theorizing about those patterns.
• Deductive reasoning begins with a theory, developing hypotheses from that theory, and then collecting and analyzing data to test the truth of those hypotheses.
• Inductive and deductive reasoning can be employed together for a more complete understanding of the research topic.
• Though researchers don’t always set out to use both inductive and deductive reasoning in their work, they sometimes find that new questions arise in the course of an investigation that can best be answered by employing both approaches.
• Identify one theory and how it helps you understand your topic and working question.

I encourage you to find a specific theory from your topic area, rather than relying only on the broad theoretical perspectives like systems theory or the strengths perspective. Those broad theoretical perspectives are okay...but I promise that searching for theories about your topic will help you conceptualize and design your research project.

• Using the theory you identified, describe what you expect the answer to be to your working question.
• Define and provide an example of idiographic causal relationships
• Describe the role of causality in quantitative research as compared to qualitative research
• Identify, define, and describe each of the main criteria for nomothetic causal relationships
• Describe the difference between and provide examples of independent, dependent, and control variables
• Define hypothesis, state a clear hypothesis, and discuss the respective roles of quantitative and qualitative research when it comes to hypotheses

Causality  refers to the idea that one event, behavior, or belief will result in the occurrence of another, subsequent event, behavior, or belief. In other words, it is about cause and effect. It seems simple, but you may be surprised to learn there is more than one way to explain how one thing causes another. How can that be? How could there be many ways to understand causality?

Think back to our discussion in Section 5.3 on paradigms [insert chapter link plus link to section 1.2]. You’ll remember the positivist paradigm as the one that believes in objectivity. Positivists look for causal explanations that are universally true for everyone, everywhere  because they seek objective truth. Interpretivists, on the other hand, look for causal explanations that are true for individuals or groups in a specific time and place because they seek subjective truths. Remember that for interpretivists, there is not one singular truth that is true for everyone, but many truths created and shared by others.

"Are you trying to generalize or nah?"

One of my favorite classroom moments occurred in the early days of my teaching career. Students were providing peer feedback on their working questions. I overheard one group who was helping someone rephrase their research question. A student asked, “Are you trying to generalize or nah?” Teaching is full of fun moments like that one. Answering that one question can help you understand how to conceptualize and design your research project.

Nomothetic causal explanations are incredibly powerful. They allow scientists to make predictions about what will happen in the future, with a certain margin of error. Moreover, they allow scientists to generalize —that is, make claims about a large population based on a smaller sample of people or items. Generalizing is important. We clearly do not have time to ask everyone their opinion on a topic or test a new intervention on every person. We need a type of causal explanation that helps us predict and estimate truth in all situations.

Generally, nomothetic causal relationships work best for explanatory research projects [INSERT SECTION LINK]. They also tend to use quantitative research: by boiling things down to numbers, one can use the universal language of mathematics to use statistics to explore those relationships. On the other hand, descriptive and exploratory projects often fit better with idiographic causality. These projects do not usually try to generalize, but instead investigate what is true for individuals, small groups, or communities at a specific point in time. You will learn about this type of causality in the next section. Here, we will assume you have an explanatory working question. For example, you may want to know about the risk and protective factors for a specific diagnosis or how a specific therapy impacts client outcomes.

What do nomothetic causal explanations look like?

Nomothetic causal explanations express relationships between variables . The term variable has a scientific definition. This one from Gillespie & Wagner (2018) "a logical grouping of attributes that can be observed and measured and is expected to vary from person to person in a population" (p. 9). [91] More practically, variables are the key concepts in your working question. You know, the things you plan to observe when you actually do your research project, conduct your surveys, complete your interviews, etc. These things have two key properties. First, they vary , as in they do not remain constant. "Age" varies by number. "Gender" varies by category. But they both vary. Second, they have attributes . So the variable "health professions" has attributes or categories, such as social worker, nurse, counselor, etc.

It's also worth reviewing what is  not a variable. Well, things that don't change (or vary) aren't variables. If you planned to do a study on how gender impacts earnings but your study only contained women, that concept would not vary . Instead, it would be a constant . Another common mistake I see in students' explanatory questions is mistaking an attribute for a variable. "Men" is not a variable. "Gender" is a variable. "Virginia" is not a variable. The variable is the "state or territory" in which someone or something is physically located.

When one variable causes another, we have what researchers call independent and dependent variables. For example, in a study investigating the impact of spanking on aggressive behavior, spanking would be the independent variable and aggressive behavior would be the dependent variable. An independent variable is the cause, and a  dependent variable  is the effect. Why are they called that? Dependent variables  depend on independent variables. If all of that gets confusing, just remember the graphical relationship in Figure 8.5.

Write out your working question, as it exists now. As we said previously in the subsection, we assume you have an explanatory research question for learning this section.

• Write out a diagram similar to Figure 8.5.
• Put your independent variable on the left and the dependent variable on the right.
• Can your variables vary?
• Do they have different attributes or categories that vary from person to person?
• How does the theory you identified in section 8.1 help you understand this causal relationship?

If the theory you've identified isn't much help to you or seems unrelated, it's a good indication that you need to read more literature about the theories related to your topic.

For some students, your working question may not be specific enough to list an independent or dependent variable clearly. You may have "risk factors" in place of an independent variable, for example. Or "effects" as a dependent variable. If that applies to your research question, get specific for a minute even if you have to revise this later. Think about which specific risk factors or effects you are interested in. Consider a few options for your independent and dependent variable and create diagrams similar to Figure 8.5.

Finally, you are likely to revisit your working question so you may have to come back to this exercise to clarify the causal relationship you want to investigate.

For a ten-cent word like "nomothetic," these causal relationships should look pretty basic to you. They should look like "x causes y." Indeed, you may be looking at your causal explanation and thinking, "wow, there are so many other things I'm missing in here." In fact, maybe my dependent variable sometimes causes changes in my independent variable! For example, a working question asking about poverty and education might ask how poverty makes it more difficult to graduate college or how high college debt impacts income inequality after graduation. Nomothetic causal relationships are slices of reality. They boil things down to two (or often more) key variables and assert a one-way causal explanation between them. This is by design, as they are trying to generalize across all people to all situations. The more complicated, circular, and often contradictory causal explanations are idiographic, which we will cover in the next section of this chapter.

Developing a hypothesis

A hypothesis   is a statement describing a researcher’s expectation regarding what they anticipate finding. Hypotheses in quantitative research are a nomothetic causal relationship that the researcher expects to determine is true or false. A hypothesis is written to describe the expected relationship between the independent and dependent variables. In other words, write the answer to your working question using your variables. That's your hypothesis! Make sure you haven't introduced new variables into your hypothesis that are not in your research question. If you have, write out your hypothesis as in Figure 8.5.

A good hypothesis should be testable using social science research methods. That is, you can use a social science research project (like a survey or experiment) to test whether it is true or not. A good hypothesis is also  specific about the relationship it explores. For example, a student project that hypothesizes, "families involved with child welfare agencies will benefit from Early Intervention programs," is not specific about what benefits it plans to investigate. For this student, I advised her to take a look at the empirical literature and theory about Early Intervention and see what outcomes are associated with these programs. This way, she could  more clearly state the dependent variable in her hypothesis, perhaps looking at reunification, attachment, or developmental milestone achievement in children and families under child welfare supervision.

Your hypothesis should be an informed prediction based on a theory or model of the social world. For example, you may hypothesize that treating mental health clients with warmth and positive regard is likely to help them achieve their therapeutic goals. That hypothesis would be based on the humanistic practice models of Carl Rogers. Using previous theories to generate hypotheses is an example of deductive research. If Rogers’ theory of unconditional positive regard is accurate, a study comparing clinicians who used it versus those who did not would show more favorable treatment outcomes for clients receiving unconditional positive regard.

Let’s consider a couple of examples. In research on sexual harassment (Uggen & Blackstone, 2004), [92] one might hypothesize, based on feminist theories of sexual harassment, that more females than males will experience specific sexually harassing behaviors. What is the causal relationship being predicted here? Which is the independent and which is the dependent variable? In this case, researchers hypothesized that a person’s sex (independent variable) would predict their likelihood to experience sexual harassment (dependent variable).

Sometimes researchers will hypothesize that a relationship will take a specific direction. As a result, an increase or decrease in one area might be said to cause an increase or decrease in another. For example, you might choose to study the relationship between age and support for legalization of marijuana. Perhaps you’ve taken a sociology class and, based on the theories you’ve read, you hypothesize that age is negatively related to support for marijuana legalization. [93] What have you just hypothesized?

You have hypothesized that as people get older, the likelihood of their supporting marijuana legalization decreases. Thus, as age (your independent variable) moves in one direction (up), support for marijuana legalization (your dependent variable) moves in another direction (down). So, a direct relationship (or positive correlation) involve two variables going in the same direction and an inverse relationship (or negative correlation) involve two variables going in opposite directions. If writing hypotheses feels tricky, it is sometimes helpful to draw them out and depict each of the two hypotheses we have just discussed.

It’s important to note that once a study starts, it is unethical to change your hypothesis to match the data you find. For example, what happens if you conduct a study to test the hypothesis from Figure 8.7 on support for marijuana legalization, but you find no relationship between age and support for legalization? It means that your hypothesis was incorrect, but that’s still valuable information. It would challenge what the existing literature says on your topic, demonstrating that more research needs to be done to figure out the factors that impact support for marijuana legalization. Don’t be embarrassed by negative results, and definitely don’t change your hypothesis to make it appear correct all along!

Criteria for establishing a nomothetic causal relationship

Let’s say you conduct your study and you find evidence that supports your hypothesis, as age increases, support for marijuana legalization decreases. Success! Causal explanation complete, right? Not quite.

You’ve only established one of the criteria for causality. The criteria for causality must include all of the following: covariation, plausibility, temporality, and nonspuriousness. In our example from Figure 8.7, we have established only one criteria—covariation. When variables covary , they vary together. Both age and support for marijuana legalization vary in our study. Our sample contains people of varying ages and varying levels of support for marijuana legalization. If, for example, we only included 16-year-olds in our study, age would be a  constant , not a variable.

Just because there might be some correlation between two variables does not mean that a causal relationship between the two is really plausible. Plausibility means that in order to make the claim that one event, behavior, or belief causes another, the claim has to make sense. It makes sense that people from previous generations would have different attitudes towards marijuana than younger generations. People who grew up in the time of Reefer Madness or the hippies may hold different views than those raised in an era of legalized medicinal and recreational use of marijuana. Plausibility is of course helped by basing your causal explanation in existing theoretical and empirical findings.

Once we’ve established that there is a plausible relationship between the two variables, we also need to establish whether the cause occurred before the effect, the criterion of temporality . A person’s age is a quality that appears long before any opinions on drug policy, so temporally the cause comes before the effect. It wouldn’t make any sense to say that support for marijuana legalization makes a person’s age increase. Even if you could predict someone’s age based on their support for marijuana legalization, you couldn’t say someone’s age was caused by their support for legalization of marijuana.

Finally, scientists must establish nonspuriousness. A spurious relationship is one in which an association between two variables appears to be causal but can in fact be explained by some third variable. This third variable is often called a confound or confounding variable because it clouds and confuses the relationship between your independent and dependent variable, making it difficult to discern the true causal relationship is.

Continuing with our example, we could point to the fact that older adults are less likely to have used marijuana recreationally. Maybe it is actually recreational use of marijuana that leads people to be more open to legalization, not their age. In this case, our confounding variable would be recreational marijuana use. Perhaps the relationship between age and attitudes towards legalization is a spurious relationship that is accounted for by previous use. This is also referred to as the third variable problem , where a seemingly true causal relationship is actually caused by a third variable not in the hypothesis. In this example, the relationship between age and support for legalization could be more about having tried marijuana than the age of the person.

Quantitative researchers are sensitive to the effects of potentially spurious relationships. As a result, they will often measure these third variables in their study, so they can control for their effects in their statistical analysis. These are called  control variables , and they refer to potentially confounding variables whose effects are controlled for mathematically in the data analysis process. Control variables can be a bit confusing, and we will discuss them more in Chapter 10, but think about it as an argument between you, the researcher, and a critic.

Researcher: “The older a person is, the less likely they are to support marijuana legalization.” Critic: “Actually, it’s more about whether a person has used marijuana before. That is what truly determines whether someone supports marijuana legalization.” Researcher: “Well, I measured previous marijuana use in my study and mathematically controlled for its effects in my analysis. Age explains most of the variation in attitudes towards marijuana legalization.”

Let’s consider a few additional, real-world examples of spuriousness. Did you know, for example, that high rates of ice cream sales have been shown to cause drowning? Of course, that’s not really true, but there is a positive relationship between the two. In this case, the third variable that causes both high ice cream sales and increased deaths by drowning is time of year, as the summer season sees increases in both (Babbie, 2010). [94]

Here’s another good one: it is true that as the salaries of Presbyterian ministers in Massachusetts rise, so too does the price of rum in Havana, Cuba. Well, duh, you might be saying to yourself. Everyone knows how much ministers in Massachusetts love their rum, right? Not so fast. Both salaries and rum prices have increased, true, but so has the price of just about everything else (Huff & Geis, 1993). [95]

Finally, research shows that the more firefighters present at a fire, the more damage is done at the scene. What this statement leaves out, of course, is that as the size of a fire increases so too does the amount of damage caused as does the number of firefighters called on to help (Frankfort-Nachmias & Leon-Guerrero, 2011). [96] In each of these examples, it is the presence of a confounding variable that explains the apparent relationship between the two original variables.

In sum, the following criteria must be met for a nomothetic causal relationship:

• The two variables must vary together.
• The relationship must be plausible.
• The cause must precede the effect in time.
• The relationship must be nonspurious (not due to a confounding variable).

The hypothetico-dedutive method

The primary way that researchers in the positivist paradigm use theories is sometimes called the hypothetico-deductive method (although this term is much more likely to be used by philosophers of science than by scientists themselves). Researchers choose an existing theory. Then, they make a prediction about some new phenomenon that should be observed if the theory is correct. Again, this prediction is called a hypothesis. The researchers then conduct an empirical study to test the hypothesis. Finally, they reevaluate the theory in light of the new results and revise it if necessary.

This process is usually conceptualized as a cycle because the researchers can then derive a new hypothesis from the revised theory, conduct a new empirical study to test the hypothesis, and so on. As Figure 8.8 shows, this approach meshes nicely with the process of conducting a research project—creating a more detailed model of “theoretically motivated” or “theory-driven” research. Together, they form a model of theoretically motivated research. 

Keep in mind the hypothetico-deductive method is only one way of using social theory to inform social science research. It starts with describing one or more existing theories, deriving a hypothesis from one of those theories, testing your hypothesis in a new study, and finally reevaluating the theory based on the results data analyses. This format works well when there is an existing theory that addresses the research question—especially if the resulting hypothesis is surprising or conflicts with a hypothesis derived from a different theory.

But what if your research question is more interpretive? What if it is less about theory-testing and more about theory-building? This is what our next chapters will cover: the process of inductively deriving theory from people's stories and experiences. This process looks different than that depicted in Figure 8.8. It still starts with your research question and answering that question by conducting a research study. But instead of testing a hypothesis you created based on a theory, you will create a theory of your own that explain the data you collected. This format works well for qualitative research questions and for research questions that existing theories do not address.

• In positivist and quantitative studies, the goal is often to understand the more general causes of some phenomenon rather than the idiosyncrasies of one particular instance, as in an idiographic causal relationship.
• Nomothetic causal explanations focus on objectivity, prediction, and generalization.
• Criteria for nomothetic causal relationships require the relationship be plausible and nonspurious; and that the cause must precede the effect in time.
• In a nomothetic causal relationship, the independent variable causes changes in the dependent variable.
• Hypotheses are statements, drawn from theory, which describe a researcher’s expectation about a relationship between two or more variables.
• Write out your working question and hypothesis.
• Defend your hypothesis in a short paragraph, using arguments based on the theory you identified in section 8.1.
• Review the criteria for a nomothetic causal relationship. Critique your short paragraph about your hypothesis using these criteria.
• Are there potentially confounding variables, issues with time order, or other problems you can identify in your reasoning?

Inductive & deductive (deductive focus)

9. Writing your research question Copyright © 2020 by Matthew DeCarlo is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Research Question vs Hypothesis: Difference and Comparison

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This kind of research is called a research paper and includes various headings that are related to the given topic.

Key Takeaways A research question is a broad inquiry into a topic, while a hypothesis is a statement that explains a phenomenon. Research questions are open-ended and exploratory, while hypotheses are specific and testable. Research questions are used in qualitative research, while hypotheses are used in quantitative research.

 Research Question vs Hypothesis

The difference between Research Question and Hypothesis is that the research question is the question whose answer needs to be found through the research paper, whereas a hypothesis is an assertion that either approves or negates the matter in question. The two also differ in their structure, aim, nature, and so on.

The research question poses a question that is then to be solved or answered through the research paper. Since it puts forth a question, it is inquisitive.

Hypothesis, on the other hand, is an assumption on the possibility or impossibility of the task in question. It is a prediction of what the results of the research might yield.

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Comparison Table

What is Research Question?

The research question is the question that forms the basis of the research. It is to find the answer to this question that one embarks on the journey of analyzing and researching a particular topic.

It can be incorporated in studies that are quantitative as well as qualitative. The research question should be concise and to the point.

The research question should be put forth or presented at the very beginning of the research study to give an idea about the topic and the answers it aims to find.

The format used to write it is that of posing a proper question. Depending on the subject, there are various methods using which one needs to frame the research question.

What is Hypothesis?

Hypothesis is a statement that suggests or predicts the outcome of the research. It predicts how a certain thing or project will pan out or behave. 

The hypothesis is mostly used in experimental and quantitative research and studies.  It is used in scientific experiments and projects, and it can also be used in other fields such as mathematics, sociology, etc.

A hypothesis can be written or derived when ample research is conducted on a specific topic or the topic in question.

Since a hypothesis is a predictive statement, it does not allow for the possibility of multiple outcomes. It leaves very little space for diversified results.

Main Differences Between Research Question and Hypothesis

• A research question allows for the possibility of multiple and varied results. On the other hand, the hypothesis does not provide a multitude of possibilities.
• A research question is inquisitive since it puts forth a question, whereas a hypothesis is predictive as it assumes the experiment’s outcome or result based on prior studies.

• https://www.researchgate.net/profile/Inga-Jenset/publication/313876542_Linking_practice_to_theory_in_teacher_education_Teacher_candidates’_opportunities_to_talk_about_field_experiences/links/58ad822d45851503be91af6f/Linking-practice-to-theory-in-teacher-education-Teacher-candidates-opportunities-to-talk-about-field-experiences.pdf
• https://academicjournals.org/journal/IJEAPS/article-abstract/334464E40792

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By Emma Smith

Emma Smith holds an MA degree in English from Irvine Valley College. She has been a Journalist since 2002, writing articles on the English language, Sports, and Law. Read more about me on her bio page .

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Frequently asked questions

What’s the difference between a research hypothesis and a statistical hypothesis.

A research hypothesis is your proposed answer to your research question. The research hypothesis usually includes an explanation (“ x affects y because …”).

A statistical hypothesis, on the other hand, is a mathematical statement about a population parameter. Statistical hypotheses always come in pairs: the null and alternative hypotheses . In a well-designed study , the statistical hypotheses correspond logically to the research hypothesis.

Frequently asked questions: Statistics

As the degrees of freedom increase, Student’s t distribution becomes less leptokurtic , meaning that the probability of extreme values decreases. The distribution becomes more and more similar to a standard normal distribution .

The three categories of kurtosis are:

• Mesokurtosis : An excess kurtosis of 0. Normal distributions are mesokurtic.
• Platykurtosis : A negative excess kurtosis. Platykurtic distributions are thin-tailed, meaning that they have few outliers .
• Leptokurtosis : A positive excess kurtosis. Leptokurtic distributions are fat-tailed, meaning that they have many outliers.

Probability distributions belong to two broad categories: discrete probability distributions and continuous probability distributions . Within each category, there are many types of probability distributions.

Probability is the relative frequency over an infinite number of trials.

For example, the probability of a coin landing on heads is .5, meaning that if you flip the coin an infinite number of times, it will land on heads half the time.

Since doing something an infinite number of times is impossible, relative frequency is often used as an estimate of probability. If you flip a coin 1000 times and get 507 heads, the relative frequency, .507, is a good estimate of the probability.

Categorical variables can be described by a frequency distribution. Quantitative variables can also be described by a frequency distribution, but first they need to be grouped into interval classes .

A histogram is an effective way to tell if a frequency distribution appears to have a normal distribution .

Plot a histogram and look at the shape of the bars. If the bars roughly follow a symmetrical bell or hill shape, like the example below, then the distribution is approximately normally distributed.

You can use the CHISQ.INV.RT() function to find a chi-square critical value in Excel.

For example, to calculate the chi-square critical value for a test with df = 22 and α = .05, click any blank cell and type:

=CHISQ.INV.RT(0.05,22)

You can use the qchisq() function to find a chi-square critical value in R.

For example, to calculate the chi-square critical value for a test with df = 22 and α = .05:

qchisq(p = .05, df = 22, lower.tail = FALSE)

You can use the chisq.test() function to perform a chi-square test of independence in R. Give the contingency table as a matrix for the “x” argument. For example:

m = matrix(data = c(89, 84, 86, 9, 8, 24), nrow = 3, ncol = 2)

chisq.test(x = m)

You can use the CHISQ.TEST() function to perform a chi-square test of independence in Excel. It takes two arguments, CHISQ.TEST(observed_range, expected_range), and returns the p value.

Chi-square goodness of fit tests are often used in genetics. One common application is to check if two genes are linked (i.e., if the assortment is independent). When genes are linked, the allele inherited for one gene affects the allele inherited for another gene.

Suppose that you want to know if the genes for pea texture (R = round, r = wrinkled) and color (Y = yellow, y = green) are linked. You perform a dihybrid cross between two heterozygous ( RY / ry ) pea plants. The hypotheses you’re testing with your experiment are:

• This would suggest that the genes are unlinked.
• This would suggest that the genes are linked.

You observe 100 peas:

• 78 round and yellow peas
• 6 round and green peas
• 4 wrinkled and yellow peas
• 12 wrinkled and green peas

Step 1: Calculate the expected frequencies

To calculate the expected values, you can make a Punnett square. If the two genes are unlinked, the probability of each genotypic combination is equal.

The expected phenotypic ratios are therefore 9 round and yellow: 3 round and green: 3 wrinkled and yellow: 1 wrinkled and green.

From this, you can calculate the expected phenotypic frequencies for 100 peas:

Step 2: Calculate chi-square

Χ 2 = 8.41 + 8.67 + 11.6 + 5.4 = 34.08

Step 3: Find the critical chi-square value

Since there are four groups (round and yellow, round and green, wrinkled and yellow, wrinkled and green), there are three degrees of freedom .

For a test of significance at α = .05 and df = 3, the Χ 2 critical value is 7.82.

Step 4: Compare the chi-square value to the critical value

Χ 2 = 34.08

Critical value = 7.82

The Χ 2 value is greater than the critical value .

Step 5: Decide whether the reject the null hypothesis

The Χ 2 value is greater than the critical value, so we reject the null hypothesis that the population of offspring have an equal probability of inheriting all possible genotypic combinations. There is a significant difference between the observed and expected genotypic frequencies ( p < .05).

The data supports the alternative hypothesis that the offspring do not have an equal probability of inheriting all possible genotypic combinations, which suggests that the genes are linked

You can use the chisq.test() function to perform a chi-square goodness of fit test in R. Give the observed values in the “x” argument, give the expected values in the “p” argument, and set “rescale.p” to true. For example:

chisq.test(x = c(22,30,23), p = c(25,25,25), rescale.p = TRUE)

You can use the CHISQ.TEST() function to perform a chi-square goodness of fit test in Excel. It takes two arguments, CHISQ.TEST(observed_range, expected_range), and returns the p value .

Both correlations and chi-square tests can test for relationships between two variables. However, a correlation is used when you have two quantitative variables and a chi-square test of independence is used when you have two categorical variables.

Both chi-square tests and t tests can test for differences between two groups. However, a t test is used when you have a dependent quantitative variable and an independent categorical variable (with two groups). A chi-square test of independence is used when you have two categorical variables.

The two main chi-square tests are the chi-square goodness of fit test and the chi-square test of independence .

A chi-square distribution is a continuous probability distribution . The shape of a chi-square distribution depends on its degrees of freedom , k . The mean of a chi-square distribution is equal to its degrees of freedom ( k ) and the variance is 2 k . The range is 0 to ∞.

As the degrees of freedom ( k ) increases, the chi-square distribution goes from a downward curve to a hump shape. As the degrees of freedom increases further, the hump goes from being strongly right-skewed to being approximately normal.

To find the quartiles of a probability distribution, you can use the distribution’s quantile function.

You can use the quantile() function to find quartiles in R. If your data is called “data”, then “quantile(data, prob=c(.25,.5,.75), type=1)” will return the three quartiles.

You can use the QUARTILE() function to find quartiles in Excel. If your data is in column A, then click any blank cell and type “=QUARTILE(A:A,1)” for the first quartile, “=QUARTILE(A:A,2)” for the second quartile, and “=QUARTILE(A:A,3)” for the third quartile.

You can use the PEARSON() function to calculate the Pearson correlation coefficient in Excel. If your variables are in columns A and B, then click any blank cell and type “PEARSON(A:A,B:B)”.

There is no function to directly test the significance of the correlation.

You can use the cor() function to calculate the Pearson correlation coefficient in R. To test the significance of the correlation, you can use the cor.test() function.

You should use the Pearson correlation coefficient when (1) the relationship is linear and (2) both variables are quantitative and (3) normally distributed and (4) have no outliers.

The Pearson correlation coefficient ( r ) is the most common way of measuring a linear correlation. It is a number between –1 and 1 that measures the strength and direction of the relationship between two variables.

This table summarizes the most important differences between normal distributions and Poisson distributions :

When the mean of a Poisson distribution is large (>10), it can be approximated by a normal distribution.

In the Poisson distribution formula, lambda (λ) is the mean number of events within a given interval of time or space. For example, λ = 0.748 floods per year.

The e in the Poisson distribution formula stands for the number 2.718. This number is called Euler’s constant. You can simply substitute e with 2.718 when you’re calculating a Poisson probability. Euler’s constant is a very useful number and is especially important in calculus.

The three types of skewness are:

• Right skew (also called positive skew ) . A right-skewed distribution is longer on the right side of its peak than on its left.
• Left skew (also called negative skew). A left-skewed distribution is longer on the left side of its peak than on its right.
• Zero skew. It is symmetrical and its left and right sides are mirror images.

Skewness and kurtosis are both important measures of a distribution’s shape.

• Skewness measures the asymmetry of a distribution.
• Kurtosis measures the heaviness of a distribution’s tails relative to a normal distribution .

The alternative hypothesis is often abbreviated as H a or H 1 . When the alternative hypothesis is written using mathematical symbols, it always includes an inequality symbol (usually ≠, but sometimes < or >).

The null hypothesis is often abbreviated as H 0 . When the null hypothesis is written using mathematical symbols, it always includes an equality symbol (usually =, but sometimes ≥ or ≤).

The t distribution was first described by statistician William Sealy Gosset under the pseudonym “Student.”

To calculate a confidence interval of a mean using the critical value of t , follow these four steps:

• Choose the significance level based on your desired confidence level. The most common confidence level is 95%, which corresponds to α = .05 in the two-tailed t table .
• Find the critical value of t in the two-tailed t table.
• Multiply the critical value of t by s / √ n .
• Add this value to the mean to calculate the upper limit of the confidence interval, and subtract this value from the mean to calculate the lower limit.

To test a hypothesis using the critical value of t , follow these four steps:

• Calculate the t value for your sample.
• Find the critical value of t in the t table .
• Determine if the (absolute) t value is greater than the critical value of t .
• Reject the null hypothesis if the sample’s t value is greater than the critical value of t . Otherwise, don’t reject the null hypothesis .

You can use the T.INV() function to find the critical value of t for one-tailed tests in Excel, and you can use the T.INV.2T() function for two-tailed tests.

You can use the qt() function to find the critical value of t in R. The function gives the critical value of t for the one-tailed test. If you want the critical value of t for a two-tailed test, divide the significance level by two.

You can use the RSQ() function to calculate R² in Excel. If your dependent variable is in column A and your independent variable is in column B, then click any blank cell and type “RSQ(A:A,B:B)”.

You can use the summary() function to view the R²  of a linear model in R. You will see the “R-squared” near the bottom of the output.

There are two formulas you can use to calculate the coefficient of determination (R²) of a simple linear regression .

The coefficient of determination (R²) is a number between 0 and 1 that measures how well a statistical model predicts an outcome. You can interpret the R² as the proportion of variation in the dependent variable that is predicted by the statistical model.

There are three main types of missing data .

Missing completely at random (MCAR) data are randomly distributed across the variable and unrelated to other variables .

Missing at random (MAR) data are not randomly distributed but they are accounted for by other observed variables.

Missing not at random (MNAR) data systematically differ from the observed values.

To tidy up your missing data , your options usually include accepting, removing, or recreating the missing data.

• Acceptance: You leave your data as is
• Listwise or pairwise deletion: You delete all cases (participants) with missing data from analyses
• Imputation: You use other data to fill in the missing data

Missing data are important because, depending on the type, they can sometimes bias your results. This means your results may not be generalizable outside of your study because your data come from an unrepresentative sample .

Missing data , or missing values, occur when you don’t have data stored for certain variables or participants.

In any dataset, there’s usually some missing data. In quantitative research , missing values appear as blank cells in your spreadsheet.

There are two steps to calculating the geometric mean :

• Multiply all values together to get their product.
• Find the n th root of the product ( n is the number of values).

Before calculating the geometric mean, note that:

• The geometric mean can only be found for positive values.
• If any value in the data set is zero, the geometric mean is zero.

The arithmetic mean is the most commonly used type of mean and is often referred to simply as “the mean.” While the arithmetic mean is based on adding and dividing values, the geometric mean multiplies and finds the root of values.

Even though the geometric mean is a less common measure of central tendency , it’s more accurate than the arithmetic mean for percentage change and positively skewed data. The geometric mean is often reported for financial indices and population growth rates.

The geometric mean is an average that multiplies all values and finds a root of the number. For a dataset with n numbers, you find the n th root of their product.

Outliers are extreme values that differ from most values in the dataset. You find outliers at the extreme ends of your dataset.

It’s best to remove outliers only when you have a sound reason for doing so.

Some outliers represent natural variations in the population , and they should be left as is in your dataset. These are called true outliers.

Other outliers are problematic and should be removed because they represent measurement errors , data entry or processing errors, or poor sampling.

You can choose from four main ways to detect outliers :

• Sorting your values from low to high and checking minimum and maximum values
• Visualizing your data with a box plot and looking for outliers
• Using the interquartile range to create fences for your data
• Using statistical procedures to identify extreme values

Outliers can have a big impact on your statistical analyses and skew the results of any hypothesis test if they are inaccurate.

These extreme values can impact your statistical power as well, making it hard to detect a true effect if there is one.

No, the steepness or slope of the line isn’t related to the correlation coefficient value. The correlation coefficient only tells you how closely your data fit on a line, so two datasets with the same correlation coefficient can have very different slopes.

To find the slope of the line, you’ll need to perform a regression analysis .

Correlation coefficients always range between -1 and 1.

The sign of the coefficient tells you the direction of the relationship: a positive value means the variables change together in the same direction, while a negative value means they change together in opposite directions.

The absolute value of a number is equal to the number without its sign. The absolute value of a correlation coefficient tells you the magnitude of the correlation: the greater the absolute value, the stronger the correlation.

These are the assumptions your data must meet if you want to use Pearson’s r :

• Both variables are on an interval or ratio level of measurement
• Data from both variables follow normal distributions
• Your data have no outliers
• Your data is from a random or representative sample
• You expect a linear relationship between the two variables

A correlation coefficient is a single number that describes the strength and direction of the relationship between your variables.

Different types of correlation coefficients might be appropriate for your data based on their levels of measurement and distributions . The Pearson product-moment correlation coefficient (Pearson’s r ) is commonly used to assess a linear relationship between two quantitative variables.

There are various ways to improve power:

• Increase the potential effect size by manipulating your independent variable more strongly,
• Increase sample size,
• Increase the significance level (alpha),
• Reduce measurement error by increasing the precision and accuracy of your measurement devices and procedures,
• Use a one-tailed test instead of a two-tailed test for t tests and z tests.

A power analysis is a calculation that helps you determine a minimum sample size for your study. It’s made up of four main components. If you know or have estimates for any three of these, you can calculate the fourth component.

• Statistical power : the likelihood that a test will detect an effect of a certain size if there is one, usually set at 80% or higher.
• Sample size : the minimum number of observations needed to observe an effect of a certain size with a given power level.
• Significance level (alpha) : the maximum risk of rejecting a true null hypothesis that you are willing to take, usually set at 5%.
• Expected effect size : a standardized way of expressing the magnitude of the expected result of your study, usually based on similar studies or a pilot study.

Null and alternative hypotheses are used in statistical hypothesis testing . The null hypothesis of a test always predicts no effect or no relationship between variables, while the alternative hypothesis states your research prediction of an effect or relationship.

Statistical analysis is the main method for analyzing quantitative research data . It uses probabilities and models to test predictions about a population from sample data.

The risk of making a Type II error is inversely related to the statistical power of a test. Power is the extent to which a test can correctly detect a real effect when there is one.

To (indirectly) reduce the risk of a Type II error, you can increase the sample size or the significance level to increase statistical power.

The risk of making a Type I error is the significance level (or alpha) that you choose. That’s a value that you set at the beginning of your study to assess the statistical probability of obtaining your results ( p value ).

The significance level is usually set at 0.05 or 5%. This means that your results only have a 5% chance of occurring, or less, if the null hypothesis is actually true.

To reduce the Type I error probability, you can set a lower significance level.

In statistics, a Type I error means rejecting the null hypothesis when it’s actually true, while a Type II error means failing to reject the null hypothesis when it’s actually false.

In statistics, power refers to the likelihood of a hypothesis test detecting a true effect if there is one. A statistically powerful test is more likely to reject a false negative (a Type II error).

If you don’t ensure enough power in your study, you may not be able to detect a statistically significant result even when it has practical significance. Your study might not have the ability to answer your research question.

While statistical significance shows that an effect exists in a study, practical significance shows that the effect is large enough to be meaningful in the real world.

Statistical significance is denoted by p -values whereas practical significance is represented by effect sizes .

There are dozens of measures of effect sizes . The most common effect sizes are Cohen’s d and Pearson’s r . Cohen’s d measures the size of the difference between two groups while Pearson’s r measures the strength of the relationship between two variables .

Effect size tells you how meaningful the relationship between variables or the difference between groups is.

A large effect size means that a research finding has practical significance, while a small effect size indicates limited practical applications.

Using descriptive and inferential statistics , you can make two types of estimates about the population : point estimates and interval estimates.

• A point estimate is a single value estimate of a parameter . For instance, a sample mean is a point estimate of a population mean.
• An interval estimate gives you a range of values where the parameter is expected to lie. A confidence interval is the most common type of interval estimate.

Both types of estimates are important for gathering a clear idea of where a parameter is likely to lie.

Standard error and standard deviation are both measures of variability . The standard deviation reflects variability within a sample, while the standard error estimates the variability across samples of a population.

The standard error of the mean , or simply standard error , indicates how different the population mean is likely to be from a sample mean. It tells you how much the sample mean would vary if you were to repeat a study using new samples from within a single population.

To figure out whether a given number is a parameter or a statistic , ask yourself the following:

• Does the number describe a whole, complete population where every member can be reached for data collection ?
• Is it possible to collect data for this number from every member of the population in a reasonable time frame?

If the answer is yes to both questions, the number is likely to be a parameter. For small populations, data can be collected from the whole population and summarized in parameters.

If the answer is no to either of the questions, then the number is more likely to be a statistic.

The arithmetic mean is the most commonly used mean. It’s often simply called the mean or the average. But there are some other types of means you can calculate depending on your research purposes:

• Weighted mean: some values contribute more to the mean than others.
• Geometric mean : values are multiplied rather than summed up.
• Harmonic mean: reciprocals of values are used instead of the values themselves.

You can find the mean , or average, of a data set in two simple steps:

• Find the sum of the values by adding them all up.
• Divide the sum by the number of values in the data set.

This method is the same whether you are dealing with sample or population data or positive or negative numbers.

The median is the most informative measure of central tendency for skewed distributions or distributions with outliers. For example, the median is often used as a measure of central tendency for income distributions, which are generally highly skewed.

Because the median only uses one or two values, it’s unaffected by extreme outliers or non-symmetric distributions of scores. In contrast, the mean and mode can vary in skewed distributions.

To find the median , first order your data. Then calculate the middle position based on n , the number of values in your data set.

A data set can often have no mode, one mode or more than one mode – it all depends on how many different values repeat most frequently.

Your data can be:

• without any mode
• unimodal, with one mode,
• bimodal, with two modes,
• trimodal, with three modes, or
• multimodal, with four or more modes.

To find the mode :

• If your data is numerical or quantitative, order the values from low to high.
• If it is categorical, sort the values by group, in any order.

Then you simply need to identify the most frequently occurring value.

The interquartile range is the best measure of variability for skewed distributions or data sets with outliers. Because it’s based on values that come from the middle half of the distribution, it’s unlikely to be influenced by outliers .

The two most common methods for calculating interquartile range are the exclusive and inclusive methods.

The exclusive method excludes the median when identifying Q1 and Q3, while the inclusive method includes the median as a value in the data set in identifying the quartiles.

For each of these methods, you’ll need different procedures for finding the median, Q1 and Q3 depending on whether your sample size is even- or odd-numbered. The exclusive method works best for even-numbered sample sizes, while the inclusive method is often used with odd-numbered sample sizes.

While the range gives you the spread of the whole data set, the interquartile range gives you the spread of the middle half of a data set.

Homoscedasticity, or homogeneity of variances, is an assumption of equal or similar variances in different groups being compared.

This is an important assumption of parametric statistical tests because they are sensitive to any dissimilarities. Uneven variances in samples result in biased and skewed test results.

Statistical tests such as variance tests or the analysis of variance (ANOVA) use sample variance to assess group differences of populations. They use the variances of the samples to assess whether the populations they come from significantly differ from each other.

Variance is the average squared deviations from the mean, while standard deviation is the square root of this number. Both measures reflect variability in a distribution, but their units differ:

• Standard deviation is expressed in the same units as the original values (e.g., minutes or meters).
• Variance is expressed in much larger units (e.g., meters squared).

Although the units of variance are harder to intuitively understand, variance is important in statistical tests .

The empirical rule, or the 68-95-99.7 rule, tells you where most of the values lie in a normal distribution :

• Around 68% of values are within 1 standard deviation of the mean.
• Around 95% of values are within 2 standard deviations of the mean.
• Around 99.7% of values are within 3 standard deviations of the mean.

The empirical rule is a quick way to get an overview of your data and check for any outliers or extreme values that don’t follow this pattern.

In a normal distribution , data are symmetrically distributed with no skew. Most values cluster around a central region, with values tapering off as they go further away from the center.

The measures of central tendency (mean, mode, and median) are exactly the same in a normal distribution.

The standard deviation is the average amount of variability in your data set. It tells you, on average, how far each score lies from the mean .

In normal distributions, a high standard deviation means that values are generally far from the mean, while a low standard deviation indicates that values are clustered close to the mean.

No. Because the range formula subtracts the lowest number from the highest number, the range is always zero or a positive number.

In statistics, the range is the spread of your data from the lowest to the highest value in the distribution. It is the simplest measure of variability .

While central tendency tells you where most of your data points lie, variability summarizes how far apart your points from each other.

Data sets can have the same central tendency but different levels of variability or vice versa . Together, they give you a complete picture of your data.

Variability is most commonly measured with the following descriptive statistics :

• Range : the difference between the highest and lowest values
• Interquartile range : the range of the middle half of a distribution
• Standard deviation : average distance from the mean
• Variance : average of squared distances from the mean

Variability tells you how far apart points lie from each other and from the center of a distribution or a data set.

Variability is also referred to as spread, scatter or dispersion.

While interval and ratio data can both be categorized, ranked, and have equal spacing between adjacent values, only ratio scales have a true zero.

For example, temperature in Celsius or Fahrenheit is at an interval scale because zero is not the lowest possible temperature. In the Kelvin scale, a ratio scale, zero represents a total lack of thermal energy.

A critical value is the value of the test statistic which defines the upper and lower bounds of a confidence interval , or which defines the threshold of statistical significance in a statistical test. It describes how far from the mean of the distribution you have to go to cover a certain amount of the total variation in the data (i.e. 90%, 95%, 99%).

If you are constructing a 95% confidence interval and are using a threshold of statistical significance of p = 0.05, then your critical value will be identical in both cases.

The t -distribution gives more probability to observations in the tails of the distribution than the standard normal distribution (a.k.a. the z -distribution).

In this way, the t -distribution is more conservative than the standard normal distribution: to reach the same level of confidence or statistical significance , you will need to include a wider range of the data.

A t -score (a.k.a. a t -value) is equivalent to the number of standard deviations away from the mean of the t -distribution .

The t -score is the test statistic used in t -tests and regression tests. It can also be used to describe how far from the mean an observation is when the data follow a t -distribution.

The t -distribution is a way of describing a set of observations where most observations fall close to the mean , and the rest of the observations make up the tails on either side. It is a type of normal distribution used for smaller sample sizes, where the variance in the data is unknown.

The t -distribution forms a bell curve when plotted on a graph. It can be described mathematically using the mean and the standard deviation .

In statistics, ordinal and nominal variables are both considered categorical variables .

Even though ordinal data can sometimes be numerical, not all mathematical operations can be performed on them.

Ordinal data has two characteristics:

• The data can be classified into different categories within a variable.
• The categories have a natural ranked order.

However, unlike with interval data, the distances between the categories are uneven or unknown.

Nominal and ordinal are two of the four levels of measurement . Nominal level data can only be classified, while ordinal level data can be classified and ordered.

Nominal data is data that can be labelled or classified into mutually exclusive categories within a variable. These categories cannot be ordered in a meaningful way.

For example, for the nominal variable of preferred mode of transportation, you may have the categories of car, bus, train, tram or bicycle.

If your confidence interval for a difference between groups includes zero, that means that if you run your experiment again you have a good chance of finding no difference between groups.

If your confidence interval for a correlation or regression includes zero, that means that if you run your experiment again there is a good chance of finding no correlation in your data.

In both of these cases, you will also find a high p -value when you run your statistical test, meaning that your results could have occurred under the null hypothesis of no relationship between variables or no difference between groups.

If you want to calculate a confidence interval around the mean of data that is not normally distributed , you have two choices:

• Find a distribution that matches the shape of your data and use that distribution to calculate the confidence interval.
• Perform a transformation on your data to make it fit a normal distribution, and then find the confidence interval for the transformed data.

The standard normal distribution , also called the z -distribution, is a special normal distribution where the mean is 0 and the standard deviation is 1.

Any normal distribution can be converted into the standard normal distribution by turning the individual values into z -scores. In a z -distribution, z -scores tell you how many standard deviations away from the mean each value lies.

The z -score and t -score (aka z -value and t -value) show how many standard deviations away from the mean of the distribution you are, assuming your data follow a z -distribution or a t -distribution .

These scores are used in statistical tests to show how far from the mean of the predicted distribution your statistical estimate is. If your test produces a z -score of 2.5, this means that your estimate is 2.5 standard deviations from the predicted mean.

The predicted mean and distribution of your estimate are generated by the null hypothesis of the statistical test you are using. The more standard deviations away from the predicted mean your estimate is, the less likely it is that the estimate could have occurred under the null hypothesis .

To calculate the confidence interval , you need to know:

• The point estimate you are constructing the confidence interval for
• The critical values for the test statistic
• The standard deviation of the sample
• The sample size

Then you can plug these components into the confidence interval formula that corresponds to your data. The formula depends on the type of estimate (e.g. a mean or a proportion) and on the distribution of your data.

The confidence level is the percentage of times you expect to get close to the same estimate if you run your experiment again or resample the population in the same way.

The confidence interval consists of the upper and lower bounds of the estimate you expect to find at a given level of confidence.

For example, if you are estimating a 95% confidence interval around the mean proportion of female babies born every year based on a random sample of babies, you might find an upper bound of 0.56 and a lower bound of 0.48. These are the upper and lower bounds of the confidence interval. The confidence level is 95%.

The mean is the most frequently used measure of central tendency because it uses all values in the data set to give you an average.

For data from skewed distributions, the median is better than the mean because it isn’t influenced by extremely large values.

The mode is the only measure you can use for nominal or categorical data that can’t be ordered.

The measures of central tendency you can use depends on the level of measurement of your data.

• For a nominal level, you can only use the mode to find the most frequent value.
• For an ordinal level or ranked data, you can also use the median to find the value in the middle of your data set.
• For interval or ratio levels, in addition to the mode and median, you can use the mean to find the average value.

Measures of central tendency help you find the middle, or the average, of a data set.

The 3 most common measures of central tendency are the mean, median and mode.

• The mode is the most frequent value.
• The median is the middle number in an ordered data set.
• The mean is the sum of all values divided by the total number of values.

Some variables have fixed levels. For example, gender and ethnicity are always nominal level data because they cannot be ranked.

However, for other variables, you can choose the level of measurement . For example, income is a variable that can be recorded on an ordinal or a ratio scale:

• At an ordinal level , you could create 5 income groupings and code the incomes that fall within them from 1–5.
• At a ratio level , you would record exact numbers for income.

If you have a choice, the ratio level is always preferable because you can analyze data in more ways. The higher the level of measurement, the more precise your data is.

The level at which you measure a variable determines how you can analyze your data.

Depending on the level of measurement , you can perform different descriptive statistics to get an overall summary of your data and inferential statistics to see if your results support or refute your hypothesis .

Levels of measurement tell you how precisely variables are recorded. There are 4 levels of measurement, which can be ranked from low to high:

• Nominal : the data can only be categorized.
• Ordinal : the data can be categorized and ranked.
• Interval : the data can be categorized and ranked, and evenly spaced.
• Ratio : the data can be categorized, ranked, evenly spaced and has a natural zero.

No. The p -value only tells you how likely the data you have observed is to have occurred under the null hypothesis .

If the p -value is below your threshold of significance (typically p < 0.05), then you can reject the null hypothesis, but this does not necessarily mean that your alternative hypothesis is true.

The alpha value, or the threshold for statistical significance , is arbitrary – which value you use depends on your field of study.

In most cases, researchers use an alpha of 0.05, which means that there is a less than 5% chance that the data being tested could have occurred under the null hypothesis.

P -values are usually automatically calculated by the program you use to perform your statistical test. They can also be estimated using p -value tables for the relevant test statistic .

P -values are calculated from the null distribution of the test statistic. They tell you how often a test statistic is expected to occur under the null hypothesis of the statistical test, based on where it falls in the null distribution.

If the test statistic is far from the mean of the null distribution, then the p -value will be small, showing that the test statistic is not likely to have occurred under the null hypothesis.

A p -value , or probability value, is a number describing how likely it is that your data would have occurred under the null hypothesis of your statistical test .

The test statistic you use will be determined by the statistical test.

You can choose the right statistical test by looking at what type of data you have collected and what type of relationship you want to test.

The test statistic will change based on the number of observations in your data, how variable your observations are, and how strong the underlying patterns in the data are.

For example, if one data set has higher variability while another has lower variability, the first data set will produce a test statistic closer to the null hypothesis , even if the true correlation between two variables is the same in either data set.

The formula for the test statistic depends on the statistical test being used.

Generally, the test statistic is calculated as the pattern in your data (i.e. the correlation between variables or difference between groups) divided by the variance in the data (i.e. the standard deviation ).

• Univariate statistics summarize only one variable  at a time.
• Bivariate statistics compare two variables .
• Multivariate statistics compare more than two variables .

The 3 main types of descriptive statistics concern the frequency distribution, central tendency, and variability of a dataset.

• Distribution refers to the frequencies of different responses.
• Measures of central tendency give you the average for each response.
• Measures of variability show you the spread or dispersion of your dataset.

Descriptive statistics summarize the characteristics of a data set. Inferential statistics allow you to test a hypothesis or assess whether your data is generalizable to the broader population.

In statistics, model selection is a process researchers use to compare the relative value of different statistical models and determine which one is the best fit for the observed data.

The Akaike information criterion is one of the most common methods of model selection. AIC weights the ability of the model to predict the observed data against the number of parameters the model requires to reach that level of precision.

AIC model selection can help researchers find a model that explains the observed variation in their data while avoiding overfitting.

In statistics, a model is the collection of one or more independent variables and their predicted interactions that researchers use to try to explain variation in their dependent variable.

You can test a model using a statistical test . To compare how well different models fit your data, you can use Akaike’s information criterion for model selection.

The Akaike information criterion is calculated from the maximum log-likelihood of the model and the number of parameters (K) used to reach that likelihood. The AIC function is 2K – 2(log-likelihood) .

Lower AIC values indicate a better-fit model, and a model with a delta-AIC (the difference between the two AIC values being compared) of more than -2 is considered significantly better than the model it is being compared to.

The Akaike information criterion is a mathematical test used to evaluate how well a model fits the data it is meant to describe. It penalizes models which use more independent variables (parameters) as a way to avoid over-fitting.

AIC is most often used to compare the relative goodness-of-fit among different models under consideration and to then choose the model that best fits the data.

A factorial ANOVA is any ANOVA that uses more than one categorical independent variable . A two-way ANOVA is a type of factorial ANOVA.

Some examples of factorial ANOVAs include:

• Testing the combined effects of vaccination (vaccinated or not vaccinated) and health status (healthy or pre-existing condition) on the rate of flu infection in a population.
• Testing the effects of marital status (married, single, divorced, widowed), job status (employed, self-employed, unemployed, retired), and family history (no family history, some family history) on the incidence of depression in a population.
• Testing the effects of feed type (type A, B, or C) and barn crowding (not crowded, somewhat crowded, very crowded) on the final weight of chickens in a commercial farming operation.

In ANOVA, the null hypothesis is that there is no difference among group means. If any group differs significantly from the overall group mean, then the ANOVA will report a statistically significant result.

Significant differences among group means are calculated using the F statistic, which is the ratio of the mean sum of squares (the variance explained by the independent variable) to the mean square error (the variance left over).

If the F statistic is higher than the critical value (the value of F that corresponds with your alpha value, usually 0.05), then the difference among groups is deemed statistically significant.

The only difference between one-way and two-way ANOVA is the number of independent variables . A one-way ANOVA has one independent variable, while a two-way ANOVA has two.

• One-way ANOVA : Testing the relationship between shoe brand (Nike, Adidas, Saucony, Hoka) and race finish times in a marathon.
• Two-way ANOVA : Testing the relationship between shoe brand (Nike, Adidas, Saucony, Hoka), runner age group (junior, senior, master’s), and race finishing times in a marathon.

All ANOVAs are designed to test for differences among three or more groups. If you are only testing for a difference between two groups, use a t-test instead.

Multiple linear regression is a regression model that estimates the relationship between a quantitative dependent variable and two or more independent variables using a straight line.

Linear regression most often uses mean-square error (MSE) to calculate the error of the model. MSE is calculated by:

• measuring the distance of the observed y-values from the predicted y-values at each value of x;
• squaring each of these distances;
• calculating the mean of each of the squared distances.

Linear regression fits a line to the data by finding the regression coefficient that results in the smallest MSE.

Simple linear regression is a regression model that estimates the relationship between one independent variable and one dependent variable using a straight line. Both variables should be quantitative.

For example, the relationship between temperature and the expansion of mercury in a thermometer can be modeled using a straight line: as temperature increases, the mercury expands. This linear relationship is so certain that we can use mercury thermometers to measure temperature.

A regression model is a statistical model that estimates the relationship between one dependent variable and one or more independent variables using a line (or a plane in the case of two or more independent variables).

A regression model can be used when the dependent variable is quantitative, except in the case of logistic regression, where the dependent variable is binary.

A t-test should not be used to measure differences among more than two groups, because the error structure for a t-test will underestimate the actual error when many groups are being compared.

If you want to compare the means of several groups at once, it’s best to use another statistical test such as ANOVA or a post-hoc test.

A one-sample t-test is used to compare a single population to a standard value (for example, to determine whether the average lifespan of a specific town is different from the country average).

A paired t-test is used to compare a single population before and after some experimental intervention or at two different points in time (for example, measuring student performance on a test before and after being taught the material).

A t-test measures the difference in group means divided by the pooled standard error of the two group means.

In this way, it calculates a number (the t-value) illustrating the magnitude of the difference between the two group means being compared, and estimates the likelihood that this difference exists purely by chance (p-value).

Your choice of t-test depends on whether you are studying one group or two groups, and whether you care about the direction of the difference in group means.

If you are studying one group, use a paired t-test to compare the group mean over time or after an intervention, or use a one-sample t-test to compare the group mean to a standard value. If you are studying two groups, use a two-sample t-test .

If you want to know only whether a difference exists, use a two-tailed test . If you want to know if one group mean is greater or less than the other, use a left-tailed or right-tailed one-tailed test .

A t-test is a statistical test that compares the means of two samples . It is used in hypothesis testing , with a null hypothesis that the difference in group means is zero and an alternate hypothesis that the difference in group means is different from zero.

Statistical significance is a term used by researchers to state that it is unlikely their observations could have occurred under the null hypothesis of a statistical test . Significance is usually denoted by a p -value , or probability value.

Statistical significance is arbitrary – it depends on the threshold, or alpha value, chosen by the researcher. The most common threshold is p < 0.05, which means that the data is likely to occur less than 5% of the time under the null hypothesis .

When the p -value falls below the chosen alpha value, then we say the result of the test is statistically significant.

A test statistic is a number calculated by a  statistical test . It describes how far your observed data is from the  null hypothesis  of no relationship between  variables or no difference among sample groups.

The test statistic tells you how different two or more groups are from the overall population mean , or how different a linear slope is from the slope predicted by a null hypothesis . Different test statistics are used in different statistical tests.

Statistical tests commonly assume that:

• the data are normally distributed
• the groups that are being compared have similar variance
• the data are independent

If your data does not meet these assumptions you might still be able to use a nonparametric statistical test , which have fewer requirements but also make weaker inferences.

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