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Methodology

Research Methods | Definitions, Types, Examples

Research methods are specific procedures for collecting and analyzing data. Developing your research methods is an integral part of your research design . When planning your methods, there are two key decisions you will make.

First, decide how you will collect data . Your methods depend on what type of data you need to answer your research question :

Qualitative vs. quantitative : Will your data take the form of words or numbers?
Primary vs. secondary : Will you collect original data yourself, or will you use data that has already been collected by someone else?
Descriptive vs. experimental : Will you take measurements of something as it is, or will you perform an experiment?

Second, decide how you will analyze the data .

For quantitative data, you can use statistical analysis methods to test relationships between variables.
For qualitative data, you can use methods such as thematic analysis to interpret patterns and meanings in the data.

Methods for collecting data, examples of data collection methods, methods for analyzing data, examples of data analysis methods, other interesting articles, frequently asked questions about research methods.

Data is the information that you collect for the purposes of answering your research question . The type of data you need depends on the aims of your research.

Qualitative vs. quantitative data

Your choice of qualitative or quantitative data collection depends on the type of knowledge you want to develop.

For questions about ideas, experiences and meanings, or to study something that can’t be described numerically, collect qualitative data .

If you want to develop a more mechanistic understanding of a topic, or your research involves hypothesis testing , collect quantitative data .


Qualitative		to broader populations. .
Quantitative		.

You can also take a mixed methods approach , where you use both qualitative and quantitative research methods.

Primary vs. secondary research

Primary research is any original data that you collect yourself for the purposes of answering your research question (e.g. through surveys , observations and experiments ). Secondary research is data that has already been collected by other researchers (e.g. in a government census or previous scientific studies).

If you are exploring a novel research question, you’ll probably need to collect primary data . But if you want to synthesize existing knowledge, analyze historical trends, or identify patterns on a large scale, secondary data might be a better choice.


Primary	.	methods.
Secondary

Descriptive vs. experimental data

In descriptive research , you collect data about your study subject without intervening. The validity of your research will depend on your sampling method .

In experimental research , you systematically intervene in a process and measure the outcome. The validity of your research will depend on your experimental design .

To conduct an experiment, you need to be able to vary your independent variable , precisely measure your dependent variable, and control for confounding variables . If it’s practically and ethically possible, this method is the best choice for answering questions about cause and effect.


Descriptive		. .
Experimental

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Research methods for collecting data
Research method	Primary or secondary?	Qualitative or quantitative?	When to use
	Primary	Quantitative	To test cause-and-effect relationships.
	Primary	Quantitative	To understand general characteristics of a population.
Interview/focus group	Primary	Qualitative	To gain more in-depth understanding of a topic.
Observation	Primary	Either	To understand how something occurs in its natural setting.
	Secondary	Either	To situate your research in an existing body of work, or to evaluate trends within a research topic.
	Either	Either	To gain an in-depth understanding of a specific group or context, or when you don’t have the resources for a large study.

Your data analysis methods will depend on the type of data you collect and how you prepare it for analysis.

Data can often be analyzed both quantitatively and qualitatively. For example, survey responses could be analyzed qualitatively by studying the meanings of responses or quantitatively by studying the frequencies of responses.

Qualitative analysis methods

Qualitative analysis is used to understand words, ideas, and experiences. You can use it to interpret data that was collected:

From open-ended surveys and interviews , literature reviews , case studies , ethnographies , and other sources that use text rather than numbers.
Using non-probability sampling methods .

Qualitative analysis tends to be quite flexible and relies on the researcher’s judgement, so you have to reflect carefully on your choices and assumptions and be careful to avoid research bias .

Quantitative analysis methods

Quantitative analysis uses numbers and statistics to understand frequencies, averages and correlations (in descriptive studies) or cause-and-effect relationships (in experiments).

You can use quantitative analysis to interpret data that was collected either:

During an experiment .
Using probability sampling methods .

Because the data is collected and analyzed in a statistically valid way, the results of quantitative analysis can be easily standardized and shared among researchers.

Research methods for analyzing data
Research method	Qualitative or quantitative?	When to use
	Quantitative	To analyze data collected in a statistically valid manner (e.g. from experiments, surveys, and observations).
Meta-analysis	Quantitative	To statistically analyze the results of a large collection of studies. Can only be applied to studies that collected data in a statistically valid manner.
	Qualitative	To analyze data collected from interviews, , or textual sources. To understand general themes in the data and how they are communicated.
	Either	To analyze large volumes of textual or visual data collected from surveys, literature reviews, or other sources. Can be quantitative (i.e. frequencies of words) or qualitative (i.e. meanings of words).

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If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

Chi square test of independence
Statistical power
Descriptive statistics
Degrees of freedom
Pearson correlation
Null hypothesis
Double-blind study
Case-control study
Research ethics
Data collection
Hypothesis testing
Structured interviews

Research bias

Hawthorne effect
Unconscious bias
Recall bias
Halo effect
Self-serving bias
Information bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

The research methods you use depend on the type of data you need to answer your research question .

If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

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Home » Background of The Study – Examples and Writing Guide

Background of The Study – Examples and Writing Guide

Table of Contents

Background of The Study

Definition:

Background of the study refers to the context, circumstances, and history that led to the research problem or topic being studied. It provides the reader with a comprehensive understanding of the subject matter and the significance of the study.

The background of the study usually includes a discussion of the relevant literature, the gap in knowledge or understanding, and the research questions or hypotheses to be addressed. It also highlights the importance of the research topic and its potential contributions to the field. A well-written background of the study sets the stage for the research and helps the reader to appreciate the need for the study and its potential significance.

How to Write Background of The Study

Here are some steps to help you write the background of the study:

Identify the Research Problem

Start by identifying the research problem you are trying to address. This problem should be significant and relevant to your field of study.

Provide Context

Once you have identified the research problem, provide some context. This could include the historical, social, or political context of the problem.

Review Literature

Conduct a thorough review of the existing literature on the topic. This will help you understand what has been studied and what gaps exist in the current research.

Identify Research Gap

Based on your literature review, identify the gap in knowledge or understanding that your research aims to address. This gap will be the focus of your research question or hypothesis.

State Objectives

Clearly state the objectives of your research . These should be specific, measurable, achievable, relevant, and time-bound (SMART).

Discuss Significance

Explain the significance of your research. This could include its potential impact on theory , practice, policy, or society.

Finally, summarize the key points of the background of the study. This will help the reader understand the research problem, its context, and its significance.

How to Write Background of The Study in Proposal

The background of the study is an essential part of any proposal as it sets the stage for the research project and provides the context and justification for why the research is needed. Here are the steps to write a compelling background of the study in your proposal:

Identify the problem: Clearly state the research problem or gap in the current knowledge that you intend to address through your research.
Provide context: Provide a brief overview of the research area and highlight its significance in the field.
Review literature: Summarize the relevant literature related to the research problem and provide a critical evaluation of the current state of knowledge.
Identify gaps : Identify the gaps or limitations in the existing literature and explain how your research will contribute to filling these gaps.
Justify the study : Explain why your research is important and what practical or theoretical contributions it can make to the field.
Highlight objectives: Clearly state the objectives of the study and how they relate to the research problem.
Discuss methodology: Provide an overview of the methodology you will use to collect and analyze data, and explain why it is appropriate for the research problem.
Conclude : Summarize the key points of the background of the study and explain how they support your research proposal.

How to Write Background of The Study In Thesis

The background of the study is a critical component of a thesis as it provides context for the research problem, rationale for conducting the study, and the significance of the research. Here are some steps to help you write a strong background of the study:

Identify the research problem : Start by identifying the research problem that your thesis is addressing. What is the issue that you are trying to solve or explore? Be specific and concise in your problem statement.
Review the literature: Conduct a thorough review of the relevant literature on the topic. This should include scholarly articles, books, and other sources that are directly related to your research question.
I dentify gaps in the literature: After reviewing the literature, identify any gaps in the existing research. What questions remain unanswered? What areas have not been explored? This will help you to establish the need for your research.
Establish the significance of the research: Clearly state the significance of your research. Why is it important to address this research problem? What are the potential implications of your research? How will it contribute to the field?
Provide an overview of the research design: Provide an overview of the research design and methodology that you will be using in your study. This should include a brief explanation of the research approach, data collection methods, and data analysis techniques.
State the research objectives and research questions: Clearly state the research objectives and research questions that your study aims to answer. These should be specific, measurable, achievable, relevant, and time-bound.
Summarize the chapter: Summarize the chapter by highlighting the key points and linking them back to the research problem, significance of the study, and research questions.

How to Write Background of The Study in Research Paper

Here are the steps to write the background of the study in a research paper:

Identify the research problem: Start by identifying the research problem that your study aims to address. This can be a particular issue, a gap in the literature, or a need for further investigation.
Conduct a literature review: Conduct a thorough literature review to gather information on the topic, identify existing studies, and understand the current state of research. This will help you identify the gap in the literature that your study aims to fill.
Explain the significance of the study: Explain why your study is important and why it is necessary. This can include the potential impact on the field, the importance to society, or the need to address a particular issue.
Provide context: Provide context for the research problem by discussing the broader social, economic, or political context that the study is situated in. This can help the reader understand the relevance of the study and its potential implications.
State the research questions and objectives: State the research questions and objectives that your study aims to address. This will help the reader understand the scope of the study and its purpose.
Summarize the methodology : Briefly summarize the methodology you used to conduct the study, including the data collection and analysis methods. This can help the reader understand how the study was conducted and its reliability.

Examples of Background of The Study

Here are some examples of the background of the study:

Problem : The prevalence of obesity among children in the United States has reached alarming levels, with nearly one in five children classified as obese.

Significance : Obesity in childhood is associated with numerous negative health outcomes, including increased risk of type 2 diabetes, cardiovascular disease, and certain cancers.

Gap in knowledge : Despite efforts to address the obesity epidemic, rates continue to rise. There is a need for effective interventions that target the unique needs of children and their families.

Problem : The use of antibiotics in agriculture has contributed to the development of antibiotic-resistant bacteria, which poses a significant threat to human health.

Significance : Antibiotic-resistant infections are responsible for thousands of deaths each year and are a major public health concern.

Gap in knowledge: While there is a growing body of research on the use of antibiotics in agriculture, there is still much to be learned about the mechanisms of resistance and the most effective strategies for reducing antibiotic use.

Edxample 3:

Problem : Many low-income communities lack access to healthy food options, leading to high rates of food insecurity and diet-related diseases.

Significance : Poor nutrition is a major contributor to chronic diseases such as obesity, type 2 diabetes, and cardiovascular disease.

Gap in knowledge : While there have been efforts to address food insecurity, there is a need for more research on the barriers to accessing healthy food in low-income communities and effective strategies for increasing access.

Examples of Background of The Study In Research

Here are some real-life examples of how the background of the study can be written in different fields of study:

Example 1 : “There has been a significant increase in the incidence of diabetes in recent years. This has led to an increased demand for effective diabetes management strategies. The purpose of this study is to evaluate the effectiveness of a new diabetes management program in improving patient outcomes.”

Example 2 : “The use of social media has become increasingly prevalent in modern society. Despite its popularity, little is known about the effects of social media use on mental health. This study aims to investigate the relationship between social media use and mental health in young adults.”

Example 3: “Despite significant advancements in cancer treatment, the survival rate for patients with pancreatic cancer remains low. The purpose of this study is to identify potential biomarkers that can be used to improve early detection and treatment of pancreatic cancer.”

Examples of Background of The Study in Proposal

Here are some real-time examples of the background of the study in a proposal:

Example 1 : The prevalence of mental health issues among university students has been increasing over the past decade. This study aims to investigate the causes and impacts of mental health issues on academic performance and wellbeing.

Example 2 : Climate change is a global issue that has significant implications for agriculture in developing countries. This study aims to examine the adaptive capacity of smallholder farmers to climate change and identify effective strategies to enhance their resilience.

Example 3 : The use of social media in political campaigns has become increasingly common in recent years. This study aims to analyze the effectiveness of social media campaigns in mobilizing young voters and influencing their voting behavior.

Example 4 : Employee turnover is a major challenge for organizations, especially in the service sector. This study aims to identify the key factors that influence employee turnover in the hospitality industry and explore effective strategies for reducing turnover rates.

Examples of Background of The Study in Thesis

Here are some real-time examples of the background of the study in the thesis:

Example 1 : “Women’s participation in the workforce has increased significantly over the past few decades. However, women continue to be underrepresented in leadership positions, particularly in male-dominated industries such as technology. This study aims to examine the factors that contribute to the underrepresentation of women in leadership roles in the technology industry, with a focus on organizational culture and gender bias.”

Example 2 : “Mental health is a critical component of overall health and well-being. Despite increased awareness of the importance of mental health, there are still significant gaps in access to mental health services, particularly in low-income and rural communities. This study aims to evaluate the effectiveness of a community-based mental health intervention in improving mental health outcomes in underserved populations.”

Example 3: “The use of technology in education has become increasingly widespread, with many schools adopting online learning platforms and digital resources. However, there is limited research on the impact of technology on student learning outcomes and engagement. This study aims to explore the relationship between technology use and academic achievement among middle school students, as well as the factors that mediate this relationship.”

Examples of Background of The Study in Research Paper

Here are some examples of how the background of the study can be written in various fields:

Example 1: The prevalence of obesity has been on the rise globally, with the World Health Organization reporting that approximately 650 million adults were obese in 2016. Obesity is a major risk factor for several chronic diseases such as diabetes, cardiovascular diseases, and cancer. In recent years, several interventions have been proposed to address this issue, including lifestyle changes, pharmacotherapy, and bariatric surgery. However, there is a lack of consensus on the most effective intervention for obesity management. This study aims to investigate the efficacy of different interventions for obesity management and identify the most effective one.

Example 2: Antibiotic resistance has become a major public health threat worldwide. Infections caused by antibiotic-resistant bacteria are associated with longer hospital stays, higher healthcare costs, and increased mortality. The inappropriate use of antibiotics is one of the main factors contributing to the development of antibiotic resistance. Despite numerous efforts to promote the rational use of antibiotics, studies have shown that many healthcare providers continue to prescribe antibiotics inappropriately. This study aims to explore the factors influencing healthcare providers’ prescribing behavior and identify strategies to improve antibiotic prescribing practices.

Example 3: Social media has become an integral part of modern communication, with millions of people worldwide using platforms such as Facebook, Twitter, and Instagram. Social media has several advantages, including facilitating communication, connecting people, and disseminating information. However, social media use has also been associated with several negative outcomes, including cyberbullying, addiction, and mental health problems. This study aims to investigate the impact of social media use on mental health and identify the factors that mediate this relationship.

Purpose of Background of The Study

The primary purpose of the background of the study is to help the reader understand the rationale for the research by presenting the historical, theoretical, and empirical background of the problem.

More specifically, the background of the study aims to:

Provide a clear understanding of the research problem and its context.
Identify the gap in knowledge that the study intends to fill.
Establish the significance of the research problem and its potential contribution to the field.
Highlight the key concepts, theories, and research findings related to the problem.
Provide a rationale for the research questions or hypotheses and the research design.
Identify the limitations and scope of the study.

When to Write Background of The Study

The background of the study should be written early on in the research process, ideally before the research design is finalized and data collection begins. This allows the researcher to clearly articulate the rationale for the study and establish a strong foundation for the research.

The background of the study typically comes after the introduction but before the literature review section. It should provide an overview of the research problem and its context, and also introduce the key concepts, theories, and research findings related to the problem.

Writing the background of the study early on in the research process also helps to identify potential gaps in knowledge and areas for further investigation, which can guide the development of the research questions or hypotheses and the research design. By establishing the significance of the research problem and its potential contribution to the field, the background of the study can also help to justify the research and secure funding or support from stakeholders.

Advantage of Background of The Study

The background of the study has several advantages, including:

Provides context: The background of the study provides context for the research problem by highlighting the historical, theoretical, and empirical background of the problem. This allows the reader to understand the research problem in its broader context and appreciate its significance.
Identifies gaps in knowledge: By reviewing the existing literature related to the research problem, the background of the study can identify gaps in knowledge that the study intends to fill. This helps to establish the novelty and originality of the research and its potential contribution to the field.
Justifies the research : The background of the study helps to justify the research by demonstrating its significance and potential impact. This can be useful in securing funding or support for the research.
Guides the research design: The background of the study can guide the development of the research questions or hypotheses and the research design by identifying key concepts, theories, and research findings related to the problem. This ensures that the research is grounded in existing knowledge and is designed to address the research problem effectively.
Establishes credibility: By demonstrating the researcher’s knowledge of the field and the research problem, the background of the study can establish the researcher’s credibility and expertise, which can enhance the trustworthiness and validity of the research.

Disadvantages of Background of The Study

Some Disadvantages of Background of The Study are as follows:

Time-consuming : Writing a comprehensive background of the study can be time-consuming, especially if the research problem is complex and multifaceted. This can delay the research process and impact the timeline for completing the study.
Repetitive: The background of the study can sometimes be repetitive, as it often involves summarizing existing research and theories related to the research problem. This can be tedious for the reader and may make the section less engaging.
Limitations of existing research: The background of the study can reveal the limitations of existing research related to the problem. This can create challenges for the researcher in developing research questions or hypotheses that address the gaps in knowledge identified in the background of the study.
Bias : The researcher’s biases and perspectives can influence the content and tone of the background of the study. This can impact the reader’s perception of the research problem and may influence the validity of the research.
Accessibility: Accessing and reviewing the literature related to the research problem can be challenging, especially if the researcher does not have access to a comprehensive database or if the literature is not available in the researcher’s language. This can limit the depth and scope of the background of the study.

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What Is Research Methodology? A Plain-Language Explanation & Definition (With Examples)

By Derek Jansen (MBA) and Kerryn Warren (PhD) | June 2020 (Last updated April 2023)

If you’re new to formal academic research, it’s quite likely that you’re feeling a little overwhelmed by all the technical lingo that gets thrown around. And who could blame you – “research methodology”, “research methods”, “sampling strategies”… it all seems never-ending!

In this post, we’ll demystify the landscape with plain-language explanations and loads of examples (including easy-to-follow videos), so that you can approach your dissertation, thesis or research project with confidence. Let’s get started.

Research Methodology 101

What exactly research methodology means
What qualitative , quantitative and mixed methods are
What sampling strategy is
What data collection methods are
What data analysis methods are
How to choose your research methodology
Example of a research methodology

What is research methodology?

Research methodology simply refers to the practical “how” of a research study. More specifically, it’s about how a researcher systematically designs a study to ensure valid and reliable results that address the research aims, objectives and research questions . Specifically, how the researcher went about deciding:

What type of data to collect (e.g., qualitative or quantitative data )
Who to collect it from (i.e., the sampling strategy )
How to collect it (i.e., the data collection method )
How to analyse it (i.e., the data analysis methods )

Within any formal piece of academic research (be it a dissertation, thesis or journal article), you’ll find a research methodology chapter or section which covers the aspects mentioned above. Importantly, a good methodology chapter explains not just what methodological choices were made, but also explains why they were made. In other words, the methodology chapter should justify the design choices, by showing that the chosen methods and techniques are the best fit for the research aims, objectives and research questions.

So, it’s the same as research design?

Not quite. As we mentioned, research methodology refers to the collection of practical decisions regarding what data you’ll collect, from who, how you’ll collect it and how you’ll analyse it. Research design, on the other hand, is more about the overall strategy you’ll adopt in your study. For example, whether you’ll use an experimental design in which you manipulate one variable while controlling others. You can learn more about research design and the various design types here .

Need a helping hand?

What are qualitative, quantitative and mixed-methods?

Qualitative, quantitative and mixed-methods are different types of methodological approaches, distinguished by their focus on words , numbers or both . This is a bit of an oversimplification, but its a good starting point for understanding.

Let’s take a closer look.

Qualitative research refers to research which focuses on collecting and analysing words (written or spoken) and textual or visual data, whereas quantitative research focuses on measurement and testing using numerical data . Qualitative analysis can also focus on other “softer” data points, such as body language or visual elements.

It’s quite common for a qualitative methodology to be used when the research aims and research questions are exploratory in nature. For example, a qualitative methodology might be used to understand peoples’ perceptions about an event that took place, or a political candidate running for president.

Contrasted to this, a quantitative methodology is typically used when the research aims and research questions are confirmatory in nature. For example, a quantitative methodology might be used to measure the relationship between two variables (e.g. personality type and likelihood to commit a crime) or to test a set of hypotheses .

As you’ve probably guessed, the mixed-method methodology attempts to combine the best of both qualitative and quantitative methodologies to integrate perspectives and create a rich picture. If you’d like to learn more about these three methodological approaches, be sure to watch our explainer video below.

What is sampling strategy?

Simply put, sampling is about deciding who (or where) you’re going to collect your data from . Why does this matter? Well, generally it’s not possible to collect data from every single person in your group of interest (this is called the “population”), so you’ll need to engage a smaller portion of that group that’s accessible and manageable (this is called the “sample”).

How you go about selecting the sample (i.e., your sampling strategy) will have a major impact on your study. There are many different sampling methods you can choose from, but the two overarching categories are probability sampling and non-probability sampling .

Probability sampling involves using a completely random sample from the group of people you’re interested in. This is comparable to throwing the names all potential participants into a hat, shaking it up, and picking out the “winners”. By using a completely random sample, you’ll minimise the risk of selection bias and the results of your study will be more generalisable to the entire population.

Non-probability sampling , on the other hand, doesn’t use a random sample . For example, it might involve using a convenience sample, which means you’d only interview or survey people that you have access to (perhaps your friends, family or work colleagues), rather than a truly random sample. With non-probability sampling, the results are typically not generalisable .

To learn more about sampling methods, be sure to check out the video below.

What are data collection methods?

As the name suggests, data collection methods simply refers to the way in which you go about collecting the data for your study. Some of the most common data collection methods include:

Interviews (which can be unstructured, semi-structured or structured)
Focus groups and group interviews
Surveys (online or physical surveys)
Observations (watching and recording activities)
Biophysical measurements (e.g., blood pressure, heart rate, etc.)
Documents and records (e.g., financial reports, court records, etc.)

The choice of which data collection method to use depends on your overall research aims and research questions , as well as practicalities and resource constraints. For example, if your research is exploratory in nature, qualitative methods such as interviews and focus groups would likely be a good fit. Conversely, if your research aims to measure specific variables or test hypotheses, large-scale surveys that produce large volumes of numerical data would likely be a better fit.

What are data analysis methods?

Data analysis methods refer to the methods and techniques that you’ll use to make sense of your data. These can be grouped according to whether the research is qualitative (words-based) or quantitative (numbers-based).

Popular data analysis methods in qualitative research include:

Qualitative content analysis
Thematic analysis
Discourse analysis
Narrative analysis
Interpretative phenomenological analysis (IPA)
Visual analysis (of photographs, videos, art, etc.)

Qualitative data analysis all begins with data coding , after which an analysis method is applied. In some cases, more than one analysis method is used, depending on the research aims and research questions . In the video below, we explore some common qualitative analysis methods, along with practical examples.

Moving on to the quantitative side of things, popular data analysis methods in this type of research include:

Descriptive statistics (e.g. means, medians, modes )
Inferential statistics (e.g. correlation, regression, structural equation modelling)

Again, the choice of which data collection method to use depends on your overall research aims and objectives , as well as practicalities and resource constraints. In the video below, we explain some core concepts central to quantitative analysis.

How do I choose a research methodology?

As you’ve probably picked up by now, your research aims and objectives have a major influence on the research methodology . So, the starting point for developing your research methodology is to take a step back and look at the big picture of your research, before you make methodology decisions. The first question you need to ask yourself is whether your research is exploratory or confirmatory in nature.

If your research aims and objectives are primarily exploratory in nature, your research will likely be qualitative and therefore you might consider qualitative data collection methods (e.g. interviews) and analysis methods (e.g. qualitative content analysis).

Conversely, if your research aims and objective are looking to measure or test something (i.e. they’re confirmatory), then your research will quite likely be quantitative in nature, and you might consider quantitative data collection methods (e.g. surveys) and analyses (e.g. statistical analysis).

Designing your research and working out your methodology is a large topic, which we cover extensively on the blog . For now, however, the key takeaway is that you should always start with your research aims, objectives and research questions (the golden thread). Every methodological choice you make needs align with those three components.

Example of a research methodology chapter

In the video below, we provide a detailed walkthrough of a research methodology from an actual dissertation, as well as an overview of our free methodology template .

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

199 Comments

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Research methodology with a simplest way i have never seen before this article.

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I am writing a APA Format paper . I using questionnaire with 120 STDs teacher for my participant. Can you write me mthology for this research. Send it through email sent. Just need a sample as an example please. My topic is ” impacts of overcrowding on students learning

Thanks for your comment.

We can’t write your methodology for you. If you’re looking for samples, you should be able to find some sample methodologies on Google. Alternatively, you can download some previous dissertations from a dissertation directory and have a look at the methodology chapters therein.

All the best with your research.

Thank you so much for this!! God Bless

Thank you. Explicit explanation

Thank you, Derek and Kerryn, for making this simple to understand. I’m currently at the inception stage of my research.

Thnks a lot , this was very usefull on my assignment

excellent explanation

I’m currently working on my master’s thesis, thanks for this! I’m certain that I will use Qualitative methodology.

Thanks a lot for this concise piece, it was quite relieving and helpful. God bless you BIG…

I am currently doing my dissertation proposal and I am sure that I will do quantitative research. Thank you very much it was extremely helpful.

Very interesting and informative yet I would like to know about examples of Research Questions as well, if possible.

I’m about to submit a research presentation, I have come to understand from your simplification on understanding research methodology. My research will be mixed methodology, qualitative as well as quantitative. So aim and objective of mixed method would be both exploratory and confirmatory. Thanks you very much for your guidance.

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Thank you immensely for this simple, easy to comprehend explanation of data collection methods. I have been stuck here for months 😩. Glad I found your piece. Super insightful.

I’m going to write synopsis which will be quantitative research method and I don’t know how to frame my topic, can I kindly get some ideas..

Thanks for this, I was really struggling.

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Thank you. Accurate and simple🥰

This article was really helpful, it helped me understanding the basic concepts of the topic Research Methodology. The examples were very clear, and easy to understand. I would like to visit this website again. Thank you so much for such a great explanation of the subject.

Thanks dude

Thank you Doctor Derek for this wonderful piece, please help to provide your details for reference purpose. God bless.

Many compliments to you

Great work , thank you very much for the simple explanation

Thank you. I had to give a presentation on this topic. I have looked everywhere on the internet but this is the best and simple explanation.

thank you, its very informative.

Well explained. Now I know my research methodology will be qualitative and exploratory. Thank you so much, keep up the good work

Well explained, thank you very much.

This is good explanation, I have understood the different methods of research. Thanks a lot.

Great work…very well explanation

Thanks Derek. Kerryn was just fantastic!

Great to hear that, Hyacinth. Best of luck with your research!

Its a good templates very attractive and important to PhD students and lectuter

Thanks for the feedback, Matobela. Good luck with your research methodology.

Thank you. This is really helpful.

You’re very welcome, Elie. Good luck with your research methodology.

Well explained thanks

This is a very helpful site especially for young researchers at college. It provides sufficient information to guide students and equip them with the necessary foundation to ask any other questions aimed at deepening their understanding.

Thanks for the kind words, Edward. Good luck with your research!

Thank you. I have learned a lot.

Great to hear that, Ngwisa. Good luck with your research methodology!

Thank you for keeping your presentation simples and short and covering key information for research methodology. My key takeaway: Start with defining your research objective the other will depend on the aims of your research question.

My name is Zanele I would like to be assisted with my research , and the topic is shortage of nursing staff globally want are the causes , effects on health, patients and community and also globally

Thanks for making it simple and clear. It greatly helped in understanding research methodology. Regards.

This is well simplified and straight to the point

Thank you Dr

I was given an assignment to research 2 publications and describe their research methodology? I don’t know how to start this task can someone help me?

Sure. You’re welcome to book an initial consultation with one of our Research Coaches to discuss how we can assist – https://gradcoach.com/book/new/ .

Thanks a lot I am relieved of a heavy burden.keep up with the good work

I’m very much grateful Dr Derek. I’m planning to pursue one of the careers that really needs one to be very much eager to know. There’s a lot of research to do and everything, but since I’ve gotten this information I will use it to the best of my potential.

Thank you so much, words are not enough to explain how helpful this session has been for me!

Thanks this has thought me alot.

Very concise and helpful. Thanks a lot

Thank Derek. This is very helpful. Your step by step explanation has made it easier for me to understand different concepts. Now i can get on with my research.

I wish i had come across this sooner. So simple but yet insightful

really nice explanation thank you so much

I’m so grateful finding this site, it’s really helpful…….every term well explained and provide accurate understanding especially to student going into an in-depth research for the very first time, even though my lecturer already explained this topic to the class, I think I got the clear and efficient explanation here, much thanks to the author.

It is very helpful material

I would like to be assisted with my research topic : Literature Review and research methodologies. My topic is : what is the relationship between unemployment and economic growth?

Its really nice and good for us.

THANKS SO MUCH FOR EXPLANATION, ITS VERY CLEAR TO ME WHAT I WILL BE DOING FROM NOW .GREAT READS.

Short but sweet.Thank you

Informative article. Thanks for your detailed information.

I’m currently working on my Ph.D. thesis. Thanks a lot, Derek and Kerryn, Well-organized sequences, facilitate the readers’ following.

great article for someone who does not have any background can even understand

I am a bit confused about research design and methodology. Are they the same? If not, what are the differences and how are they related?

Thanks in advance.

concise and informative.

Thank you very much

How can we site this article is Harvard style?

Very well written piece that afforded better understanding of the concept. Thank you!

Am a new researcher trying to learn how best to write a research proposal. I find your article spot on and want to download the free template but finding difficulties. Can u kindly send it to my email, the free download entitled, “Free Download: Research Proposal Template (with Examples)”.

Thank too much

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It was very helpful, a well-written document with precise information.

how do i reference this?

MLA Jansen, Derek, and Kerryn Warren. “What (Exactly) Is Research Methodology?” Grad Coach, June 2021, gradcoach.com/what-is-research-methodology/.

APA Jansen, D., & Warren, K. (2021, June). What (Exactly) Is Research Methodology? Grad Coach. https://gradcoach.com/what-is-research-methodology/

Your explanation is easily understood. Thank you

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hello sir/ma’am, i didn’t find yet that what type of research methodology i am using. because i am writing my report on CSR and collect all my data from websites and articles so which type of methodology i should write in dissertation report. please help me. i am from India.

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Pilot Study in Research: Definition & Examples

Julia Simkus

Editor at Simply Psychology

BA (Hons) Psychology, Princeton University

Julia Simkus is a graduate of Princeton University with a Bachelor of Arts in Psychology. She is currently studying for a Master's Degree in Counseling for Mental Health and Wellness in September 2023. Julia's research has been published in peer reviewed journals.

Learn about our Editorial Process

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

A pilot study, also known as a feasibility study, is a small-scale preliminary study conducted before the main research to check the feasibility or improve the research design.

Pilot studies can be very important before conducting a full-scale research project, helping design the research methods and protocol.

How Does it Work?

Pilot studies are a fundamental stage of the research process. They can help identify design issues and evaluate a study’s feasibility, practicality, resources, time, and cost before the main research is conducted.

It involves selecting a few people and trying out the study on them. It is possible to save time and, in some cases, money by identifying any flaws in the procedures designed by the researcher.

A pilot study can help the researcher spot any ambiguities (i.e., unusual things), confusion in the information given to participants, or problems with the task devised.

Sometimes the task is too hard, and the researcher may get a floor effect because none of the participants can score at all or can complete the task – all performances are low.

The opposite effect is a ceiling effect, when the task is so easy that all achieve virtually full marks or top performances and are “hitting the ceiling.”

This enables researchers to predict an appropriate sample size, budget accordingly, and improve the study design before performing a full-scale project.

Pilot studies also provide researchers with preliminary data to gain insight into the potential results of their proposed experiment.

However, pilot studies should not be used to test hypotheses since the appropriate power and sample size are not calculated. Rather, pilot studies should be used to assess the feasibility of participant recruitment or study design.

By conducting a pilot study, researchers will be better prepared to face the challenges that might arise in the larger study. They will be more confident with the instruments they will use for data collection.

Multiple pilot studies may be needed in some studies, and qualitative and/or quantitative methods may be used.

To avoid bias, pilot studies are usually carried out on individuals who are as similar as possible to the target population but not on those who will be a part of the final sample.

Feedback from participants in the pilot study can be used to improve the experience for participants in the main study. This might include reducing the burden on participants, improving instructions, or identifying potential ethical issues.

Experiment Pilot Study

In a pilot study with an experimental design , you would want to ensure that your measures of these variables are reliable and valid.

You would also want to check that you can effectively manipulate your independent variables and that you can control for potential confounding variables.

A pilot study allows the research team to gain experience and training, which can be particularly beneficial if new experimental techniques or procedures are used.

Questionnaire Pilot Study

It is important to conduct a questionnaire pilot study for the following reasons:

Check that respondents understand the terminology used in the questionnaire.
Check that emotive questions are not used, as they make people defensive and could invalidate their answers.
Check that leading questions have not been used as they could bias the respondent’s answer.
Ensure that the questionnaire can be completed in a reasonable amount of time. If it’s too long, respondents may lose interest or not have enough time to complete it, which could affect the response rate and the data quality.

By identifying and addressing issues in the pilot study, researchers can reduce errors and risks in the main study. This increases the reliability and validity of the main study’s results.

Assessing the practicality and feasibility of the main study

Testing the efficacy of research instruments

Identifying and addressing any weaknesses or logistical problems

Collecting preliminary data

Estimating the time and costs required for the project

Determining what resources are needed for the study

Identifying the necessity to modify procedures that do not elicit useful data

Adding credibility and dependability to the study

Pretesting the interview format

Enabling researchers to develop consistent practices and familiarize themselves with the procedures in the protocol

Addressing safety issues and management problems

Limitations

Require extra costs, time, and resources.

Do not guarantee the success of the main study.

Contamination (ie: if data from the pilot study or pilot participants are included in the main study results).

Funding bodies may be reluctant to fund a further study if the pilot study results are published.

Do not have the power to assess treatment effects due to small sample size.

Viscocanalostomy: A Pilot Study (Carassa, Bettin, Fiori, & Brancato, 1998)
WHO International Pilot Study of Schizophrenia (Sartorius, Shapiro, Kimura, & Barrett, 1972)
Stephen LaBerge of Stanford University ran a series of experiments in the 80s that investigated lucid dreaming. In 1985, he performed a pilot study that demonstrated that time perception is the same as during wakefulness. Specifically, he had participants go into a state of lucid dreaming and count out ten seconds, signaling the start and end with pre-determined eye movements measured with the EOG.
Negative Word-of-Mouth by Dissatisfied Consumers: A Pilot Study (Richins, 1983)
A pilot study and randomized controlled trial of the mindful self‐compassion program (Neff & Germer, 2013)
Pilot study of secondary prevention of posttraumatic stress disorder with propranolol (Pitman et al., 2002)
In unstructured observations, the researcher records all relevant behavior without a system. There may be too much to record, and the behaviors recorded may not necessarily be the most important, so the approach is usually used as a pilot study to see what type of behaviors would be recorded.
Perspectives of the use of smartphones in travel behavior studies: Findings from a literature review and a pilot study (Gadziński, 2018)

Further Information

Lancaster, G. A., Dodd, S., & Williamson, P. R. (2004). Design and analysis of pilot studies: recommendations for good practice. Journal of evaluation in clinical practice, 10 (2), 307-312.
Thabane, L., Ma, J., Chu, R., Cheng, J., Ismaila, A., Rios, L. P., … & Goldsmith, C. H. (2010). A tutorial on pilot studies: the what, why and how. BMC Medical Research Methodology, 10 (1), 1-10.
Moore, C. G., Carter, R. E., Nietert, P. J., & Stewart, P. W. (2011). Recommendations for planning pilot studies in clinical and translational research. Clinical and translational science, 4 (5), 332-337.

Carassa, R. G., Bettin, P., Fiori, M., & Brancato, R. (1998). Viscocanalostomy: a pilot study. European journal of ophthalmology, 8 (2), 57-61.

Gadziński, J. (2018). Perspectives of the use of smartphones in travel behaviour studies: Findings from a literature review and a pilot study. Transportation Research Part C: Emerging Technologies, 88 , 74-86.

In J. (2017). Introduction of a pilot study. Korean Journal of Anesthesiology, 70 (6), 601–605. https://doi.org/10.4097/kjae.2017.70.6.601

LaBerge, S., LaMarca, K., & Baird, B. (2018). Pre-sleep treatment with galantamine stimulates lucid dreaming: A double-blind, placebo-controlled, crossover study. PLoS One, 13 (8), e0201246.

Leon, A. C., Davis, L. L., & Kraemer, H. C. (2011). The role and interpretation of pilot studies in clinical research. Journal of psychiatric research, 45 (5), 626–629. https://doi.org/10.1016/j.jpsychires.2010.10.008

Malmqvist, J., Hellberg, K., Möllås, G., Rose, R., & Shevlin, M. (2019). Conducting the Pilot Study: A Neglected Part of the Research Process? Methodological Findings Supporting the Importance of Piloting in Qualitative Research Studies. International Journal of Qualitative Methods. https://doi.org/10.1177/1609406919878341

Neff, K. D., & Germer, C. K. (2013). A pilot study and randomized controlled trial of the mindful self‐compassion program. Journal of Clinical Psychology, 69 (1), 28-44.

Pitman, R. K., Sanders, K. M., Zusman, R. M., Healy, A. R., Cheema, F., Lasko, N. B., … & Orr, S. P. (2002). Pilot study of secondary prevention of posttraumatic stress disorder with propranolol. Biological psychiatry, 51 (2), 189-192.

Richins, M. L. (1983). Negative word-of-mouth by dissatisfied consumers: A pilot study. Journal of Marketing, 47 (1), 68-78.

Sartorius, N., Shapiro, R., Kimura, M., & Barrett, K. (1972). WHO International Pilot Study of Schizophrenia1. Psychological medicine, 2 (4), 422-425.

Teijlingen, E. R; V. Hundley (2001). The importance of pilot studies, Social research UPDATE, (35)

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Home Market Research

What is Research: Definition, Methods, Types & Examples

The search for knowledge is closely linked to the object of study; that is, to the reconstruction of the facts that will provide an explanation to an observed event and that at first sight can be considered as a problem. It is very human to seek answers and satisfy our curiosity. Let’s talk about research.

Content Index

What is Research?

What are the characteristics of research.

Comparative analysis chart

Qualitative methods

Quantitative methods, 8 tips for conducting accurate research.

Research is the careful consideration of study regarding a particular concern or research problem using scientific methods. According to the American sociologist Earl Robert Babbie, “research is a systematic inquiry to describe, explain, predict, and control the observed phenomenon. It involves inductive and deductive methods.”

Inductive methods analyze an observed event, while deductive methods verify the observed event. Inductive approaches are associated with qualitative research , and deductive methods are more commonly associated with quantitative analysis .

Research is conducted with a purpose to:

Identify potential and new customers
Understand existing customers
Set pragmatic goals
Develop productive market strategies
Address business challenges
Put together a business expansion plan
Identify new business opportunities
Good research follows a systematic approach to capture accurate data. Researchers need to practice ethics and a code of conduct while making observations or drawing conclusions.
The analysis is based on logical reasoning and involves both inductive and deductive methods.
Real-time data and knowledge is derived from actual observations in natural settings.
There is an in-depth analysis of all data collected so that there are no anomalies associated with it.
It creates a path for generating new questions. Existing data helps create more research opportunities.
It is analytical and uses all the available data so that there is no ambiguity in inference.
Accuracy is one of the most critical aspects of research. The information must be accurate and correct. For example, laboratories provide a controlled environment to collect data. Accuracy is measured in the instruments used, the calibrations of instruments or tools, and the experiment’s final result.

What is the purpose of research?

There are three main purposes:

Exploratory: As the name suggests, researchers conduct exploratory studies to explore a group of questions. The answers and analytics may not offer a conclusion to the perceived problem. It is undertaken to handle new problem areas that haven’t been explored before. This exploratory data analysis process lays the foundation for more conclusive data collection and analysis.

LEARN ABOUT: Descriptive Analysis

Descriptive: It focuses on expanding knowledge on current issues through a process of data collection. Descriptive research describe the behavior of a sample population. Only one variable is required to conduct the study. The three primary purposes of descriptive studies are describing, explaining, and validating the findings. For example, a study conducted to know if top-level management leaders in the 21st century possess the moral right to receive a considerable sum of money from the company profit.

LEARN ABOUT: Best Data Collection Tools

Explanatory: Causal research or explanatory research is conducted to understand the impact of specific changes in existing standard procedures. Running experiments is the most popular form. For example, a study that is conducted to understand the effect of rebranding on customer loyalty.

Here is a comparative analysis chart for a better understanding:


Approach used	Unstructured	Structured	Highly structured
Conducted through	Asking questions	Asking questions	By using hypotheses.
Time	Early stages of decision making	Later stages of decision making	Later stages of decision making

It begins by asking the right questions and choosing an appropriate method to investigate the problem. After collecting answers to your questions, you can analyze the findings or observations to draw reasonable conclusions.

When it comes to customers and market studies, the more thorough your questions, the better the analysis. You get essential insights into brand perception and product needs by thoroughly collecting customer data through surveys and questionnaires . You can use this data to make smart decisions about your marketing strategies to position your business effectively.

To make sense of your study and get insights faster, it helps to use a research repository as a single source of truth in your organization and manage your research data in one centralized data repository .

Types of research methods and Examples

Research methods are broadly classified as Qualitative and Quantitative .

Both methods have distinctive properties and data collection methods .

Qualitative research is a method that collects data using conversational methods, usually open-ended questions . The responses collected are essentially non-numerical. This method helps a researcher understand what participants think and why they think in a particular way.

Types of qualitative methods include:

One-to-one Interview
Focus Groups
Ethnographic studies
Text Analysis

Quantitative methods deal with numbers and measurable forms . It uses a systematic way of investigating events or data. It answers questions to justify relationships with measurable variables to either explain, predict, or control a phenomenon.

Types of quantitative methods include:

Survey research
Descriptive research
Correlational research

LEARN MORE: Descriptive Research vs Correlational Research

Remember, it is only valuable and useful when it is valid, accurate, and reliable. Incorrect results can lead to customer churn and a decrease in sales.

It is essential to ensure that your data is:

Valid – founded, logical, rigorous, and impartial.
Accurate – free of errors and including required details.
Reliable – other people who investigate in the same way can produce similar results.
Timely – current and collected within an appropriate time frame.
Complete – includes all the data you need to support your business decisions.

Gather insights

Identify the main trends and issues, opportunities, and problems you observe. Write a sentence describing each one.
Keep track of the frequency with which each of the main findings appears.
Make a list of your findings from the most common to the least common.
Evaluate a list of the strengths, weaknesses, opportunities, and threats identified in a SWOT analysis .
Prepare conclusions and recommendations about your study.
Act on your strategies
Look for gaps in the information, and consider doing additional inquiry if necessary
Plan to review the results and consider efficient methods to analyze and interpret results.

Review your goals before making any conclusions about your study. Remember how the process you have completed and the data you have gathered help answer your questions. Ask yourself if what your analysis revealed facilitates the identification of your conclusions and recommendations.

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What is the Background of the Study and How to Write It

What is the Background of the Study in Research?

The background of the study is the first section of a research paper and gives context surrounding the research topic. The background explains to the reader where your research journey started, why you got interested in the topic, and how you developed the research question that you will later specify. That means that you first establish the context of the research you did with a general overview of the field or topic and then present the key issues that drove your decision to study the specific problem you chose.

Once the reader understands where you are coming from and why there was indeed a need for the research you are going to present in the following—because there was a gap in the current research, or because there is an obvious problem with a currently used process or technology—you can proceed with the formulation of your research question and summarize how you are going to address it in the rest of your manuscript.

Why is the Background of the Study Important?

No matter how surprising and important the findings of your study are, if you do not provide the reader with the necessary background information and context, they will not be able to understand your reasons for studying the specific problem you chose and why you think your study is relevant. And more importantly, an editor who does not share your enthusiasm for your work (because you did not fill them in on all the important details) will very probably not even consider your manuscript worthy of their and the reviewers’ time and will immediately send it back to you.

To avoid such desk rejections , you need to make sure you pique the reader’s interest and help them understand the contribution of your work to the specific field you study, the more general research community, or the public. Introducing the study background is crucial to setting the scene for your readers.

Background of the Study Structure

Before writing your study background, it is essential to understand what to include. The following elements should all be included in the background and are presented in greater detail in the next section:

A general overview of the topic and why it is important (overlaps with establishing the “importance of the topic” in the Introduction)
The current state of the research on the topic or on related topics in the field
Controversies about current knowledge or specific past studies that undergird your research methodology
Any claims or assumptions that have been made by researchers, institutions, or politicians that might need to be clarified
Methods and techniques used in the study or from which your study deviated in some way

Presenting the Study Background

As you begin introducing your background, you first need to provide a general overview and include the main issues concerning the topic. Depending on whether you do “basic” (with the aim of providing further knowledge) or “applied” research (to establish new techniques, processes, or products), this is either a literature review that summarizes all relevant earlier studies in the field or a description of the process (e.g., vote counting) or practice (e.g., diagnosis of a specific disease) that you think is problematic or lacking and needs a solution.

Example s of a general overview

If you study the function of a Drosophila gene, for example, you can explain to the reader why and for whom the study of fly genetics is relevant, what is already known and established, and where you see gaps in the existing literature. If you investigated how the way universities have transitioned into online teaching since the beginning of the Covid-19 pandemic has affected students’ learning progress, then you need to present a summary of what changes have happened around the world, what the effects of those changes have been so far, and where you see problems that need to be addressed. Note that you need to provide sources for every statement and every claim you make here, to establish a solid foundation of knowledge for your own study.

Describing the current state of knowledge

When the reader understands the main issue(s), you need to fill them in more specifically on the current state of the field (in basic research) or the process/practice/product use you describe (in practical/applied research). Cite all relevant studies that have already reported on the Drosophila gene you are interested in, have failed to reveal certain functions of it, or have suggested that it might be involved in more processes than we know so far. Or list the reports from the education ministries of the countries you are interested in and highlight the data that shows the need for research into the effects of the Corona-19 pandemic on teaching and learning.

Discussing controversies, claims, and assumptions

Are there controversies regarding your topic of interest that need to be mentioned and/or addressed? For example, if your research topic involves an issue that is politically hot, you can acknowledge this here. Have any earlier claims or assumptions been made, by other researchers, institutions, or politicians, that you think need to be clarified?

Mentioning methodologies and approaches

While putting together these details, you also need to mention methodologies : What methods/techniques have been used so far to study what you studied and why are you going to either use the same or a different approach? Are any of the methods included in the literature review flawed in such a way that your study takes specific measures to correct or update? While you shouldn’t spend too much time here justifying your methods (this can be summarized briefly in the rationale of the study at the end of the Introduction and later in the Discussion section), you can engage with the crucial methods applied in previous studies here first.

When you have established the background of the study of your research paper in such a logical way, then the reader should have had no problem following you from the more general information you introduced first to the specific details you added later. You can now easily lead over to the relevance of your research, explain how your work fits into the bigger picture, and specify the aims and objectives of your study. This latter part is usually considered the “ statement of the problem ” of your study. Without a solid research paper background, this statement will come out of nowhere for the reader and very probably raise more questions than you were planning to answer.

Where does the study background section go in a paper?

Unless you write a research proposal or some kind of report that has a specific “Background” chapter, the background of your study is the first part of your introduction section . This is where you put your work in context and provide all the relevant information the reader needs to follow your rationale. Make sure your background has a logical structure and naturally leads into the statement of the problem at the very end of the introduction so that you bring everything together for the reader to judge the relevance of your work and the validity of your approach before they dig deeper into the details of your study in the methods section .

Consider Receiving Professional Editing Services

Now that you know how to write a background section for a research paper, you might be interested in our AI Text Editor at Wordvice AI. And be sure to receive professional editing services , including academic editing and proofreading , before submitting your manuscript to journals. On the Wordvice academic resources website, you can also find many more articles and other resources that can help you with writing the other parts of your research paper , with making a research paper outline before you put everything together, or with writing an effective cover letter once you are ready to submit.

Educational resources and simple solutions for your research journey

What is the Background of a Study and How to Write It (Examples Included)

Have you ever found yourself struggling to write a background of the study for your research paper? You’re not alone. While the background of a study is an essential element of a research manuscript, it’s also one of the most challenging pieces to write. This is because it requires researchers to provide context and justification for their research, highlight the significance of their study, and situate their work within the existing body of knowledge in the field.

Despite its challenges, the background of a study is crucial for any research paper. A compelling well-written background of the study can not only promote confidence in the overall quality of your research analysis and findings, but it can also determine whether readers will be interested in knowing more about the rest of the research study.

In this article, we’ll explore the key elements of the background of a study and provide simple guidelines on how to write one effectively. Whether you’re a seasoned researcher or a graduate student working on your first research manuscript, this post will explain how to write a background for your study that is compelling and informative.

Table of Contents

What is the background of a study ?

Typically placed in the beginning of your research paper, the background of a study serves to convey the central argument of your study and its significance clearly and logically to an uninformed audience. The background of a study in a research paper helps to establish the research problem or gap in knowledge that the study aims to address, sets the stage for the research question and objectives, and highlights the significance of the research. The background of a study also includes a review of relevant literature, which helps researchers understand where the research study is placed in the current body of knowledge in a specific research discipline. It includes the reason for the study, the thesis statement, and a summary of the concept or problem being examined by the researcher. At times, the background of a study can may even examine whether your research supports or contradicts the results of earlier studies or existing knowledge on the subject.

How is the background of a study different from the introduction?

It is common to find early career researchers getting confused between the background of a study and the introduction in a research paper. Many incorrectly consider these two vital parts of a research paper the same and use these terms interchangeably. The confusion is understandable, however, it’s important to know that the introduction and the background of the study are distinct elements and serve very different purposes.

The basic different between the background of a study and the introduction is kind of information that is shared with the readers . While the introduction provides an overview of the specific research topic and touches upon key parts of the research paper, the background of the study presents a detailed discussion on the existing literature in the field, identifies research gaps, and how the research being done will add to current knowledge.
The introduction aims to capture the reader’s attention and interest and to provide a clear and concise summary of the research project. It typically begins with a general statement of the research problem and then narrows down to the specific research question. It may also include an overview of the research design, methodology, and scope. The background of the study outlines the historical, theoretical, and empirical background that led to the research question to highlight its importance. It typically offers an overview of the research field and may include a review of the literature to highlight gaps, controversies, or limitations in the existing knowledge and to justify the need for further research.
Both these sections appear at the beginning of a research paper. In some cases the introduction may come before the background of the study , although in most instances the latter is integrated into the introduction itself. The length of the introduction and background of a study can differ based on the journal guidelines and the complexity of a specific research study.

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To put it simply, the background of the study provides context for the study by explaining how your research fills a research gap in existing knowledge in the field and how it will add to it. The introduction section explains how the research fills this gap by stating the research topic, the objectives of the research and the findings – it sets the context for the rest of the paper.

Where is the background of a study placed in a research paper?

T he background of a study is typically placed in the introduction section of a research paper and is positioned after the statement of the problem. Researchers should try and present the background of the study in clear logical structure by dividing it into several sections, such as introduction, literature review, and research gap. This will make it easier for the reader to understand the research problem and the motivation for the study.

So, when should you write the background of your study ? It’s recommended that researchers write this section after they have conducted a thorough literature review and identified the research problem, research question, and objectives. This way, they can effectively situate their study within the existing body of knowledge in the field and provide a clear rationale for their research.

Creating an effective background of a study structure

Given that the purpose of writing the background of your study is to make readers understand the reasons for conducting the research, it is important to create an outline and basic framework to work within. This will make it easier to write the background of the study and will ensure that it is comprehensive and compelling for readers.

While creating a background of the study structure for research papers, it is crucial to have a clear understanding of the essential elements that should be included. Make sure you incorporate the following elements in the background of the study section :

Present a general overview of the research topic, its significance, and main aims; this may be like establishing the “importance of the topic” in the introduction.
Discuss the existing level of research done on the research topic or on related topics in the field to set context for your research. Be concise and mention only the relevant part of studies, ideally in chronological order to reflect the progress being made.
Highlight disputes in the field as well as claims made by scientists, organizations, or key policymakers that need to be investigated. This forms the foundation of your research methodology and solidifies the aims of your study.
Describe if and how the methods and techniques used in the research study are different from those used in previous research on similar topics.

By including these critical elements in the background of your study , you can provide your readers with a comprehensive understanding of your research and its context.

What is the background of a study and how to write it

How to write a background of the study in research papers ?

Now that you know the essential elements to include, it’s time to discuss how to write the background of the study in a concise and interesting way that engages audiences. The best way to do this is to build a clear narrative around the central theme of your research so that readers can grasp the concept and identify the gaps that the study will address. While the length and detail presented in the background of a study could vary depending on the complexity and novelty of the research topic, it is imperative to avoid wordiness. For research that is interdisciplinary, mentioning how the disciplines are connected and highlighting specific aspects to be studied helps readers understand the research better.

While there are different styles of writing the background of a study , it always helps to have a clear plan in place. Let us look at how to write a background of study for research papers.

Identify the research problem: Begin the background by defining the research topic, and highlighting the main issue or question that the research aims to address. The research problem should be clear, specific, and relevant to the field of study. It should be framed using simple, easy to understand language and must be meaningful to intended audiences.
Craft an impactful statement of the research objectives: While writing the background of the study it is critical to highlight the research objectives and specific goals that the study aims to achieve. The research objectives should be closely related to the research problem and must be aligned with the overall purpose of the study.
Conduct a review of available literature: When writing the background of the research , provide a summary of relevant literature in the field and related research that has been conducted around the topic. Remember to record the search terms used and keep track of articles that you read so that sources can be cited accurately. Ensure that the literature you include is sourced from credible sources.
Address existing controversies and assumptions: It is a good idea to acknowledge and clarify existing claims and controversies regarding the subject of your research. For example, if your research topic involves an issue that has been widely discussed due to ethical or politically considerations, it is best to address them when writing the background of the study .
Present the relevance of the study: It is also important to provide a justification for the research. This is where the researcher explains why the study is important and what contributions it will make to existing knowledge on the subject. Highlighting key concepts and theories and explaining terms and ideas that may feel unfamiliar to readers makes the background of the study content more impactful.
Proofread to eliminate errors in language, structure, and data shared: Once the first draft is done, it is a good idea to read and re-read the draft a few times to weed out possible grammatical errors or inaccuracies in the information provided. In fact, experts suggest that it is helpful to have your supervisor or peers read and edit the background of the study . Their feedback can help ensure that even inadvertent errors are not overlooked.

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How to avoid mistakes in writing the background of a study

While figuring out how to write the background of a study , it is also important to know the most common mistakes authors make so you can steer clear of these in your research paper.

Write the background of a study in a formal academic tone while keeping the language clear and simple. Check for the excessive use of jargon and technical terminology that could confuse your readers.
Avoid including unrelated concepts that could distract from the subject of research. Instead, focus your discussion around the key aspects of your study by highlighting gaps in existing literature and knowledge and the novelty and necessity of your study.
Provide relevant, reliable evidence to support your claims and citing sources correctly; be sure to follow a consistent referencing format and style throughout the paper.
Ensure that the details presented in the background of the study are captured chronologically and organized into sub-sections for easy reading and comprehension.
Check the journal guidelines for the recommended length for this section so that you include all the important details in a concise manner.

By keeping these tips in mind, you can create a clear, concise, and compelling background of the study for your research paper. Take this example of a background of the study on the impact of social media on mental health.

Social media has become a ubiquitous aspect of modern life, with people of all ages, genders, and backgrounds using platforms such as Facebook, Instagram, and Twitter to connect with others, share information, and stay updated on news and events. While social media has many potential benefits, including increased social connectivity and access to information, there is growing concern about its impact on mental health. Research has suggested that social media use is associated with a range of negative mental health outcomes, including increased rates of anxiety, depression, and loneliness. This is thought to be due, in part, to the social comparison processes that occur on social media, whereby users compare their lives to the idealized versions of others that are presented online. Despite these concerns, there is also evidence to suggest that social media can have positive effects on mental health. For example, social media can provide a sense of social support and community, which can be beneficial for individuals who are socially isolated or marginalized. Given the potential benefits and risks of social media use for mental health, it is important to gain a better understanding of the mechanisms underlying these effects. This study aims to investigate the relationship between social media use and mental health outcomes, with a particular focus on the role of social comparison processes. By doing so, we hope to shed light on the potential risks and benefits of social media use for mental health, and to provide insights that can inform interventions and policies aimed at promoting healthy social media use.

To conclude, the background of a study is a crucial component of a research manuscript and must be planned, structured, and presented in a way that attracts reader attention, compels them to read the manuscript, creates an impact on the minds of readers and sets the stage for future discussions.

A well-written background of the study not only provides researchers with a clear direction on conducting their research, but it also enables readers to understand and appreciate the relevance of the research work being done.

Frequently Asked Questions (FAQs) on background of the study

Q: How does the background of the study help the reader understand the research better?

The background of the study plays a crucial role in helping readers understand the research better by providing the necessary context, framing the research problem, and establishing its significance. It helps readers:

understand the larger framework, historical development, and existing knowledge related to a research topic
identify gaps, limitations, or unresolved issues in the existing literature or knowledge
outline potential contributions, practical implications, or theoretical advancements that the research aims to achieve
and learn the specific context and limitations of the research project

Q: Does the background of the study need citation?

Yes, the background of the study in a research paper should include citations to support and acknowledge the sources of information and ideas presented. When you provide information or make statements in the background section that are based on previous studies, theories, or established knowledge, it is important to cite the relevant sources. This establishes credibility, enables verification, and demonstrates the depth of literature review you’ve done.

Q: What is the difference between background of the study and problem statement?

The background of the study provides context and establishes the research’s foundation while the problem statement clearly states the problem being addressed and the research questions or objectives.

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Inductive vs. Deductive Research Approach

Significance of a Study: Revisiting the “So What” Question

Open Access
First Online: 03 December 2022

Cite this chapter

You have full access to this open access chapter

James Hiebert 6 ,
Jinfa Cai 7 ,
Stephen Hwang 7 ,
Anne K Morris 6 &
Charles Hohensee 6

Part of the book series: Research in Mathematics Education ((RME))

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Every researcher wants their study to matter—to make a positive difference for their professional communities. To ensure your study matters, you can formulate clear hypotheses and choose methods that will test them well, as described in Chaps. 1, 2, 3 and 4. You can go further, however, by considering some of the terms commonly used to describe the importance of studies, terms like significance, contributions, and implications. As you clarify for yourself the meanings of these terms, you learn that whether your study matters depends on how convincingly you can argue for its importance. Perhaps most surprising is that convincing others of its importance rests with the case you make before the data are ever gathered. The importance of your hypotheses should be apparent before you test them. Are your predictions about things the profession cares about? Can you make them with a striking degree of precision? Are the rationales that support them compelling? You are answering the “So what?” question as you formulate hypotheses and design tests of them. This means you can control the answer. You do not need to cross your fingers and hope as you collect data.

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Part I. Setting the Groundwork

One of the most common questions asked of researchers is “So what?” What difference does your study make? Why are the findings important? The “so what” question is one of the most basic questions, often perceived by novice researchers as the most difficult question to answer. Indeed, addressing the “so what” question continues to challenge even experienced researchers. It is not always easy to articulate a convincing argument for the importance of your work. It can be especially difficult to describe its importance without falling into the trap of making claims that reach beyond the data.

That this issue is a challenge for researchers is illustrated by our analysis of reviewer comments for JRME . About one-third of the reviews for manuscripts that were ultimately rejected included concerns about the importance of the study. Said another way, reviewers felt the “So what?” question had not been answered. To paraphrase one journal reviewer, “The manuscript left me unsure of what the contribution of this work to the field’s knowledge is, and therefore I doubt its significance.” We expect this is a frequent concern of reviewers for all research journals.

Our goal in this chapter is to help you navigate the pressing demands of journal reviewers, editors, and readers for demonstrating the importance of your work while staying within the bounds of acceptable claims based on your results. We will begin by reviewing what we have said about these issues in previous chapters. We will then clarify one of the confusing aspects of developing appropriate arguments—the absence of consensus definitions of key terms such as significance, contributions, and implications. Based on the definitions we propose, we will examine the critical role of alignment for realizing the potential significance of your study. Because the importance of your study is communicated through your evolving research paper, we will fold suggestions for writing your paper into the discussion of creating and executing your study.

The picture illustrates a description - A confusing aspect of developing appropriate arguments is the absence of consensus definitions of some key terms.

We laid the groundwork in Chap. 1 for what we consider to be important research in education:

In our view, the ultimate goal of education is to offer all students the best possible learning opportunities. So, we believe the ultimate purpose of scientific inquiry in education is to support the improvement of learning opportunities for all students…. If there is no way to imagine a connection to improving learning opportunities for students, even a distant connection, we recommend you reconsider whether it is an important hypothesis within the education community.

Of course, you might prefer another “ultimate purpose” for research in education. That’s fine. The critical point is that the argument for the importance of the hypotheses you are testing should be connected to the value of a long-term goal you can describe. As long as most of the educational community agrees with this goal, and you can show how testing your hypotheses will move the field forward to achieving this goal, you will have developed a convincing argument for the importance of your work.

In Chap. 2 , we argued the importance of your hypotheses can and should be established before you collect data. Your theoretical framework should carry the weight of your argument because it should describe how your hypotheses will extend what is already known. Your methods should then show that you will test your hypotheses in an appropriate way—in a way that will allow you to detect how the results did, and did not, confirm the hypotheses. This will, in turn, allow you to formulate revised hypotheses. We described establishing the importance of your study by saying, “The importance can come from the fact that, based on the results, you will be able to offer revised hypotheses that help the field better understand an issue relevant for improving all students’ learning opportunities.”

The ideas from Chaps. 1 , 2 , and 3 go a long way toward setting the parameters for what counts as an important study and how its importance can be determined. Chapter 4 focused on ensuring that the importance of a study can be realized. The next section fills in the details by proposing definitions for the most common terms used to claim importance: significance, contributions, and implications.

You might notice that we do not have a chapter dedicated to discussing the presentation of the findings—that is, a “results” chapter. We do not mean to imply that presenting results is trivial. However, we believe that if you follow our recommendations for writing your evolving research paper, presenting the results will be quite straightforward. The key is to present your results so they can be most easily compared with your predictions. This means, among other things, organizing your presentation of results according to your earlier presentation of hypotheses.

Part II. Clarifying Importance by Revisiting the Definitions of Key Terms

What does it mean to say your findings are significant? Statistical significance is clear. There are widely accepted standards for determining the statistical significance of findings. But what about educational significance? Is this the same as claiming that your study makes an important contribution? Or, that your study has important implications? Different researchers might answer these questions in different ways. When key terms like these are overused, their definitions gradually broaden or shift, and they can lose their meaning. That is unfortunate, because it creates confusion about how to develop claims for the importance of a study.

By clarifying the definitions, we hope to clarify what is required to claim that a study is significant , that it makes a contribution , and that it has important implications . Not everyone defines the terms as we do. Our definitions are probably a bit narrower or more targeted than those you may encounter elsewhere. Depending on where you want to publish your study, you may want to adapt your use of these terms to match more closely the expectations of a particular journal. But the way we define and address these terms is not antithetical to common uses. And we believe ridding the terms of unnecessary overlap allows us to discriminate among different key concepts with respect to claims for the importance of research studies. It is not necessary to define the terms exactly as we have, but it is critical that the ideas embedded in our definitions be distinguished and that all of them be taken into account when examining the importance of a study.

We will use the following definitions:

Significance: The importance of the problem, questions, and/or hypotheses for improving the learning opportunities for all students (you can substitute a different long-term goal if its value is widely shared). Significance can be determined before data are gathered. Significance is an attribute of the research problem , not the research findings .

Contributions : The value of the findings for revising the hypotheses, making clear what has been learned, what is now better understood.

Implications : Deductions about what can be concluded from the findings that are not already included in “contributions.” The most common deductions in educational research are for improving educational practice. Deductions for research practice that are not already defined as contributions are often suggestions about research methods that are especially useful or methods to avoid.

Significance

The significance of a study is built by formulating research questions and hypotheses you connect through a careful argument to a long-term goal of widely shared value (e.g., improving learning opportunities for all students). Significance applies both to the domain in which your study is located and to your individual study. The significance of the domain is established by choosing a goal of widely shared value and then identifying a domain you can show is connected to achieving the goal. For example, if the goal to which your study contributes is improving the learning opportunities for all students, your study might aim to understand more fully how things work in a domain such as teaching for conceptual understanding, or preparing teachers to attend to all students, or designing curricula to support all learners, or connecting learning opportunities to particular learning outcomes.

The significance of your individual study is something you build ; it is not predetermined or self-evident. Significance of a study is established by making a case for it, not by simply choosing hypotheses everyone already thinks are important. Although you might believe the significance of your study is obvious, readers will need to be convinced.

The picture illustrates a description- Significance can be determined before data are gathered. Significance is an attribute of the research problems.

Significance is something you develop in your evolving research paper. The theoretical framework you present connects your study to what has been investigated previously. Your argument for significance of the domain comes from the significance of the line of research of which your study is a part. The significance of your study is developed by showing, through the presentation of your framework, how your study advances this line of research. This means the lion’s share of your answer to the “So what?” question will be developed as part of your theoretical framework.

Although defining significance as located in your paper prior to presenting results is not a definition universally shared among educational researchers, it is becoming an increasingly common view. In fact, there is movement toward evaluating the significance of a study based only on the first sections of a research paper—the sections prior to the results (Makel et al., 2021 ).

In addition to addressing the “So what?” question, your theoretical framework can address another common concern often voiced by readers: “What is so interesting? I could have predicted those results.” Predictions do not need to be surprising to be interesting and significant. The significance comes from the rationales that show how the predictions extend what is currently known. It is irrelevant how many researchers could have made the predictions. What makes a study significant is that the theoretical framework and the predictions make clear how the study will increase the field’s understanding toward achieving a goal of shared value.

The picture represents a description-What makes a study significant in the theoretical framework and the predictions make clear how it will increase the field's understanding.

An important consequence of interpreting significance as a carefully developed argument for the importance of your research study within a larger domain is that it reveals the advantage of conducting a series of connected studies rather than single, disconnected studies. Building the significance of a research study requires time and effort. Once you have established significance for a particular study, you can build on this same argument for related studies. This saves time, allows you to continue to refine your argument across studies, and increases the likelihood your studies will contribute to the field.

Contributions

As we have noted, in fields as complicated as education, it is unlikely that your predictions will be entirely accurate. If the problem you are investigating is significant, the hypotheses will be formulated in such a way that they extend a line of research to understand more deeply phenomena related to students’ learning opportunities or another goal of shared value. Often, this means investigating the conditions under which phenomena occur. This gets complicated very quickly, so the data you gather will likely differ from your predictions in a variety of ways. The contributions your study makes will depend on how you interpret these results in light of the original hypotheses.

The picture represents a description-A study's contribution lies in the value of its findings for revising the hypotheses, making clear what has been learned.

Contributions Emerge from Revisions to your Hypotheses

We view interpreting results as a process of comparing the data with the predictions and then examining the way in which hypotheses should be revised to more fully account for the results. Revising will almost always be warranted because, as we noted, predictions are unlikely to be entirely accurate. For example, if researchers expect Outcome A to occur under specified conditions but find that it does not occur to the extent predicted or actually does occur but without all the conditions, they must ask what changes to the hypotheses are needed to predict more accurately the conditions under which Outcome A occurred. Are there, for example, essential conditions that were not anticipated and that should be included in the revised hypotheses?

Consider an example from a recently published study (Wang et al., 2021 ). A team of researchers investigated the following research question: “How are students’ perceptions of their parents’ expectations related to students’ mathematics-related beliefs and their perceived mathematics achievement?” The researchers predicted that students’ perceptions of their parents’ expectations would be highly related to students’ mathematics-related beliefs and their perceived mathematics achievement. The rationale was based largely on prior research that had consistently found parents’ general educational expectations to be highly correlated with students’ achievement.

The findings showed that Chinese high school students’ perceptions of their parents’ educational expectations were positively related to these students’ mathematics-related beliefs. In other words, students who believed their parents expected them to attain higher levels of education had more desirable mathematics-related beliefs.

However, students’ perceptions of their parents’ expectations about mathematics achievement were not related to students’ mathematics-related beliefs in the same way as the more general parental educational expectations. Students who reported that their parents had no specific expectations possessed more desirable mathematics-related beliefs than all other subgroups. In addition, these students tended to perceive their mathematics achievement rank in their class to be higher on average than students who reported that their parents expressed some level of expectation for mathematics achievement.

Because this finding was not predicted, the researchers revised the original hypothesis. Their new prediction was that students who believe their parents have no specific mathematics achievement expectations possess more positive mathematics-related beliefs and higher perceived mathematics achievement than students who believe their parents do have specific expectations. They developed a revised rationale that drew on research on parental pressure and mathematics anxiety, positing that parents’ specific mathematics achievement expectations might increase their children’s sense of pressure and anxiety, thus fostering less positive mathematics-related beliefs. The team then conducted a follow-up study. Their findings aligned more closely with the new predictions and affirmed the better explanatory power of the revised rationale. The contributions of the study are found in this increased explanatory power—in the new understandings of this phenomenon contained in the revisions to the rationale.

Interpreting findings in order to revise hypotheses is not a straightforward task. Usually, the rationales blend multiple constructs or variables and predict multiple outcomes, with different outcomes connected to different research questions and addressed by different sets of data. Nevertheless, the contributions of your study depend on specifying the differences between your original hypotheses and your revised hypotheses. What can you explain now that you could not explain before?

We believe that revising hypotheses is an optimal response to any question of contributions because a researcher’s initial hypotheses plus the revisions suggested by the data are the most productive way to tie a study into the larger chain of research of which it is a part. Revised hypotheses represent growth in knowledge. Building on other researchers’ revised hypotheses and revising them further by more explicitly and precisely describing the conditions that are expected to influence the outcomes in the next study accumulates knowledge in a form that can be recorded, shared, built upon, and improved.

The significance of your study is presented in the opening sections of your evolving research paper whereas the contributions are presented in the final section, after the results. In fact, the central focus in this “Discussion” section should be a specification of the contributions (note, though, that this guidance may not fully align with the requirements of some journals).

Contributions Answer the Question of Generalizability

A common and often contentious, confusing issue that can befuddle novice and experienced researchers alike is the generalizability of results. All researchers prefer to believe the results they report apply to more than the sample of participants in their study. How important would a study be if the results applied only to, say, two fourth-grade classrooms in one school, or to the exact same tasks used as measures? How do you decide to which larger population (of students or tasks) your results could generalize? How can you state your claims so they are precisely those justified by the data?

To illustrate the challenge faced by researchers in answering these questions, we return to the JRME reviewers. We found that 30% of the reviews expressed concerns about the match between the results and the claims. For manuscripts that ultimately received a decision of Reject, the majority of reviewers said the authors had overreached—the claims were not supported by the data. In other words, authors generalized their claims beyond those that could be justified.

The Connection Between Contributions and Generalizability

In our view, claims about contributions can be examined productively alongside considerations of generalizability. To make the case for this view, we need to back up a bit. Recall that the purpose of research is to understand a phenomenon. To understand a phenomenon, you need to determine the conditions under which it occurs. Consequently, productive hypotheses specify the conditions under which the predictions hold and explain why and how these conditions make a difference. And the conditions set the parameters on generalizability. They identify when, where, and for whom the effect or situation will occur. So, hypotheses describe the extent of expected generalizability, and revised hypotheses that contain the contributions recalibrate generalizability and offer new predictions within these parameters.

An Example That Illustrates the Connection

In Chap. 4 , we introduced an example with a research question asking whether second graders improve their understanding of place value after a specially designed instructional intervention. We suggested asking a few second and third graders to complete your tasks to see if they generated the expected variation in performance. Suppose you completed this pilot study and now have satisfactory tasks. What conditions might influence the effect of the intervention? After careful study, you developed rationales that supported three conditions: the entry level of students’ understanding, the way in which the intervention is implemented, and the classroom norms that set expectations for students’ participation.

Suppose your original hypotheses predicted the desired effect of the intervention only if the students possessed an understanding of several concepts on which place value is built, only if the intervention was implemented with fidelity to the detailed instructional guidelines, and only if classroom norms encouraged students to participate in small-group work and whole-class discussions. Your claims of generalizability will apply to second-grade settings with these characteristics.

Now suppose you designed the study so the intervention occurred in five second-grade classrooms that agreed to participate. The pre-intervention assessment showed all students with the minimal level of entry understanding. The same well-trained teacher was employed to teach the intervention in all five classrooms, none of which included her own students. And you learned from prior observations and reports of the classroom teachers that three of the classrooms operated with the desired classroom norms, but two did not. Because of these conditions, your study is now designed to test one of your hypotheses—the desired effect will occur only if classroom norms encouraged students to participate in small-group work and whole-class discussions. This is the only condition that will vary; the other two (prior level of understanding and fidelity of implementation) are the same across classrooms so you will not learn how these affect the results.

Suppose the classrooms performed equally well on the post-intervention assessments. You expected lower performance in the two classrooms with less student participation, so you need to revise your hypotheses. The challenge is to explain the higher-than-expected performance of these students. Because you were interested in understanding the effects of this condition, you observed several lessons in all the classrooms during the intervention. You can now use this information to explain why the intervention worked equally well in classrooms with different norms.

Your revised hypothesis captures this part of your study’s contribution. You can now say more about the ways in which the intervention can help students improve their understanding of place value because you have different information about the role of classroom norms. This, in turn, allows you to specify more precisely the nature and extent of the generalizability of your findings. You now can generalize your findings to classrooms with different norms. However, because you did not learn more about the impact of students’ entry level understandings or of different kinds of implementation, the generalizability along these dimensions remains as limited as before.

This example is simplified. In many studies, the findings will be more complicated, and more conditions will likely be identified, some of which were anticipated and some of which emerged while conducting the study and analyzing the data. Nevertheless, the point is that generalizability should be tied to the conditions that are expected to affect the results. Further, unanticipated conditions almost always appear, so generalizations should be conservative and made with caution and humility. They are likely to change after testing the new predictions.

Contributions Are Assured When Hypotheses Are Significant and Methods Are Appropriate and Aligned

We have argued that the contributions of your study are produced by the revised hypotheses you can formulate based on your results. Will these revisions always represent contributions to the field? What if the revisions are minor? What if your results do not inform revisions to your hypotheses?

We will answer these questions briefly now and then develop them further in Part IV of this chapter. The answer to the primary question is “yes,” your revisions will always be a contribution to the field if (1) your hypotheses are significant and (2) you crafted appropriate methods to test the hypotheses. This is true even if your revisions are minor or if your data are not as informative as you expected. However, this is true only if you meet the two conditions in the earlier sentence. The first condition (significant hypotheses) can be satisfied by following the suggestions in the earlier section on significance. The second condition (appropriate methods) is addressed further in Part III in this chapter.

Implications

Before examining the role of methods in connecting significance with important contributions, we elaborate briefly our definition of “implications.” We reserve implications for the conclusions you can logically deduce from your findings that are not already presented as contributions. This means that, like contributions, implications are presented in the Discussion section of your research paper.

Many educational researchers present two types of implications: implications for future research and implications for practice. Although we are aware of this common usage, we believe our definition of “contributions” cover these implications. Clarifying why we call these “contributions” will explain why we largely reserve the word “implications” for recommendations regarding methods.

Implications for Future Research

Implications for future research often include (1) recommendations for empirical studies that would extend the findings of this study, (2) inferences about the usefulness of theoretical constructs, and (3) conclusions about the advisability of using particular kinds of methods. Given our earlier definitions, we prefer to label the first two types of implications as contributions.

Consider recommendations for empirical studies. After analyzing the data and presenting the results, we have suggested you compare the results with those predicted, revise the rationales for the original predictions to account for the results, and make new predictions based on the revised rationales. It is precisely these new predictions that can form the basis for recommending future research. Testing these new predictions is what would most productively extend this line of research. It can sometimes sound as if researchers are recommending future studies based on hunches about what research might yield useful findings. But researchers can do better than this. It would be more productive to base recommendations on a careful analysis of how the predictions of the original study could be sharpened and improved.

Now consider inferences about the usefulness of theoretical constructs. Our argument for labeling these inferences as contributions is similar. Rationales for predictions are where the relevant theoretical constructs are located. Revisions to these rationales based on the differences between the results and the predictions reveal the theoretical constructs that were affirmed to support accurate predictions and those that must be revised. In our view, usefulness is determined through this revision process.

Implications that do not come under our meaning of contributions are in the third type of implications, namely the appropriateness of methods for generating rich contributions. These kinds of implications are produced by your evaluation of your methods: research design, sampling procedures, tasks, data collection procedures, and data analyses. Although not always included in the discussion of findings, we believe it would be helpful for researchers to identify particular methods that were useful for conducting their study and those that should be modified or avoided. We believe these are appropriately called implications.

Implications for Practice

If the purpose of research is to better understand how to improve learning opportunities for all students, then it is appropriate to consider whether implications for improving educational practice can be drawn from the results of a study. How are these implications formulated? This is an important question because, in our view, these claims often come across as an afterthought, “Oh, I need to add some implications for practice.” But here is the sobering reality facing researchers: By any measure, the history of educational research shows that identifying these implications has had little positive effect on practice.

Perhaps the most challenging task for researchers who attempt to draw implications for practice is to interpret their findings for appropriate settings. A researcher who studied the instructional intervention for second graders on place value and found that average performance in the intervention classrooms improved more than in the textbook classrooms might be tempted to draw implications for practice. What should the researcher say? That second-grade teachers should adopt the intervention? Such an implication would be an overreach because, as we noted earlier, the findings cannot be generalized to all second-grade classrooms. Moreover, an improvement in average performance does not mean the intervention was better for all students.

The challenge is to identify the conditions under which the intervention would improve the learning opportunities for all students. Some of these conditions will be identified as the theoretical framework is built because the predictions need to account for these conditions. But some will be unforeseen, and some that are identified will not be informed by the findings. Recall that, in the study described earlier, a condition of entry level of understanding was hypothesized but the design of the study did not allow the researcher to draw any conclusions about its effect.

What can researchers say about implications for practice given the complexities involved in generalizing findings to other settings? We offer two recommendations. First, because it is difficult to specify all the conditions under which a phenomenon occurs, it is rarely appropriate to prescribe an educational practice. Researchers cannot anticipate the conditions under which individual teachers operate, conditions that often require adaptation of a suggested practice rather than implementation of a practice as prescribed.

Our second recommendation comes from returning to the purpose for educational research—to understand more fully how to improve learning opportunities for all students (or to achieve another goal of widely shared value). As we have described, understanding comes primarily from building and reevaluating rationales for your predictions. If you reach a new understanding related to improving learning opportunities, an understanding that could have practical implications, we recommend you share this understanding as an implication for practice.

For example, suppose the researcher who found better average performance of second graders after the intervention on place value had also studied several conditions under which performance improved. And suppose the researcher found that most students who did not improve their performance misunderstood a concept that appeared early in the intervention (e.g., the multiplicative relationship between positional values of a numeral). An implication for practice the researcher might share would be to describe the potential importance of understanding this concept early in the sequence of activities if teachers try out this intervention.

If you use our definitions, these implications for practice would be presented as contributions because they emerge directly from reevaluating and revising your rationales. We believe it is appropriate to use “Contributions” as the heading for this section in the Discussion section of your research paper. However, if editors prefer “Implications” we recommend following their suggestion.

We want to be clear that the terms you use for the different ways your study is important is not critical. We chose to define the terms significance, contributions, and implications in very specific and not universally shared ways to distinguish all the meanings of importance you should consider. Some of these can be established through your theoretical framework, some by the revisions of your hypotheses, and some by reflecting on the value of particular methods. The important thing, from our point of view, is that the ideas we defined for each of these terms are distinguished and recognized as specific ways of determining the importance of your study.

Part III. The Role of Methods in Determining Contributions

We have argued that every part of the study (and of the evolving research paper) should be aligned. All parts should be connected through a coherent chain of reasoning. In this chapter, we argue that the chain of reasoning is not complete until the methods are presented and the results are interpreted and discussed. The methods of the study create a bridge that connects the introductory material (research questions, theoretical framework, literature review, hypotheses) with the results and interpretations.

The role that methods play in scientific inquiry is to ensure that your hypotheses will be tested appropriately so the significance of your study will yield its potential contributions. To do this, the methods must do more than follow the standard guidelines and be technically correct (see Chap. 4 ). They must also fit with the surrounding parts of the study. We call this coherence.

The picture represents a description-The role that methods play in scientific inquiry is to ensure that your hypotheses will be tested appropriately for contributions.

Coherence Across the Phases of Scientific Inquiry

Coherence means the parts of a whole are fully aligned. When doing scientific inquiry, the early parts or phases should motivate the later phases. The methods you use should be motivated or explained by the earlier phases (e.g., research questions, theoretical framework, hypotheses). Your methods, in turn, should produce results that can be interpreted by comparing them with your predictions. Methods are aligned with earlier phases when you can use the rationales contained in your hypotheses to decide what kinds of data are needed to test your predictions, how best to gather these kinds of data, and what analyses should be performed (see Chap. 4 and Cai et al., 2019a ).

For a visual representation of this coherence, see Fig. 5.1 . Each box identifies an aspect of scientific inquiry. Hypotheses (shown in Box 1) include the rationales and predictions. Because the rationales encompass the theoretical framework and the literature review, Box 1 establishes the significance of the study. Box 2 represents the methods, which we defined in Chap. 4 as the entire set of procedures you will use, including the basic design, measures for collecting data, and analytic approaches. In Fig. 5.1 , the hypothesis in Box 1 points you to the methods you will use. That is, you will choose methods that provide data for analyses that will generate results or findings (Box 3) that allow you to make comparisons against your predictions. Based on those comparisons, you will revise your hypotheses and derive the contributions and implications of your study (Box 4).

The Chain of Coherence That Runs Through All Parts of a Research Study

We intend Fig. 5.1 to carry several messages. One is that coherence of a study and the associated research paper require all aspects of the study to flow from one into the other. Each set of prior entries must motivate and justify the next one. For example, the data and analyses you intend to gather and use in Box 2 (Methods) must be those that are motivated and explained by the research question and hypothesis (prediction and rationale) in Box 1.

A second message in the figure is that coherence includes Box 4, “Discussion.” Aligned with the first three boxes, the fourth box flows from these boxes but is also constrained by them. The contributions and implications authors describe in the Discussion section of the paper cannot go beyond what is allowed by the original hypotheses and the revisions to these hypotheses indicated by the findings.

Methods Enable Significance to Yield Contributions

We begin this section by identifying a third message conveyed in Fig. 5.1 . The methods of the study, represented by Box 2, provide a bridge that connects the significance of the study (Box 1) with the contributions of the study (Box 4). The results (Box 3) indicate the nature of the contributions by determining the revisions to the original hypotheses.

In our view, the connecting role played by the methods is often underappreciated. Crafting appropriate methods aligned with the significance of the study, on one hand, and the interpretations, on the other, can determine whether a study is judged to make a contribution.

If the hypotheses are established as significant, and if appropriate methods are used to test the predictions, the study will make important contributions even if the data are not statistically significant. We can say this another way. When researchers establish the significance of the hypotheses (i.e., convince readers they are of interest to the field) and use methods that provide a sound test of these hypotheses, the data they present will be of interest regardless of how they turn out. This is why Makel et al. ( 2021 ) endorse a review process for publication that emphasizes the significance of the study as presented in the first sections of a research paper.

Treating the methods as connecting the introductory arguments to the interpretations of data prevent researchers from making a common mistake: When writing the research paper, some researchers lose track of the research questions and/or the predictions. In other words, results are presented but are not interpreted as answers to the research questions or compared with the predictions. It is as if the introductory material of the paper begins one story, and the interpretations of results ends a different story. Lack of alignment makes it impossible to tell one coherent story.

A final point is that the alignment of a study cannot be evaluated and appreciated if the methods are not fully described. Methods must be described clearly and completely in the research paper so readers can see how they flow from the earlier phases of the study and how they yield the data presented. We suggested in Chap. 4 a rule of thumb for deciding whether the methods have been fully described: “Readers should be able to replicate the study if they wish.”

Part IV. Special Considerations that Affect a Study’s Contributions

We conclude Chap. 5 by addressing two additional issues that can affect how researchers interpret the results and make claims about the contributions of a study. Usually, researchers deal with these issues in the Discussion section of their research paper, but we believe it is useful to consider them as you plan and conduct your study. The issues can be posed as questions: How should I treat the limitations of my study? How should I deal with findings that are completely unexpected?

Limitations of a Study

We can identify two kinds of limitations: (1) limitations that constrain your ability to interpret your results because of unfortunate choices you made, and (2) limitations that constrain your ability to generalize your results because of missing variables you could not fit into the scope of your study or did not anticipate. We recommend different ways of dealing with these.

Limitations Due to Unfortunate Choices

All researchers make unfortunate choices. These are mistakes that could have been prevented. Often, they are choices in how a study was designed and/or executed. Maybe the sample did not have the characteristics assumed, or a task did not assess what was expected, or the intervention was not implemented as planned. Although many unfortunate choices can be prevented by thinking through the consequences of every decision or by conducting a well-designed pilot study or two, some will occur anyway. How should you deal with them?

The consequence of unfortunate choices is that the data do not test the hypotheses as precisely or completely as hoped. When this happens, the data must be interpreted with these constraints in mind. Almost always, this limits the researcher to making fewer or narrower claims than desired about differences and similarities between the results and the predictions. Usually this means conclusions about the ways in which the rationales must be revised require extra qualifications. In other words, claims about contributions of the study must be made with extra caution.

Research papers frequently include a subsection in the Discussion called “Limitations of the Study.” Researchers often use this subsection to identify the study’s limitations by describing the unfortunate choices, but they do not always spell out how these limitations should affect the contributions of the paper. Sometimes, it appears that researchers are simply checking off a requirement to identify the limitations by saying something like “The results should be interpreted with caution.” But this does not help readers understand exactly what cautions should be applied and it does not hold researchers accountable for the limitations.

We recommend something different. We suggest you do the hard work of figuring out how the data should be interpreted in light of the limitations and share these details with the readers. You might do this when the results are presented or when you interpret them. Rather than presenting your claims about the contributions of the study and then saying readers should interpret these with “caution” because of the study’s limitations, we suggest presenting only those interpretations and claims of contributions that can be made with the limitations in mind.

The picture illustrates a description-We suggest you do the hard work of figuring out how the data should be interpreted in light of the limitations and share details.

One way to think about the constraints you will likely need to impose on your interpretations is in terms of generalizability. Recall that earlier in this chapter, we described the close relationship between contributions and generalizability. When generalizability is restricted, so are contributions.

Limitations Due to Missing Variables

Because of the complexity of problems, questions, and hypotheses explored in educational research, researchers are unlikely to anticipate in their studies all the variables that affect the data and results. In addition, tradeoffs often must be made. Researchers cannot study everything at once, so decisions must be made about which variables to study carefully and which to either control or ignore.

In the earlier example of studying whether second graders improve their understanding of place value after a specially designed instructional intervention, the researcher identified three variables that were expected to influence the effect of the intervention: students’ entry level of understanding, implementation of the intervention, and norms of the classrooms in which the intervention was implemented. The researcher decided to control the implementation variable by hiring one experienced teacher to implement the intervention in all the classrooms. This meant the variable of individual teacher differences was not included in the study and the researcher could not generalize to classrooms with these differences.

Some researchers might see controlling the implementation of the intervention as a limitation. We do not. As a factor that is not allowed to vary, it constrains the generalizations a researcher can make, but we believe these kinds of controlled variables are better treated as opportunities for future research. Perhaps the researcher’s observations in the classroom provided information that could be used to make some predictions about which elements of the intervention are essential and which are optional—about which aspects of the intervention must be implemented as written and which can vary with different teachers. When revising the rationales to show what was learned in this study, the researcher could include rationales for new, tentative predictions about the effects of the intervention in classrooms where implementation differed in specified ways. These predictions create a genuine contribution of the study. If you use our definitions, these new predictions, often presented under “implications for future research,” would be presented as “contributions.”

Notice that if you follow our advice, you would not need to include a separate section in the Discussion of your paper labeled “Limitations.” We acknowledge, however, that some journal editors recommend such a subsection. In this case, we suggest you include this subsection along with treating the two different kinds of limitations as we recommend. You can do both.

Dealing with Unexpected Findings

Researchers are often faced with unexpected and perhaps surprising results, even when they have developed a convincing theoretical framework, posed research questions tightly connected to this framework, presented predictions about expected outcomes, and selected methods that appropriately test these predictions. Indeed, the unexpected findings can be the most interesting and valuable products of the study. They can range from mildly surprising to “Wow. I didn’t expect that.” How should researchers treat such findings? Our answer is based on two principles.

The first principle is that the value of research does not lie in whether the predictions are completely accurate but in helping the field learn more about the explanatory power of theoretical frameworks. That is, the value lies in the increased understanding of phenomena generated by examining the ability of theoretical frameworks (or rationales) to predict outcomes and explain results. The second principle, a corollary to the first, is to treat unexpected findings in a way that is most educative for the reader.

Based on our arguments to this point, you could guess we will say there will always be unexpected findings. Predicted answers to significant research questions in education will rarely, if ever, be entirely accurate. So, you can count on dealing with unexpected findings.

Consistent with the two principles above, your goal should be to use unexpected findings to understand more fully the phenomenon under investigation. We recommend one of three different paths. The choice of which path to take depends on what you decide after reflecting again on the decisions you made at each phase of the study.

The first path is appropriate when researchers reexamine their theoretical framework in light of the unexpected findings and decide that it is still a compelling framework based on previous work. They reason that readers are likely to have been convinced by this framework and would likely have made similar predictions. In this case, we believe that it is educative for researchers to (a) summarize their initial framework, (b) present the findings and distinguish those that were aligned with the predictions from those that were not, and (c) explain why the theoretical framework was inadequate and propose changes to the framework that would have created more alignment with the unexpected findings.

Revisions to initial hypotheses are especially useful if they include explanations for why a researcher might have been wrong (and researchers who ask significant questions in domains as complex as education are almost always wrong in some way). Depending on the ways in which the revised framework differs from the original, the authors have two options. If the revised framework is an expansion of the original, it would be appropriate for the authors to propose directions for future research that would extend this study. Alternatively, if the revised framework is still largely within the scope of the original study and consists of revisions to the original hypotheses, the revisions could guide a second study to check the adequacy of the revisions. This second study could be conducted by the same researchers (perhaps before the final manuscript is written and presented as two parts of the same report) or it could be proposed in the Discussion as a specific study that could be conducted by other researchers.

The second path is appropriate when researchers reexamine their theoretical framework in light of the unexpected findings and recognize serious flaws in the framework. The flaws could result from a number of factors, including defining elements of the framework in too general a way to formulate well-grounded hypotheses, failing to include a variable, or not accounting carefully enough for the previous work in this domain, both theoretical and empirical. In many of these cases, readers would not be well served by reading a poorly developed framework and then learning that the framework, which had not been convincing, did not accurately predict the results. Before scrapping the study and starting over, we suggest stepping back and reexamining the framework. Is it possible to develop a more coherent, complete, and convincing framework? Would this framework predict the results more accurately? If the findings remain unexpected based on the predictions generated by this revised, more compelling framework, then the first path applies.

It is likely that the new framework will better predict the findings. After all, the researchers now know the findings they will report. However, it is unlikely that the framework will accurately predict all the findings. This is because the framework is not built around the findings of this study of which authors are now aware (but have not yet been presented). Frameworks are built on research and theory already published. This means the redesigned framework is built from exactly the same empirical findings and theoretical arguments available before the study was conducted. The redesigned framework also is constrained by needing to justify exactly those methods used in the study. The redesigned framework cannot justify different methods or even slightly altered methods. The task for researchers is to show how the new theoretical framework necessarily generates, using the same methods, the predictions they present in the research paper. Just as before, it is unlikely this framework can account for all the findings. Just as before, after presenting the results the researchers should explain why they believe particular hypotheses were confirmed and why others should be revised, even in small ways, based on the findings reported. Researchers can now use these findings to revise the hypotheses presented in the paper. The point we are making is that we believe it is acceptable to reconstruct frameworks before writing research reports if doing so would be more educative for the reader.

Finally, the third path becomes appropriate when researchers, in reexamining their theoretical framework, trace the problem to a misalignment between the methods they used and the theoretical framework or the research questions. Perhaps the researchers recognize that the tasks they used did not yield data that could test the predictions and address the research questions. Or perhaps the researchers realize that the sample they selected would likely have been heavily influenced by a factor they failed to take into account. In other words, the researchers decide that the unexpected findings were due to a problem with the methods they used, not with the framework or the accompanying predictions. In this case, we recommend that the researchers correct the methodological problems and conduct the study again.

Part V. A Few Suggestions for Structuring Your Discussion Section

Writing the Discussion section of your research paper can be overwhelming given all our suggestions about what to include in this section. Here are a few tips that might help you create a simple template for this section.

We recommend the Discussion begin with a brief summary of the main results, especially those you will interpret in this section. This summary should not contain new data or results not previously presented in the paper.

The Discussion could then move to presenting the contributions in the ways we have described. To do this you could point out the ways in which the results differed from the predictions and suggest revisions to your rationales that would have better predicted the results. Doing this will show how the contributions of your study extend what is known beyond the research you drew on to build your original rationale. You can then propose how to extend your contributions to research by proposing future research studies that would test your new predictions. If you believe the revisions you make to your rationales produce new insights or understandings that could be helpful for educational practitioners, you can identify these contributions to practice as well. This comprises the bulk of the Discussion section.

If you have embedded the limitations in earlier sections of the paper, you will have presented your results and interpreted your findings constrained by these limitations. If you choose (or are asked) to describe limitations in the Discussion, you could identify the limitations and then point to the ways they affected your interpretations of the findings. Finally, the Discussion could conclude with the implications of the study for methodological choices that could improve research in the domain in which your study is located or how future studies could overcome the limitations you identified.

Because we are providing guidance on writing your research paper for publication, we will reiterate here that you should investigate the expectations and conventions of the journal to which you will submit your paper. Usually, it will be acceptable to use the terms “significance,” “contributions,” and “implications” as we have defined them. However, if the editors expect you to use the terms differently, follow the editors’ expectations. Our definitions in this chapter are meant to help you think clearly about the different ways you can make a case for the importance of your research. What matters is that you have carefully built and described a coherent chain of scientific inquiry that allows your study to translate the significance of your research problem into contributions to the field.

We began the chapter with the “So what?” question. The question looks simple and straightforward but is challenging and complicated. Its simple appearance can lead researchers to believe it should have a simple answer. But it almost never does. In this chapter, we tried to address the many complications that arise when answering the question. We hope you now have some new insights and new tools for answering the question in your next study.

Cai, J., Morris, A., Hohensee, C., Hwang, S., Robison, V., Cirillo, M., Kramer, S. L., & Hiebert, J. (2019a). Choosing and justifying robust methods for educational research. Journal for Research in Mathematics Education, 50 (4), 342–348. https://doi.org/10.5951/jresematheduc.50.2.0114

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Hiebert, J., Cai, J., Hwang, S., Morris, A.K., Hohensee, C. (2023). Significance of a Study: Revisiting the “So What” Question. In: Doing Research: A New Researcher’s Guide. Research in Mathematics Education. Springer, Cham. https://doi.org/10.1007/978-3-031-19078-0_5

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What is Research Methodology? Definition, Types, and Examples

Research methodology 1,2 is a structured and scientific approach used to collect, analyze, and interpret quantitative or qualitative data to answer research questions or test hypotheses. A research methodology is like a plan for carrying out research and helps keep researchers on track by limiting the scope of the research. Several aspects must be considered before selecting an appropriate research methodology, such as research limitations and ethical concerns that may affect your research.

The research methodology section in a scientific paper describes the different methodological choices made, such as the data collection and analysis methods, and why these choices were selected. The reasons should explain why the methods chosen are the most appropriate to answer the research question. A good research methodology also helps ensure the reliability and validity of the research findings. There are three types of research methodology—quantitative, qualitative, and mixed-method, which can be chosen based on the research objectives.

What is research methodology ?

A research methodology describes the techniques and procedures used to identify and analyze information regarding a specific research topic. It is a process by which researchers design their study so that they can achieve their objectives using the selected research instruments. It includes all the important aspects of research, including research design, data collection methods, data analysis methods, and the overall framework within which the research is conducted. While these points can help you understand what is research methodology, you also need to know why it is important to pick the right methodology.

Why is research methodology important?

Having a good research methodology in place has the following advantages: 3

Helps other researchers who may want to replicate your research; the explanations will be of benefit to them.
You can easily answer any questions about your research if they arise at a later stage.
A research methodology provides a framework and guidelines for researchers to clearly define research questions, hypotheses, and objectives.
It helps researchers identify the most appropriate research design, sampling technique, and data collection and analysis methods.
A sound research methodology helps researchers ensure that their findings are valid and reliable and free from biases and errors.
It also helps ensure that ethical guidelines are followed while conducting research.
A good research methodology helps researchers in planning their research efficiently, by ensuring optimum usage of their time and resources.

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Types of research methodology.

There are three types of research methodology based on the type of research and the data required. 1

Quantitative research methodology focuses on measuring and testing numerical data. This approach is good for reaching a large number of people in a short amount of time. This type of research helps in testing the causal relationships between variables, making predictions, and generalizing results to wider populations.
Qualitative research methodology examines the opinions, behaviors, and experiences of people. It collects and analyzes words and textual data. This research methodology requires fewer participants but is still more time consuming because the time spent per participant is quite large. This method is used in exploratory research where the research problem being investigated is not clearly defined.
Mixed-method research methodology uses the characteristics of both quantitative and qualitative research methodologies in the same study. This method allows researchers to validate their findings, verify if the results observed using both methods are complementary, and explain any unexpected results obtained from one method by using the other method.

What are the types of sampling designs in research methodology?

Sampling 4 is an important part of a research methodology and involves selecting a representative sample of the population to conduct the study, making statistical inferences about them, and estimating the characteristics of the whole population based on these inferences. There are two types of sampling designs in research methodology—probability and nonprobability.

Probability sampling

In this type of sampling design, a sample is chosen from a larger population using some form of random selection, that is, every member of the population has an equal chance of being selected. The different types of probability sampling are:

Systematic —sample members are chosen at regular intervals. It requires selecting a starting point for the sample and sample size determination that can be repeated at regular intervals. This type of sampling method has a predefined range; hence, it is the least time consuming.
Stratified —researchers divide the population into smaller groups that don’t overlap but represent the entire population. While sampling, these groups can be organized, and then a sample can be drawn from each group separately.
Cluster —the population is divided into clusters based on demographic parameters like age, sex, location, etc.
Convenience —selects participants who are most easily accessible to researchers due to geographical proximity, availability at a particular time, etc.
Purposive —participants are selected at the researcher’s discretion. Researchers consider the purpose of the study and the understanding of the target audience.
Snowball —already selected participants use their social networks to refer the researcher to other potential participants.
Quota —while designing the study, the researchers decide how many people with which characteristics to include as participants. The characteristics help in choosing people most likely to provide insights into the subject.

What are data collection methods?

During research, data are collected using various methods depending on the research methodology being followed and the research methods being undertaken. Both qualitative and quantitative research have different data collection methods, as listed below.

Qualitative research 5

One-on-one interviews: Helps the interviewers understand a respondent’s subjective opinion and experience pertaining to a specific topic or event
Document study/literature review/record keeping: Researchers’ review of already existing written materials such as archives, annual reports, research articles, guidelines, policy documents, etc.
Focus groups: Constructive discussions that usually include a small sample of about 6-10 people and a moderator, to understand the participants’ opinion on a given topic.
Qualitative observation : Researchers collect data using their five senses (sight, smell, touch, taste, and hearing).

Quantitative research 6

Sampling: The most common type is probability sampling.
Interviews: Commonly telephonic or done in-person.
Observations: Structured observations are most commonly used in quantitative research. In this method, researchers make observations about specific behaviors of individuals in a structured setting.
Document review: Reviewing existing research or documents to collect evidence for supporting the research.
Surveys and questionnaires. Surveys can be administered both online and offline depending on the requirement and sample size.

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What are data analysis methods.

The data collected using the various methods for qualitative and quantitative research need to be analyzed to generate meaningful conclusions. These data analysis methods 7 also differ between quantitative and qualitative research.

Quantitative research involves a deductive method for data analysis where hypotheses are developed at the beginning of the research and precise measurement is required. The methods include statistical analysis applications to analyze numerical data and are grouped into two categories—descriptive and inferential.

Descriptive analysis is used to describe the basic features of different types of data to present it in a way that ensures the patterns become meaningful. The different types of descriptive analysis methods are:

Measures of frequency (count, percent, frequency)
Measures of central tendency (mean, median, mode)
Measures of dispersion or variation (range, variance, standard deviation)
Measure of position (percentile ranks, quartile ranks)

Inferential analysis is used to make predictions about a larger population based on the analysis of the data collected from a smaller population. This analysis is used to study the relationships between different variables. Some commonly used inferential data analysis methods are:

Correlation: To understand the relationship between two or more variables.
Cross-tabulation: Analyze the relationship between multiple variables.
Regression analysis: Study the impact of independent variables on the dependent variable.
Frequency tables: To understand the frequency of data.
Analysis of variance: To test the degree to which two or more variables differ in an experiment.

Qualitative research involves an inductive method for data analysis where hypotheses are developed after data collection. The methods include:

Content analysis: For analyzing documented information from text and images by determining the presence of certain words or concepts in texts.
Narrative analysis: For analyzing content obtained from sources such as interviews, field observations, and surveys. The stories and opinions shared by people are used to answer research questions.
Discourse analysis: For analyzing interactions with people considering the social context, that is, the lifestyle and environment, under which the interaction occurs.
Grounded theory: Involves hypothesis creation by data collection and analysis to explain why a phenomenon occurred.
Thematic analysis: To identify important themes or patterns in data and use these to address an issue.

How to choose a research methodology?

Here are some important factors to consider when choosing a research methodology: 8

Research objectives, aims, and questions —these would help structure the research design.
Review existing literature to identify any gaps in knowledge.
Check the statistical requirements —if data-driven or statistical results are needed then quantitative research is the best. If the research questions can be answered based on people’s opinions and perceptions, then qualitative research is most suitable.
Sample size —sample size can often determine the feasibility of a research methodology. For a large sample, less effort- and time-intensive methods are appropriate.
Constraints —constraints of time, geography, and resources can help define the appropriate methodology.

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How to write a research methodology .

A research methodology should include the following components: 3,9

Research design —should be selected based on the research question and the data required. Common research designs include experimental, quasi-experimental, correlational, descriptive, and exploratory.
Research method —this can be quantitative, qualitative, or mixed-method.
Reason for selecting a specific methodology —explain why this methodology is the most suitable to answer your research problem.
Research instruments —explain the research instruments you plan to use, mainly referring to the data collection methods such as interviews, surveys, etc. Here as well, a reason should be mentioned for selecting the particular instrument.
Sampling —this involves selecting a representative subset of the population being studied.
Data collection —involves gathering data using several data collection methods, such as surveys, interviews, etc.
Data analysis —describe the data analysis methods you will use once you’ve collected the data.
Research limitations —mention any limitations you foresee while conducting your research.
Validity and reliability —validity helps identify the accuracy and truthfulness of the findings; reliability refers to the consistency and stability of the results over time and across different conditions.
Ethical considerations —research should be conducted ethically. The considerations include obtaining consent from participants, maintaining confidentiality, and addressing conflicts of interest.

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Frequently Asked Questions

Q1. What are the key components of research methodology?

A1. A good research methodology has the following key components:

Research design
Data collection procedures
Data analysis methods
Ethical considerations

Q2. Why is ethical consideration important in research methodology?

A2. Ethical consideration is important in research methodology to ensure the readers of the reliability and validity of the study. Researchers must clearly mention the ethical norms and standards followed during the conduct of the research and also mention if the research has been cleared by any institutional board. The following 10 points are the important principles related to ethical considerations: 10

Participants should not be subjected to harm.
Respect for the dignity of participants should be prioritized.
Full consent should be obtained from participants before the study.
Participants’ privacy should be ensured.
Confidentiality of the research data should be ensured.
Anonymity of individuals and organizations participating in the research should be maintained.
The aims and objectives of the research should not be exaggerated.
Affiliations, sources of funding, and any possible conflicts of interest should be declared.
Communication in relation to the research should be honest and transparent.
Misleading information and biased representation of primary data findings should be avoided.

Q3. What is the difference between methodology and method?

A3. Research methodology is different from a research method, although both terms are often confused. Research methods are the tools used to gather data, while the research methodology provides a framework for how research is planned, conducted, and analyzed. The latter guides researchers in making decisions about the most appropriate methods for their research. Research methods refer to the specific techniques, procedures, and tools used by researchers to collect, analyze, and interpret data, for instance surveys, questionnaires, interviews, etc.

Research methodology is, thus, an integral part of a research study. It helps ensure that you stay on track to meet your research objectives and answer your research questions using the most appropriate data collection and analysis tools based on your research design.

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Research methodologies. Pfeiffer Library website. Accessed August 15, 2023. https://library.tiffin.edu/researchmethodologies/whatareresearchmethodologies
Types of research methodology. Eduvoice website. Accessed August 16, 2023. https://eduvoice.in/types-research-methodology/
The basics of research methodology: A key to quality research. Voxco. Accessed August 16, 2023. https://www.voxco.com/blog/what-is-research-methodology/
Sampling methods: Types with examples. QuestionPro website. Accessed August 16, 2023. https://www.questionpro.com/blog/types-of-sampling-for-social-research/
What is qualitative research? Methods, types, approaches, examples. Researcher.Life blog. Accessed August 15, 2023. https://researcher.life/blog/article/what-is-qualitative-research-methods-types-examples/
What is quantitative research? Definition, methods, types, and examples. Researcher.Life blog. Accessed August 15, 2023. https://researcher.life/blog/article/what-is-quantitative-research-types-and-examples/
Data analysis in research: Types & methods. QuestionPro website. Accessed August 16, 2023. https://www.questionpro.com/blog/data-analysis-in-research/#Data_analysis_in_qualitative_research
Factors to consider while choosing the right research methodology. PhD Monster website. Accessed August 17, 2023. https://www.phdmonster.com/factors-to-consider-while-choosing-the-right-research-methodology/
What is research methodology? Research and writing guides. Accessed August 14, 2023. https://paperpile.com/g/what-is-research-methodology/
Ethical considerations. Business research methodology website. Accessed August 17, 2023. https://research-methodology.net/research-methodology/ethical-considerations/

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Language and Grammar Rules for Academic Writing

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Definitions

ORI Introduction to RCR: Chapter 3. The Protection of Human Subjects

Researchers are responsible for obtaining appropriate approval before conducting research involving human subjects. The need for approval rests on three seemingly obvious but not always easy-to-interpret considerations: 1) whether the work qualifies as research, 2) whether it involves human subjects, and 3) whether it is exempt. All three considerations are discussed in the Common Rule and guide decision making about the use of human subjects in research. The authority to make decisions about the need for approval rests with the Institutional Review Board (IRB, discussed below) or other appropriate institutional officials.

Research. The Common Rule defines research as “systematic investigation, including research development, testing and evaluation, designed to develop or contribute to generalizable knowledge” (§ 46.102(d), see box, next page, for full definition). This means that a project or study is research if it:

is conducted with the intention of drawing conclusions that have some general applicability and
uses a commonly accepted scientific method.

The random collection of information about individuals that has no general applicability is not research. Scientific investigation that leads to generalizable knowledge is.

Human subjects. Human subjects are “living individual(s) about whom an investigator conducting research obtains: (1) data through intervention or interaction with the individual; or (2) identifiable private information” (§ 46.102(f), see box, next page, for full definition). Humans are considered subjects and covered by Federal regulations if the researcher:

interacts or intervenes directly with them, or
collects identifiable private information.

If one of these two conditions applies and if the project or study qualifies as research, then institutional approval is needed before any work is undertaken.

Exempt research. Some studies that involve humans may be exempt from the requirements in the Federal regulations. Studies that fall into the following categories could qualify for exemptions, including:

research conducted in established or commonly accepted educational settings;
research involving the use of educational tests;
research involving the collection or study of existing data, documents, records, pathological specimens, or diagnostic specimens, if unidentifiable or publicly available;
research and demonstration projects which are conducted by or subject to the approval of department or agency heads; or
taste and food quality evaluation and consumer acceptance studies.

It is critically important to note, however, that decisions about whether studies are exempt from the requirements of the Common Rule must be made by an IRB or an appropriate institutional official and not by the investigator.

45 CFR 46.102

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You’re on a business trip in Oakland, CA. You've been working late in downtown and now you're looking for a place nearby to grab a late dinner. You decided to check Zomato to try and find somewhere to eat. (Don't begin searching yet).

Look around on the home page. Does anything seem interesting to you?
How would you go about finding a place to eat near you in Downtown Oakland? You want something kind of quick, open late, not too expensive, and with a good rating.
What do the reviews say about the restaurant you've chosen?
What was the most important factor for you in choosing this spot?
You're currently close to the 19th St Bart station, and it's 9PM. How would you get to this restaurant? Do you think you'll be able to make it before closing time?
Your friend recommended you to check out a place called Belly while you're in Oakland. Try to find where it is, when it's open, and what kind of food options they have.
Now go to any restaurant's page and try to leave a review (don't actually submit it).

What was the worst thing about your experience?

It was hard to find the bart station. The collections not being able to be sorted was a bit of a bummer

What other aspects of the experience could be improved?

Feedback from the owners would be nice

What did you like about the website?

The flow was good, lots of bright photos

What other comments do you have for the owner of the website?

I like that you can sort by what you are looking for and i like the idea of collections

You're going on a vacation to Italy next month, and you want to learn some basic Italian for getting around while there. You decided to try Duolingo.

Please begin by downloading the app to your device.
Choose Italian and get started with the first lesson (stop once you reach the first question).
Now go all the way through the rest of the first lesson, describing your thoughts as you go.
Get your profile set up, then view your account page. What information and options are there? Do you feel that these are useful? Why or why not?
After a week in Italy, you're going to spend a few days in Austria. How would you take German lessons on Duolingo?
What other languages does the app offer? Do any of them interest you?

I felt like there could have been a little more of an instructional component to the lesson.

It would be cool if there were some feature that could allow two learners studying the same language to take lessons together. I imagine that their screens would be synced and they could go through lessons together and chat along the way.

Overall, the app was very intuitive to use and visually appealing. I also liked the option to connect with others.

Overall, the app seemed very helpful and easy to use. I feel like it makes learning a new language fun and almost like a game. It would be nice, however, if it contained more of an instructional portion.

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What is Qualitative Research Design? Definition, Types, Examples and Best Practices

What is Qualitative Research Design?

Qualitative research design is defined as a systematic and flexible approach to conducting research that focuses on understanding and interpreting the complexity of human phenomena.

Unlike quantitative research, which seeks to measure and quantify variables, qualitative research is concerned with exploring the underlying meanings, patterns, and perspectives that shape individuals’ experiences and behaviors. This type of research design is particularly useful when studying social and cultural phenomena, as it allows researchers to delve deeply into the context and nuances of a particular subject.

In qualitative research, data is often collected through methods such as interviews, focus groups, participant observation, and document analysis. These methods aim to gather rich, detailed information that can provide insights into the subjective experiences of individuals or groups.

Researchers employing qualitative design are often interested in exploring social processes, cultural norms, and the lived experiences of participants. The emphasis is on understanding the depth and context of the phenomena under investigation, rather than generating statistical generalizations.

One key characteristic of qualitative research design is its iterative nature. The research process is dynamic and may evolve as new insights emerge. Researchers continually engage with the data, refining their questions and methods based on ongoing analysis.

This flexibility allows for a more organic and responsive exploration of the research topic, making it well-suited for complex and multifaceted inquiries.

Qualitative research design also involves careful consideration of ethical concerns, as researchers often work closely with participants to gather personal and sensitive information.

Establishing trust, maintaining confidentiality, and ensuring participants’ autonomy are critical aspects of ethical practice in qualitative research. In summary, qualitative research design is a holistic and interpretive approach that prioritizes understanding the intricacies of human experience, offering depth and context to our comprehension of social and cultural phenomena.

Key Characteristics of Qualitative Research Design

Qualitative research design is characterized by several key features that distinguish it from quantitative approaches. Here are some of the essential characteristics:

Open-ended Nature: Qualitative research is open-ended and flexible, allowing for the exploration of complex social phenomena without preconceived hypotheses. Researchers often start with broad questions and adapt their focus based on emerging insights.
Rich Descriptions: Qualitative research emphasizes rich and detailed descriptions of the subject under investigation. This depth helps capture the context, nuances, and subtleties of human experiences, behaviors, and social phenomena.
Subjective Understanding: Qualitative researchers acknowledge the role of the researcher in shaping the study. The subjective interpretations and perspectives of both researchers and participants are considered valuable for understanding the phenomena being studied.
Interpretive Approach: Rather than seeking universal laws or generalizations, qualitative research aims to interpret and make sense of the meanings and patterns inherent in the data. Interpretation is often context-dependent and involves understanding the social and cultural context in which the study takes place.
Non-probability Sampling: Qualitative studies typically use non-probability sampling methods, such as purposeful or snowball sampling, to select participants deliberately chosen for their relevance to the research question. Sample sizes are often small but information-rich, allowing for a deep understanding of the selected cases.
Inductive Reasoning: Qualitative data analysis is often inductive, meaning that it involves identifying patterns, themes, and categories that emerge from the data itself. Researchers let the data shape the analysis, rather than fitting it into preconceived categories.
Coding and Categorization: Researchers use coding techniques to systematically organize and categorize data. This involves assigning labels or codes to segments of data based on recurring themes or patterns.
Flexible Design: Qualitative research design is adaptable and allows for changes in research questions, methods, and strategies as the study progresses. This flexibility accommodates the evolving nature of the research process.
Iterative Nature: Researchers engage in an iterative process of data collection, analysis, and refinement. As new insights emerge, researchers may revisit previous stages of the research, leading to a deeper and more nuanced understanding of the subject.

By embracing these key characteristics, qualitative research design offers a holistic and contextualized approach to studying the complexities of human behavior, culture, and social phenomena.

Key Components of Qualitative Research Design

Qualitative research design involves several key components that shape the overall framework and methodology of the study. These components help guide researchers in conducting in-depth investigations into the complexities of human experiences, behaviors, and social phenomena. Here are the key components of qualitative research design:

Central Inquiry: Qualitative research begins with a well-defined central research question or objective. This question guides the entire study and determines the focus of data collection and analysis. The question is often broad and open-ended to allow for exploration and discovery.
Rationale: Researchers provide a clear rationale for why the study is being conducted, outlining its significance and relevance. This may involve identifying gaps in existing literature, addressing practical problems, or contributing to theoretical debates.
Theoretical Framework: Qualitative studies often draw on existing theories or conceptual frameworks to guide their inquiry. The theoretical lens helps shape the research design and provides a basis for interpreting findings.
Study Design: Researchers decide on the overall approach to the study, whether it’s a case study, ethnography, grounded theory, phenomenology, or another qualitative design. The choice depends on the research question and the nature of the phenomenon under investigation.
Sampling Strategy: Qualitative research employs purposeful or theoretical sampling to select participants who can provide rich and relevant information related to the research question. Sampling decisions are made to ensure diversity and depth in the data.
Interviews: In-depth interviews are a common method in qualitative research. These interviews are typically semi-structured, allowing for flexibility while ensuring key topics are covered.
Observation: Researchers may engage in direct observation of participants in natural settings. This can involve participant observation, where the researcher becomes part of the environment, or non-participant observation, where the researcher remains separate.
Document Analysis: Researchers analyze existing documents, artifacts, or texts relevant to the study, such as diaries, letters, organizational records, or media content.
Thematic Analysis: Researchers identify and analyze recurring themes or patterns in the data. This involves coding and categorizing data to uncover underlying meanings and concepts.
Constant Comparative Analysis: Common in grounded theory, this method involves comparing data as it is collected, allowing researchers to refine categories and theories iteratively.
Narrative Analysis: Focuses on the stories people tell, examining the structure and content of narratives to understand the meaning-making process.
Informed Consent: Researchers obtain informed consent from participants, explaining the purpose of the study, potential risks, and ensuring participants have the right to withdraw at any time.
Confidentiality and Anonymity: Researchers take measures to protect the privacy of participants by ensuring that their identities and personal information are kept confidential or anonymized.
Credibility: Establishing credibility involves demonstrating that the study accurately represents participants’ perspectives. Techniques such as member checking, peer debriefing, and prolonged engagement contribute to credibility.
Transferability: Researchers aim to make the study findings applicable to similar contexts. Detailed descriptions and thick descriptions enhance the transferability of qualitative research.
Dependability and Confirmability: Ensuring dependability involves maintaining consistency in data collection and analysis, while confirmability ensures that findings are rooted in the data rather than researcher bias.
Reflexivity: Researchers acknowledge their role in shaping the study and consider how their background, experiences, and biases may influence the research process and interpretation of findings. Reflexivity enhances transparency and the researcher’s self-awareness.

By carefully considering and integrating these key components, qualitative researchers can design studies that yield rich, contextually grounded insights into the social phenomena they aim to explore.

Types of Qualitative Research Design

Qualitative research design encompasses various approaches, each suited to different research questions and objectives. Here are some common types of qualitative research designs:

Focus: Ethnography involves immersing the researcher in the natural environment of the participants to observe and understand their behaviors, practices, and cultural context.
Data Collection: Researchers often use participant observation, interviews, and document analysis to gather data.
Example: An anthropologist immersed in a remote tribe might live with the community for an extended period, participating in their daily activities, conducting interviews, and documenting observations. By doing so, the researcher gains a deep understanding of the tribe’s cultural practices, social relationships, and the significance of rituals in their way of life.
Focus: Phenomenology explores the lived experiences of individuals to uncover the essence of a phenomenon.
Data Collection: In-depth interviews and sometimes participant observation are common methods.
Purpose: It seeks to understand the subjective meaning individuals attribute to an experience.
In a study on the lived experiences of cancer survivors, researchers might conduct in-depth interviews to explore the subjective meaning individuals attach to their diagnosis, treatment, and recovery. Phenomenology seeks to uncover the essence of these experiences, capturing the emotional, psychological, and social dimensions that shape survivors’ perspectives on their journey through cancer.
Focus: Grounded theory aims to develop a theory grounded in the data, allowing patterns and concepts to emerge organically.
Data Collection: It involves constant comparative analysis of interviews or observations, with coding and categorization.
Purpose: This approach is used when researchers want to generate theories or concepts based on the data itself.
Research on retirement transitions using grounded theory might involve interviewing retirees from various backgrounds. Through constant comparison and iterative analysis, researchers may identify emerging themes and categories, ultimately developing a theory that explains the commonalities and variations in retirees’ experiences as they navigate this life stage.
Focus: Case studies delve deeply into a specific case or context to understand it in detail.
Data Collection: Multiple sources of data, such as interviews, observations, and documents, are used to provide a comprehensive view.
Purpose: Case studies are useful for exploring complex phenomena within their real-life context.
A case study on a company’s crisis response could involve a detailed examination of communication strategies, decision-making processes, and the organizational dynamics during a specific crisis. By analyzing the case in-depth, researchers gain insights into how the company’s actions and decisions influenced the outcome of the crisis and what lessons can be learned for future situations.
Focus: Narrative research examines the stories people tell to understand how individuals construct meaning and identity.
Data Collection: It involves collecting and analyzing narratives through interviews, personal accounts, or written documents.
Purpose: Narrative research is often used to explore personal or cultural stories and their implications.
Examining the life stories of refugees may involve collecting and analyzing personal narratives through interviews or written accounts. Researchers explore how displacement has shaped the refugees’ identities, relationships, and perceptions of home, providing a nuanced understanding of their experiences through the lens of storytelling.
Focus: Action research involves collaboration between researchers and participants to identify and solve practical problems.
Data Collection: Researchers collect data through cycles of planning, acting, observing, and reflecting.
Purpose: It is geared towards facilitating positive change in a particular context or community.
In an educational setting, action research might involve teachers and researchers collaborating to address a specific classroom challenge. Through cycles of planning, implementing interventions, and reflecting, the aim is to improve teaching practices and student learning outcomes, with the findings contributing to both practical solutions and the broader understanding of effective pedagogy.
Focus: Content analysis examines the content of written, visual, or audio materials to identify patterns or themes.
Data Collection: Researchers systematically analyze texts, images, or media content using coding and categorization.
Purpose: It is often used to study communication, media, or cultural artifacts.
A content analysis of news articles covering a specific social issue, such as climate change, could involve systematically coding and categorizing language and themes. This approach allows researchers to identify patterns in media discourse, explore public perceptions, and understand how the issue is framed in the media.
Focus: Critical ethnography combines ethnographic methods with a critical perspective to examine power structures and social inequalities.
Data Collection: Researchers engage in participant observation, interviews, and document analysis with a focus on social justice issues.
Purpose: This approach aims to explore and challenge existing power dynamics and social structures.
A critical ethnography examining gender dynamics in a workplace might involve observing daily interactions, conducting interviews, and analyzing policies. Researchers, guided by a critical perspective, aim to uncover power imbalances, stereotypes, and systemic inequalities within the organizational culture, contributing to a deeper understanding of gender dynamics in the workplace.
Focus: Similar to grounded theory, constructivist grounded theory acknowledges the role of the researcher in shaping interpretations.
Data Collection: It involves a flexible approach to data collection, including interviews, observations, or documents.
Purpose: This approach recognizes the co-construction of meaning between researchers and participants.
In a study on the experiences of individuals with chronic illness, researchers employing constructivist grounded theory might engage in open-ended interviews and data collection. The focus is on co-constructing meanings with participants, acknowledging the dynamic relationship between the researcher and those being studied, ultimately leading to a theory that reflects the collaborative nature of knowledge creation.

These qualitative research designs offer diverse methods for exploring and understanding the complexities of human experiences, behaviors, and social phenomena. The choice of design depends on the research question, the context of the study, and the desired depth of understanding.

Best practices for Qualitative Research Design

Qualitative research design requires careful planning and execution to ensure the credibility, reliability, and richness of the findings. Here are some best practices to consider when designing qualitative research:

Clearly articulate the research questions or objectives to guide the study. Ensure they are specific, open-ended, and aligned with the qualitative research approach.
Select a qualitative research design that aligns with the research questions and objectives. Consider approaches such as ethnography, phenomenology, grounded theory, or case study based on the nature of the study.
Conduct a comprehensive literature review to understand existing theories, concepts, and research related to the study. This helps situate the research within the broader scholarly context.
Use purposeful or theoretical sampling to select participants who can provide rich information related to the research questions. Aim for diversity in participants to capture a range of perspectives.
Clearly outline the data collection methods, such as interviews, observations, or document analysis. Develop detailed protocols, guides, or questionnaires to maintain consistency across data collection sessions.
Prioritize building trust and rapport with participants. Clearly communicate the study’s purpose, obtain informed consent, and establish a comfortable environment for open and honest discussions.
Adhere to ethical guidelines throughout the research process. Protect participant confidentiality, respect their autonomy, and obtain ethical approval from relevant review boards.
Pilot the data collection instruments and procedures with a small sample to identify and address any ambiguities, refine questions, and enhance the overall quality of data collection.
Use a systematic approach to analyze data, such as thematic analysis, constant comparison, or narrative analysis. Maintain transparency in the coding process, and consider inter-coder reliability if multiple researchers are involved.
Acknowledge and document the researcher’s background, biases, and perspectives. Practice reflexivity by continually reflecting on how the researcher’s positionality may influence the study.
Enhance the credibility of findings by using multiple data sources and methods. Triangulation helps validate results and provides a more comprehensive understanding of the research topic.
Consider member checking, where researchers share preliminary findings with participants to validate interpretations. This process enhances the credibility and trustworthiness of the study.
Keep a detailed journal documenting decisions, reflections, and insights throughout the research process. This journal helps provide transparency and can contribute to the rigor of the study.
Aim for data saturation, the point at which new data no longer provide additional insights. Saturation ensures thorough exploration of the research questions and increases the robustness of the findings.
Clearly document the research process, from design to findings. Provide a detailed and transparent account of the study methodology, facilitating the reproducibility and evaluation of the research.

By incorporating these best practices, qualitative researchers can enhance the rigor, credibility, and relevance of their studies, ultimately contributing valuable insights to the field.

Interested in learning more about the fields of product, research, and design? Search our articles here for helpful information spanning a wide range of topics!

Usability Testing Questions for Improving User’s Experience

14 best performance testing tools for application reliability, a complete guide to usability testing methods for better ux, ux mapping methods and how to create effective maps.

Nursing Research (NURS 3321/4325/5366)

Introduction
Understand What Quantitative Research Is
Understand What Qualitative Research Is
Sage Methods Map
Step 1: Accessing CINAHL
Step 2: Create a Keyword Search
Step 3: Create a Subject Heading Search
Step 4: Repeat Steps 1-3 for Second Concept
Step 5: Repeat Steps 1-3 for Quantitative Terms
Step 6: Combining All Searches
Step 7: Adding Limiters
Step 8: Save Your Search!
What Kind of Article is This?
PICO Keyword Search Strategy
PICO Keyword Search
PICO Subject Heading Search
Combining Keyword and Subject Heading Searches
Adding Filters/Limiters
Finding Health Statistics
Find Clinical Guidelines This link opens in a new window
APA Format & Citations This link opens in a new window

What is Quantitative Research?

Quantitative methodology is the dominant research framework in the social sciences. it refers to a set of strategies, techniques and assumptions used to study psychological, social and economic processes through the exploration of numeric patterns . quantitative research gathers a range of numeric data. some of the numeric data is intrinsically quantitative (e.g. personal income), while in other cases the numeric structure is imposed (e.g. ‘on a scale from 1 to 10, how depressed did you feel last week’). the collection of quantitative information allows researchers to conduct simple to extremely sophisticated statistical analyses that aggregate the data (e.g. averages, percentages), show relationships among the data (e.g. ‘students with lower grade point averages tend to score lower on a depression scale’) or compare across aggregated data (e.g. the usa has a higher gross domestic product than spain). quantitative research includes methodologies such as questionnaires, structured observations or experiments and stands in contrast to qualitative research. qualitative research involves the collection and analysis of narratives and/or open-ended observations through methodologies such as interviews, focus groups or ethnographies..

Coghlan, D., Brydon-Miller, M. (2014). The SAGE encyclopedia of action research (Vols. 1-2). London, : SAGE Publications Ltd doi: 10.4135/9781446294406

What is the purpose of quantitative research?

The purpose of quantitative research is to generate knowledge and create understanding about the social world. Quantitative research is used by social scientists, including communication researchers, to observe phenomena or occurrences affecting individuals. Social scientists are concerned with the study of people. Quantitative research is a way to learn about a particular group of people, known as a sample population. Using scientific inquiry, quantitative research relies on data that are observed or measured to examine questions about the sample population.

Allen, M. (2017). The SAGE encyclopedia of communication research methods (Vols. 1-4). Thousand Oaks, CA: SAGE Publications, Inc doi: 10.4135/9781483381411

How do I know if the study is a quantitative design? What type of quantitative study is it?

Quantitative Research Designs: Descriptive non-experimental, Quasi-experimental or Experimental?

Studies do not always explicitly state what kind of research design is being used. You will need to know how to decipher which design type is used. The following video will help you determine the quantitative design type.

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2024 Voices of Gen Z Study

Three young individuals stroll along a sidewalk, enjoying their time together in a lively urban setting.

Gen Z feels optimistic about the future. How do we best prepare them for it?

The Walton Family Foundation and Gallup’s new 2024 Voices of Gen Z Study builds on one of the largest and most comprehensive national research surveys tracking the sentiments and attitudes of Gen Z. The latest research focuses on Gen Z’s views about themselves, their schools, and their future prospects.

This year’s findings show that just over half (51%) of Gen Z believe they are thriving in their lives.

The survey reveals a generation deeply committed to meaningful engagement in both work and school, with a growing desire to contribute to their communities and pursue their passions. They are driven by a quest for meaningful relationships, a strong sense of community, and a purposeful life.

Key areas for improvement identified by Gen Z include increasing hands-on learning opportunities, expanding mental health support, and providing greater exposure to diverse post-secondary pathways and practical skills.

While Gen Z remains optimistic about their future, too many feel underprepared for college and careers. They want an education that provides real-world experiences. Mental health—which is closely related to their overall life satisfaction and future outlook—also continues to pose a significant challenge for many Gen Zers. Only 21% report having “excellent” mental health, while more say their mental health is only fair (24%) or poor (7%).

Explore the latest findings to learn how we can better support Gen Z in achieving their goals and unlocking their full potential.

The vast majority of gen zers believe they have a great future ahead of them., however, they are less confident in their preparedness for the future. .

Only half of Gen Z feel prepared for the future. Students whose school offers opportunities to learn skills relevant to a job they want or how to interview are more likely to feel prepared to succeed in their future careers.

Despite that, only 35% of K-12 students feel they are learning skills relevant to their future careers and just 23% have opportunities to work on projects related to jobs they want.

Beyond feeling unprepared for their future careers, many Gen Zers are also worried about their ability to manage their “adult” lives. Less than half of Gen Z think they will be prepared to manage their money and finances (46%) or buy a home (31%).

Harnessing Gen Z’s optimism requires targeted improvements that prepare them for future success. By creating educational programs that support Gen Z in connecting to mentors in their community, developing career-relevant skills and building readiness for their adult lives, we can better equip Gen Z to succeed.

Fostering engagement in school and work is critical to ensuring Gen Z remains optimistic about the future.

Gen Z is looking for supportive relationships with their teachers and a curriculum that interests them.

Engagement in school continues to be a key predictor of Gen Z’s future outlook and life satisfaction. Students who find their subjects interesting and challenging report higher engagement and a more positive perspective on their lives. However, recent data shows a decline in this type of engagement:

This is a 10% drop from 2023.

This is an 8% drop from last year.

The relationships between students and their teachers play a significant role in boosting engagement. Over 70% of Gen Z identify their best teachers as those who care about them as individuals, while 63% feel most excited about learning when their teachers are passionate about their subjects.

Ultimately, focusing on developing meaningful relationships with mentors and role models and creating a strong sense of belonging and community in the classroom is key to improving Gen Z’s engagement and overall well-being.

Hands-on learning can help students bridge the gap between school and career.

Connecting classroom learning to practical experiences is essential..

The key to truly understanding concepts is applying them to real life. Hands-on learning and real-world applications not only prepare Gen Z students for college and the workforce but also empower them to make meaningful contributions to their communities.

Hands-on activities include simulations, demonstrations, and experiments that make learning interactive.

Schools can increase engagement with curriculum that is relevant to the real world and the lives of students.

Despite this, only 35% of K-12 students feel they are acquiring skills relevant to their future careers. Investing in educational programs that focus on practical skills and career readiness can significantly enhance student engagement and ensure a smoother transition from K-12 to the next stages of their lives.

College remains the primary post-graduation pathway for Gen Z.

A lack of exposure is keeping students from exploring pathways outside of college..

While college remains a popular path, many Gen Zers feel unprepared to succeed in college. Others are interested in exploring other paths that align with their passions and goals.

Two-thirds of Gen Z report frequent discussions with adults about attending college.

Just 18% of students are aware of opportunities like apprenticeships and vocational training.

Among Gen Z adults, only one in four feel very prepared to succeed in their careers.

Furthermore, many Gen Z students have limited knowledge of non-college pathways. Fewer than one in four high schoolers has had many conversations about non-college pathways, such as apprenticeships and internships. Raising awareness about non-college pathways can help more students discover meaningful career options that match their interests and skills.

Gen Z is reimagining what success in life looks like.

Their definition of success is largely driven by the quality of their relationships and a sense of purpose. .

Gen Z defines a “great life” as one where they are happy, can live comfortably and have close relationships with their friends and families.

Wealth, buying a house, and having children are far less important for a great life.

66% of Gen Z feel a sense of connection with something larger than themselves.

In terms of their careers, Gen Z values jobs that allow them to live comfortably (77%) and pursue their passions (70%) over becoming wealthy (31%) or managers (12%). Combined, the findings indicate a desire for balance between reaching financial stability and maintaining personal fulfillment and strong relationships.

We can better support Gen Zers to lead happy and fulfilling lives by helping them to connect with one another, and discover and develop their purpose and passions.

Latest Resources

Gen Zers to Their Parents: When We Are Upset, Just Listen

New research developed in partnership with psychologist Dr. Lisa Damour reveals that most young people experience happiness on a daily basis, but many also feel stress, anxiety and sadness. According to the findings, most Gen Z youth want space and a listening ear when experiencing intense emotions.

Two students talking together in the library with two more students working with each other behind them.

Report Card 2024: New Perspectives from Students on U.S. Schools

The Walton Family Foundation partnered with Gallup to survey students across the country for the second year in a row. According to students, schools have room to grow in areas like making learning exciting for students and preparing them for their future careers.

High School Teacher explaining electronics circuits To Pupils learning In Technology course

Pilot AI Tool Generates Math Hints to Help Students Solve Problems

A new AI Pilot tool, The Math Hint Generator Chatbot demo, serves as a virtual tutor to help kids “get unstuck.”

Help & FAQ

Developing a consensus-based definition of out-of-hospital clinical deterioration: A Delphi study

Department of Paramedicine
Epidemiology and Preventive Medicine Alfred Hospital

Research output : Contribution to journal › Article › Research › peer-review

Background: Clinical deterioration is a time-critical medical emergency requiring rapid recognition and intervention. Deteriorating patients are seen across various healthcare settings, including the out-of-hospital (OOH) environment. OOH care is an evolving area of medicine where decisions are made regarding priority and timing of clinical interventions, ongoing management, and transport to appropriate care. To date, the literature lacks a standardised definition of OOH clinical deterioration. Objective: The objective of this study was to create a consensus-based definition of OOH clinical deterioration informed by emergency medicine health professionals. Methods: A Delphi study consisting three rounds was conducted electronically between June 2020 and January 2021. The expert panel consisted of 30 clinicians, including emergency physicians and paramedics. Results: A consensus-based definition of OOH clinical deterioration was identified as changes from a patient's baseline physiological status resulting in their condition worsening. These changes primarily take the form of measurable vital signs and assessable symptoms but should be evaluated in conjunction with the history of events and pertinent risk factors. Clinicians should be suspicious that a patient could deteriorate when changes occur in one or more of the following vital signs: respiratory rate, heart rate, blood pressure, Glasgow Coma Scale, oxygen saturation, electrocardiogram, and skin colour. Almost all participants (92%) indicated an early warning system would be helpful to assist timely recognition of deteriorating patients. Conclusion: The creation of a consensus-based definition of OOH clinical deterioration can serve as a starting point for the development and validation of OOH-specific early warning systems. Moreover, a standardised definition allows meaningful comparisons to be made across health services and ensures consistency in future research. This study has shown recognition of OOH clinical deterioration to be a complex issue requiring further research. Improving our understanding of key factors contributing to deterioration can assist timely recognition and intervention, potentially reducing unnecessary morbidity and mortality.

Original language	English
Pages (from-to)	318-325
Number of pages	8
Journal
Volume	37
Issue number	2
DOIs
Publication status	Published - Mar 2024

Clinical deterioration
Deteriorating patient
Out-of-hospital
Prehospital

Access to Document

10.1016/j.aucc.2023.05.008 Licence: CC BY-NC-ND

Defining and Identifying Members of a Research Study Population: CTSA-Affiliated Faculty Members

Jonathan d. eldredge.

1 Biomedical Informatics Research, Training, and Scholarship Unit, Health Sciences Library and Informatics Center, University of New Mexico, Albuquerque, NM 87131-0001

Edward F. Weagel

2 Clinical and Translational Science Center, University of New Mexico, Albuquerque, NM 87131-0001

Philip J. Kroth

An early step in research projects that involve humans consists of composing a clear and detailed definition of the study population. All experimental, observational, and qualitative research designs involving human subjects should define the study population in order to determine the eligibility of individuals for a study. The defined population then will become the basis for applying the research results to other relevant populations. Clearly defining a study population early in the research process also helps assure the overall validity of the study results.

Many research reports fail to define or describe a study population adequately. The Consolidated Standards of Reporting Trials (CONSORT) represent perhaps the most rigorous set of standards for reporting research results in a transparent and uniform manner. The CONSORT Guidelines observe that, “Despite their importance, eligibility criteria are often not reported adequately” [ 1 ]. Defining the study population in a research project involves inductive reasoning, critical thinking, and pragmatic project management skills. Yet, few research methods books or articles explicitly address the many issues related to rigorously defining and identifying members of study populations.

This research methods article provides guidance on defining a study population. The authors hope to fill a gap in the literature by describing the particular challenges of defining a study population of faculty members affiliated with a translational sciences center at a major health sciences center in the US. The National Institutes of Health’s (NIH) Clinical & Translational Sciences Award (CTSA) program funds (NIH Award # UL1TR000041) the University of New Mexico’s Clinical and Translational Sciences Center (CTSC). The authors briefly describe their methodology and results. They include examples from their own research, as well as from other research studies involving a variety of study populations, to illustrate key points. Finally, the authors reflect on their “lessons learned” from their experiences with this research project.

During April through July 2012, the authors sought to compile a complete and accurate list of all faculty members currently employed by the University of New Mexico with a formal affiliation with its CTSA-funded CTSC. All faculty members eligible for the study had an “affiliated” role with the CTSC while maintaining a formal appointed role(s), primarily with individual academic departments such as Advanced Nurse Practice, Biochemistry, Radiopharmaceutical Sciences, or Pediatrics. There were no formal or uniformly standardized faculty roles within the CTSC, simply an undefined “affiliated” status.

Inclusion and Exclusion Criteria

The CTSC maintained four lists used primarily for email notifications to individuals to keep them informed about various CTSC activities. None of these lists met the requirements for the project due to a lack of accuracy or completeness. One “Publicity” list used for distributing general public affairs announcements included individuals both affiliated and unaffiliated with the university. The affiliated individuals included non-faculty members as well as faculty members. A second “Teaching” list for faculty members who taught in CTSC-sponsored courses was both accurate and current, but it excluded many faculty members with non-teaching roles, such as clinicians or investigators. A third “Funded” list included only individuals who had received research funds, primarily pilot grants, directly from the CTSC. This third list of researchers excluded many others with formal CTSC teaching or administrative roles. The third list also included faculty members no longer employed by the university. A fourth “Administrative” list included mostly staff, although it contained some individual faculty members with administrative roles whose names did not necessarily appear on the other three lists.

The authors followed several iterative steps to compile a complete and accurate list by drawing selectively from all of the aforementioned lists with the goal of identifying faculty members associated with the CTSC. The inductive definition and identification phases of the study reported here consisted of dual, interactive processes.

The target population needed to consist of currently employed tenured or tenure track full-time faculty members at the University of New Mexico who fulfilled at least one of the following qualifications:

Receiving funds from the CTSC for research projects
Teaching in the CTSC-sponsored graduate curricula
Holding administrative positions within the CTSC

For each successive phase of the inductive analytic process the authors more precisely defined their study population of currently employed CTSC-affiliated university faculty members. The authors verified current faculty employment status with the health sciences center’s contracts offices in the appropriate school or administrative units, such as the School of Medicine, College of Nursing, or College of Pharmacy. The authors also verified the current employment status of potentially eligible university faculty members outside the health sciences center, but still within the same institution’s departments, such as Psychology and Sociology.

The final list excluded all formerly affiliated faculty who had left the university, past or present staff members, part-time or adjunct faculty members, research faculty members no longer funded by the CTSC, and others appearing on the various lists who, for a number of reasons, did not meet the inclusion criteria.

Affiliated Master List

The list of “Teaching” faculty members affiliated with the CTSC provided the most accurate and complete account, so it became the starting point for this target population-identification phase.

Truth tables [ 2 – 4 ] usually are used for disentangling more complex relationships or potentially causal variables, but they seemed well suited for this more fundamental purpose of identifying a target population. The truth table depicted in Table 1 gauged the strengths and weaknesses of the four different existing CTSC lists against the emerging definition of what constituted a member of the new fifth “Affiliated” (shaded far right-hand column on Table 1 ) study population list. The three authors had 17 combined years of experience in working with the CTSC. The first and third authors were among the founding leaders who designed the graduate CTSC-sponsored curricula. The second author has held a senior administrative informatics post in the CTSC for the past three years. These roles helped the authors evaluate the accuracy and completeness of these CTSC lists.

Truth Table for Identifying CTSA-Affiliated Faculty Members

	Publicity	Teaching	Funded	Administrative	Affiliated

Current employment only	N	Y	N	Y	Y
Tenured or tenure track only	N	Y	N	N	Y
Full-time employment only	N	N	N	Y	Y
University affiliation only	N	Y	Y	Y	Y
All study target population members	N	N	N	N	Y

The “Affiliated” list emerged from the process described in this methods article. All other lists on this table already existed and were used for consultation in arriving at the “Affiliated” list in the far right-hand column.

The authors compiled a complete and accurate list of 108 CTSC-affiliated faculty members following several months of investigation using the process outlined above. The authors were highly confident that the list included all faculty members who belonged in the study population, while excluding all others. This “Affiliated” list was stratified by (1) clinical, (2) basic science, (3) pharmacy, or (4) nursing and other faculty members, before a random selection process began for the subsequent study process.

Inductive Processes

Plato and Aristotle, as well as many early physical scientists in more recent centuries such as Bacon and Mill, pioneered inductive approaches [ 5 ]. In its purest form, an inductivist approach begins “with a collection of data, empirical observations or measurements of some kind, and build[s] theoretical categories and propositions from [the] relationships discovered among the data” [ 6 ]. Pure inductivism tends to introduce biases, however [ 7 ]. More modern forms of inductivism have emerged in the form of techniques for developing clearly delineated categorizations and classifications that have high fidelity with describing logical groupings in the real world.

Induction contrasts with the other major form of logical reasoning known as deduction. Whereas induction assembles more and more specific observations in order to form a generalization, deduction begins with a generalization and then applies this generalization to specific instances [ 8 ]. Deduction assumes that the generalization of interest consists of an agreed-upon fact, principle, or theory [ 9 ].

Inductive approaches are more common than might be recognized immediately. While rarely noted explicitly, investigators often employ inductive approaches at different junctures of the research process. Investigators might use inductive processes, for example, to generate hypotheses or research questions. Investigators also apply inductive approaches to define study populations. Clinicians use inductive techniques when formulating patient diagnoses. Librarians and information science professionals, moreover, use inductive processes while engaged in classification processes, such as indexing or meta-tagging.

Analytic Induction

The analytic induction approach “involves an iterative testing and retesting of theoretical ideas using the data” [ 10 ]. Investigators use analytic induction to develop more clearly defined concepts or categories by examining individual cases against emerging concepts [ 11 ]. It seeks to remain faithful to empirical observations of a limited number of cases as any categorizations take shape, while ensuring that investigators avoid jumping to premature conclusions. It abstracts from some detailed observations and then, in a quasi-deductive way, it tests those abstractions against more and more cases. The abstractions are revised if they prove too inflexible or too narrow to incorporate any apparent deviant cases [ 12 ].

Analytic inductive approaches orient one unfamiliar with a new phenomenon to make sense out of it. In a way, analytic inductivism creates order out of disorder, or at least order amidst relative confusion. A zoologist, for example, upon encountering a new species, might initially categorize the animal by color. Eventually the zoologist finds this narrow color categorization scheme to be insufficient over successive cases to accurately describe additional examples of the new species, which are otherwise similar, except for the different colors. The zoologist instead shifts focus to the presence of vertebrae in the new species as a possibly more precise categorization system for uniquely defining the species [ 13 ].

Becker illustrated the similar use of analytic inductivism in the social sciences when he wrote: “Once we have isolated … a generic feature of some social relation or process and given it a name, and thus created a concept, we can look for the same phenomenon in places other than where we found it” [ 14 ]. Inductive analysis in a broader sense offers great flexibility in defining previously nebulous categories and classifications, whether in the physical sciences, in the social sciences, or in clinical settings [ 15 ]. Inductive analysis can only serve as a starting point that initiates further exploration to validate concepts or systems in the real world. Neither inductive nor deductive logic approaches alone will suffice in the research process [ 16 ]. Yet, inductivism offers the advantage of viewing even a familiar research problem from a fresh perspective [ 17 ].

In this study, the analytic inductive aspect of defining a study population relied on reacting to existing lists in order to more precisely define the final criteria for identifying all members of the study population. Questions arose about the current employment status of certain faculty members who had left the university when their names still appeared on a list of faculty members funded through the CTSC, to cite one example. As noted already, the simplified truth table displayed in Table 1 guided the dual definitional and identification processes.

The study population description needs to offer a clear definition as to who belongs to the study population and who should be excluded from the study population. The Uniform Requirements for Manuscripts Submitted to Biomedical Journals remind investigators to be explicit about the selection criteria for inclusion and exclusion that define a study population, adding that “The guiding principle should be clarity about how and why a study was done in a particular way” [ 18 ]. Some researchers recommend including a table that delineates inclusion criteria [ 19 ]. The left-hand column in Table 1 provides an example of such inclusion criteria.

Investigators should employ standardized, commonly understood definitions of groups whenever possible in developing a study population description. The population of affiliated faculty members described in this article adhered to common conceptions of faculty members. Imagine the potential complexity of defining a study population associated with more nebulous concepts. Comstock et al. reveal, in their analysis of multiple epidemiological studies, how different investigators employed their own operational definitions for the concepts of race and ethnicity, rather than standardized definitions of these concepts [ 20 ]. The net result of these multiple definitions undermined the utility of comparing the studies. Gobbens et al. similarly discovered that the commonly referenced phrase “medical frailty in the elderly” did not have a standard definition, despite some investigators’ misconceptions to the contrary [ 21 ]. The research community in such circumstances might need to convene a consensus conference to arrive at a commonly understood definition [ 22 ].

Investigators need to be alert to (and, if necessary, to correct for) the possibility that their inclusion and exclusion criteria reflect gender, racial, ethnic, economic, or information disparities biases [ 23 ]. The first author made such a mistake, years ago while conducting a study of rural health care providers. The author assumed that most rural inhabitants had access to the same high-quality Internet connections as urban residents. Not much later, the field informants for the study population fortunately corrected this misunderstanding [ 24 ]. Attitudes, behaviors, or skills within a study population might change over time in other circumstances. If that change itself serves as a focus of the study, this dynamic needs to be addressed in the research strategy. For example, if a research study wishes to gauge the effectiveness of library or informatics training on a defined population, but some members of the initial study population acquire the training via different venues, then the investigators need to identify and exclude these individuals from the study population.

Who applies the stated inclusion criteria to determine eligibility? The authors defined the study population and then recruited participants directly from that defined population in every study that they have conducted. One experiment involving first-year medical students used enrollment lists of students in good academic standing to define the study population [ 25 ]. What if a study recruits individuals by asking prospective participants to self-identify? In such circumstances, study population members’ own self-definitions might not align with investigators’ definitions. One study found that immigrant and refugee youth self-defined themselves in ways within the contexts of their own experiences [ 26 ]. Investigators must clearly define and promulgate their definition of a study population early in the study to avoid enrollment of ineligible individuals either later in the study or at other study sites [ 27 ].

Validity refers to the close proximity of a defined concept and what the investigators are measuring [ 28 ]. For the central example in this article, a poorly defined or recruited study population of putatively CTSC-affiliated faculty members that includes ineligible individuals such as staff members who are not engaged in the same types of roles will jeopardize the credibility of the study. The inadvertent inclusion of faculty not affiliated with the CTSC or retired faculty members would have similarly threatened the integrity of the study. Critics could justifiably deem this, or any other study, to be flawed due to sloppiness in the assembling of the study population.

Verification

The authors implemented this study in a highly controlled university environment where many resources were available to verify the accuracy of individual members of the defined study population. The authors had an online employee directory, a faculty contracts office, and departmental administrators who often could quickly resolve identity or status issues. The authors additionally had their own extensive 17 years of cumulated experience with the CTSC to aid in the resolution of identity issues with a high degree of fidelity. Many research studies occur in less-controlled “field” contexts that lack such verification resources or expert knowledge.

Simpson et al. have noted that “Although it is recommended that study eligibility criteria should be clear, objective and precise, they are often complex and open to interpretation” [ 29 ]. The authors hope that the recommendations in this article will help others to streamline and lend greater credibility to their own studies involving human populations. The authors could not locate a study to serve as an exemplar of a perfectly defined and identified study population. Instead, they identified a series of large scale cohort studies in the International Journal of Epidemiology that do offer definitions of study populations that are both concise and precise. The cohorts involve study populations as diverse as Japanese diabetics, Danish nurses, secondary school students in Amsterdam, twins, German uranium miners, and Thai university students [ 30 – 35 ]. Readers might find these examples helpful for their own research studies.

Lessons Learned

Institutions and organizations often function and build decisions on definitions of groups that are not well defined. Such definitions will serve episodic needs connected with an initiative or program in the short-run, rather than as master lists capable of serving long-term institutional needs. In the example at the University of New Mexico, many official CTSC communications contain the words “affiliated” or “affiliation” when referring to faculty with an institutional relationship, when no definition exists to guide one in interpreting what these words mean. All organizations reflect at least some ambiguity when defining affiliated faculty groups. Still, many investigators wanting to conduct organizationally related research often assume, at least initially, that the organizations have rigorously defined all group definitions.

Real world experience reveals wide gaps between aspirational goals and actual practices when defining populations. Motivating organizations to construct well-thought-out definitions poses challenges, since doing so might force these organizations to confront very difficult structural, hierarchal, political, or cultural conflicts. Some examples of these myriad conflicts may involve resolving ambiguities about budgeting, organizational authority, or political problems. As an example, defining who is affiliated with a CTSA at an institution may impact the authority academic departments or other research centers have over their own primarily affiliated faculty members. These kinds of probing discussions are often difficult to pursue smoothly and might be politically charged.

Despite these challenges, organizations that invest the time and resources to more rigorously define these group definitions will reap the previously described long-term benefits. Perhaps analogous to swallowing bad-tasting medicine, working out important group definitions will improve organizational health by helping the organization to engage in difficult, complex, or otherwise unpleasant issues.

Defining a study population early in the design stages of a research project will help to facilitate a smooth implementation phase. Clear definitions inform the value of applying research results to relevant populations for real world purposes. Importantly, a carefully and accurately defined study population enhances the completed study’s validity. This article describes the many challenges in defining a research study population and their potential solutions.

Acknowledgments

Funding Support

Health Sciences Library and Informatics Center, University of New Mexico, Albuquerque and Clinical and Translational Sciences Center, University of New Mexico. NIH Award # UL1TR000041.

IMAGES

Definition Of Research
Research
PPT
What is Research
Schematic representation of the research study
Quantitative Research Sample Definition of Terms

COMMENTS

Study designs: Part 1
Research study design is a framework, or the set of methods and procedures used to collect and analyze data on variables specified in a particular research problem. ... An interventional study has to be, by definition, a prospective study since the investigator determines the exposure for each study participant and then follows them to observe ...
A Practical Guide to Writing Quantitative and Qualitative Research
INTRODUCTION. Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses.1,2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results.3,4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the ...
Significance of the Study
Definition: Significance of the study in research refers to the potential importance, relevance, or impact of the research findings. It outlines how the research contributes to the existing body of knowledge, what gaps it fills, or what new understanding it brings to a particular field of study. In general, the significance of a study can be ...
What is Scientific Research and How Can it be Done?
Research conducted for the purpose of contributing towards science by the systematic collection, interpretation and evaluation of data and that, too, in a planned manner is called scientific research: a researcher is the one who conducts this research. The results obtained from a small group through scientific studies are socialised, and new ...
Research Methods
Research methods are specific procedures for collecting and analyzing data. Developing your research methods is an integral part of your research design. When planning your methods, there are two key decisions you will make. First, decide how you will collect data. Your methods depend on what type of data you need to answer your research question:
Background of The Study
Here are the steps to write the background of the study in a research paper: Identify the research problem: Start by identifying the research problem that your study aims to address. This can be a particular issue, a gap in the literature, or a need for further investigation. Conduct a literature review: Conduct a thorough literature review to ...
What Is Research Methodology? Definition + Examples
As we mentioned, research methodology refers to the collection of practical decisions regarding what data you'll collect, from who, how you'll collect it and how you'll analyse it. Research design, on the other hand, is more about the overall strategy you'll adopt in your study. For example, whether you'll use an experimental design ...
Pilot Study in Research: Definition & Examples
Advantages. Limitations. Examples. A pilot study, also known as a feasibility study, is a small-scale preliminary study conducted before the main research to check the feasibility or improve the research design. Pilot studies can be very important before conducting a full-scale research project, helping design the research methods and protocol.
What is Research
Research is the careful consideration of study regarding a particular concern or research problem using scientific methods. According to the American sociologist Earl Robert Babbie, "research is a systematic inquiry to describe, explain, predict, and control the observed phenomenon. It involves inductive and deductive methods.".
Research
Research Definition. Research is a careful and detailed study into a specific problem, concern, or issue using the scientific method. It's the adult form of the science fair projects back in ...
What is the Background of the Study and How to Write It
The background of the study is the first section of a research paper and gives context surrounding the research topic. The background explains to the reader where your research journey started, why you got interested in the topic, and how you developed the research question that you will later specify. That means that you first establish the ...
What Is Research, and Why Do People Do It?
Abstractspiepr Abs1. Every day people do research as they gather information to learn about something of interest. In the scientific world, however, research means something different than simply gathering information. Scientific research is characterized by its careful planning and observing, by its relentless efforts to understand and explain ...
What is the Background of a Study and How to Write It
The background of a study in a research paper helps to establish the research problem or gap in knowledge that the study aims to address, sets the stage for the research question and objectives, and highlights the significance of the research. The background of a study also includes a review of relevant literature, which helps researchers ...
In brief: What types of studies are there?
There are various types of scientific studies such as experiments and comparative analyses, observational studies, surveys, or interviews. The choice of study type will mainly depend on the research question being asked. When making decisions, patients and doctors need reliable answers to a number of questions.
Significance of a Study: Revisiting the "So What" Question
Significance of a study is established by making a case for it, not by simply choosing hypotheses everyone already thinks are important. Although you might believe the significance of your study is obvious, readers will need to be convinced. Significance is something you develop in your evolving research paper.
Definition of research study
research study. (REE-serch STUH-dee) A scientific study of nature that sometimes includes processes involved in health and disease. For example, clinical trials are research studies that involve people. These studies may be related to new ways to screen, prevent, diagnose, and treat disease. They may also study certain outcomes and certain ...
What is Research Methodology? Definition, Types, and Examples
Definition, Types, and Examples. Research methodology 1,2 is a structured and scientific approach used to collect, analyze, and interpret quantitative or qualitative data to answer research questions or test hypotheses. A research methodology is like a plan for carrying out research and helps keep researchers on track by limiting the scope of ...
PDF Definition of Key Terms in Your Dissertation: How to Decide What to
the concepts that will be used throughout your whole research, allowing readers to have a better understanding of the concepts, factors, and context in your study. • It makes sure that readers will grasp the fundamental concepts of your study in the specific way you are using them. Quite often, people have different and various
Definitions
The Common Rule defines research as "systematic investigation, including research development, testing and evaluation, designed to develop or contribute to generalizable knowledge" (§ 46.102 (d), see box, next page, for full definition). This means that a project or study is research if it: uses a commonly accepted scientific method.
What is Qualitative Research Design? Definition, Types, Examples and
Study Design: Researchers decide on the overall approach to the study, whether it's a case study, ethnography, grounded theory, phenomenology, or another qualitative design. The choice depends on the research question and the nature of the phenomenon under investigation.
Types of studies and research design
Types of study design. Medical research is classified into primary and secondary research. Clinical/experimental studies are performed in primary research, whereas secondary research consolidates available studies as reviews, systematic reviews and meta-analyses. Three main areas in primary research are basic medical research, clinical research ...
(PDF) What is research? A conceptual understanding
Research is a systematic endeavor to acquire understanding, broaden knowledge, or find answers to unanswered questions. It is a methodical and structured undertaking to investigate the natural and ...
Nursing Research (NURS 3321/4325/5366)
What is Quantitative Research? Quantitative methodology is the dominant research framework in the social sciences. It refers to a set of strategies, techniques and assumptions used to study psychological, social and economic processes through the exploration of numeric patterns.Quantitative research gathers a range of numeric data.
Understanding Research Study Designs
Ranganathan P. Understanding Research Study Designs. Indian J Crit Care Med 2019;23 (Suppl 4):S305-S307. Keywords: Clinical trials as topic, Observational studies as topic, Research designs. We use a variety of research study designs in biomedical research. In this article, the main features of each of these designs are summarized. Go to:
2024 Voices of Gen Z Study
The Walton Family Foundation and Gallup's new 2024 Voices of Gen Z Study builds on one of the largest and most comprehensive national research surveys tracking the sentiments and attitudes of Gen Z. The latest research focuses on Gen Z's views about themselves, their schools, and their future prospects.
Developing a consensus-based definition of out-of-hospital clinical
Moreover, a standardised definition allows meaningful comparisons to be made across health services and ensures consistency in future research. This study has shown recognition of OOH clinical deterioration to be a complex issue requiring further research.
Defining and Identifying Members of a Research Study Population: CTSA
An early step in research projects that involve humans consists of composing a clear and detailed definition of the study population. All experimental, observational, and qualitative research designs involving human subjects should define the study population in order to determine the eligibility of individuals for a study.

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Research Methods | Definitions, Types, Examples

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Background of The Study – Examples and Writing Guide

Background of The Study

How to Write Background of The Study

Identify the Research Problem

Provide Context

Review Literature

Identify Research Gap

State Objectives

Discuss Significance

How to Write Background of The Study in Proposal

How to Write Background of The Study In Thesis

How to Write Background of The Study in Research Paper

Examples of Background of The Study

Examples of Background of The Study In Research

Examples of Background of The Study in Proposal

Examples of Background of The Study in Thesis

Examples of Background of The Study in Research Paper

Purpose of Background of The Study

When to Write Background of The Study

Advantage of Background of The Study

Disadvantages of Background of The Study

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Pilot Study in Research: Definition & Examples

How Does it Work?

Experiment Pilot Study

Questionnaire Pilot Study

Limitations

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What is Research: Definition, Methods, Types & Examples

What is Research?

Qualitative methods

What is the purpose of research?

Types of research methods and Examples

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What is the Background of the Study and How to Write It

What is the Background of the Study in Research?

Why is the Background of the Study Important?

Table of Contents:

Background of the Study Structure

Presenting the Study Background

Example s of a general overview