research findings quantitative

To display these descriptive statistics in a paper, one might create a table like Table 2. Note that for discrete variables, we use the value label in the table, not the value.

If we were then to discuss our descriptive statistics in a quantitative paper, we might write something like this (note that we do not need to repeat every single detail from the table, as readers can peruse the table themselves):

This analysis relies on four variables from the 2021 General Social Survey: occupational prestige score, age, highest degree earned, and whether the respondent was born in the United States. Descriptive statistics for all four variables are shown in Table 2. The median occupational prestige score is 47, with a range from 16 to 80. 50% of respondents had occupational prestige scores scores between 35 and 59. The median age of respondents is 53, with a range from 18 to 89. 50% of respondents are between ages 37 and 66. Both variables have little skew. Highest degree earned ranges from less than high school to a graduate degree; the median respondent has earned an associate’s degree, while the modal response (given by 39.8% of the respondents) is a high school degree. 88.8% of respondents were born in the United States.

Crosstabulation

When presenting the results of a crosstabulation, we simplify the table so that it highlights the most important information—the column percentages—and include the significance and association below the table. Consider the SPSS output below.

Table 4 shows how a table suitable for include in a paper might look if created from the SPSS output in Table 3. Note that we use asterisks to indicate the significance level of the results: * means p < 0.05; ** means p < 0.01; *** means p < 0.001; and no stars mean p > 0.05 (and thus that the result is not significant). Also note than N is the abbreviation for the number of respondents.

If we were going to discuss the results of this crosstabulation in a quantitative research paper, the discussion might look like this:

A crosstabulation of respondent’s class identification and their highest degree earned, with class identification as the independent variable, is significant, with a Spearman correlation of 0.419, as shown in Table 4. Among lower class and working class respondents, more than 50% had earned a high school degree. Less than 20% of poor respondents and less than 40% of working-class respondents had earned more than a high school degree. In contrast, the majority of middle class and upper class respondents had earned at least a bachelor’s degree. In fact, 50% of upper class respondents had earned a graduate degree.

Correlation

When presenting a correlating matrix, one of the most important things to note is that we only present half the table so as not to include duplicated results. Think of the line through the table where empty cells exist to represent the correlation between a variable and itself, and include only the triangle of data either above or below that line of cells. Consider the output in Table 5.

Table 6 shows what the contents of Table 5 might look like when a table is constructed in a fashion suitable for publication.

If we were to discuss the results of this bivariate correlation analysis in a quantitative paper, the discussion might look like this:

Bivariate correlations were run among variables measuring age, occupational prestige, the highest year of school respondents completed, and family income in constant 1986 dollars, as shown in Table 6. Correlations between age and highest year of school completed and between age and family income are not significant. All other correlations are positive and significant at the p<0.001 level. The correlation between age and occupational prestige is weak; the correlations between income and occupational prestige and between income and educational attainment are moderate, and the correlation between education and occupational prestige is strong.

To present the results of a regression, we create one table that includes all of the key information from the multiple tables of SPSS output. This includes the R 2 and significance of the regression, either the B or the beta values (different analysts have different preferences here) for each variable, and the standard error and significance of each variable. Consider the SPSS output in Table 7.

The regression output in shown in Table 7 contains a lot of information. We do not include all of this information when making tables suitable for publication. As can be seen in Table 8, we include the Beta (or the B), the standard error, and the significance asterisk for each variable; the R 2 and significance for the overall regression; the degrees of freedom (which tells readers the sample size or N); and the constant; along with the key to p/significance values.

If we were to discuss the results of this regression in a quantitative paper, the results might look like this:

Table 8 shows the results of a regression in which age, occupational prestige, and highest year of school completed are the independent variables and family income is the dependent variable. The regression results are significant, and all of the independent variables taken together explain 15.6% of the variance in family income. Age is not a significant predictor of income, while occupational prestige and educational attainment are. Educational attainment has a larger effect on family income than does occupational prestige. For every year of additional education attained, family income goes up on average by $3,988.545; for every one-unit increase in occupational prestige score, family income goes up on average by $522.887. [1]

• Choose two discrete variables and three continuous variables from a dataset of your choice. Produce appropriate descriptive statistics on all five of the variables and create a table of the results suitable for inclusion in a paper.
• Using the two discrete variables you have chosen, produce an appropriate crosstabulation, with significance and measure of association. Create a table of the results suitable for inclusion in a paper.
• Using the three continuous variables you have chosen, produce a correlation matrix. Create a table of the results suitable for inclusion in a paper.
• Using the three continuous variables you have chosen, produce a multivariate linear regression. Create a table of the results suitable for inclusion in a paper.
• Write a methods section describing the dataset, analytical methods, and variables you utilized in questions 1, 2, 3, and 4 and explaining the results of your descriptive analysis.
• Write a findings section explaining the results of the analyses you performed in questions 2, 3, and 4.
• Note that the actual numberical increase comes from the B values, which are shown in the SPSS output in Table 7 but not in the reformatted Table 8. ↵

Social Data Analysis Copyright © 2021 by Mikaila Mariel Lemonik Arthur is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Home Market Research

Quantitative Research: What It Is, Practices & Methods

Quantitative research involves analyzing and gathering numerical data to uncover trends, calculate averages, evaluate relationships, and derive overarching insights. It’s used in various fields, including the natural and social sciences. Quantitative data analysis employs statistical techniques for processing and interpreting numeric data.

Research designs in the quantitative realm outline how data will be collected and analyzed with methods like experiments and surveys. Qualitative methods complement quantitative research by focusing on non-numerical data, adding depth to understanding. Data collection methods can be qualitative or quantitative, depending on research goals. Researchers often use a combination of both approaches to gain a comprehensive understanding of phenomena.

What is Quantitative Research?

Quantitative research is a systematic investigation of phenomena by gathering quantifiable data and performing statistical, mathematical, or computational techniques. Quantitative research collects statistically significant information from existing and potential customers using sampling methods and sending out online surveys , online polls , and questionnaires , for example.

One of the main characteristics of this type of research is that the results can be depicted in numerical form. After carefully collecting structured observations and understanding these numbers, it’s possible to predict the future of a product or service, establish causal relationships or Causal Research , and make changes accordingly. Quantitative research primarily centers on the analysis of numerical data and utilizes inferential statistics to derive conclusions that can be extrapolated to the broader population.

An example of a quantitative research study is the survey conducted to understand how long a doctor takes to tend to a patient when the patient walks into the hospital. A patient satisfaction survey can be administered to ask questions like how long a doctor takes to see a patient, how often a patient walks into a hospital, and other such questions, which are dependent variables in the research. This kind of research method is often employed in the social sciences, and it involves using mathematical frameworks and theories to effectively present data, ensuring that the results are logical, statistically sound, and unbiased.

Data collection in quantitative research uses a structured method and is typically conducted on larger samples representing the entire population. Researchers use quantitative methods to collect numerical data, which is then subjected to statistical analysis to determine statistically significant findings. This approach is valuable in both experimental research and social research, as it helps in making informed decisions and drawing reliable conclusions based on quantitative data.

Quantitative Research Characteristics

Quantitative research has several unique characteristics that make it well-suited for specific projects. Let’s explore the most crucial of these characteristics so that you can consider them when planning your next research project:

• Structured tools: Quantitative research relies on structured tools such as surveys, polls, or questionnaires to gather quantitative data . Using such structured methods helps collect in-depth and actionable numerical data from the survey respondents, making it easier to perform data analysis.
• Sample size: Quantitative research is conducted on a significant sample size  representing the target market . Appropriate Survey Sampling methods, a fundamental aspect of quantitative research methods, must be employed when deriving the sample to fortify the research objective and ensure the reliability of the results.
• Close-ended questions: Closed-ended questions , specifically designed to align with the research objectives, are a cornerstone of quantitative research. These questions facilitate the collection of quantitative data and are extensively used in data collection processes.
• Prior studies: Before collecting feedback from respondents, researchers often delve into previous studies related to the research topic. This preliminary research helps frame the study effectively and ensures the data collection process is well-informed.
• Quantitative data: Typically, quantitative data is represented using tables, charts, graphs, or other numerical forms. This visual representation aids in understanding the collected data and is essential for rigorous data analysis, a key component of quantitative research methods.
• Generalization of results: One of the strengths of quantitative research is its ability to generalize results to the entire population. It means that the findings derived from a sample can be extrapolated to make informed decisions and take appropriate actions for improvement based on numerical data analysis.

Quantitative Research Methods

Quantitative research methods are systematic approaches used to gather and analyze numerical data to understand and draw conclusions about a phenomenon or population. Here are the quantitative research methods:

• Primary quantitative research methods
• Secondary quantitative research methods

Primary Quantitative Research Methods

Primary quantitative research is the most widely used method of conducting market research. The distinct feature of primary research is that the researcher focuses on collecting data directly rather than depending on data collected from previously done research. Primary quantitative research design can be broken down into three further distinctive tracks and the process flow. They are:

A. Techniques and Types of Studies

There are multiple types of primary quantitative research. They can be distinguished into the four following distinctive methods, which are:

01. Survey Research

Survey Research is fundamental for all quantitative outcome research methodologies and studies. Surveys are used to ask questions to a sample of respondents, using various types such as online polls, online surveys, paper questionnaires, web-intercept surveys , etc. Every small and big organization intends to understand what their customers think about their products and services, how well new features are faring in the market, and other such details.

By conducting survey research, an organization can ask multiple survey questions , collect data from a pool of customers, and analyze this collected data to produce numerical results. It is the first step towards collecting data for any research. You can use single ease questions . A single-ease question is a straightforward query that elicits a concise and uncomplicated response.

This type of research can be conducted with a specific target audience group and also can be conducted across multiple groups along with comparative analysis . A prerequisite for this type of research is that the sample of respondents must have randomly selected members. This way, a researcher can easily maintain the accuracy of the obtained results as a huge variety of respondents will be addressed using random selection. 

Traditionally, survey research was conducted face-to-face or via phone calls. Still, with the progress made by online mediums such as email or social media, survey research has also spread to online mediums.There are two types of surveys , either of which can be chosen based on the time in hand and the kind of data required:

Cross-sectional surveys: Cross-sectional surveys are observational surveys conducted in situations where the researcher intends to collect data from a sample of the target population at a given point in time. Researchers can evaluate various variables at a particular time. Data gathered using this type of survey is from people who depict similarity in all variables except the variables which are considered for research . Throughout the survey, this one variable will stay constant.

• Cross-sectional surveys are popular with retail, SMEs, and healthcare industries. Information is garnered without modifying any parameters in the variable ecosystem.
• Multiple samples can be analyzed and compared using a cross-sectional survey research method.
• Multiple variables can be evaluated using this type of survey research.
• The only disadvantage of cross-sectional surveys is that the cause-effect relationship of variables cannot be established as it usually evaluates variables at a particular time and not across a continuous time frame.

Longitudinal surveys: Longitudinal surveys are also observational surveys , but unlike cross-sectional surveys, longitudinal surveys are conducted across various time durations to observe a change in respondent behavior and thought processes. This time can be days, months, years, or even decades. For instance, a researcher planning to analyze the change in buying habits of teenagers over 5 years will conduct longitudinal surveys.

• In cross-sectional surveys, the same variables were evaluated at a given time, and in longitudinal surveys, different variables can be analyzed at different intervals.
• Longitudinal surveys are extensively used in the field of medicine and applied sciences. Apart from these two fields, they are also used to observe a change in the market trend analysis , analyze customer satisfaction, or gain feedback on products/services.
• In situations where the sequence of events is highly essential, longitudinal surveys are used.
• Researchers say that when research subjects need to be thoroughly inspected before concluding, they rely on longitudinal surveys.

02. Correlational Research

A comparison between two entities is invariable. Correlation research is conducted to establish a relationship between two closely-knit entities and how one impacts the other, and what changes are eventually observed. This research method is carried out to give value to naturally occurring relationships, and a minimum of two different groups are required to conduct this quantitative research method successfully. Without assuming various aspects, a relationship between two groups or entities must be established.

Researchers use this quantitative research design to correlate two or more variables using mathematical analysis methods. Patterns, relationships, and trends between variables are concluded as they exist in their original setup. The impact of one of these variables on the other is observed, along with how it changes the relationship between the two variables. Researchers tend to manipulate one of the variables to attain the desired results.

Ideally, it is advised not to make conclusions merely based on correlational research. This is because it is not mandatory that if two variables are in sync that they are interrelated.

Example of Correlational Research Questions :

• The relationship between stress and depression.
• The equation between fame and money.
• The relation between activities in a third-grade class and its students.

03. Causal-comparative Research

This research method mainly depends on the factor of comparison. Also called quasi-experimental research , this quantitative research method is used by researchers to conclude the cause-effect equation between two or more variables, where one variable is dependent on the other independent variable. The independent variable is established but not manipulated, and its impact on the dependent variable is observed. These variables or groups must be formed as they exist in the natural setup. As the dependent and independent variables will always exist in a group, it is advised that the conclusions are carefully established by keeping all the factors in mind.

Causal-comparative research is not restricted to the statistical analysis of two variables but extends to analyzing how various variables or groups change under the influence of the same changes. This research is conducted irrespective of the type of relationship that exists between two or more variables. Statistical analysis plan is used to present the outcome using this quantitative research method.

Example of Causal-Comparative Research Questions:

• The impact of drugs on a teenager. The effect of good education on a freshman. The effect of substantial food provision in the villages of Africa.

04. Experimental Research

Also known as true experimentation, this research method relies on a theory. As the name suggests, experimental research is usually based on one or more theories. This theory has yet to be proven before and is merely a supposition. In experimental research, an analysis is done around proving or disproving the statement. This research method is used in natural sciences. Traditional research methods are more effective than modern techniques.

There can be multiple theories in experimental research. A theory is a statement that can be verified or refuted.

After establishing the statement, efforts are made to understand whether it is valid or invalid. This quantitative research method is mainly used in natural or social sciences as various statements must be proved right or wrong.

• Traditional research methods are more effective than modern techniques.
• Systematic teaching schedules help children who struggle to cope with the course.
• It is a boon to have responsible nursing staff for ailing parents.

B. Data Collection Methodologies

The second major step in primary quantitative research is data collection. Data collection can be divided into sampling methods and data collection using surveys and polls.

01. Data Collection Methodologies: Sampling Methods

There are two main sampling methods for quantitative research: Probability and Non-probability sampling .

Probability sampling: A theory of probability is used to filter individuals from a population and create samples in probability sampling . Participants of a sample are chosen by random selection processes. Each target audience member has an equal opportunity to be selected in the sample.

There are four main types of probability sampling:

• Simple random sampling: As the name indicates, simple random sampling is nothing but a random selection of elements for a sample. This sampling technique is implemented where the target population is considerably large.
• Stratified random sampling: In the stratified random sampling method , a large population is divided into groups (strata), and members of a sample are chosen randomly from these strata. The various segregated strata should ideally not overlap one another.
• Cluster sampling: Cluster sampling is a probability sampling method using which the main segment is divided into clusters, usually using geographic segmentation and demographic segmentation parameters.
• Systematic sampling: Systematic sampling is a technique where the starting point of the sample is chosen randomly, and all the other elements are chosen using a fixed interval. This interval is calculated by dividing the population size by the target sample size.

Non-probability sampling: Non-probability sampling is where the researcher’s knowledge and experience are used to create samples. Because of the researcher’s involvement, not all the target population members have an equal probability of being selected to be a part of a sample.

There are five non-probability sampling models:

• Convenience sampling: In convenience sampling , elements of a sample are chosen only due to one prime reason: their proximity to the researcher. These samples are quick and easy to implement as there is no other parameter of selection involved.
• Consecutive sampling: Consecutive sampling is quite similar to convenience sampling, except for the fact that researchers can choose a single element or a group of samples and conduct research consecutively over a significant period and then perform the same process with other samples.
• Quota sampling: Using quota sampling , researchers can select elements using their knowledge of target traits and personalities to form strata. Members of various strata can then be chosen to be a part of the sample as per the researcher’s understanding.
• Snowball sampling: Snowball sampling is conducted with target audiences who are difficult to contact and get information. It is popular in cases where the target audience for analysis research is rare to put together.
• Judgmental sampling: Judgmental sampling is a non-probability sampling method where samples are created only based on the researcher’s experience and research skill .

02. Data collection methodologies: Using surveys & polls

Once the sample is determined, then either surveys or polls can be distributed to collect the data for quantitative research.

Using surveys for primary quantitative research

A survey is defined as a research method used for collecting data from a pre-defined group of respondents to gain information and insights on various topics of interest. The ease of survey distribution and the wide number of people it can reach depending on the research time and objective makes it one of the most important aspects of conducting quantitative research.

Fundamental levels of measurement – nominal, ordinal, interval, and ratio scales

Four measurement scales are fundamental to creating a multiple-choice question in a survey. They are nominal, ordinal, interval, and ratio measurement scales without the fundamentals of which no multiple-choice questions can be created. Hence, it is crucial to understand these measurement levels to develop a robust survey.

Use of different question types

To conduct quantitative research, close-ended questions must be used in a survey. They can be a mix of multiple question types, including multiple-choice questions like semantic differential scale questions , rating scale questions , etc.

Survey Distribution and Survey Data Collection

In the above, we have seen the process of building a survey along with the research design to conduct primary quantitative research. Survey distribution to collect data is the other important aspect of the survey process. There are different ways of survey distribution. Some of the most commonly used methods are:

• Email: Sending a survey via email is the most widely used and effective survey distribution method. This method’s response rate is high because the respondents know your brand. You can use the QuestionPro email management feature to send out and collect survey responses.
• Buy respondents: Another effective way to distribute a survey and conduct primary quantitative research is to use a sample. Since the respondents are knowledgeable and are on the panel by their own will, responses are much higher.
• Embed survey on a website: Embedding a survey on a website increases a high number of responses as the respondent is already in close proximity to the brand when the survey pops up.
• Social distribution: Using social media to distribute the survey aids in collecting a higher number of responses from the people that are aware of the brand.
• QR code: QuestionPro QR codes store the URL for the survey. You can print/publish this code in magazines, signs, business cards, or on just about any object/medium.
• SMS survey: The SMS survey is a quick and time-effective way to collect a high number of responses.
• Offline Survey App: The QuestionPro App allows users to circulate surveys quickly, and the responses can be collected both online and offline.

Survey example

An example of a survey is a short customer satisfaction (CSAT) survey that can quickly be built and deployed to collect feedback about what the customer thinks about a brand and how satisfied and referenceable the brand is.

Using polls for primary quantitative research

Polls are a method to collect feedback using close-ended questions from a sample. The most commonly used types of polls are election polls and exit polls . Both of these are used to collect data from a large sample size but using basic question types like multiple-choice questions.

C. Data Analysis Techniques

The third aspect of primary quantitative research design is data analysis . After collecting raw data, there must be an analysis of this data to derive statistical inferences from this research. It is important to relate the results to the research objective and establish the statistical relevance of the results.

Remember to consider aspects of research that were not considered for the data collection process and report the difference between what was planned vs. what was actually executed.

It is then required to select precise Statistical Analysis Methods , such as SWOT, Conjoint, Cross-tabulation, etc., to analyze the quantitative data.

• SWOT analysis: SWOT Analysis stands for the acronym of Strengths, Weaknesses, Opportunities, and Threat analysis. Organizations use this statistical analysis technique to evaluate their performance internally and externally to develop effective strategies for improvement.
• Conjoint Analysis: Conjoint Analysis is a market analysis method to learn how individuals make complicated purchasing decisions. Trade-offs are involved in an individual’s daily activities, and these reflect their ability to decide from a complex list of product/service options.
• Cross-tabulation: Cross-tabulation is one of the preliminary statistical market analysis methods which establishes relationships, patterns, and trends within the various parameters of the research study.
• TURF Analysis: TURF Analysis , an acronym for Totally Unduplicated Reach and Frequency Analysis, is executed in situations where the reach of a favorable communication source is to be analyzed along with the frequency of this communication. It is used for understanding the potential of a target market.

Inferential statistics methods such as confidence interval, the margin of error, etc., can then be used to provide results.

Secondary Quantitative Research Methods

Secondary quantitative research or desk research is a research method that involves using already existing data or secondary data. Existing data is summarized and collated to increase the overall effectiveness of the research.

This research method involves collecting quantitative data from existing data sources like the internet, government resources, libraries, research reports, etc. Secondary quantitative research helps to validate the data collected from primary quantitative research and aid in strengthening or proving, or disproving previously collected data.

The following are five popularly used secondary quantitative research methods:

• Data available on the internet: With the high penetration of the internet and mobile devices, it has become increasingly easy to conduct quantitative research using the internet. Information about most research topics is available online, and this aids in boosting the validity of primary quantitative data.
• Government and non-government sources: Secondary quantitative research can also be conducted with the help of government and non-government sources that deal with market research reports. This data is highly reliable and in-depth and hence, can be used to increase the validity of quantitative research design.
• Public libraries: Now a sparingly used method of conducting quantitative research, it is still a reliable source of information, though. Public libraries have copies of important research that was conducted earlier. They are a storehouse of valuable information and documents from which information can be extracted.
• Educational institutions: Educational institutions conduct in-depth research on multiple topics, and hence, the reports that they publish are an important source of validation in quantitative research.
• Commercial information sources: Local newspapers, journals, magazines, radio, and TV stations are great sources to obtain data for secondary quantitative research. These commercial information sources have in-depth, first-hand information on market research, demographic segmentation, and similar subjects.

Quantitative Research Examples

Some examples of quantitative research are:

• A customer satisfaction template can be used if any organization would like to conduct a customer satisfaction (CSAT) survey . Through this kind of survey, an organization can collect quantitative data and metrics on the goodwill of the brand or organization in the customer’s mind based on multiple parameters such as product quality, pricing, customer experience, etc. This data can be collected by asking a net promoter score (NPS) question , matrix table questions, etc. that provide data in the form of numbers that can be analyzed and worked upon.
• Another example of quantitative research is an organization that conducts an event, collecting feedback from attendees about the value they see from the event. By using an event survey , the organization can collect actionable feedback about the satisfaction levels of customers during various phases of the event such as the sales, pre and post-event, the likelihood of recommending the organization to their friends and colleagues, hotel preferences for the future events and other such questions.

What are the Advantages of Quantitative Research?

There are many advantages to quantitative research. Some of the major advantages of why researchers use this method in market research are:

Collect Reliable and Accurate Data:

Quantitative research is a powerful method for collecting reliable and accurate quantitative data. Since data is collected, analyzed, and presented in numbers, the results obtained are incredibly reliable and objective. Numbers do not lie and offer an honest and precise picture of the conducted research without discrepancies. In situations where a researcher aims to eliminate bias and predict potential conflicts, quantitative research is the method of choice.

Quick Data Collection:

Quantitative research involves studying a group of people representing a larger population. Researchers use a survey or another quantitative research method to efficiently gather information from these participants, making the process of analyzing the data and identifying patterns faster and more manageable through the use of statistical analysis. This advantage makes quantitative research an attractive option for projects with time constraints.

Wider Scope of Data Analysis:

Quantitative research, thanks to its utilization of statistical methods, offers an extensive range of data collection and analysis. Researchers can delve into a broader spectrum of variables and relationships within the data, enabling a more thorough comprehension of the subject under investigation. This expanded scope is precious when dealing with complex research questions that require in-depth numerical analysis.

Eliminate Bias:

One of the significant advantages of quantitative research is its ability to eliminate bias. This research method leaves no room for personal comments or the biasing of results, as the findings are presented in numerical form. This objectivity makes the results fair and reliable in most cases, reducing the potential for researcher bias or subjectivity.

In summary, quantitative research involves collecting, analyzing, and presenting quantitative data using statistical analysis. It offers numerous advantages, including the collection of reliable and accurate data, quick data collection, a broader scope of data analysis, and the elimination of bias, making it a valuable approach in the field of research. When considering the benefits of quantitative research, it’s essential to recognize its strengths in contrast to qualitative methods and its role in collecting and analyzing numerical data for a more comprehensive understanding of research topics.

Best Practices to Conduct Quantitative Research

Here are some best practices for conducting quantitative research:

• Differentiate between quantitative and qualitative: Understand the difference between the two methodologies and apply the one that suits your needs best.
• Choose a suitable sample size: Ensure that you have a sample representative of your population and large enough to be statistically weighty.
• Keep your research goals clear and concise: Know your research goals before you begin data collection to ensure you collect the right amount and the right quantity of data.
• Keep the questions simple: Remember that you will be reaching out to a demographically wide audience. Pose simple questions for your respondents to understand easily.

Quantitative Research vs Qualitative Research

Quantitative research and qualitative research are two distinct approaches to conducting research, each with its own set of methods and objectives. Here’s a comparison of the two:

Quantitative Research

• Objective: The primary goal of quantitative research is to quantify and measure phenomena by collecting numerical data. It aims to test hypotheses, establish patterns, and generalize findings to a larger population.
• Data Collection: Quantitative research employs systematic and standardized approaches for data collection, including techniques like surveys, experiments, and observations that involve predefined variables. It is often collected from a large and representative sample.
• Data Analysis: Data is analyzed using statistical techniques, such as descriptive statistics, inferential statistics, and mathematical modeling. Researchers use statistical tests to draw conclusions and make generalizations based on numerical data.
• Sample Size: Quantitative research often involves larger sample sizes to ensure statistical significance and generalizability.
• Results: The results are typically presented in tables, charts, and statistical summaries, making them highly structured and objective.
• Generalizability: Researchers intentionally structure quantitative research to generate outcomes that can be helpful to a larger population, and they frequently seek to establish causative connections.
• Emphasis on Objectivity: Researchers aim to minimize bias and subjectivity, focusing on replicable and objective findings.

Qualitative Research

• Objective: Qualitative research seeks to gain a deeper understanding of the underlying motivations, behaviors, and experiences of individuals or groups. It explores the context and meaning of phenomena.
• Data Collection: Qualitative research employs adaptable and open-ended techniques for data collection, including methods like interviews, focus groups, observations, and content analysis. It allows participants to express their perspectives in their own words.
• Data Analysis: Data is analyzed through thematic analysis, content analysis, or grounded theory. Researchers focus on identifying patterns, themes, and insights in the data.
• Sample Size: Qualitative research typically involves smaller sample sizes due to the in-depth nature of data collection and analysis.
• Results: Findings are presented in narrative form, often in the participants’ own words. Results are subjective, context-dependent, and provide rich, detailed descriptions.
• Generalizability: Qualitative research does not aim for broad generalizability but focuses on in-depth exploration within a specific context. It provides a detailed understanding of a particular group or situation.
• Emphasis on Subjectivity: Researchers acknowledge the role of subjectivity and the researcher’s influence on the Research Process . Participant perspectives and experiences are central to the findings.

Researchers choose between quantitative and qualitative research methods based on their research objectives and the nature of the research question. Each approach has its advantages and drawbacks, and the decision between them hinges on the particular research objectives and the data needed to address research inquiries effectively.

Quantitative research is a structured way of collecting and analyzing data from various sources. Its purpose is to quantify the problem and understand its extent, seeking results that someone can project to a larger population.

Companies that use quantitative rather than qualitative research typically aim to measure magnitudes and seek objectively interpreted statistical results. So if you want to obtain quantitative data that helps you define the structured cause-and-effect relationship between the research problem and the factors, you should opt for this type of research.

At QuestionPro , we have various Best Data Collection Tools and features to conduct investigations of this type. You can create questionnaires and distribute them through our various methods. We also have sample services or various questions to guarantee the success of your study and the quality of the collected data.

Quantitative research is a systematic and structured approach to studying phenomena that involves the collection of measurable data and the application of statistical, mathematical, or computational techniques for analysis.

Quantitative research is characterized by structured tools like surveys, substantial sample sizes, closed-ended questions, reliance on prior studies, data presented numerically, and the ability to generalize findings to the broader population.

The two main methods of quantitative research are Primary quantitative research methods, involving data collection directly from sources, and Secondary quantitative research methods, which utilize existing data for analysis.

1.Surveying to measure employee engagement with numerical rating scales. 2.Analyzing sales data to identify trends in product demand and market share. 4.Examining test scores to assess the impact of a new teaching method on student performance. 4.Using website analytics to track user behavior and conversion rates for an online store.

1.Differentiate between quantitative and qualitative approaches. 2.Choose a representative sample size. 3.Define clear research goals before data collection. 4.Use simple and easily understandable survey questions.

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Quantitative research: Understanding the approaches and key elements

Quantitative research has many benefits and challenges but understanding how to properly conduct it can lead to a successful marketing research project.

Choosing the right quantitative approach

Editor’s note: Allison Von Borstel is the associate director of creative analytics at The Sound. This is an edited version of an article that originally appeared under the title “ Understanding Quantitative Research Approaches .”

What is quantitative research?

The systematic approaches that ground quantitative research involve hundreds or thousands of data points for one research project. The wonder of quantitative research is that each data point, or row in a spreadsheet, is a person and has a human story to tell. 

Quantitative research aggregates voices and distills them into numbers that uncover trends, illuminates relationships and correlations that inform decision-making with solid evidence and clarity.

The benefits of quantitative approach es

Why choose a quantitative   approach? Because you want a very clear story grounded in statistical rigor as a guide to making smart, data-backed decisions. 

Quantitative approaches shine because they:

Involve a lot of people

Large sample sizes (think hundreds or thousands) enable researchers to generalize findings because the sample is representative of the total population.  

They are grounded in statistical rigor

Allowing for precise measurement and analysis of data, providing statistically significant results that bolster confidence in research.

Reduce bias

Structured data collection and analysis methods enhance the reliability of findings. 

Boost efficiency

Quantitative methods often follow a qualitative phase, allowing researchers to validate findings by reporting the perspective of hundreds of people in a fraction of the time. 

Widen the analysis’ scope

The copious data collected in just a 20-minute (max) survey positions researchers to evaluate a broad spectrum of variables within the data. This thorough comprehension is instrumental when dealing with complex questions that require in-depth analysis. 

Quantitative approaches have hurdles, which include:

Limited flexibility

Once a survey is fielded, or data is gathered, there’s no opportunity to ask a live follow-up question. While it is possible to follow-up with the same people for two surveys, the likelihood of sufficient responses is small. 

Battling bots

One of the biggest concerns in data quality is making sure data represents people and not bots. 

Missing body language cues

Numbers, words and even images lack the cues that a researcher could pick up on during an interview. Unlike in a qualitative focus group, where one might deduce that a person is uncertain of an answer, in quantitative research, a static response is what the researcher works with.

Understanding quantitative research methods 

Quantitative approaches approach research from the same starting point as qualitative approaches – grounded in business objectives with a specific group of people to study. 

Once research has kicked off, the business objective thoroughly explored and the approach selected, research follows a general outline:  

Consider what data is needed

Think about what type of information needs to be gathered, with an approach in mind. While most quantitative research involves numbers, words and images also count.

• Numbers: Yes, the stereotypical rows of numbers in spreadsheets. Rows that capture people’s opinions and attitudes and are coded to numbers for comparative analytics. Numerical analysis is used for everything from descriptive statistics to regression/predictive analysis. 
• Words:  Text analysis employs a machine learning model to identify sentiment, emotion and meaning of text. Often used for sentiment analysis or content classification, it can be applied to single-word responses, elaborate open-ends, reviews or even social media posts.
• Images: Image analysis extracts meaningful information from images. A computer vision model that takes images as inputs and outputs numerical information (e.g., having a sample upload their favorite bag of chips and yielding the top three brands).

Design a survey

Create a survey to capture the data needed to address the objective. During this process, different pathways could be written to get a dynamic data set (capturing opinions that derive from various lived experiences). Survey logic is also written to provide a smooth UX experience for respondents.    

Prepare the data

The quality of quantitative research rests heavily on the quality of data. After data is collected (typically by fielding a survey or collecting already-existing data, more on that in a bit), it’s time to clean the data. 

Begin the analysis process

Now that you have a robust database (including numbers, words or images), it’s time to listen to the story that the data tells. Depending on the research approach used, advanced analytics come into play to tease out insights and nuances for the business objective. 

Tell the story

Strip the quantitative jargon and convey the insights from the research. Just because it’s quantitative research does not mean the results have to be told in a monotone drone with a monochrome chart. Answer business objectives dynamically, knowing that research is grounded in statistically sound information. 

The two options: Primary vs. secondary research

The two methods that encompass quantitative approaches are primary (collecting data oneself) and secondary (relying on already existing data).

Primary  research  is primarily used  

Most research involves primary data collection – where the researcher collects data directly. The main approach in primary research is survey data collection.  

The types of survey questions

Span various measurement scales (nominal, ordinal, interval and ratio) using a mix of question types (single and multi-choice, scales, matrix or open-ends).  

Analysis methods

Custom surveys yield great data for a variety of methods in market analysis. Here are a couple favorites: 

• Crosstabulation : Used to uncover insights that might not be obvious at first glance. This analysis organizes data into categories, revealing trends or patterns between variables. 
• Sentiment analysis: Used to sift through text to gauge emotions, opinions and attitudes. This method helps understand perception, fine-tune strategies and effectively respond to feedback.
• Market sizing: Used to map out the dimensions of a market. By calculating the total potential demand for a product or service in a specific market, this method reveals the scope of opportunities needed to make informed decisions about investment and growth strategies. 
• Conjoint analysis : Used to uncover what people value most in products or services. It breaks down features into bits and pieces and asks people to choose their ideal combo. By analyzing these preferences, this analysis reveals the hidden recipe for customer satisfaction.
• Job-To-Be-Done : Used to understand the underlying human motivations that drive people to act. People are multifaceted and experience a myriad of situations each day – meaning that a brand’s competition isn’t limited to in-category. 
• Segmentation: Used to identify specific cohorts within a greater population. It groups people with similar characteristics, behaviors or needs together. This method helps tailor products or services to specific groups, boosting satisfaction and sales.

Statistical rigor

Regardless of method, a quantitative approach then enables researchers to draw inferences and make predictions based upon the confidence in the data (looking at confidence intervals, margin of error, etc.)

Let’s not forget secondary research

By accessing a wide range of existing information, this research can be a cost-effective way to gain insights or can supplement primary research findings. 

Here are popular options: 

Government sources

Government sources can be extremely in-depth, can range across multiple industries and markets and reflect millions of people. This type of data is often instrumental for longitudinal or cultural trends analysis. 

Educational institutions

Research universities conduct in-depth studies on a variety of topics, often aggregating government data, nonprofit data and primary data.  

Client data

This includes any research that was conducted for or by companies before the   present research project. Whether it’s data gathered from customer reviews or prior quantitative work, these secondary resources can help extend findings and detect trends by connecting past data to future data.

Quantitative research enhances research projects

Quantitative research approaches are so much more than “how much” or “how many,” they reveal the   why   behind people’s actions, emotions and behaviors. By using standardized collection methods, like surveys, quant instills confidence and rigor in findings.

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Qualitative vs Quantitative Research Methods & Data Analysis

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.

Learn about our Editorial Process

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.

The main difference between quantitative and qualitative research is the type of data they collect and analyze.

Quantitative data is information about quantities, and therefore numbers, and qualitative data is descriptive, and regards phenomenon which can be observed but not measured, such as language.

• Quantitative research collects numerical data and analyzes it using statistical methods. The aim is to produce objective, empirical data that can be measured and expressed numerically. Quantitative research is often used to test hypotheses, identify patterns, and make predictions.
• Qualitative research gathers non-numerical data (words, images, sounds) to explore subjective experiences and attitudes, often via observation and interviews. It aims to produce detailed descriptions and uncover new insights about the studied phenomenon.

On This Page:

What Is Qualitative Research?

Qualitative research is the process of collecting, analyzing, and interpreting non-numerical data, such as language. Qualitative research can be used to understand how an individual subjectively perceives and gives meaning to their social reality.

Qualitative data is non-numerical data, such as text, video, photographs, or audio recordings. This type of data can be collected using diary accounts or in-depth interviews and analyzed using grounded theory or thematic analysis.

Qualitative research is multimethod in focus, involving an interpretive, naturalistic approach to its subject matter. This means that qualitative researchers study things in their natural settings, attempting to make sense of, or interpret, phenomena in terms of the meanings people bring to them. Denzin and Lincoln (1994, p. 2)

Interest in qualitative data came about as the result of the dissatisfaction of some psychologists (e.g., Carl Rogers) with the scientific study of psychologists such as behaviorists (e.g., Skinner ).

Since psychologists study people, the traditional approach to science is not seen as an appropriate way of carrying out research since it fails to capture the totality of human experience and the essence of being human.  Exploring participants’ experiences is known as a phenomenological approach (re: Humanism ).

Qualitative research is primarily concerned with meaning, subjectivity, and lived experience. The goal is to understand the quality and texture of people’s experiences, how they make sense of them, and the implications for their lives.

Qualitative research aims to understand the social reality of individuals, groups, and cultures as nearly as possible as participants feel or live it. Thus, people and groups are studied in their natural setting.

Some examples of qualitative research questions are provided, such as what an experience feels like, how people talk about something, how they make sense of an experience, and how events unfold for people.

Research following a qualitative approach is exploratory and seeks to explain ‘how’ and ‘why’ a particular phenomenon, or behavior, operates as it does in a particular context. It can be used to generate hypotheses and theories from the data.

Qualitative Methods

There are different types of qualitative research methods, including diary accounts, in-depth interviews , documents, focus groups , case study research , and ethnography .

The results of qualitative methods provide a deep understanding of how people perceive their social realities and in consequence, how they act within the social world.

The researcher has several methods for collecting empirical materials, ranging from the interview to direct observation, to the analysis of artifacts, documents, and cultural records, to the use of visual materials or personal experience. Denzin and Lincoln (1994, p. 14)

Here are some examples of qualitative data:

Interview transcripts : Verbatim records of what participants said during an interview or focus group. They allow researchers to identify common themes and patterns, and draw conclusions based on the data. Interview transcripts can also be useful in providing direct quotes and examples to support research findings.

Observations : The researcher typically takes detailed notes on what they observe, including any contextual information, nonverbal cues, or other relevant details. The resulting observational data can be analyzed to gain insights into social phenomena, such as human behavior, social interactions, and cultural practices.

Unstructured interviews : generate qualitative data through the use of open questions.  This allows the respondent to talk in some depth, choosing their own words.  This helps the researcher develop a real sense of a person’s understanding of a situation.

Diaries or journals : Written accounts of personal experiences or reflections.

Notice that qualitative data could be much more than just words or text. Photographs, videos, sound recordings, and so on, can be considered qualitative data. Visual data can be used to understand behaviors, environments, and social interactions.

Qualitative Data Analysis

Qualitative research is endlessly creative and interpretive. The researcher does not just leave the field with mountains of empirical data and then easily write up his or her findings.

Qualitative interpretations are constructed, and various techniques can be used to make sense of the data, such as content analysis, grounded theory (Glaser & Strauss, 1967), thematic analysis (Braun & Clarke, 2006), or discourse analysis .

For example, thematic analysis is a qualitative approach that involves identifying implicit or explicit ideas within the data. Themes will often emerge once the data has been coded .

Key Features

• Events can be understood adequately only if they are seen in context. Therefore, a qualitative researcher immerses her/himself in the field, in natural surroundings. The contexts of inquiry are not contrived; they are natural. Nothing is predefined or taken for granted.
• Qualitative researchers want those who are studied to speak for themselves, to provide their perspectives in words and other actions. Therefore, qualitative research is an interactive process in which the persons studied teach the researcher about their lives.
• The qualitative researcher is an integral part of the data; without the active participation of the researcher, no data exists.
• The study’s design evolves during the research and can be adjusted or changed as it progresses. For the qualitative researcher, there is no single reality. It is subjective and exists only in reference to the observer.
• The theory is data-driven and emerges as part of the research process, evolving from the data as they are collected.

Limitations of Qualitative Research

• Because of the time and costs involved, qualitative designs do not generally draw samples from large-scale data sets.
• The problem of adequate validity or reliability is a major criticism. Because of the subjective nature of qualitative data and its origin in single contexts, it is difficult to apply conventional standards of reliability and validity. For example, because of the central role played by the researcher in the generation of data, it is not possible to replicate qualitative studies.
• Also, contexts, situations, events, conditions, and interactions cannot be replicated to any extent, nor can generalizations be made to a wider context than the one studied with confidence.
• The time required for data collection, analysis, and interpretation is lengthy. Analysis of qualitative data is difficult, and expert knowledge of an area is necessary to interpret qualitative data. Great care must be taken when doing so, for example, looking for mental illness symptoms.

Advantages of Qualitative Research

• Because of close researcher involvement, the researcher gains an insider’s view of the field. This allows the researcher to find issues that are often missed (such as subtleties and complexities) by the scientific, more positivistic inquiries.
• Qualitative descriptions can be important in suggesting possible relationships, causes, effects, and dynamic processes.
• Qualitative analysis allows for ambiguities/contradictions in the data, which reflect social reality (Denscombe, 2010).
• Qualitative research uses a descriptive, narrative style; this research might be of particular benefit to the practitioner as she or he could turn to qualitative reports to examine forms of knowledge that might otherwise be unavailable, thereby gaining new insight.

What Is Quantitative Research?

Quantitative research involves the process of objectively collecting and analyzing numerical data to describe, predict, or control variables of interest.

The goals of quantitative research are to test causal relationships between variables , make predictions, and generalize results to wider populations.

Quantitative researchers aim to establish general laws of behavior and phenomenon across different settings/contexts. Research is used to test a theory and ultimately support or reject it.

Quantitative Methods

Experiments typically yield quantitative data, as they are concerned with measuring things.  However, other research methods, such as controlled observations and questionnaires , can produce both quantitative information.

For example, a rating scale or closed questions on a questionnaire would generate quantitative data as these produce either numerical data or data that can be put into categories (e.g., “yes,” “no” answers).

Experimental methods limit how research participants react to and express appropriate social behavior.

Findings are, therefore, likely to be context-bound and simply a reflection of the assumptions that the researcher brings to the investigation.

There are numerous examples of quantitative data in psychological research, including mental health. Here are a few examples:

Another example is the Experience in Close Relationships Scale (ECR), a self-report questionnaire widely used to assess adult attachment styles .

The ECR provides quantitative data that can be used to assess attachment styles and predict relationship outcomes.

Neuroimaging data : Neuroimaging techniques, such as MRI and fMRI, provide quantitative data on brain structure and function.

This data can be analyzed to identify brain regions involved in specific mental processes or disorders.

For example, the Beck Depression Inventory (BDI) is a clinician-administered questionnaire widely used to assess the severity of depressive symptoms in individuals.

The BDI consists of 21 questions, each scored on a scale of 0 to 3, with higher scores indicating more severe depressive symptoms. 

Quantitative Data Analysis

Statistics help us turn quantitative data into useful information to help with decision-making. We can use statistics to summarize our data, describing patterns, relationships, and connections. Statistics can be descriptive or inferential.

Descriptive statistics help us to summarize our data. In contrast, inferential statistics are used to identify statistically significant differences between groups of data (such as intervention and control groups in a randomized control study).

• Quantitative researchers try to control extraneous variables by conducting their studies in the lab.
• The research aims for objectivity (i.e., without bias) and is separated from the data.
• The design of the study is determined before it begins.
• For the quantitative researcher, the reality is objective, exists separately from the researcher, and can be seen by anyone.
• Research is used to test a theory and ultimately support or reject it.

Limitations of Quantitative Research

• Context: Quantitative experiments do not take place in natural settings. In addition, they do not allow participants to explain their choices or the meaning of the questions they may have for those participants (Carr, 1994).
• Researcher expertise: Poor knowledge of the application of statistical analysis may negatively affect analysis and subsequent interpretation (Black, 1999).
• Variability of data quantity: Large sample sizes are needed for more accurate analysis. Small-scale quantitative studies may be less reliable because of the low quantity of data (Denscombe, 2010). This also affects the ability to generalize study findings to wider populations.
• Confirmation bias: The researcher might miss observing phenomena because of focus on theory or hypothesis testing rather than on the theory of hypothesis generation.

Advantages of Quantitative Research

• Scientific objectivity: Quantitative data can be interpreted with statistical analysis, and since statistics are based on the principles of mathematics, the quantitative approach is viewed as scientifically objective and rational (Carr, 1994; Denscombe, 2010).
• Useful for testing and validating already constructed theories.
• Rapid analysis: Sophisticated software removes much of the need for prolonged data analysis, especially with large volumes of data involved (Antonius, 2003).
• Replication: Quantitative data is based on measured values and can be checked by others because numerical data is less open to ambiguities of interpretation.
• Hypotheses can also be tested because of statistical analysis (Antonius, 2003).

Antonius, R. (2003). Interpreting quantitative data with SPSS . Sage.

Black, T. R. (1999). Doing quantitative research in the social sciences: An integrated approach to research design, measurement and statistics . Sage.

Braun, V. & Clarke, V. (2006). Using thematic analysis in psychology . Qualitative Research in Psychology , 3, 77–101.

Carr, L. T. (1994). The strengths and weaknesses of quantitative and qualitative research : what method for nursing? Journal of advanced nursing, 20(4) , 716-721.

Denscombe, M. (2010). The Good Research Guide: for small-scale social research. McGraw Hill.

Denzin, N., & Lincoln. Y. (1994). Handbook of Qualitative Research. Thousand Oaks, CA, US: Sage Publications Inc.

Glaser, B. G., Strauss, A. L., & Strutzel, E. (1968). The discovery of grounded theory; strategies for qualitative research. Nursing research, 17(4) , 364.

Minichiello, V. (1990). In-Depth Interviewing: Researching People. Longman Cheshire.

Punch, K. (1998). Introduction to Social Research: Quantitative and Qualitative Approaches. London: Sage

Further Information

• Mixed methods research
• Designing qualitative research
• Methods of data collection and analysis
• Introduction to quantitative and qualitative research
• Checklists for improving rigour in qualitative research: a case of the tail wagging the dog?
• Qualitative research in health care: Analysing qualitative data
• Qualitative data analysis: the framework approach
• Using the framework method for the analysis of
• Qualitative data in multi-disciplinary health research
• Content Analysis
• Grounded Theory
• Thematic Analysis

• Chapter Seven: Presenting Your Results

This chapter serves as the culmination of the previous chapters, in that it focuses on how to present the results of one's study, regardless of the choice made among the three methods. Writing in academics has a form and style that you will want to apply not only to report your own research, but also to enhance your skills at reading original research published in academic journals. Beyond the basic academic style of report writing, there are specific, often unwritten assumptions about how quantitative, qualitative, and critical/rhetorical studies should be organized and the information they should contain. This chapter discusses how to present your results in writing, how to write accessibly, how to visualize data, and how to present your results in person.  

• Chapter One: Introduction
• Chapter Two: Understanding the distinctions among research methods
• Chapter Three: Ethical research, writing, and creative work
• Chapter Four: Quantitative Methods (Part 1)
• Chapter Four: Quantitative Methods (Part 2 - Doing Your Study)
• Chapter Four: Quantitative Methods (Part 3 - Making Sense of Your Study)
• Chapter Five: Qualitative Methods (Part 1)
• Chapter Five: Qualitative Data (Part 2)
• Chapter Six: Critical / Rhetorical Methods (Part 1)
• Chapter Six: Critical / Rhetorical Methods (Part 2)

Written Presentation of Results

Once you've gone through the process of doing communication research – using a quantitative, qualitative, or critical/rhetorical methodological approach – the final step is to  communicate  it.

The major style manuals (the APA Manual, the MLA Handbook, and Turabian) are very helpful in documenting the structure of writing a study, and are highly recommended for consultation. But, no matter what style manual you may use, there are some common elements to the structure of an academic communication research paper.

Title Page :

This is simple: Your Paper's Title, Your Name, Your Institutional Affiliation (e.g., University), and the Date, each on separate lines, centered on the page. Try to make your title both descriptive (i.e., it gives the reader an idea what the study is about) and interesting (i.e., it is catchy enough to get one's attention).

For example, the title, "The uncritical idealization of a compensated psychopath character in a popular book series," would not be an inaccurate title for a published study, but it is rather vague and exceedingly boring. That study's author fortunately chose the title, "A boyfriend to die for: Edward Cullen as compensated psychopath in Stephanie Meyer's  Twilight ," which is more precisely descriptive, and much more interesting (Merskin, 2011). The use of the colon in academic titles can help authors accomplish both objectives: a catchy but relevant phrase, followed by a more clear explanation of the article's topic.

In some instances, you might be asked to write an abstract, which is a summary of your paper that can range in length from 75 to 250 words. If it is a published paper, it is useful to include key search terms in this brief description of the paper (the title may already have a few of these terms as well). Although this may be the last thing your write, make it one of the best things you write, because this may be the first thing your audience reads about the paper (and may be the only thing read if it is written badly). Summarize the problem/research question, your methodological approach, your results and conclusions, and the significance of the paper in the abstract.

Quantitative and qualitative studies will most typically use the rest of the section titles noted below. Critical/rhetorical studies will include many of the same steps, but will often have different headings. For example, a critical/rhetorical paper will have an introduction, definition of terms, and literature review, followed by an analysis (often divided into sections by areas of investigation) and ending with a conclusion/implications section. Because critical/rhetorical research is much more descriptive, the subheadings in such a paper are often times not generic subheads like "literature review," but instead descriptive subheadings that apply to the topic at hand, as seen in the schematic below. Because many journals expect the article to follow typical research paper headings of introduction, literature review, methods, results, and discussion, we discuss these sections briefly next.

Introduction:

As you read social scientific journals (see chapter 1 for examples), you will find that they tend to get into the research question quickly and succinctly. Journal articles from the humanities tradition tend to be more descriptive in the introduction. But, in either case, it is good to begin with some kind of brief anecdote that gets the reader engaged in your work and lets the reader understand why this is an interesting topic. From that point, state your research question, define the problem (see Chapter One) with an overview of what we do and don't know, and finally state what you will do, or what you want to find out. The introduction thus builds the case for your topic, and is the beginning of building your argument, as we noted in chapter 1.

By the end of the Introduction, the reader should know what your topic is, why it is a significant communication topic, and why it is necessary that you investigate it (e.g., it could be there is gap in literature, you will conduct valuable exploratory research, or you will provide a new model for solving some professional or social problem).

Literature Review:

The literature review summarizes and organizes the relevant books, articles, and other research in this area. It sets up both quantitative and qualitative studies, showing the need for the study. For critical/rhetorical research, the literature review often incorporates the description of the historical context and heuristic vocabulary, with key terms defined in this section of the paper. For more detail on writing a literature review, see Appendix 1.

The methods of your paper are the processes that govern your research, where the researcher explains what s/he did to solve the problem. As you have seen throughout this book, in communication studies, there are a number of different types of research methods. For example, in quantitative research, one might conduct surveys, experiments, or content analysis. In qualitative research, one might instead use interviews and observations. Critical/rhetorical studies methods are more about the interpretation of texts or the study of popular culture as communication. In creative communication research, the method may be an interpretive performance studies or filmmaking. Other methods used sometimes alone, or in combination with other methods, include legal research, historical research, and political economy research.

In quantitative and qualitative research papers, the methods will be most likely described according to the APA manual standards. At the very least, the methods will include a description of participants, data collection, and data analysis, with specific details on each of these elements. For example, in an experiment, the researcher will describe the number of participants, the materials used, the design of the experiment, the procedure of the experiment, and what statistics will be used to address the hypotheses/research questions.

Critical/rhetorical researchers rarely have a specific section called "methods," as opposed to quantitative and qualitative researchers, but rather demonstrate the method they use for analysis throughout the writing of their piece.

Helping your reader understand the methods you used for your study is important not only for your own study's credibility, but also for possible replication of your study by other researchers. A good guideline to keep in mind is  transparency . You want to be as clear as possible in describing the decisions you made in designing your study, gathering and analyzing your data so that the reader can retrace your steps and understand how you came to the conclusions you formed. A research study can be very good, but if it is not clearly described so that others can see how the results were determined or obtained, then the quality of the study and its potential contributions are lost.

After you completed your study, your findings will be listed in the results section. Particularly in a quantitative study, the results section is for revisiting your hypotheses and reporting whether or not your results supported them, and the statistical significance of the results. Whether your study supported or contradicted your hypotheses, it's always helpful to fully report what your results were. The researcher usually organizes the results of his/her results section by research question or hypothesis, stating the results for each one, using statistics to show how the research question or hypothesis was answered in the study.

The qualitative results section also may be organized by research question, but usually is organized by themes which emerged from the data collected. The researcher provides rich details from her/his observations and interviews, with detailed quotations provided to illustrate the themes identified. Sometimes the results section is combined with the discussion section.

Critical/rhetorical researchers would include their analysis often with different subheadings in what would be considered a "results" section, yet not labeled specifically this way.

Discussion:

In the discussion section, the researcher gives an appraisal of the results. Here is where the researcher considers the results, particularly in light of the literature review, and explains what the findings mean. If the results confirmed or corresponded with the findings of other literature, then that should be stated. If the results didn't support the findings of previous studies, then the researcher should develop an explanation of why the study turned out this way. Sometimes, this section is called a "conclusion" by researchers.

References:

In this section, all of the literature cited in the text should have full references in alphabetical order. Appendices: Appendix material includes items like questionnaires used in the study, photographs, documents, etc. An alphabetical letter is assigned for each piece (e.g. Appendix A, Appendix B), with a second line of title describing what the appendix contains (e.g. Participant Informed Consent, or  New York Times  Speech Coverage). They should be organized consistently with the order in which they are referenced in the text of the paper. The page numbers for appendices are consecutive with the paper and reference list.

Tables/Figures:

Tables and figures are referenced in the text, but included at the end of the study and numbered consecutively. (Check with your professor; some like to have tables and figures inserted within the paper's main text.) Tables generally are data in a table format, whereas figures are diagrams (such as a pie chart) and drawings (such as a flow chart).

Accessible Writing

As you may have noticed, academic writing does have a language (e.g., words like heuristic vocabulary and hypotheses) and style (e.g., literature reviews) all its own. It is important to engage in that language and style, and understand how to use it to  communicate effectively in an academic context . Yet, it is also important to remember that your analyses and findings should also be written to be accessible. Writers should avoid excessive jargon, or—even worse—deploying jargon to mask an incomplete understanding of a topic.

The scourge of excessive jargon in academic writing was the target of a famous hoax in 1996. A New York University physics professor submitted an article, " Transgressing the Boundaries: Toward a Transformative Hermeneutics of Quantum Gravity ," to a special issue of the academic journal  Social Text  devoted to science and postmodernism. The article was designed to point out how dense academic jargon can sometimes mask sloppy thinking. As the professor, Alan Sokal, had expected, the article was published. One sample sentence from the article reads:

It has thus become increasingly apparent that physical "reality", no less than social "reality", is at bottom a social and linguistic construct; that scientific "knowledge", far from being objective, reflects and encodes the dominant ideologies and power relations of the culture that produced it; that the truth claims of science are inherently theory-laden and self-referential; and consequently, that the discourse of the scientific community, for all its undeniable value, cannot assert a privileged epistemological status with respect to counter-hegemonic narratives emanating from dissident or marginalized communities. (Sokal, 1996. pp. 217-218)

According to the journal's editor, about six reviewers had read the article but didn't suspect that it was phony. A public debate ensued after Sokal revealed his hoax. Sokal said he worried that jargon and intellectual fads cause academics to lose contact with the real world and "undermine the prospect for progressive social critique" ( Scott, 1996 ). The APA Manual recommends to avoid using technical vocabulary where it is not needed or relevant or if the technical language is overused, thus becoming jargon. In short, the APA argues that "scientific jargon...grates on the reader, encumbers the communication of information, and wastes space" (American Psychological Association, 2010, p. 68).

Data Visualization

Images and words have long existed on the printed page of manuscripts, yet, until recently, relatively few researchers possessed the resources to effectively combine images combined with words (Tufte, 1990, 1983). Communication scholars are only now becoming aware of this dimension in research as computer technologies have made it possible for many people to produce and publish multimedia presentations.

Although visuals may seem to be anathema to the primacy of the written word in research, they are a legitimate way, and at times the best way, to present ideas. Visual scholar Lester Faigley et al. (2004) explains how data visualizations have become part of our daily lives:

Visualizations can shed light on research as well. London-based David McCandless specializes in visualizing interesting research questions, or in his words "the questions I wanted answering" (2009, p. 7). His images include a graph of the  peak times of the year for breakups  (based on Facebook status updates), a  radiation dosage chart , and some  experiments with the Google Ngram Viewer , which charts the appearance of keywords in millions of books over hundreds of years.

The  public domain image  below creatively maps U.S. Census data of the outflow of people from California to other states between 1995 and 2000.

Visualizing one's research is possible in multiple ways. A simple technology, for example, is to enter data into a spreadsheet such as Excel, and select  Charts  or  SmartArt  to generate graphics. A number of free web tools can also transform raw data into useful charts and graphs.  Many Eyes , an open source data visualization tool (sponsored by IBM Research), says its goal "is to 'democratize' visualization and to enable a new social kind of data analysis" (IBM, 2011). Another tool,  Soundslides , enables users to import images and audio to create a photographic slideshow, while the program handles all of the background code. Other tools, often open source and free, can help visual academic research into interactive maps; interactive, image-based timelines; interactive charts; and simple 2-D and 3-D animations. Adobe Creative Suite (which includes popular software like Photoshop) is available on most computers at universities, but open source alternatives exist as well.  Gimp  is comparable to Photoshop, and it is free and relatively easy to use.

One online performance studies journal,  Liminalities , is an excellent example of how "research" can be more than just printed words. In each issue, traditional academic essays and book reviews are often supported photographs, while other parts of an issue can include video, audio, and multimedia contributions. The journal, founded in 2005, treats performance itself as a methodology, and accepts contribution in html, mp3, Quicktime, and Flash formats.

For communication researchers, there is also a vast array of visual digital archives available online. Many of these archives are located at colleges and universities around the world, where digital librarians are spearheading a massive effort to make information—print, audio, visual, and graphic—available to the public as part of a global information commons. For example, the University of Iowa has a considerable digital archive including historical photos documenting American railroads and a database of images related to geoscience. The University of Northern Iowa has a growing Special Collections Unit that includes digital images of every UNI Yearbook between 1905 and 1923 and audio files of UNI jazz band performances. Researchers at he University of Michigan developed  OAIster , a rich database that has joined thousands of digital archives in one searchable interface. Indeed, virtually every academic library is now digitizing all types of media, not just texts, and making them available for public viewing and, when possible, for use in presenting research. In addition to academic collections, the  Library of Congress  and the  National Archives  offer an ever-expanding range of downloadable media; commercial, user-generated databases such as Flickr, Buzznet, YouTube and Google Video offer a rich resource of images that are often free of copyright constraints (see Chapter 3 about Creative Commons licenses) and nonprofit endeavors, such as the  Internet Archive , contain a formidable collection of moving images, still photographs, audio files (including concert recordings), and open source software.

Presenting your Work in Person

As Communication students, it's expected that you are not only able to communicate your research project in written form but also in person.

Before you do any oral presentation, it's good to have a brief "pitch" ready for anyone who asks you about your research. The pitch is routine in Hollywood: a screenwriter has just a few minutes to present an idea to a producer. Although your pitch will be more sophisticated than, say, " Snakes on a Plane " (which unfortunately was made into a movie), you should in just a few lines be able to explain the gist of your research to anyone who asks. Developing this concise description, you will have some practice in distilling what might be a complicated topic into one others can quickly grasp.

Oral presentation

In most oral presentations of research, whether at the end of a semester, or at a research symposium or conference, you will likely have just 10 to 20 minutes. This is probably not enough time to read the entire paper aloud, which is not what you should do anyway if you want people to really listen (although, unfortunately some make this mistake). Instead, the point of the presentation should be to present your research in an interesting manner so the listeners will want to read the whole thing. In the presentation, spend the least amount of time on the literature review (a very brief summary will suffice) and the most on your own original contribution. In fact, you may tell your audience that you are only presenting on one portion of the paper, and that you would be happy to talk more about your research and findings in the question and answer session that typically follows. Consider your presentation the beginning of a dialogue between you and the audience. Your tone shouldn't be "I have found everything important there is to find, and I will cram as much as I can into this presentation," but instead "I found some things you will find interesting, but I realize there is more to find."

Turabian (2007) has a helpful chapter on presenting research. Most important, she emphasizes, is to remember that your audience members are listeners, not readers. Thus, recall the lessons on speech making in your college oral communication class. Give an introduction, tell them what the problem is, and map out what you will present to them. Organize your findings into a few points, and don't get bogged down in minutiae. (The minutiae are for readers to find if they wish, not for listeners to struggle through.) PowerPoint slides are acceptable, but don't read them. Instead, create an outline of a few main points, and practice your presentation.

Turabian  suggests an introduction of not more than three minutes, which should include these elements:

• The research topic you will address (not more than a minute).
• Your research question (30 seconds or less)
• An answer to "so what?" – explaining the relevance of your research (30 seconds)
• Your claim, or argument (30 seconds or less)
• The map of your presentation structure (30 seconds or less)

As Turabian (2007) suggests, "Rehearse your introduction, not only to get it right, but to be able to look your audience in the eye as you give it. You can look down at notes later" (p. 125).

Poster presentation

In some symposiums and conferences, you may be asked to present at a "poster" session. Instead of presenting on a panel of 4-5 people to an audience, a poster presenter is with others in a large hall or room, and talks one-on-one with visitors who look at the visual poster display of the research. As in an oral presentation, a poster highlights just the main point of the paper. Then, if visitors have questions, the author can informally discuss her/his findings.

To attract attention, poster presentations need to be nicely designed, or in the words of an advertising professor who schedules poster sessions at conferences, "be big, bold, and brief" ( Broyles , 2011). Large type (at least 18 pt.), graphics, tables, and photos are recommended.

A poster presentation session at a conference, by David Eppstein (Own work) [CC-BY-SA-3.0 ( www.creativecommons.org/licenses/by-sa/3.0 )], via Wikimedia Commons]

The Association for Education in Journalism and Mass Communication (AEJMC) has a  template for making an effective poster presentation . Many universities, copy shops, and Internet services also have large-scale printers, to print full-color research poster designs that can be rolled up and transported in a tube.

Judging Others' Research

After taking this course, you should have a basic knowledge of research methods. There will still be some things that may mystify you as a reader of other's research. For example, you may not be able to interpret the coefficients for statistical significance, or make sense of a complex structural equation. Some specialized vocabulary may still be difficult.

But, you should understand how to critically review research. For example, imagine you have been asked to do a blind (i.e., the author's identity is concealed) "peer review" of communication research for acceptance to a conference, or publication in an academic journal. For most  conferences  and  journals , submissions are made online, where editors can manage the flow and assign reviews to papers. The evaluations reviewers make are based on the same things that we have covered in this book. For example, the conference for the AEJMC ask reviewers to consider (on a five-point scale, from Excellent to Poor) a number of familiar research dimensions, including the paper's clarity of purpose, literature review, clarity of research method, appropriateness of research method, evidence presented clearly, evidence supportive of conclusions, general writing and organization, and the significance of the contribution to the field.

Beyond academia, it is likely you will more frequently apply the lessons of research methods as a critical consumer of news, politics, and everyday life. Just because some expert cites a number or presents a conclusion doesn't mean it's automatically true. John Allen Paulos, in his book  A Mathematician reads the newspaper , suggests some basic questions we can ask. "If statistics were presented, how were they obtained? How confident can we be of them? Were they derived from a random sample or from a collection of anecdotes? Does the correlation suggest a causal relationship, or is it merely a coincidence?" (1997, p. 201).

Through the study of research methods, we have begun to build a critical vocabulary and understanding to ask good questions when others present "knowledge." For example, if Candidate X won a straw poll in Iowa, does that mean she'll get her party's nomination? If Candidate Y wins an open primary in New Hampshire, does that mean he'll be the next president? If Candidate Z sheds a tear, does it matter what the context is, or whether that candidate is a man or a woman? What we learn in research methods about validity, reliability, sampling, variables, research participants, epistemology, grounded theory, and rhetoric, we can consider whether the "knowledge" that is presented in the news is a verifiable fact, a sound argument, or just conjecture.

American Psychological Association (2010). Publication manual of the American Psychological Association (6th ed.). Washington, DC: Author.

Broyles, S. (2011). "About poster sessions." AEJMC.  http://www.aejmc.org/home/2013/01/about-poster-sessions/ .

Faigley, L., George, D., Palchik, A., Selfe, C. (2004).  Picturing texts . New York: W.W. Norton & Company.

IBM (2011). Overview of Many Eyes.  http://www.research.ibm.com/social/projects_manyeyes.shtml .

McCandless, D. (2009).  The visual miscellaneum . New York: Collins Design.

Merskin, D. (2011). A boyfriend to die for: Edward Cullen as compensated psychopath in Stephanie Meyer's  Twilight. Journal of Communication Inquiry  35: 157-178. doi:10.1177/0196859911402992

Paulos, J. A. (1997).  A mathematician reads the newspaper . New York: Anchor.

Scott, J. (1996, May 18). Postmodern gravity deconstructed, slyly.  New York Times , http://www.nytimes.com/books/98/11/15/specials/sokal-text.html .

Sokal, A. (1996). Transgressing the boundaries: towards a transformative hermeneutics of quantum gravity.  Social Text  46/47, 217-252.

Tufte, E. R. (1990).  Envisioning information . Cheshire, CT: Graphics Press.

Tufte, E. R. (1983).  The visual display of quantitative information . Cheshire, CT: Graphics Press.

Turabian, Kate L. (2007).  A manual for writers of research papers, theses, and dissertations: Chicago style guide for students and researchers  (7th ed.). Chicago: University of Chicago Press.

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Reporting Research Results in APA Style | Tips & Examples

Published on December 21, 2020 by Pritha Bhandari . Revised on January 17, 2024.

The results section of a quantitative research paper is where you summarize your data and report the findings of any relevant statistical analyses.

The APA manual provides rigorous guidelines for what to report in quantitative research papers in the fields of psychology, education, and other social sciences.

Use these standards to answer your research questions and report your data analyses in a complete and transparent way.

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Table of contents

What goes in your results section, introduce your data, summarize your data, report statistical results, presenting numbers effectively, what doesn’t belong in your results section, frequently asked questions about results in apa.

In APA style, the results section includes preliminary information about the participants and data, descriptive and inferential statistics, and the results of any exploratory analyses.

Include these in your results section:

• Participant flow and recruitment period. Report the number of participants at every stage of the study, as well as the dates when recruitment took place.
• Missing data . Identify the proportion of data that wasn’t included in your final analysis and state the reasons.
• Any adverse events. Make sure to report any unexpected events or side effects (for clinical studies).
• Descriptive statistics . Summarize the primary and secondary outcomes of the study.
• Inferential statistics , including confidence intervals and effect sizes. Address the primary and secondary research questions by reporting the detailed results of your main analyses.
• Results of subgroup or exploratory analyses, if applicable. Place detailed results in supplementary materials.

Write up the results in the past tense because you’re describing the outcomes of a completed research study.

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Before diving into your research findings, first describe the flow of participants at every stage of your study and whether any data were excluded from the final analysis.

Participant flow and recruitment period

It’s necessary to report any attrition, which is the decline in participants at every sequential stage of a study. That’s because an uneven number of participants across groups sometimes threatens internal validity and makes it difficult to compare groups. Be sure to also state all reasons for attrition.

If your study has multiple stages (e.g., pre-test, intervention, and post-test) and groups (e.g., experimental and control groups), a flow chart is the best way to report the number of participants in each group per stage and reasons for attrition.

Also report the dates for when you recruited participants or performed follow-up sessions.

Missing data

Another key issue is the completeness of your dataset. It’s necessary to report both the amount and reasons for data that was missing or excluded.

Data can become unusable due to equipment malfunctions, improper storage, unexpected events, participant ineligibility, and so on. For each case, state the reason why the data were unusable.

Some data points may be removed from the final analysis because they are outliers—but you must be able to justify how you decided what to exclude.

If you applied any techniques for overcoming or compensating for lost data, report those as well.

Adverse events

For clinical studies, report all events with serious consequences or any side effects that occured.

Descriptive statistics summarize your data for the reader. Present descriptive statistics for each primary, secondary, and subgroup analysis.

Don’t provide formulas or citations for commonly used statistics (e.g., standard deviation) – but do provide them for new or rare equations.

Descriptive statistics

The exact descriptive statistics that you report depends on the types of data in your study. Categorical variables can be reported using proportions, while quantitative data can be reported using means and standard deviations . For a large set of numbers, a table is the most effective presentation format.

Include sample sizes (overall and for each group) as well as appropriate measures of central tendency and variability for the outcomes in your results section. For every point estimate , add a clearly labelled measure of variability as well.

Be sure to note how you combined data to come up with variables of interest. For every variable of interest, explain how you operationalized it.

According to APA journal standards, it’s necessary to report all relevant hypothesis tests performed, estimates of effect sizes, and confidence intervals.

When reporting statistical results, you should first address primary research questions before moving onto secondary research questions and any exploratory or subgroup analyses.

Present the results of tests in the order that you performed them—report the outcomes of main tests before post-hoc tests, for example. Don’t leave out any relevant results, even if they don’t support your hypothesis.

Inferential statistics

For each statistical test performed, first restate the hypothesis , then state whether your hypothesis was supported and provide the outcomes that led you to that conclusion.

Report the following for each hypothesis test:

• the test statistic value,
• the degrees of freedom ,
• the exact p- value (unless it is less than 0.001),
• the magnitude and direction of the effect.

When reporting complex data analyses, such as factor analysis or multivariate analysis, present the models estimated in detail, and state the statistical software used. Make sure to report any violations of statistical assumptions or problems with estimation.

Effect sizes and confidence intervals

For each hypothesis test performed, you should present confidence intervals and estimates of effect sizes .

Confidence intervals are useful for showing the variability around point estimates. They should be included whenever you report population parameter estimates.

Effect sizes indicate how impactful the outcomes of a study are. But since they are estimates, it’s recommended that you also provide confidence intervals of effect sizes.

Subgroup or exploratory analyses

Briefly report the results of any other planned or exploratory analyses you performed. These may include subgroup analyses as well.

Subgroup analyses come with a high chance of false positive results, because performing a large number of comparison or correlation tests increases the chances of finding significant results.

If you find significant results in these analyses, make sure to appropriately report them as exploratory (rather than confirmatory) results to avoid overstating their importance.

While these analyses can be reported in less detail in the main text, you can provide the full analyses in supplementary materials.

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To effectively present numbers, use a mix of text, tables , and figures where appropriate:

• To present three or fewer numbers, try a sentence ,
• To present between 4 and 20 numbers, try a table ,
• To present more than 20 numbers, try a figure .

Since these are general guidelines, use your own judgment and feedback from others for effective presentation of numbers.

Tables and figures should be numbered and have titles, along with relevant notes. Make sure to present data only once throughout the paper and refer to any tables and figures in the text.

Formatting statistics and numbers

It’s important to follow capitalization , italicization, and abbreviation rules when referring to statistics in your paper. There are specific format guidelines for reporting statistics in APA , as well as general rules about writing numbers .

If you are unsure of how to present specific symbols, look up the detailed APA guidelines or other papers in your field.

It’s important to provide a complete picture of your data analyses and outcomes in a concise way. For that reason, raw data and any interpretations of your results are not included in the results section.

It’s rarely appropriate to include raw data in your results section. Instead, you should always save the raw data securely and make them available and accessible to any other researchers who request them.

Making scientific research available to others is a key part of academic integrity and open science.

Interpretation or discussion of results

This belongs in your discussion section. Your results section is where you objectively report all relevant findings and leave them open for interpretation by readers.

While you should state whether the findings of statistical tests lend support to your hypotheses, refrain from forming conclusions to your research questions in the results section.

Explanation of how statistics tests work

For the sake of concise writing, you can safely assume that readers of your paper have professional knowledge of how statistical inferences work.

In an APA results section , you should generally report the following:

• Participant flow and recruitment period.
• Missing data and any adverse events.
• Descriptive statistics about your samples.
• Inferential statistics , including confidence intervals and effect sizes.
• Results of any subgroup or exploratory analyses, if applicable.

According to the APA guidelines, you should report enough detail on inferential statistics so that your readers understand your analyses.

• the test statistic value
• the degrees of freedom
• the exact p value (unless it is less than 0.001)
• the magnitude and direction of the effect

You should also present confidence intervals and estimates of effect sizes where relevant.

In APA style, statistics can be presented in the main text or as tables or figures . To decide how to present numbers, you can follow APA guidelines:

• To present three or fewer numbers, try a sentence,
• To present between 4 and 20 numbers, try a table,
• To present more than 20 numbers, try a figure.

Results are usually written in the past tense , because they are describing the outcome of completed actions.

The results chapter or section simply and objectively reports what you found, without speculating on why you found these results. The discussion interprets the meaning of the results, puts them in context, and explains why they matter.

In qualitative research , results and discussion are sometimes combined. But in quantitative research , it’s considered important to separate the objective results from your interpretation of them.

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Conducting and Writing Quantitative and Qualitative Research

Edward barroga.

1 Department of Medical Education, Showa University School of Medicine, Tokyo, Japan.

Glafera Janet Matanguihan

2 Department of Biological Sciences, Messiah University, Mechanicsburg, PA, USA.

Atsuko Furuta

Makiko arima, shizuma tsuchiya, chikako kawahara, yusuke takamiya.

Comprehensive knowledge of quantitative and qualitative research systematizes scholarly research and enhances the quality of research output. Scientific researchers must be familiar with them and skilled to conduct their investigation within the frames of their chosen research type. When conducting quantitative research, scientific researchers should describe an existing theory, generate a hypothesis from the theory, test their hypothesis in novel research, and re-evaluate the theory. Thereafter, they should take a deductive approach in writing the testing of the established theory based on experiments. When conducting qualitative research, scientific researchers raise a question, answer the question by performing a novel study, and propose a new theory to clarify and interpret the obtained results. After which, they should take an inductive approach to writing the formulation of concepts based on collected data. When scientific researchers combine the whole spectrum of inductive and deductive research approaches using both quantitative and qualitative research methodologies, they apply mixed-method research. Familiarity and proficiency with these research aspects facilitate the construction of novel hypotheses, development of theories, or refinement of concepts.

Graphical Abstract

INTRODUCTION

Novel research studies are conceptualized by scientific researchers first by asking excellent research questions and developing hypotheses, then answering these questions by testing their hypotheses in ethical research. 1 , 2 , 3 Before they conduct novel research studies, scientific researchers must possess considerable knowledge of both quantitative and qualitative research. 2

In quantitative research, researchers describe existing theories, generate and test a hypothesis in novel research, and re-evaluate existing theories deductively based on their experimental results. 1 , 4 , 5 In qualitative research, scientific researchers raise and answer research questions by performing a novel study, then propose new theories by clarifying their results inductively. 1 , 6

RATIONALE OF THIS ARTICLE

When researchers have a limited knowledge of both research types and how to conduct them, this can result in substandard investigation. Researchers must be familiar with both types of research and skilled to conduct their investigations within the frames of their chosen type of research. Thus, meticulous care is needed when planning quantitative and qualitative research studies to avoid unethical research and poor outcomes.

Understanding the methodological and writing assumptions 7 , 8 underpinning quantitative and qualitative research, especially by non-Anglophone researchers, is essential for their successful conduct. Scientific researchers, especially in the academe, face pressure to publish in international journals 9 where English is the language of scientific communication. 10 , 11 In particular, non-Anglophone researchers face challenges related to linguistic, stylistic, and discourse differences. 11 , 12 Knowing the assumptions of the different types of research will help clarify research questions and methodologies, easing the challenge and help.

SEARCH FOR RELEVANT ARTICLES

To identify articles relevant to this topic, we adhered to the search strategy recommended by Gasparyan et al. 7 We searched through PubMed, Scopus, Directory of Open Access Journals, and Google Scholar databases using the following keywords: quantitative research, qualitative research, mixed-method research, deductive reasoning, inductive reasoning, study design, descriptive research, correlational research, experimental research, causal-comparative research, quasi-experimental research, historical research, ethnographic research, meta-analysis, narrative research, grounded theory, phenomenology, case study, and field research.

AIMS OF THIS ARTICLE

This article aims to provide a comparative appraisal of qualitative and quantitative research for scientific researchers. At present, there is still a need to define the scope of qualitative research, especially its essential elements. 13 Consensus on the critical appraisal tools to assess the methodological quality of qualitative research remains lacking. 14 Framing and testing research questions can be challenging in qualitative research. 2 In the healthcare system, it is essential that research questions address increasingly complex situations. Therefore, research has to be driven by the kinds of questions asked and the corresponding methodologies to answer these questions. 15 The mixed-method approach also needs to be clarified as this would appear to arise from different philosophical underpinnings. 16

This article also aims to discuss how particular types of research should be conducted and how they should be written in adherence to international standards. In the US, Europe, and other countries, responsible research and innovation was conceptualized and promoted with six key action points: engagement, gender equality, science education, open access, ethics and governance. 17 , 18 International ethics standards in research 19 as well as academic integrity during doctoral trainings are now integral to the research process. 20

POTENTIAL BENEFITS FROM THIS ARTICLE

This article would be beneficial for researchers in further enhancing their understanding of the theoretical, methodological, and writing aspects of qualitative and quantitative research, and their combination.

Moreover, this article reviews the basic features of both research types and overviews the rationale for their conduct. It imparts information on the most common forms of quantitative and qualitative research, and how they are carried out. These aspects would be helpful for selecting the optimal methodology to use for research based on the researcher’s objectives and topic.

This article also provides information on the strengths and weaknesses of quantitative and qualitative research. Such information would help researchers appreciate the roles and applications of both research types and how to gain from each or their combination. As different research questions require different types of research and analyses, this article is anticipated to assist researchers better recognize the questions answered by quantitative and qualitative research.

Finally, this article would help researchers to have a balanced perspective of qualitative and quantitative research without considering one as superior to the other.

TYPES OF RESEARCH

Research can be classified into two general types, quantitative and qualitative. 21 Both types of research entail writing a research question and developing a hypothesis. 22 Quantitative research involves a deductive approach to prove or disprove the hypothesis that was developed, whereas qualitative research involves an inductive approach to create a hypothesis. 23 , 24 , 25 , 26

In quantitative research, the hypothesis is stated before testing. In qualitative research, the hypothesis is developed through inductive reasoning based on the data collected. 27 , 28 For types of data and their analysis, qualitative research usually includes data in the form of words instead of numbers more commonly used in quantitative research. 29

Quantitative research usually includes descriptive, correlational, causal-comparative / quasi-experimental, and experimental research. 21 On the other hand, qualitative research usually encompasses historical, ethnographic, meta-analysis, narrative, grounded theory, phenomenology, case study, and field research. 23 , 25 , 28 , 30 A summary of the features, writing approach, and examples of published articles for each type of qualitative and quantitative research is shown in Table 1 . 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43

QUANTITATIVE RESEARCH

Deductive approach.

The deductive approach is used to prove or disprove the hypothesis in quantitative research. 21 , 25 Using this approach, researchers 1) make observations about an unclear or new phenomenon, 2) investigate the current theory surrounding the phenomenon, and 3) hypothesize an explanation for the observations. Afterwards, researchers will 4) predict outcomes based on the hypotheses, 5) formulate a plan to test the prediction, and 6) collect and process the data (or revise the hypothesis if the original hypothesis was false). Finally, researchers will then 7) verify the results, 8) make the final conclusions, and 9) present and disseminate their findings ( Fig. 1A ).

Types of quantitative research

The common types of quantitative research include (a) descriptive, (b) correlational, c) experimental research, and (d) causal-comparative/quasi-experimental. 21

Descriptive research is conducted and written by describing the status of an identified variable to provide systematic information about a phenomenon. A hypothesis is developed and tested after data collection, analysis, and synthesis. This type of research attempts to factually present comparisons and interpretations of findings based on analyses of the characteristics, progression, or relationships of a certain phenomenon by manipulating the employed variables or controlling the involved conditions. 44 Here, the researcher examines, observes, and describes a situation, sample, or variable as it occurs without investigator interference. 31 , 45 To be meaningful, the systematic collection of information requires careful selection of study units by precise measurement of individual variables 21 often expressed as ranges, means, frequencies, and/or percentages. 31 , 45 Descriptive statistical analysis using ANOVA, Student’s t -test, or the Pearson coefficient method has been used to analyze descriptive research data. 46

Correlational research is performed by determining and interpreting the extent of a relationship between two or more variables using statistical data. This involves recognizing data trends and patterns without necessarily proving their causes. The researcher studies only the data, relationships, and distributions of variables in a natural setting, but does not manipulate them. 21 , 45 Afterwards, the researcher establishes reliability and validity, provides converging evidence, describes relationship, and makes predictions. 47

Experimental research is usually referred to as true experimentation. The researcher establishes the cause-effect relationship among a group of variables making up a study using the scientific method or process. This type of research attempts to identify the causal relationships between variables through experiments by arbitrarily controlling the conditions or manipulating the variables used. 44 The scientific manuscript would include an explanation of how the independent variable was manipulated to determine its effects on the dependent variables. The write-up would also describe the random assignments of subjects to experimental treatments. 21

Causal-comparative/quasi-experimental research closely resembles true experimentation but is conducted by establishing the cause-effect relationships among variables. It may also be conducted to establish the cause or consequences of differences that already exist between, or among groups of individuals. 48 This type of research compares outcomes between the intervention groups in which participants are not randomized to their respective interventions because of ethics- or feasibility-related reasons. 49 As in true experiments, the researcher identifies and measures the effects of the independent variable on the dependent variable. However, unlike true experiments, the researchers do not manipulate the independent variable.

In quasi-experimental research, naturally formed or pre-existing groups that are not randomly assigned are used, particularly when an ethical, randomized controlled trial is not feasible or logical. 50 The researcher identifies control groups as those which have been exposed to the treatment variable, and then compares these with the unexposed groups. The causes are determined and described after data analysis, after which conclusions are made. The known and unknown variables that could still affect the outcome are also included. 7

QUALITATIVE RESEARCH

Inductive approach.

Qualitative research involves an inductive approach to develop a hypothesis. 21 , 25 Using this approach, researchers answer research questions and develop new theories, but they do not test hypotheses or previous theories. The researcher seldom examines the effectiveness of an intervention, but rather explores the perceptions, actions, and feelings of participants using interviews, content analysis, observations, or focus groups. 25 , 45 , 51

Distinctive features of qualitative research

Qualitative research seeks to elucidate about the lives of people, including their lived experiences, behaviors, attitudes, beliefs, personality characteristics, emotions, and feelings. 27 , 30 It also explores societal, organizational, and cultural issues. 30 This type of research provides a good story mimicking an adventure which results in a “thick” description that puts readers in the research setting. 52

The qualitative research questions are open-ended, evolving, and non-directional. 26 The research design is usually flexible and iterative, commonly employing purposive sampling. The sample size depends on theoretical saturation, and data is collected using in-depth interviews, focus groups, and observations. 27

In various instances, excellent qualitative research may offer insights that quantitative research cannot. Moreover, qualitative research approaches can describe the ‘lived experience’ perspectives of patients, practitioners, and the public. 53 Interestingly, recent developments have looked into the use of technology in shaping qualitative research protocol development, data collection, and analysis phases. 54

Qualitative research employs various techniques, including conversational and discourse analysis, biographies, interviews, case-studies, oral history, surveys, documentary and archival research, audiovisual analysis, and participant observations. 26

Conducting qualitative research

To conduct qualitative research, investigators 1) identify a general research question, 2) choose the main methods, sites, and subjects, and 3) determine methods of data documentation access to subjects. Researchers also 4) decide on the various aspects for collecting data (e.g., questions, behaviors to observe, issues to look for in documents, how much (number of questions, interviews, or observations), 5) clarify researchers’ roles, and 6) evaluate the study’s ethical implications in terms of confidentiality and sensitivity. Afterwards, researchers 7) collect data until saturation, 8) interpret data by identifying concepts and theories, and 9) revise the research question if necessary and form hypotheses. In the final stages of the research, investigators 10) collect and verify data to address revisions, 11) complete the conceptual and theoretical framework to finalize their findings, and 12) present and disseminate findings ( Fig. 1B ).

Types of qualitative research

The different types of qualitative research include (a) historical research, (b) ethnographic research, (c) meta-analysis, (d) narrative research, (e) grounded theory, (f) phenomenology, (g) case study, and (h) field research. 23 , 25 , 28 , 30

Historical research is conducted by describing past events, problems, issues, and facts. The researcher gathers data from written or oral descriptions of past events and attempts to recreate the past without interpreting the events and their influence on the present. 6 Data is collected using documents, interviews, and surveys. 55 The researcher analyzes these data by describing the development of events and writes the research based on historical reports. 2

Ethnographic research is performed by observing everyday life details as they naturally unfold. 2 It can also be conducted by developing in-depth analytical descriptions of current systems, processes, and phenomena or by understanding the shared beliefs and practices of a particular group or culture. 21 The researcher collects extensive narrative non-numerical data based on many variables over an extended period, in a natural setting within a specific context. To do this, the researcher uses interviews, observations, and active participation. These data are analyzed by describing and interpreting them and developing themes. A detailed report of the interpreted data is then provided. 2 The researcher immerses himself/herself into the study population and describes the actions, behaviors, and events from the perspective of someone involved in the population. 23 As examples of its application, ethnographic research has helped to understand a cultural model of family and community nursing during the coronavirus disease 2019 outbreak. 56 It has also been used to observe the organization of people’s environment in relation to cardiovascular disease management in order to clarify people’s real expectations during follow-up consultations, possibly contributing to the development of innovative solutions in care practices. 57

Meta-analysis is carried out by accumulating experimental and correlational results across independent studies using a statistical method. 21 The report is written by specifying the topic and meta-analysis type. In the write-up, reporting guidelines are followed, which include description of inclusion criteria and key variables, explanation of the systematic search of databases, and details of data extraction. Meta-analysis offers in-depth data gathering and analysis to achieve deeper inner reflection and phenomenon examination. 58

Narrative research is performed by collecting stories for constructing a narrative about an individual’s experiences and the meanings attributed to them by the individual. 9 It aims to hear the voice of individuals through their account or experiences. 17 The researcher usually conducts interviews and analyzes data by storytelling, content review, and theme development. The report is written as an in-depth narration of events or situations focused on the participants. 2 , 59 Narrative research weaves together sequential events from one or two individuals to create a “thick” description of a cohesive story or narrative. 23 It facilitates understanding of individuals’ lives based on their own actions and interpretations. 60

Grounded theory is conducted by engaging in an inductive ground-up or bottom-up strategy of generating a theory from data. 24 The researcher incorporates deductive reasoning when using constant comparisons. Patterns are detected in observations and then a working hypothesis is created which directs the progression of inquiry. The researcher collects data using interviews and questionnaires. These data are analyzed by coding the data, categorizing themes, and describing implications. The research is written as a theory and theoretical models. 2 In the write-up, the researcher describes the data analysis procedure (i.e., theoretical coding used) for developing hypotheses based on what the participants say. 61 As an example, a qualitative approach has been used to understand the process of skill development of a nurse preceptor in clinical teaching. 62 A researcher can also develop a theory using the grounded theory approach to explain the phenomena of interest by observing a population. 23

Phenomenology is carried out by attempting to understand the subjects’ perspectives. This approach is pertinent in social work research where empathy and perspective are keys to success. 21 Phenomenology studies an individual’s lived experience in the world. 63 The researcher collects data by interviews, observations, and surveys. 16 These data are analyzed by describing experiences, examining meanings, and developing themes. The researcher writes the report by contextualizing and reporting the subjects’ experience. This research approach describes and explains an event or phenomenon from the perspective of those who have experienced it. 23 Phenomenology understands the participants’ experiences as conditioned by their worldviews. 52 It is suitable for a deeper understanding of non-measurable aspects related to the meanings and senses attributed by individuals’ lived experiences. 60

Case study is conducted by collecting data through interviews, observations, document content examination, and physical inspections. The researcher analyzes the data through a detailed identification of themes and the development of narratives. The report is written as an in-depth study of possible lessons learned from the case. 2

Field research is performed using a group of methodologies for undertaking qualitative inquiries. The researcher goes directly to the social phenomenon being studied and observes it extensively. In the write-up, the researcher describes the phenomenon under the natural environment over time with no implantation of controls or experimental conditions. 45

DIFFERENCES BETWEEN QUANTITATIVE AND QUALITATIVE RESEARCH

Scientific researchers must be aware of the differences between quantitative and qualitative research in terms of their working mechanisms to better understand their specific applications. This knowledge will be of significant benefit to researchers, especially during the planning process, to ensure that the appropriate type of research is undertaken to fulfill the research aims.

In terms of quantitative research data evaluation, four well-established criteria are used: internal validity, external validity, reliability, and objectivity. 23 The respective correlating concepts in qualitative research data evaluation are credibility, transferability, dependability, and confirmability. 30 Regarding write-up, quantitative research papers are usually shorter than their qualitative counterparts, which allows the latter to pursue a deeper understanding and thus producing the so-called “thick” description. 29

Interestingly, a major characteristic of qualitative research is that the research process is reversible and the research methods can be modified. This is in contrast to quantitative research in which hypothesis setting and testing take place unidirectionally. This means that in qualitative research, the research topic and question may change during literature analysis, and that the theoretical and analytical methods could be altered during data collection. 44

Quantitative research focuses on natural, quantitative, and objective phenomena, whereas qualitative research focuses on social, qualitative, and subjective phenomena. 26 Quantitative research answers the questions “what?” and “when?,” whereas qualitative research answers the questions “why?,” “how?,” and “how come?.” 64

Perhaps the most important distinction between quantitative and qualitative research lies in the nature of the data being investigated and analyzed. Quantitative research focuses on statistical, numerical, and quantitative aspects of phenomena, and employ the same data collection and analysis, whereas qualitative research focuses on the humanistic, descriptive, and qualitative aspects of phenomena. 26 , 28

Structured versus unstructured processes

The aims and types of inquiries determine the difference between quantitative and qualitative research. In quantitative research, statistical data and a structured process are usually employed by the researcher. Quantitative research usually suggests quantities (i.e., numbers). 65 On the other hand, researchers typically use opinions, reasons, verbal statements, and an unstructured process in qualitative research. 63 Qualitative research is more related to quality or kind. 65

In quantitative research, the researcher employs a structured process for collecting quantifiable data. Often, a close-ended questionnaire is used wherein the response categories for each question are designed in which values can be assigned and analyzed quantitatively using a common scale. 66 Quantitative research data is processed consecutively from data management, then data analysis, and finally to data interpretation. Data should be free from errors and missing values. In data management, variables are defined and coded. In data analysis, statistics (e.g., descriptive, inferential) as well as central tendency (i.e., mean, median, mode), spread (standard deviation), and parameter estimation (confidence intervals) measures are used. 67

In qualitative research, the researcher uses an unstructured process for collecting data. These non-statistical data may be in the form of statements, stories, or long explanations. Various responses according to respondents may not be easily quantified using a common scale. 66

Composing a qualitative research paper resembles writing a quantitative research paper. Both papers consist of a title, an abstract, an introduction, objectives, methods, findings, and discussion. However, a qualitative research paper is less regimented than a quantitative research paper. 27

Quantitative research as a deductive hypothesis-testing design

Quantitative research can be considered as a hypothesis-testing design as it involves quantification, statistics, and explanations. It flows from theory to data (i.e., deductive), focuses on objective data, and applies theories to address problems. 45 , 68 It collects numerical or statistical data; answers questions such as how many, how often, how much; uses questionnaires, structured interview schedules, or surveys 55 as data collection tools; analyzes quantitative data in terms of percentages, frequencies, statistical comparisons, graphs, and tables showing statistical values; and reports the final findings in the form of statistical information. 66 It uses variable-based models from individual cases and findings are stated in quantified sentences derived by deductive reasoning. 24

In quantitative research, a phenomenon is investigated in terms of the relationship between an independent variable and a dependent variable which are numerically measurable. The research objective is to statistically test whether the hypothesized relationship is true. 68 Here, the researcher studies what others have performed, examines current theories of the phenomenon being investigated, and then tests hypotheses that emerge from those theories. 4

Quantitative hypothesis-testing research has certain limitations. These limitations include (a) problems with selection of meaningful independent and dependent variables, (b) the inability to reflect subjective experiences as variables since variables are usually defined numerically, and (c) the need to state a hypothesis before the investigation starts. 61

Qualitative research as an inductive hypothesis-generating design

Qualitative research can be considered as a hypothesis-generating design since it involves understanding and descriptions in terms of context. It flows from data to theory (i.e., inductive), focuses on observation, and examines what happens in specific situations with the aim of developing new theories based on the situation. 45 , 68 This type of research (a) collects qualitative data (e.g., ideas, statements, reasons, characteristics, qualities), (b) answers questions such as what, why, and how, (c) uses interviews, observations, or focused-group discussions as data collection tools, (d) analyzes data by discovering patterns of changes, causal relationships, or themes in the data; and (e) reports the final findings as descriptive information. 61 Qualitative research favors case-based models from individual characteristics, and findings are stated using context-dependent existential sentences that are justifiable by inductive reasoning. 24

In qualitative research, texts and interviews are analyzed and interpreted to discover meaningful patterns characteristic of a particular phenomenon. 61 Here, the researcher starts with a set of observations and then moves from particular experiences to a more general set of propositions about those experiences. 4

Qualitative hypothesis-generating research involves collecting interview data from study participants regarding a phenomenon of interest, and then using what they say to develop hypotheses. It involves the process of questioning more than obtaining measurements; it generates hypotheses using theoretical coding. 61 When using large interview teams, the key to promoting high-level qualitative research and cohesion in large team methods and successful research outcomes is the balance between autonomy and collaboration. 69

Qualitative data may also include observed behavior, participant observation, media accounts, and cultural artifacts. 61 Focus group interviews are usually conducted, audiotaped or videotaped, and transcribed. Afterwards, the transcript is analyzed by several researchers.

Qualitative research also involves scientific narratives and the analysis and interpretation of textual or numerical data (or both), mostly from conversations and discussions. Such approach uncovers meaningful patterns that describe a particular phenomenon. 2 Thus, qualitative research requires skills in grasping and contextualizing data, as well as communicating data analysis and results in a scientific manner. The reflective process of the inquiry underscores the strengths of a qualitative research approach. 2

Combination of quantitative and qualitative research

When both quantitative and qualitative research methods are used in the same research, mixed-method research is applied. 25 This combination provides a complete view of the research problem and achieves triangulation to corroborate findings, complementarity to clarify results, expansion to extend the study’s breadth, and explanation to elucidate unexpected results. 29

Moreover, quantitative and qualitative findings are integrated to address the weakness of both research methods 29 , 66 and to have a more comprehensive understanding of the phenomenon spectrum. 66

For data analysis in mixed-method research, real non-quantitized qualitative data and quantitative data must both be analyzed. 70 The data obtained from quantitative analysis can be further expanded and deepened by qualitative analysis. 23

In terms of assessment criteria, Hammersley 71 opined that qualitative and quantitative findings should be judged using the same standards of validity and value-relevance. Both approaches can be mutually supportive. 52

Quantitative and qualitative research must be carefully studied and conducted by scientific researchers to avoid unethical research and inadequate outcomes. Quantitative research involves a deductive process wherein a research question is answered with a hypothesis that describes the relationship between independent and dependent variables, and the testing of the hypothesis. This investigation can be aptly termed as hypothesis-testing research involving the analysis of hypothesis-driven experimental studies resulting in a test of significance. Qualitative research involves an inductive process wherein a research question is explored to generate a hypothesis, which then leads to the development of a theory. This investigation can be aptly termed as hypothesis-generating research. When the whole spectrum of inductive and deductive research approaches is combined using both quantitative and qualitative research methodologies, mixed-method research is applied, and this can facilitate the construction of novel hypotheses, development of theories, or refinement of concepts.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

• Conceptualization: Barroga E, Matanguihan GJ.
• Data curation: Barroga E, Matanguihan GJ, Furuta A, Arima M, Tsuchiya S, Kawahara C, Takamiya Y, Izumi M.
• Formal analysis: Barroga E, Matanguihan GJ, Furuta A, Arima M, Tsuchiya S, Kawahara C.
• Investigation: Barroga E, Matanguihan GJ, Takamiya Y, Izumi M.
• Methodology: Barroga E, Matanguihan GJ, Furuta A, Arima M, Tsuchiya S, Kawahara C, Takamiya Y, Izumi M.
• Project administration: Barroga E, Matanguihan GJ.
• Resources: Barroga E, Matanguihan GJ, Furuta A, Arima M, Tsuchiya S, Kawahara C, Takamiya Y, Izumi M.
• Supervision: Barroga E.
• Validation: Barroga E, Matanguihan GJ, Furuta A, Arima M, Tsuchiya S, Kawahara C, Takamiya Y, Izumi M.
• Visualization: Barroga E, Matanguihan GJ.
• Writing - original draft: Barroga E, Matanguihan GJ.
• Writing - review & editing: Barroga E, Matanguihan GJ, Furuta A, Arima M, Tsuchiya S, Kawahara C, Takamiya Y, Izumi M.

Quantitative Observation: Everything You Need To Know

What’s the best way to gather data that doesn’t leave you second-guessing?

If you’re dealing with research, you know how important it is to get solid, reliable data.

That’s where quantitative observation steps in.

In this article, we’ll look into everything you need to know about quantitative observation.

We’ll cover what it is, how it’s different from qualitative observation, and why it’s so widely used across various fields like education, healthcare, and marketing.

By the end, you’ll see why this method is a go-to for researchers who need precise, measurable results:

What is quantitative observation?

Quantitative observation is a research method that involves collecting and analyzing numerical data about people, objects, or events. It’s often used to measure specific variables, such as frequency, duration, or intensity. Quantitative observation can be conducted in various settings, including laboratories, classrooms, and public places.

Quantitative and qualitative observation – what’s the difference?

When it comes to research, you’ll often hear about two main types of observations: quantitative and qualitative .

Both have their place, but they’re pretty different in what they focus on and how they’re used.

Let’s break it down.

Focus on numbers vs. descriptions

Quantitative observations are all about numbers. If you can count it, measure it, or express it in figures, it falls into the quantitative camp.

Think of things like:

• the temperature of a room,
• the number of people in a line,
• or the speed of a car.

This type of observation gives you hard data that you can analyze and compare.

On the other hand, qualitative observations focus on descriptions. They’re about the qualities of what you’re observing.

For example, instead of saying, “The car is going 60 mph,” you’d say, “The car is moving quickly.” It’s more about what something is like than how much there is of it.

Objectivity vs. subjectivity

Quantitative observations are usually more objective. The data you gather isn’t influenced by opinions or feelings – it’s just numbers . This makes it reliable when you’re looking for facts that can be backed up by statistical analysis.

Qualitative observations, however, are more subjective.

They depend on the observer’s perspective and interpretation. Two people might describe the same event differently, which can make this type of observation more varied and rich, but also less consistent.

Measurable data vs. rich detail

When you gather quantitative data, you’re looking for specific measurements.

This might include things like:

• or quantity.

It’s precise and can be used in graphs, charts, and statistical models.

Qualitative data, though, is more about the details that don’t fit into neat little boxes.

It includes things like colors, textures, feelings, and experiences. This data is harder to measure, but it adds depth and context to your research.

Standardization vs. flexibility

Quantitative observation methods are usually standardized. You use the same tools and processes each time to make sure your data is consistent. This is great for making comparisons across different studies or groups.

Qualitative observation, in contrast, is more flexible. It allows you to explore your subject in a more open-ended way, which can lead to new insights and understanding that you might miss with a more rigid approach.

So, whether you’re counting heads or describing feelings, both quantitative and qualitative observations play important roles in research. Each brings something valuable to the table, helping you see the full picture.

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The benefits of quantitative observations

Quantitative observation has attractive advantages, and the most important ones are:

It provides objective and reliable data that can be analyzed statistically

When you’re collecting quantitative observation data, you’re gathering facts that are clear-cut and free from personal bias.

This makes the data objective and reliable, which is a big deal in scientific research.

With these numbers in hand, you can engage in statistical analysis, where patterns and relationships start to emerge.

The beauty of this approach is that it strips away guesswork, leaving you with solid evidence that can back up your findings.

Unlike qualitative observation, which leans on descriptions, quantitative observations give you something concrete to work with.

It allows for precise measurement and comparison of variables

When it comes to measuring and comparing variables, quantitative research is the tool of choice.

Quantitative observation methods focus on capturing exact values – whether it’s the height of a plant, the number of customers, or the temperature of a liquid.

This precision is key in the research process because it lets you compare different factors head-to-head.

With standardized observation techniques, the data you gather is consistent and reliable across the board.

It doesn’t matter if you’re working on a big project or just trying to understand a small detail, quantitative observations help you keep everything measured and comparable.

It can be used to test hypotheses and identify patterns and trends

In scientific research, testing hypotheses is a key part of the job.

Quantitative observation research plays a huge role here.

Thanks to gathering quantitative data through systematic observation, you can put your ideas to the test.

The numbers you collect can either support your hypothesis or show you where things aren’t adding up.

Plus, as you gather more data, you start to see patterns and trends that weren’t obvious at first.

This is where quantitative and qualitative observation work hand in hand.

The hard numbers from quantitative research point you in the right direction, while qualitative observations add the context you need to understand the bigger picture.

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What is nominal data?

Where is quantitative observation applied? Top use cases

Quantitative observation can be used in a variety of fields, including:

Marketing: measuring customer behavior and preferences

Imagine a store tracking how many customers stop to look at a new product display or how long they spend browsing a particular aisle.

These numbers tell a story about what catches people’s attention and what doesn’t.

For instance, a study published in the International Journal of Advertising explored the effectiveness of retail window displays as part of the marketing mix.

The researchers worked with Boots the Chemists and Nottingham Business School to measure how window display design influences consumer-buying behavior.

They found that connecting buying behavior to specific marketing elements, like window displays, made sales forecasting more predictable.

If a lot of people are lingering by a new clothing line but not buying, it might suggest they’re interested but need a nudge, maybe a sale or better positioning.

This kind of data helps businesses tweak their strategies to match customer behavior.

Education: assessing student engagement and learning outcomes

In education, teachers often use quantitative observation to see how students are engaging with their lessons.

For example, a study presented in the Journal of Educational Psychology introduced the Behavioral Engagement Related to Instruction (BERI) protocol.

This protocol was specifically designed for large university classrooms to measure student engagement levels through quantitative observation data.

The BERI protocol involves tracking student behaviors in real-time, offering teachers immediate feedback on how well students are engaging with the material.

For instance, if students are actively participating in discussions or focusing on tasks during lectures, the data collected can show high levels of engagement.

On the other hand, if students appear distracted or disengaged, the data can highlight areas where the teaching method might need adjustment.

These numbers help educators identify which teaching strategies are working and which might need a different approach. If the protocol shows that students are more engaged during interactive lessons compared to traditional lectures, it indicates a need to incorporate more interactive elements into the curriculum.

This kind of targeted feedback helps instructors refine their methods to improve student learning outcomes.

Psychology: studying human behavior and cognition

Psychologists use quantitative observation to dig into the details of human behavior.

For example, a well-known study in the field of memory research conducted by Ebbinghaus in the late 19th century focused on how quickly people forget information.

In this study, participants were asked to memorize lists of nonsense syllables, and then their recall was tested at different time intervals.

The researchers measured how many syllables participants could remember after varying lengths of time, such as immediately after learning, after a few hours, and after several days.

The numbers collected from these tests helped to map out the “forgetting curve,” which shows that memory retention decreases sharply soon after learning but then levels off over time.

This type of quantitative data is often used in psychology, as it helps researchers understand how memory works and how factors like stress or fatigue might impact recall.

Sociology: investigating social phenomena and trends

In sociology, quantitative observation helps researchers understand broader social trends.

A notable study published in the American Political Science Review examined voting behavior across various neighborhoods in a large metropolitan area.

The researchers collected quantitative data on voter turnout by tracking the number of people who participated in elections in different districts over several election cycles.

The study revealed that neighborhoods with lower voter turnout often had higher levels of economic disadvantage, lower educational attainment, and less access to transportation.

These patterns were not immediately obvious without the data. By analyzing the numbers, sociologists were able to identify the social factors that contributed to lower voting rates.

This type of research helps sociologists understand the underlying reasons for such trends and suggests potential interventions.

For instance, the findings might prompt community programs aimed at increasing voter education or improving access to polling stations.

Quantitative observation in sociology is essential for uncovering these hidden patterns and driving efforts to address social inequalities.

Healthcare: evaluating the effectiveness of medical treatments and interventions

In healthcare, quantitative observation is useful for evaluating the effectiveness of medical treatments.

A well-known example is the clinical trial of the drug Streptomycin in the treatment of tuberculosis, conducted in the late 1940s.

This was one of the first randomized controlled trials (RCTs) in medical history, which set the standard for future clinical research.

In this study, researchers quantitatively observed and recorded the number of patients who showed improvement in their tuberculosis symptoms after taking Streptomycin compared to those who received a placebo.

The results showed a statistically significant improvement in the recovery rates among those treated with the drug, confirming its effectiveness.

This study provided clear evidence of the drug’s efficacy, shaping the future of tuberculosis treatment and demonstrating the power of quantitative observation in healthcare.

Thanks to systematically tracking patient outcomes, healthcare professionals were able to make informed decisions about adopting Streptomycin as a standard treatment.

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SurveyLab for quantitative observation – how does it work?

SurveyLab is a tool that takes quantitative observation to the next level.

If you’re looking to gather precise data and gain deep insights, this platform has you covered.

With SurveyLab, you can create online tests that score automatically and make data collection straightforward.

It doesn’t matter if you’re measuring customer satisfaction, employee engagement, or any other metric, the platform’s scoring mechanism helps you keep everything in check.

• One of the standout features is the ability to set up complex satisfaction indicators and key performance indicators (KPIs). These metrics give you a clear picture of what’s working and what needs attention.
• Plus, with the advanced analytical tools that SurveyLab offers, you can engage in data analysis and discover patterns you might have missed otherwise.
• The platform lets you generate graphical reports that make your findings easy to understand and share. And if you need to dig deeper, you can export the data for further analysis.

But SurveyLab isn’t just about gathering data – it’s about making sense of it.

The combination of scoring, metrics, data collection, and data analysis tools means you can conduct quantitative observations that lead to real, actionable insights.

It’s like having a full toolkit at your disposal, ready to help you make informed decisions based on solid data.

Ready to see how SurveyLab can change your quantitative observation efforts?

Try it today and access the insights that will drive your success.

And for more educational content, check our blog out .

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Home » Quantitative Research: Definition, Methods, and Examples

Quantitative Research: Definition, Methods, and Examples

June 13, 2023 max 8min read.

This article covers:

What Is Quantitative Research?

Quantitative research methods .

• Data Collection and Analysis

Types of Quantitative Research

• Advantages and Disadvantages of Quantitative Research

Examples of Quantitative Research

Picture this: you’re a product or project manager and must make a crucial decision. You need data-driven insights to guide your choices, understand customer preferences, and predict market trends. That’s where quantitative research comes into play. It’s like having a secret weapon that empowers you to make informed decisions confidently.

Quantitative research is all about numbers, statistics, and measurable data. It’s a systematic approach that allows you to gather and analyze numerical information to uncover patterns, trends, and correlations. 

Quantitative research provides concrete, objective data to drive your strategies, whether conducting surveys, analyzing large datasets, or crunching numbers.

In this article, we’ll dive and learn all about quantitative research; get ready to uncover the power of numbers.

Quantitative Research Definition:

Quantitative research is a systematic and objective approach to collecting, analyzing, and interpreting numerical data. It measures and quantifies variables, employing statistical methods to uncover patterns, relationships, and trends.

Quantitative research gets utilized across a wide range of fields, including market research, social sciences, psychology, economics, and healthcare. It follows a structured methodology that uses standardized instruments, such as surveys, experiments, or polls, to collect data. This data is then analyzed using statistical techniques to uncover patterns and relationships.

The purpose of quantitative research is to measure and quantify variables, assess the connections between variables, and draw objective and generalizable conclusions. Its benefits are numerous:

• Rigorous and scientific approach : Quantitative research provides a comprehensive and scientific approach to studying phenomena. It enables researchers to gather empirical evidence and draw reliable conclusions based on solid data.
• Evidence-based decision-making : By utilizing quantitative research, researchers can make evidence-based decisions. It helps in developing informed strategies and evaluating the effectiveness of interventions or policies by relying on data-driven insights.
• Advancement of knowledge : Quantitative research contributes to the advancement of knowledge by building upon existing theories. It expands understanding in various fields and informs future research directions, allowing for continued growth and development.

Here are various quantitative research methods:

Survey research : This method involves collecting data from a sample of individuals through questionnaires, interviews, or online surveys. Surveys gather information about people’s attitudes, opinions, behaviors, and characteristics.

Experimentation: It is a research method that allows researchers to determine cause-and-effect relationships. In an experiment, participants randomly get assigned to different groups. While the other group does not receive treatment or intervention, one group does. The outcomes of the two groups then get measured to analyze the effects of the treatment or intervention.

Here are the steps involved in an experiment:

• Define the research question. What do you want to learn about?
• Develop a hypothesis. What do you think the answer to your research question is?
• Design the experiment. How will you manipulate the variables and measure the outcomes?
• Recruit participants. Who will you study?
• Randomly assign participants to groups. This ensures that the groups are as similar as possible.
• Apply the treatments or interventions. This is what the researcher is attempting to test the effects of.
• Measure the outcomes. This is how the researcher will determine whether the treatments or interventions had any effect.
• Analyze the data. This is how the researcher will determine whether the results support the hypothesis.
• Draw conclusions. What do the results mean?
• Content analysis : Content analysis is a systematic approach to analyzing written, verbal, or visual communication. Researchers identify and categorize specific content, themes, or patterns in various forms of media, such as books, articles, speeches, or social media posts.
• Secondary data analysis : It is a research method that involves analyzing data already collected by someone else. This data can be from various sources, such as government reports, previous research studies, or large datasets like surveys or medical records. 

Researchers use secondary data analysis to answer new research questions or gain additional insights into a topic.

Data Collection and Analysis for Quantitative Research

Quantitative research is research that uses numbers and statistics to answer questions. It often measures things like attitudes, behaviors, and opinions.

There are three main methods for collecting quantitative data:

• Surveys and questionnaires: These are structured instruments used to gather data from a sample of people.
• Experiments and controlled observations: These are conducted in a controlled setting to measure variables and determine cause-and-effect relationships.
• Existing data sources (secondary data): This data gets collected from databases, archives, or previous studies.

Data preprocessing and cleaning is the first step in data analysis. It involves identifying and correcting errors, removing outliers, and ensuring the data is consistent.

Descriptive statistics is a branch of statistics that deals with the description of the data. It summarizes and describes the data using central tendency, variability, and shape measures.

Inferential statistics again comes under statistics which deals with the inference of properties of a population from a sample. It tests hypotheses, estimates parameters, and makes predictions.

Here are some of the most common inferential statistical techniques:

• Hypothesis testing : This assesses the significance of relationships or differences between variables.
• Confidence intervals : This estimates the range within which population parameters likely fall.
• Correlation and regression analysis : This examines relationships and predicts outcomes based on variables.
• Analysis of variance (ANOVA) : This compare means across multiple groups or conditions.

Statistical software and tools for data analysis can perform complex statistical analyses efficiently. Some of the most popular statistical software packages include SPSS, SAS, and R.

Here are some of the main types of quantitative research methodology:

• Descriptive research describes a particular population’s characteristics, trends, or behaviors. For example, a descriptive study might look at the average height of students in a school, the number of people who voted in an election, or the types of food people eat.
• Correlational research checks the relationship between two or more variables. For example, a correlational study might examine the relationship between income and happiness or stress and weight gain. Correlational research can show that two variables are related but cannot show that one variable causes the other.
• Experimental research is a type of research that investigates cause-and-effect relationships. In an experiment, researchers manipulate one variable (the independent variable) and measure the impact on another variable (the dependent variable). This allows researchers to make inferences about the relationship between the two variables.
• Quasi-experimental research is similar to experimental research. However, it does not involve random assignment of participants to groups. This can be due to practical or ethical considerations, such as when assigning people to receive a new medication randomly is impossible. In quasi-experimental research, researchers try to control for other factors affecting the results, such as the participant’s age, gender, or health status.
• Longitudinal research studies change patterns over an extended time. For example, a longitudinal study might examine how children’s reading skills develop over a few years or how people’s attitudes change as they age. But longitudinal research can be expensive and time-consuming. Still, it can offer valuable insights into how people and things change over time.

 Advantages and Disadvantages of Quantitative Research

Here are the advantages and downsides of quantitative research:

Advantages of Quantitative Research:

• Objectivity: Quantitative research aims to be objective and unbiased. This is because it relies on numbers and statistical methods, which reduce the potential for researcher bias and subjective interpretation.
• Generalizability: Quantitative research often involves large sample sizes, which increases the likelihood of obtaining representative data. The study findings are more likely to apply to a wider population.
• Replicability: Using standardized procedures and measurement instruments in quantitative research enhances replicability. This means that other researchers can repeat the study using the same methods to test the reliability of the findings.
• Statistical analysis: Quantitative research employs various statistical techniques for data analysis. This allows researchers to identify data patterns, relationships, and associations. Additionally, statistical analysis can provide precision and help draw objective conclusions.
• Numerical precision: Quantitative research produces numerical data that can be analyzed using mathematical calculations. This numeric precision allows for clear comparisons and quantitative interpretations.

Disadvantages of Quantitative Research :

• Lack of Contextual Understanding : Quantitative research often focuses on measurable variables, which may limit the exploration of complex phenomena. It may overlook the social, cultural, and contextual factors that could influence the research findings.
• Limited Insight : While quantitative research can identify correlations and associations, it may not uncover underlying causes or explanations of these relationships. It may provide answers to “what” and “how much,” but not necessarily “why.”
• Potential for Simplification : The quantification of data can lead to oversimplification, as it may reduce complex phenomena into numerical values. This simplification may overlook nuances and intricacies important to understanding the research topic fully.
• Cost and Time-Intensive : Quantitative research requires significant resources. It includes time, funding, and specialized expertise. Researchers must collect and analyze large amounts of numerical data, which can be lengthy and expensive.
• Limited Flexibility : A systematic and planned strategy typically gets employed in quantitative research. It signifies the researcher’s use of a predetermined data collection and analysis approach. As a result, you may be more confident that your study gets conducted consistently and equitably. But it may also make it more difficult for the researcher to change the research plan or pose additional inquiries while gathering data. This could lead to missing valuable insights.

Here are some real-life examples of quantitative research:

• Market Research : Quantitative market research is a type of market research that uses numerical data to understand consumer preferences, buying behavior, and market trends. This data typically gets gathered through surveys and questionnaires, which are then analyzed to make informed business decisions.
• Health Studies : Quantitative research, such as clinical trials and epidemiological research, is vital in health studies. Researchers collect numerical data on treatment effectiveness, disease prevalence, risk factors, and patient outcomes. This data is then analyzed statistically to draw conclusions and make evidence-based recommendations for healthcare practices.
• Educational Research : Quantitative research is used extensively in educational studies to examine various aspects of learning, teaching methods, and academic achievement. Researchers collect data through standardized tests, surveys, or observations. The reason for this approach is to analyze factors influencing student performance, educational interventions, and educational policy effectiveness.
• Social Science Surveys : Social science researchers often employ quantitative research methods. The aim here is to study social phenomena and gather data on individuals’ or groups’ attitudes, beliefs, and behaviors. Large-scale surveys collect numerical data, then statistically analyze to identify patterns, trends, and associations within the population.
• Opinion Polls : Opinion polls and public opinion research rely heavily on quantitative research techniques. Polling organizations conduct surveys with representative samples of the population. The companies do this intending to gather numerical data on public opinions, political preferences, and social attitudes. The data then gets analyzed to gauge public sentiment and predict election outcomes or public opinion on specific issues.
• Economic Research : Quantitative research is widely used in economic studies to analyze economic indicators, trends, and patterns. Economists collect numerical data on GDP, inflation, employment, and consumer spending. Statistical analysis of this data helps understand economic phenomena, forecast future trends, and inform economic policy decisions.

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Qualitative research is about understanding and exploring something in depth. It uses non-numerical data, like interviews, observations, and open-ended survey responses, to gather rich, descriptive insights. Quantitative research is about measuring and analyzing relationships between variables using numerical data.

Quantitative research gets characterized by the following:

• The collection of numerical information
• The use of statistical analysis
• The goal of measuring and quantifying phenomena
• The purpose of examining relationships between variables
• The purpose of generalizing findings to a larger population
• The use of large sample sizes
• The use of structured surveys or experiments
• The usage of statistical techniques to analyze data objectively

The primary goal of quantitative research is to gather numerical data and analyze it statistically to uncover patterns, relationships, and trends. It aims to provide objective and generalizable insights using systematic data collection methods, standardized instruments, and statistical analysis techniques. Quantitative research seeks to test hypotheses, make predictions, and inform decision-making in various fields.

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Speaker 1: Welcome to this overview of quantitative research methods. This tutorial will give you the big picture of quantitative research and introduce key concepts that will help you determine if quantitative methods are appropriate for your project study. First, what is educational research? Educational research is a process of scholarly inquiry designed to investigate the process of instruction and learning, the behaviors, perceptions, and attributes of students and teachers, the impact of institutional processes and policies, and all other areas of the educational process. The research design may be quantitative, qualitative, or a mixed methods design. The focus of this overview is quantitative methods. The general purpose of quantitative research is to explain, predict, investigate relationships, describe current conditions, or to examine possible impacts or influences on designated outcomes. Quantitative research differs from qualitative research in several ways. It works to achieve different goals and uses different methods and design. This table illustrates some of the key differences. Qualitative research generally uses a small sample to explore and describe experiences through the use of thick, rich descriptions of detailed data in an attempt to understand and interpret human perspectives. It is less interested in generalizing to the population as a whole. For example, when studying bullying, a qualitative researcher might learn about the experience of the victims and the experience of the bully by interviewing both bullies and victims and observing them on the playground. Quantitative studies generally use large samples to test numerical data by comparing or finding correlations among sample attributes so that the findings can be generalized to the population. If quantitative researchers were studying bullying, they might measure the effects of a bully on the victim by comparing students who are victims and students who are not victims of bullying using an attitudinal survey. In conducting quantitative research, the researcher first identifies the problem. For Ed.D. research, this problem represents a gap in practice. For Ph.D. research, this problem represents a gap in the literature. In either case, the problem needs to be of importance in the professional field. Next, the researcher establishes the purpose of the study. Why do you want to do the study, and what do you intend to accomplish? This is followed by research questions which help to focus the study. Once the study is focused, the researcher needs to review both seminal works and current peer-reviewed primary sources. Based on the research question and on a review of prior research, a hypothesis is created that predicts the relationship between the study's variables. Next, the researcher chooses a study design and methods to test the hypothesis. These choices should be informed by a review of methodological approaches used to address similar questions in prior research. Finally, appropriate analytical methods are used to analyze the data, allowing the researcher to draw conclusions and inferences about the data, and answer the research question that was originally posed. In quantitative research, research questions are typically descriptive, relational, or causal. Descriptive questions constrain the researcher to describing what currently exists. With a descriptive research question, one can examine perceptions or attitudes as well as more concrete variables such as achievement. For example, one might describe a population of learners by gathering data on their age, gender, socioeconomic status, and attributes towards their learning experiences. Relational questions examine the relationship between two or more variables. The X variable has some linear relationship to the Y variable. Causal inferences cannot be made from this type of research. For example, one could study the relationship between students' study habits and achievements. One might find that students using certain kinds of study strategies demonstrate greater learning, but one could not state conclusively that using certain study strategies will lead to or cause higher achievement. Causal questions, on the other hand, are designed to allow the researcher to draw a causal inference. A causal question seeks to determine if a treatment variable in a program had an effect on one or more outcome variables. In other words, the X variable influences the Y variable. For example, one could design a study that answered the question of whether a particular instructional approach caused students to learn more. The research question serves as a basis for posing a hypothesis, a predicted answer to the research question that incorporates operational definitions of the study's variables and is rooted in the literature. An operational definition matches a concept with a method of measurement, identifying how the concept will be quantified. For example, in a study of instructional strategies, the hypothesis might be that students of teachers who use Strategy X will exhibit greater learning than students of teachers who do not. In this study, one would need to operationalize learning by identifying a test or instrument that would measure learning. This approach allows the researcher to create a testable hypothesis. Relational and causal research relies on the creation of a null hypothesis, a version of the research hypothesis that predicts no relationship between variables or no effect of one variable on another. When writing the hypothesis for a quantitative question, the null hypothesis and the research or alternative hypothesis use parallel sentence structure. In this example, the null hypothesis states that there will be no statistical difference between groups, while the research or alternative hypothesis states that there will be a statistical difference between groups. Note also that both hypothesis statements operationalize the critical thinking skills variable by identifying the measurement instrument to be used. Once the research questions and hypotheses are solidified, the researcher must select a design that will create a situation in which the hypotheses can be tested and the research questions answered. Ideally, the research design will isolate the study's variables and control for intervening variables so that one can be certain of the relationships being tested. In educational research, however, it is extremely difficult to establish sufficient controls in the complex social settings being studied. In our example of investigating the impact of a certain instructional strategy in the classroom on student achievement, each day the teacher uses a specific instructional strategy. After school, some of the students in her class receive tutoring. Other students have parents that are very involved in their child's academic progress and provide learning experiences in the home. These students may do better because they received extra help, not because the teacher's instructional strategy is more effective. Unless the researcher can control for the intervening variable of extra help, it will be impossible to effectively test the study's hypothesis. Quantitative research designs can fall into two broad categories, experimental and quasi-experimental. Classic experimental designs are those that randomly assign subjects to either a control or treatment comparison group. The researcher can then compare the treatment group to the control group to test for an intervention's effect, known as a between-subject design. It is important to note that the control group may receive a standard treatment or may receive a treatment of any kind. Quasi-experimental designs do not randomly assign subjects to groups, but rather take advantage of existing groups. A researcher can still have a control and comparison group, but assignment to the groups is not random. The use of a control group is not required. However, the researcher may choose a design in which a single group is pre- and post-tested, known as a within-subjects design. Or a single group may receive only a post-test. Since quasi-experimental designs lack random assignment, the researcher should be aware of the threats to validity. Educational research often attempts to measure abstract variables such as attitudes, beliefs, and feelings. Surveys can capture data about these hard-to-measure variables, as well as other self-reported information such as demographic factors. A survey is an instrument used to collect verifiable information from a sample population. In quantitative research, surveys typically include questions that ask respondents to choose a rating from a scale, select one or more items from a list, or other responses that result in numerical data. Studies that use surveys or tests need to include strategies that establish the validity of the instrument used. There are many types of validity that need to be addressed. Face validity. Does the test appear at face value to measure what it is supposed to measure? Content validity. Content validity includes both item validity and sampling validity. Item validity ensures that the individual test items deal only with the subject being addressed. Sampling validity ensures that the range of item topics is appropriate to the subject being studied. For example, item validity might be high, but if all the items only deal with one aspect of the subjects, then sampling validity is low. Content validity can be established by having experts in the field review the test. Concurrent validity. Does a new test correlate with an older, established test that measures the same thing? Predictive validity. Does the test correlate with another related measure? For example, GRE tests are used at many colleges because these schools believe that a good grade on this test increases the probability that the student will do well at the college. Linear regression can establish the predictive validity of a test. Construct validity. Does the test measure the construct it is intended to measure? Establishing construct validity can be a difficult task when the constructs being measured are abstract. But it can be established by conducting a number of studies in which you test hypotheses regarding the construct, or by completing a factor analysis to ensure that you have the number of constructs that you say you have. In addition to ensuring the validity of instruments, the quantitative researcher needs to establish their reliability as well. Strategies for establishing reliability include Test retest. Correlates scores from two different administrations of the same test. Alternate forms. Correlates scores from administrations of two different forms of the same test. Split half reliability. Treats each half of one test or survey as a separate administration and correlates the results from each. Internal consistency. Uses Cronbach's coefficient alpha to calculate the average of all possible split halves. Quantitative research almost always relies on a sample that is intended to be representative of a larger population. There are two basic sampling strategies, random and non-random, and a number of specific strategies within each of these approaches. This table provides examples of each of the major strategies. The next section of this tutorial provides an overview of the procedures in conducting quantitative data analysis. There are specific procedures for conducting the data collection, preparing for and analyzing data, presenting the findings, and connecting to the body of existing research. This process ensures that the research is conducted as a systematic investigation that leads to credible results. Data comes in various sizes and shapes, and it is important to know about these so that the proper analysis can be used on the data. In 1946, S.S. Stevens first described the properties of measurement systems that allowed decisions about the type of measurement and about the attributes of objects that are preserved in numbers. These four types of data are referred to as nominal, ordinal, interval, and ratio. First, let's examine nominal data. With nominal data, there is no number value that indicates quantity. Instead, a number has been assigned to represent a certain attribute, like the number 1 to represent male and the number 2 to represent female. In other words, the number is just a label. You could also assign numbers to represent race, religion, or any other categorical information. Nominal data only denotes group membership. With ordinal data, there is again no indication of quantity. Rather, a number is assigned for ranking order. For example, satisfaction surveys often ask respondents to rank order their level of satisfaction with services or programs. The next level of measurement is interval data. With interval data, there are equal distances between two values, but there is no natural zero. A common example is the Fahrenheit temperature scale. Differences between the temperature measurements make sense, but ratios do not. For instance, 20 degrees Fahrenheit is not twice as hot as 10 degrees Fahrenheit. You can add and subtract interval level data, but they cannot be divided or multiplied. Finally, we have ratio data. Ratio is the same as interval, however ratios, means, averages, and other numerical formulas are all possible and make sense. Zero has a logical meaning, which shows the absence of, or having none of. Examples of ratio data are height, weight, speed, or any quantities based on a scale with a natural zero. In summary, nominal data can only be counted. Ordinal data can be counted and ranked. Interval data can also be added and subtracted, and ratio data can also be used in ratios and other calculations. Determining what type of data you have is one of the most important aspects of quantitative analysis. Depending on the research question, hypotheses, and research design, the researcher may choose to use descriptive and or inferential statistics to begin to analyze the data. Descriptive statistics are best illustrated when viewed through the lens of America's pastimes. Sports, weather, economy, stock market, and even our retirement portfolio are presented in a descriptive analysis. Basic terminology for descriptive statistics are terms that we are most familiar in this discipline. Frequency, mean, median, mode, range, variance, and standard deviation. Simply put, you are describing the data. Some of the most common graphic representations of data are bar graphs, pie graphs, histograms, and box and whisker graphs. Attempting to reach conclusions and make causal inferences beyond graphic representations or descriptive analyses is referred to as inferential statistics. In other words, examining the college enrollment of the past decade in a certain geographical region would assist in estimating what the enrollment for the next year might be. Frequently in education, the means of two or more groups are compared. When comparing means to assist in answering a research question, one can use a within-group, between-groups, or mixed-subject design. In a within-group design, the researcher compares measures of the same subjects across time, therefore within-group, or under different treatment conditions. This can also be referred to as a dependent-group design. The most basic example of this type of quasi-experimental design would be if a researcher conducted a pretest of a group of students, subjected them to a treatment, and then conducted a post-test. The group has been measured at different points in time. In a between-group design, subjects are assigned to one of the two or more groups. For example, Control, Treatment 1, Treatment 2. Ideally, the sampling and assignment to groups would be random, which would make this an experimental design. The researcher can then compare the means of the treatment group to the control group. When comparing two groups, the researcher can gain insight into the effects of the treatment. In a mixed-subjects design, the researcher is testing for significant differences between two or more independent groups while subjecting them to repeated measures. Choosing a statistical test to compare groups depends on the number of groups, whether the data are nominal, ordinal, or interval, and whether the data meet the assumptions for parametric tests. Nonparametric tests are typically used with nominal and ordinal data, while parametric tests use interval and ratio-level data. In addition to this, some further assumptions are made for parametric tests that the data are normally distributed in the population, that participant selection is independent, and the selection of one person does not determine the selection of another, and that the variances of the groups being compared are equal. The assumption of independent participant selection cannot be violated, but the others are more flexible. The t-test assesses whether the means of two groups are statistically different from each other. This analysis is appropriate whenever you want to compare the means of two groups, and especially appropriate as the method of analysis for a quasi-experimental design. When choosing a t-test, the assumptions are that the data are parametric. The analysis of variance, or ANOVA, assesses whether the means of more than two groups are statistically different from each other. When choosing an ANOVA, the assumptions are that the data are parametric. The chi-square test can be used when you have non-parametric data and want to compare differences between groups. The Kruskal-Wallis test can be used when there are more than two groups and the data are non-parametric. Correlation analysis is a set of statistical tests to determine whether there are linear relationships between two or more sets of variables from the same list of items or individuals, for example, achievement and performance of students. The tests provide a statistical yes or no as to whether a significant relationship or correlation exists between the variables. A correlation test consists of calculating a correlation coefficient between two variables. Again, there are parametric and non-parametric choices based on the assumptions of the data. Pearson R correlation is widely used in statistics to measure the strength of the relationship between linearly related variables. Spearman-Rank correlation is a non-parametric test that is used to measure the degree of association between two variables. Spearman-Rank correlation test does not assume any assumptions about the distribution. Spearman-Rank correlation test is used when the Pearson test gives misleading results. Often a Kendall-Taw is also included in this list of non-parametric correlation tests to examine the strength of the relationship if there are less than 20 rankings. Linear regression and correlation are similar and often confused. Sometimes your methodologist will encourage you to examine both the calculations. Calculate linear correlation if you measured both variables, x and y. Make sure to use the Pearson parametric correlation coefficient if you are certain you are not violating the test assumptions. Otherwise, choose the Spearman non-parametric correlation coefficient. If either variable has been manipulated using an intervention, do not calculate a correlation. While linear regression does indicate the nature of the relationship between two variables, like correlation, it can also be used to make predictions because one variable is considered explanatory while the other is considered a dependent variable. Establishing validity is a critical part of quantitative research. As with the nature of quantitative research, there is a defined approach or process for establishing validity. This also allows for the findings transferability. For a study to be valid, the evidence must support the interpretations of the data, the data must be accurate, and their use in drawing conclusions must be logical and appropriate. Construct validity concerns whether what you did for the program was what you wanted to do, or whether what you observed was what you wanted to observe. Construct validity concerns whether the operationalization of your variables are related to the theoretical concepts you are trying to measure. Are you actually measuring what you want to measure? Internal validity means that you have evidence that what you did in the study, i.e., the program, caused what you observed, i.e., the outcome, to happen. Conclusion validity is the degree to which conclusions drawn about relationships in the data are reasonable. External validity concerns the process of generalizing, or the degree to which the conclusions in your study would hold for other persons in other places and at other times. Establishing reliability and validity to your study is one of the most critical elements of the research process. Once you have decided to embark upon the process of conducting a quantitative study, use the following steps to get started. First, review research studies that have been conducted on your topic to determine what methods were used. Consider the strengths and weaknesses of the various data collection and analysis methods. Next, review the literature on quantitative research methods. Every aspect of your research has a body of literature associated with it. Just as you would not confine yourself to your course textbooks for your review of research on your topic, you should not limit yourself to your course texts for your review of methodological literature. Read broadly and deeply from the scholarly literature to gain expertise in quantitative research. Additional self-paced tutorials have been developed on different methodologies and techniques associated with quantitative research. Make sure that you complete all of the self-paced tutorials and review them as often as needed. You will then be prepared to complete a literature review of the specific methodologies and techniques that you will use in your study. Thank you for watching.

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• Published: 06 September 2024

An ecological approach to understanding transitions and tensions in complex learning contexts

• Luke McCrone   ORCID: orcid.org/0000-0003-4133-1853 1 &
• Martyn Kingsbury 1  

npj Science of Learning volume  9 , Article number:  54 ( 2024 ) Cite this article

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The move away from transmission-based lecturing toward a more student-centred active learning approach is well evidenced in STEM higher education. However, the examination of active learning has generally remained confined to formal timetabled contexts, with assumptions made that students independently manage the transition between timetabled and non-timetabled learning. This paper introduces research findings from a mixed methods study that used an ecological approach when investigating student transitions between a formal lecture theatre and adjacent informal breakout space in a UK STEM university. Using quantitative occupancy monitoring data to analyse usage patterns of both spaces, in combination with qualitative ethnographic observations and field interviews, permitted a purposeful exploration of student engagement with transitions within and between the two learning spaces. The ecological approach aided the discovery of spatial, pedagogic and agentic transitions and tensions, which subsequently informed strategic modification of space across the institution to facilitate the adoption of active learning pedagogy.

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Introduction.

There is increasingly widespread recognition in UK higher education that traditional transmission lecturing is less effective than more student-centred active learning, particularly in science, technology, engineering, and mathematics (STEM) fields 1 , 2 . Active learning aligns with the social constructivist perspective 3 , where instructional activities require learners to actively construct knowledge and integrate it with existing knowledge and experiences 4 . A separate body of literature has investigated and evidenced the relationship between this learning activity and the role of physical space 5 . Traditional learning spaces like lecture theatres that are designed for transmission-based pedagogies present challenges for enabling more active pedagogies 6 . The development of active learning classrooms, for example, signals a gradual shift away from the spatial and pedagogic assumptions underpinning these traditional approaches, which have prevailed since the time they were used for ancient oral traditions 7 . However, much of the literature has focussed on active learning within formal timetabled contexts 2 , with assumptions made that students independently manage transitions across timetabled and non-timetabled learning 8 . Turning our attention to informal learning spaces 9 , particularly those spaces adjacent to formal classrooms like corridors 10 , can help us to develop a more holistic understanding of active learning and the challenges of transitioning in both space and pedagogic intent.

The institution researched in this paper has grappled with transforming its curriculum, pedagogy and built estate to achieve its strategic objectives linked to active learning, technology enhancement, and equality, diversity and inclusion. To gain insight into the impact of these strategic changes, the present research explored how students physically transition between timetabled and non-timetabled learning spaces 8 . However, the underlying institutional tension between promoting active pedagogies and teaching those pedagogies within traditional educational infrastructure, whilst not impossible, presented challenging complexity. This tension is particularly pertinent as pedagogies and spaces diversify with the emergence of hybrid, apprenticeship-based and lifelong learning and the evolution of spaces that blur boundaries such as makerspaces 11 . With ‘misunderstanding’ being considered one of three drivers of unsustainable development 12 , there is first a need to discard less appropriate models of how complex, dynamic systems such as universities truly work. Several scholars, such as Ronald Barnett 13 and Ian Kinchin 14 , have consequently challenged the higher education sector to think more ecologically. By drawing from ‘ecology’, a biological term used to inspect the complexity of the interrelationship between organisms and their environment 16 , they argue that we stand a better chance of addressing important issues relating to sustainability, widening participation and lifelong learning while equipping students for rapidly changing future needs and challenges 12 .

Ecological principles have been applied to human social and mental activity since the 1970s 17 and can be similarly drawn upon in this context: interconnectedness, systems thinking, resilience, continuous learning, sustainability, and biodiversity of people and ideas 15 . Ecological systems theory, for example, has broadened our understanding of human development, positing that individuals shape their own learning while simultaneously influencing other people and activities in both their immediate and more remotely connected environments 18 , 19 . This thinking has since extended to ‘ecological leadership’ to perceive leaders as members embedded and distributed within the ecosystem who can become aware of the institution’s ‘natural history’ 14 . This contextual history is best uncovered using ethnography to retrieve a diversity of voices from different settings and to observe the activities and beliefs of those living in those settings 20 . The notion of an ‘ecological university’ therefore embraces the imagination and creativity of individual learners who transform ideas, relationships, materials, and themselves while supporting actors in gaining an understanding of their context, each other, and the epistemic environments in which they learn 21 . Common-pool resource theory posits that the collective management of tangible and intangible resources, such as physical and social spaces, can maximise sustainable output and foster a sense of ‘place’ in universities 22 . Providing opportunities and conditions in which students can cultivate a shared sense of ownership and agency in spaces therefore seems to be important 23 and can be supported via participatory design-based approaches 10 and other initiatives that distribute leadership 24 .

In setting out to study physical transitions between a lecture theatre and adjacent informal space, we soon realised from our initial data that students were navigating several transitions. Therefore, in this paper, ‘transition’ refers both to students moving between these physical spaces, as well as to changes in pedagogic intent, such as transitioning from teacher-centred didactic delivery to student-centred group-based learning. ‘Tensions’ represent the conflicting pressures that often drive or result from these changes. We therefore use the terms transition and tension in this paper to describe multiple phenomena. By understanding how students are engaging with these transitions and tensions between formal and adjacent spaces, this paper seeks to make a unique contribution to our understanding of active learning and pedagogic space. We argue that through the use of mixed methods 25 and an ecological approach, the institution became more aware of these transitions and tensions within the context of its natural history 14 . The ecological approach has assisted in understanding the complex interaction between learning space, pedagogy and agency, as well as the conditions under which students can actively learn. This awareness has informed subsequent modifications to institutional spaces as part of a more sustainable, inclusive and productive learning ecology.

Spatial transitions and tensions

Lecture theatre and adjacent breakout space occupancy data were analysed in the context of the timetable to better understand the relative usage patterns before and after timetabled activity; the term ‘spatial’ is hence used here to describe the intersection between the physical spaces and the timetable. We defined two types of frequently recurring timetable configurations, type 1 and type 2 (see Fig. 1 ), to study transitions between the two spaces in different contexts. The nature of student engagement with and pedagogic potential of these transition periods in each timetable configuration was subsequently further investigated using a mixed methods approach. This approach generated data that informed the conceptualisation of these transition periods as on-ramp (space and time just before the timetabled lecture) and off-ramp (space and time just after the timetabled lecture) transitions, labelled in Fig. 1 .

Definition of type 1 and type 2 timetable configurations that were repeatable and analysable for the lecture theatre with on-ramp and off-ramp transitions labelled.

The group of students ‘meaningfully’ engaging in on-ramp and/or off-ramp transitions was defined as the percentage of the incoming or outgoing lecture cohort who remained in the breakout space for longer than 5 min. These transitions are annotated as opposing arrows in Fig. 2 which displays a type 2 timetable occupancy profile to mark periods in which student cohorts seemed to enter or exit the lecture theatre and gather in the adjacent breakout space. The architectural divide between the two spaces resulted in predictable inflows and outflows of users that were able to be visualised using occupancy plots (like in Fig. 2 ).

Lecture theatre and breakout space occupancy plot labelled with type 2 timetable and occupancy changes as cohorts transition into (on-ramp) and out of (off-ramp) the lecture theatre.

Although our interest was primarily in students associated with the lecture cohort, the anonymity of the occupancy data meant that the identities of these students could only be inferred; ethnographic observations clarified the apparent identities and activities of users, while field interviews enabled us to confirm these demographic details for key instances. Ethnographic observation and interviews in the adjacent breakout space confirmed that, in general, most students were waiting and preparing for the timetabled lecture during the on-ramp transition period and reflecting and planning together during the off-ramp transition period. This qualitative data collection hence supported our characterisation of the breakout space as an area of pedagogical potential proximal to the lecture theatre. Being equipped with comfortable furniture and amenities better-supported students in their engagement with this potential during on-ramp and off-ramp transitions and associated learning activities.

The automated recording of occupancy data enabled the analysis of occupancy patterns across the expected, timetabled working week for 6 weeks with 14 days (120 h) data studied in more detail to confirm and further investigate the type 1 and type 2 timetable configurations. Subtracting lecture theatre and breakout space occupancy during timetable crossover periods provided data from which averages could be calculated for the percentage of year 1 or year 2 cohorts remaining and engaging with transitions in the breakout space. The average percentages provide insight into the potential influence of the timetable configuration on whether students were more or less likely to use the adjacent breakout space during transition periods. Based on the arithmetic mean, more (43%) of the students meaningfully engaged in the off-ramp transition during the ‘bridge’ space following the 1st year lecture in the type 2 timetable, whereas less (19%) of the students meaningfully engaged in the off-ramp transition in the type 1 timetable (see Fig. 1 ). The type 1 configuration seemed to provide less incentive for 1st year students to remain in the breakout space beyond their 9:00–11:00 lectures given that their timetabled expected interactions were not in that space. This was confirmed in field interviews, for example, a 1st year chemical engineering student was observed leaving her 10:00–11:00 chemistry lecture and explained how she typically used the breakout space:

“I usually tend to just quickly go over stuff, often with friends…purely because of its location and proximity to the lecture theatre”.

This behaviour of quickly reviewing material was mirrored during observation of other students leaving the same lecture 10 min prior, two of whom were overheard sharing “I kind of lost it after…” and “Did you understand…” as they stood to leave the lecture theatre just after the session had finished. While two students were observed asking the lecturer questions at the front of the lecture theatre, all but three immediately exited the lecture theatre and breakout space, likely heading to the campus library, to buy lunch or return to their hall accommodation.

In the type 1 timetable, 20% of the students meaningfully engaged in the on-ramp transition in the breakout space prior to their 2nd year lecture, whereas more (52%) of the 1st year students engaged in the on-ramp transition in the type 2 timetable (see Fig. 1 ). These statistics further support the potential of the ‘bridge’ period which might be understood as the interaction between the 1st year off-ramp and on-ramp transition periods in the type 2 timetable configuration. Ethnographic observation of student engagement within this bridge period revealed tensions between the expected timetabled teaching activity and more self-directed non-timetabled space and/or time. While occupancy analysis provided cohort-level insight into the spatial transitions within this bridge space and/or time (see Fig. 2 for example), observations aided in the discovery of less obvious complexities, such as tensions and potential within and between spaces and/or time. Field interviews with students deepened this understanding by confirming differences in student intent during this bridge time to either remain in the lecture theatre or breakout space ahead of the second lecture or migrate elsewhere to buy coffee or lunch, for example. Two 1st year chemical engineering students who departed their first lecture just before 11:00 (at the start of a bridge period) were observed moving together to a sofa in the breakout space. Overhearing their conversation, one admitted “The one thing I didn’t get…” to the other, who responded by confessing “How did he go from these three to these…” whilst pointing at paper notes. This observation strongly suggested that the two students were discussing difficult content from their fluid mechanics lecture, which was confirmed in a field interview in which they explained “Yes we were discussing concepts from the lecture” and that they “use this space between lectures”, stating their reason for using it as:

“It is purely because of the convenient location being by our lectures and that it is quite quiet. It is also quite spacious”.

The bridge period on this same day was dissimilarly used by a group of five 1st year students from the same cohort who chose to remain in the lecture theatre until around 11:26. Whilst they opted to use the lecture theatre instead of the breakout space, they seemed to engage in similar behaviour by clarifying misunderstandings with each other, before leaving the space ahead of their next lecture at 12:00. These findings suggest that the type 2 configuration may have the potential for more meaningful transition engagement than the type 1 configuration by providing 1st year students (who planned to attend both lectures) with a ‘bridge’ 11:00–12:00 containing 1 h of self-directed pedagogic potential sandwiched between 2 h of formal teaching contact.

Further understanding how these timetable configurations create the potential for connection and transition between timetabled activities might lead to alternative ways of informing timetable and instructional design and supporting active learning. While the mixed methods approach assisted in understanding transitions and tensions, a deeper understanding of this dynamic system eventually became limited by the language and concepts adopted. Taking an ecological approach helped us to overcome this limitation by enabling us to broaden the ethnographic focus on spatial transitions and tensions to also include those concerning pedagogy and agency. Furthermore, this approach crucially led to thinking about spaces not only as separate ecosystems but also as interconnected and integrated ecological zones within which there is different potential.

Pedagogic transitions and tensions

Conceptualising the lecture theatre and breakout space as part of an interconnected ecological zone with on-ramp and off-ramp transitions provided an alternative way of thinking about the flows of people and information between the timetabled and non-timetabled periods. Ethnographically observing these transitions provided more nuanced insight into the tensions and student behaviours typical of these periods. The physical configuration, timetabled intention and historic usage of the lecture theatre created fixed patterns of expected behaviour, which centred around transmission-based approaches to teaching and learning where the teacher typically stands at the front and lectures to students who absorb information in the row-by-row seating area. The fixed power dynamic of the lecture theatre can often suppress student interaction and result in ‘failed’, hidden, and postponed pedagogic interactions 8 . ‘Failed’ pedagogic interactions are characterised by periods in which students with internal confusion feel disempowered and disinclined to raise their hand and ask questions, even when invited to do so by the lecturer. In instances where students ask a question, the lecturer may often simply provide an answer and, in so doing, maintain the expected flow of transmitted information and reinforce the power dynamic. While some lecturers occasionally deviate from this pedagogic style by, for example, encouraging class discussion using real-time surveys, they often feel limited by the inherent expectation of the physical space.

By choosing not to volunteer misunderstandings in front of the class during such ‘failed’ pedagogic interactions, some students can instead undergo hidden pedagogic interactions. These can be observed as brief periods of whispering and question exchange between neighbouring students or as students consulting personal technology during the lecture. They are termed ‘hidden’ because they do not explicitly conform to transmission-based pedagogic expectations and are often actively discouraged by the teacher who commands students’ attention. One lecturer was observed responding to this type of student interaction in a 1st year lecture by telling the whole class to “please listen carefully, this is extremely important” and further reinforcing the expected dynamic by responding to perceived distraction with “no talking please”.

Other students may respond differently to their internal confusion by ‘postponing’ their questions to the end of the lecture. Some students undergoing these postponed pedagogic interactions during an off-ramp transition were observed gathering at the front of the lecture theatre to question the lecturer individually and more privately. Furthermore, the sociogram in Fig. 3 typifies group learning behaviour observed during an off-ramp transition period in the breakout space. In this case, the sociogram was recorded as soon as students left a 2nd year lecture timetabled until 16:00 to observe how members of the cohort engaged with the transition between the lecture theatre and adjacent space. The sociogram was recorded on a day configured with a type 2 timetable during an off-ramp transition much like the final one labelled on the occupancy plot in Fig. 2 (for a different day). As seen in the sociogram, a large group of eight students was observed engaging in a postponed pedagogic interaction in which they left the lecture theatre and immediately organised themselves around a table in the breakout space. The size of the group meant it was inappropriate to approach them for a field interview, yet their conversational tone and behaviour suggested they were discussing something work-related and possibly in relation to a “project”. The sociogram helped us to identify and approach a different chemical engineering student for a field interview in the corner of the breakout space who had also left the 2nd year lecture. She shared that she used the breakout space “because of its location” and that it is “quite comfortable because of the furniture”. This supports the view that the presence of such amenities and affordances outside of the lecture theatre can support students’ postponed pedagogic interactions.

Digitised sociogram of the breakout space showing user behaviour and interaction during an off-ramp transition immediately after a 2nd year lecture in the adjacent lecture theatre.

As both teachers and students are subject to power dynamics and associated expectations that promote the transmission/absorption of information during timetabled learning, passive transmission-based learning is often perceived as a lower-risk activity than more student-centred active learning. In suppressing certain pedagogic potentials, a ‘pedagogic tension’ exists, within which students can sometimes manage the tension and more freely exercise their pedagogic intent. These pedagogic potentials were observed as ‘failed’, hidden and postponed pedagogic interactions, the behavioural signatures of which were visualised as sociograms (such as in Fig. 3 ) and confirmed using field interviews. Conceptualising this pedagogic tension between traditional passive learning and active learning as an ecological phenomenon changed how we thought about the space between the lecture theatre and the breakout space, while also recognising more broadly the opportunities for strategic changes in pedagogic mindset and behaviour.

Agentic transitions and tensions

Investigating how agency and ‘ownership’ transitioned between the timetabled lecture theatre and non-timetabled breakout space and/or time periods aided in obtaining a better understanding of the conditions needed for inclusive active learning. Transmission-based teaching created pressure for students to experience ‘failed’, hidden and postponed pedagogic intent. On-ramp and off-ramp periods were ethnographically determined to be important spaces for supporting these transitions and managing tensions, as well as key sites of strategic change. Field interviews confirmed that students perceived less ownership of the lecture theatre than the teacher, who predominantly controlled the lecture interaction. Students also confirmed that they perceived the adjacent breakout area as a more democratic space, including one student who explained how they “can talk here and make some noise” whilst feeling “free to do anything”. In informal conversations, teachers concurred that they found it helpful to answer one-on-one student questions in a more informal setting like the breakout space.

The fixed power dynamic and potential fear of judgement often implicit in ‘typical’ lecture theatre interactions seemed to emphasise a more democratic power dynamic in the breakout space, such that students felt emancipated to direct questions and take more risks in their learning in this adjacent space. Student-directed questioning of peers and the teacher, as they physically moved from the lecture theatre into the breakout space during off-ramp transitions, suggested a renewed sense of agentic ownership in their learning. The affordances and flexibility of the breakout space meant that students under their own control could form pairs and small learning groups more easily than in the row-by-row lecture theatre. Ethnographic observations confirmed that this apparently incidental and procedural activity resulted in important dialogue and sharing between students, their peers and the teacher. The ‘agentic tension’ between the teacher-owned lecture theatre and the student-owned breakout space might be understood more broadly as an important tension between the historic way of learning traditionally in a lecture theatre and the pedagogic intent for learners to discover and explore via more active learning. Understanding this agentic tension also led to new ways of considering/managing the pedagogic balance between teacher-centred transmission and student-centred independent learning; this potential balance is dynamic and different for every student, cohort and teacher.

The public nature of the breakout space also led to the possibility of tensions between different user groups who perceived differing ownership of the space. These tensions changed throughout the academic year—based on who frequented the adjacent lecture theatre and surrounding spaces—and influenced the perceived agency of different users. The lecture theatre predominantly served early-year undergraduate students, who, as a function of their habitual ingress and egress via the breakout space, somewhat resulted in cohort familiarity and feelings of ownership; other research has shown that the intensity of these feelings can increase with the time spent there 26 . Studying on-ramp and off-ramp transitions and tensions between the two spaces provided insight into how different user groups interacted and vied for space ownership depending on their planned or unplanned activity. Later-year undergraduate students, for example, reported a change in their use of the breakout space (since their time as early-year undergraduates) based on their changed location of timetabled activities. This can be demonstrated by the field interview response of a 3rd year chemical engineering student who was interviewed with her friend in the breakout space:

“I like departmental spaces because of convenience and because of the micro community from being around other departmental members and friends. However, I would also say the way my friends and I use departmental spaces has changed over the course of our degree, as we now use different spaces to 1st year students, for example”.

Similarly, postgraduate students confessed to a greater sense of ownership during the summer months when undergraduate students no longer had timetabled teaching. This is exemplified by the interview response of a PhD student in chemical engineering who described the breakout space as:

“Not a place for work, because the undergraduates are here—sometimes I can’t even come here to eat because it is so full. Undergraduates tend to do group work here a lot as they have nowhere else.”

These agentic tensions therefore governed the sense of ownership different user groups felt in the space throughout the academic year and influenced how these users could enact certain activities. Framing these dynamic and temporally changing agentic tensions as ecological phenomena supported a deeper understanding of their nature and impact on student pedagogic interactions.

This paper has used data from a mixed methods study of student pedagogic interactions in a traditional lecture theatre and adjacent transitional space to illustrate the potential of an ecological approach, when investigating complex transitions and tensions between timetabled and non-timetabled learning spaces in a STEM university context. Three different categories of transitions and tensions between these spaces have been defined: ‘spatial transitions and tensions’, which exist as a function of timetables and architectural divides between formal and informal space and the associated expectation to learn in the formal timetabled space and/or time; ‘pedagogic transitions and tensions’, which exist between teacher-centred transmission intent within a space and student-centred intent, which can result in ‘failed’, hidden and postponed pedagogic interactions where students negotiate their own space for tackling misunderstanding; and ‘agentic transitions and tensions’, which occur between the fixed power dynamic of the formal lecture theatre and the more democratic nature of the adjacent informal space, resulting in differing agentic ownership between students and teachers in the different learning spaces.

Initially thinking about the lecture theatre and breakout space as fixed, isolated entities presented a barrier to understanding these complex interactions and tensions. We postulate that the ecological concept of the ‘ecotone’ may significantly advance our understanding of complex interactions between space and pedagogy in transition and tension. From an ecological perspective, ecotones not only represent zones of overlap and transition—for example, in estuarine intertidal zones between ocean and river ecosystems—but also exist as a distinct third ecosystem subject to conflicting tidal and river forces which create tensions and alter its position and makeup 27 . The increased biodiversity of edge species within an ecotone, which translates etymologically to mean ‘ecologies in tension’, emerges from the opportunities afforded by two ecosystems coexisting in an interconnected and dynamic way. By being less encumbered by the inertia and rules of the adjoining spaces, ecotones present opportunities for transition and potential evolutionary innovations that feedback into the core 28 . Although the use of the ecotone concept in higher education is limited 27 , 29 and presents challenges by applying a metaphor from nature to model social systems 30 , its application in our research suggests its potential to transform our understanding of complex phenomena within evolving university systems.

Adopting an interrelated, dynamic view of the complex context of learning in a changing situation is necessary, given a rapidly changing context within which increasingly diverse models of learning can have a specific educational meaning. Although this paper has introduced the ecotone concept as a way of thinking about how space, pedagogy and agency interact and how tensions between them inhibit or enable learning, there may be other potential applications of the ecotone. For example, the concept could help us to think about a diverse group of students, each with individual cultural and learning expectations; different initial knowledge and skills; and different strategies, approaches and goals working together. Managing possible tensions within these groups while embracing a ‘biodiversity’ of people and ideas can become an important source of system resilience 31 . Perceiving this diversity as a source of positive pedagogic potential can inform the design of timetables and spaces for more productive active learning.

With the World Economic Forum anticipating the ‘metaverse’ as one of the top ten emerging technologies 32 , it is arguably important to think about how this new ‘in-between’ reality will enhance connectivity between people. For example, there has been a recent move away from dualistic definitions of online and physical space to ‘onlife space’ 33 , which encapsulates both the physical and virtual realms and acknowledges the fundamental role of information technology in changing and activating physical spaces. The ecotone might be a credible metaphor for conceptually framing these onlife spaces, helping to acknowledge transitions and tensions that exist between reality and virtuality; the concept has separately been positioned as a useful metaphor for conceptualising the ‘…third space at the intersection of analogue, digital, and postdigital learning spaces’ 29 .

With the institution’s desire to increase active learning pedagogy, our findings encouraged a reflection on the extent to which existing learning spaces and timetabled pedagogic interactions were always appropriate for students to navigate transitions between traditional transmission-based learning and more active learning. Applying the ecotone metaphor first helped with recognising the formal and informal spaces as greater than the sum of their parts, which facilitated a more nuanced data analysis and wider institutional discussion that raised awareness of tensions. It also subsequently evidenced the redesign of several lecture theatres, with the intent of providing spaces better designed to facilitate more flexible pedagogic and agentic transitions within both timetabled and non-timetabled periods. However, redesigning traditional lecture theatres to be more architecturally aligned with active learning classrooms 7 is time- and cost-intensive and will mostly empower local changes to teacher and student agency. The already disproportionate capital investment into UK university education space, exceeded only by staff budgets, renders a broader redesign of all campus lecture theatres difficult 22 .

By instead using a mixed-method ecological approach to highlight the pedagogic potential of informal transitional spaces that exist between these more formal spaces and interactions, ecotone thinking can empower teachers to reconsider their use of existing spaces such as the lecture theatre and breakout space introduced in this paper. For instance, inviting students to move between such spaces can aid group formation and/or transitions between transmission-based lecture segments and more active group-based interactions. The understanding gained from these incidental and purposeful transitions between formal and informal spaces informed a series of student-staff partnership projects that redesigned ‘transitional spaces’ just outside of lecture theatres 10 . The ecological thinking therefore resulted in a ‘regenerative design’ approach 34 in which spaces were redesigned more cost-effectively, teachers could evolve their perceived and actual use of space and students could better connect their timetabled learning to various contexts.

The sector-wide shift from an industrial model of universities towards an ecological model 14 could itself be thought of as an ecotone within which students, teachers, policymakers, administrators and leaders operate and negotiate change. The ecotone concept may be valuable as a framework for considering complex, dynamic situations in which two or more things interact with the possibility of tensions existing between them. Higher education institutions that embrace an ecological mindset might be better prepared to navigate disruptions, whether they stem from technological advancements, shifts in student demographics, or global events such as the COVID-19 pandemic. The goal might be to move from a transactional to a transformative view of higher education 29 , which better supports the development of an ‘autonomous lifelong, life-wide learner, a capable knowledge worker, and a critical citizen’ 24 who has the capacity to change, learn and tackle the wicked challenges that the world faces.

Settings and participants

This research aimed to explore the nature of transitions within and between a ‘traditional’ lecture theatre used for timetabled activity and an adjacent informal breakout space (see Fig. 4 floorplan) in the institution’s chemical engineering department. This research setting was selected because of the interesting tension in activity between the lecture theatre—a row-by-row space with a 160-seat capacity serving 1st and 2nd year undergraduate classes of 30–150 students—and the public nature of the breakout space, used freely by a diverse group of users from inside and outside the department. This tension made the setting ideal for exploring transitions and potentials between different types of learning activities. The configuration and pedagogical use of the lecture theatre exemplified the ‘status quo’ across the STEM institution at the time of data collection. This provided a useful starting point for understanding existing learning behaviours and transitions amid the institution’s strategic transformation of learning space, curriculum and pedagogy. Furthermore, studying this setting was opportune following the institution’s investment in occupancy monitoring technology, which generated anonymous occupancy data for both spaces. The occupancy technology automatically collected data 24 h per day, 7 days per week, all year round allowing us to identify occupancy patterns and transitions and target specific days and times of interest. The wider, longer-term occupancy dataset was initially analysed at a higher, less granular level to select an appropriate setting for the research. Once the lecture theatre and breakout space had been selected, around 120 h of occupancy data spanning two academic years were analysed in more granular detail to investigate cohort transitions between the learning spaces, including for different timetable configurations.

Breakout space at the base shows a variety of furniture types and entrances to other spaces, including the traditional lecture theatre above with raked row-by-row seating for transmission-based teaching.

This occupancy data informed our subsequent targeting of data collection resources when employing the qualitative methods 25 , enabling a more efficient and purposeful targeting of observations. We conducted 30 observations on 29 separate weekdays within 22 weeks across a period of 11 months. Given observations lasted between 30 min and 90 min, depending on if timetabled lecture sessions had also been observed, observations totalled around 24 h and were always conducted within the confines of the academic day (09:00–17:00) and term time. Straight after some observations, we also conducted a total of 21 field interviews with 25 student participants (as some interviews involved more than one participant).

Participants were purposefully sampled for field interviews based on their observed learning behaviours during transitions between timetabled and non-timetabled spaces and periods. As these field interviews were designed to be brief (5–10 min) and non-disruptive, we retrieved the degree type and year of study from participants, opting to note other demographic details based on our observation to avoid taking too much time. Of the 25 field interview participants, close to 50% were from the 1st and 2nd year undergraduate chemical engineering cohorts observed in lecture sessions; 4 interview participants were students from other departments and 4 participants were studying postgraduate degrees, demonstrating the user diversity of the breakout space. For a notional 40-h study week, the early-year undergraduate cohorts were expected to attend teaching contact for 25% of their time, with the remaining 75% of the time being spent independently studying. This emphasis on informal work is dictated by the degree assessment, which in the early years consisted of 10% practical, 20% coursework and 70% examination. The curricular requirement for formal and informal learning in this degree context made the transitions between timetabled and non-timetabled learning particularly interesting, and typical of many other STEM subjects taught at this institution.

A mixed method for understanding transitions and tensions

The combination of quantitative space occupancy data with qualitative insights from ethnographic observations and field interviews led to a deeper understanding of how students perceive and engage with the learning spaces and the transitions between them 25 . The wider, longer-term occupancy dataset was initially analysed at a higher, less granular level for various spaces across the campus to identify potentially interesting settings in which qualitative data collection might be targeted; our secondary use of this occupancy data deviated from its intended institutional purpose of optimising room bookings and space efficiency. Occupancy insights for the lecture theatre and breakout space helped to reveal the ecological nature of the learning environment with inflow, outflow, transition and dwelling of people within and between the spaces. The occupancy data also guided the more resource-intensive targeting of ethnographic observations and field interviews which allowed us to interpret the meaning and nuance of those transitions and possible associated tensions.

‘Naturalistic’ ethnographic observation protocols 35 were chosen because they are non-participant and minimise the chances of participants altering their behaviour; as a younger researcher, I was able to remain inconspicuous as an observer. Observations were recorded as field notes on a laptop and were sometimes supplemented with floorplan-based sociograms which captured person-person and person-space interactions within 10-min snapshots of breakout space activity (see Fig. 3 for example). Sociograms were mostly recorded during timetable transition periods (such as immediately after a lecture when students would flow into the breakout space) and helped us to target field interviews by visualising the broader context, identifying specific individuals or small groups who exhibited interesting learning behaviour whilst being sensitive to who we approached. Field interviews were initiated with verbal consent due to the informal low-risk nature of interviews, which posed structured questions pertaining to how exactly participants were using the space (as compared to the observation), how often they typically used the space, why they chose to use this specific space and where else they would typically carry out the same activity on campus. Informed consent was not used for these brief in situ field interviews, a decision approved as part of two detailed applications (reference numbers EERP1718-021 and EERP1819-012) made to Imperial College’s Education Ethics Review Process. The ethics committee agreed that using informed consent protocols for 5–10-min field interviews posed an inappropriate time cost for participants, whilst potentially affecting the quality of collected data by distancing it from the observed behaviour. Retrieving brief verbal consent posed less risk and was arguably more commensurate with the uncontentious informal nature of questioning.

Thematic analysis 36 was used to analyse the ethnographic observation and field interview data by developing ‘themes’ in the qualitative data. These themes are patterns of shared meaning unified by a central organising concept, which emerge inductively from drawing relationships between different segments of the data that are assigned smaller units of meaning called codes. Analysis began during the collection of ethnographic field notes given these inscriptions were shaped by what I ‘saw’ and the choices I made as an ethnographer. To minimise bias, we remained reflexive and used our positioning and contextual knowledge, including when looking at patterns in the objective occupancy data or when using those results to target subsequent observations, to repeatedly make meaning of the data and develop a critical perspective 20 . Observation and field interview data were separately analysed in NVivo software to develop codes and themes which could then be understood holistically.

The mixed method approach and analysis of that data embodied ecological principles by dynamically and reflexively moving between remote analysis of cohort-level occupancy patterns and in situ observations and field interviews. This triangulation supported a more complete understanding of phenomena and a more resourceful collection of authentic data. The use of this approach in other settings in the institution has informed the development of infrastructure, spaces and places in which transitions and tensions are better managed and learning ecologies are able to organically develop.

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Some of the data collected in the research study is available, as limited by ethical approval, and can be requested by contacting the corresponding author (L.M.).

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Acknowledgements

We thank those involved in the development and execution of the institutional strategy that funded and motivated this research. We also thank fellow members of the Centre for Higher Education Research and Scholarship for their input into the thinking behind the research. The open access fee was paid from the Imperial College London Open Access Fund. Finally, we thank all the participants who contributed valuable insights and experience during the data collection.

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McCrone, L., Kingsbury, M. An ecological approach to understanding transitions and tensions in complex learning contexts. npj Sci. Learn. 9 , 54 (2024). https://doi.org/10.1038/s41539-024-00267-1

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• Sara Lafuerza   ORCID: orcid.org/0000-0003-2126-6505 1 ,
• Christian Gorini   ORCID: orcid.org/0000-0003-3123-4822 1 ,
• Alain Rabaute   ORCID: orcid.org/0000-0003-1369-0218 1 ,
• Didier Granjeon   ORCID: orcid.org/0000-0002-1457-6671 2 ,
• Rémy Deschamps   ORCID: orcid.org/0000-0002-0888-3456 2 ,
• Eric Fouache   ORCID: orcid.org/0000-0002-5392-0615 3 ,
• Mina Jafari 4 &
• Pierre-Yves Lagrée   ORCID: orcid.org/0000-0002-3931-6622 4  

This study investigates the sediment dynamics resulting from the extreme Storm Alex in October 2020 in the Roya Valley and its tributaries in the Alpes-Maritimes region, France. The storm, triggered by a low-pressure system, led to unprecedented rainfall, causing extensive flooding and erosion in the region. Despite limited pre-flood data, the study employs aerial and satellite imagery, digital elevation models, and field surveys to quantify sediment mobilization and its effects on the Viévola alluvial fan in the Roya Valley. The Roya Valley’s complex geomorphology, characterized by steep gradients, gullies, and torrential streams, played a significant role in sediment transport. The study reveals that the Dente and Rabay torrents were major sediment contributors, with gullies in these areas producing substantial erosion. Bank erosion in the Dente valley was particularly prominent, attributed to geological factors and glacial deposits. The analysis, relying on topographical comparisons and digital data, assesses sediment volumes eroded and deposited during the event. Despite challenges in data quality, the study offers valuable insights into sediment dynamics during extreme hydro-sedimentary events. The Viévola catchment area is a focal point, emphasizing the importance of scree and fluvio-glacial deposits as primary sources of sediment. The findings emphasize the need for improved pre-event data and monitoring in mountainous regions susceptible to extreme events. The study’s methodology, despite limitations, contributes to a better understanding of geomorphic responses to extreme events. Expanding similar studies to cover a wider range of catchment areas and incorporating field data offers potential for enhanced hazard assessment and management strategies. The research underscores the critical role of sediment transport in shaping landscapes and impacting human infrastructure during extreme flood events.

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Acknowledgements

We express our sincere gratitude to all those who have contributed to this research. The authors acknowledge the support of the ANR-17-CE03-004-01 project, the “coup de pouce” project of ISTeP Sorbonne University, and the doctoral fellowship from the French Research Ministry for funding this study. Additionally, we would like to extend our thanks to the Institute of Ocean and Environmental Transition of the Alliance Sorbonne University for financing three field campaigns. We are grateful to the Monastery of Saorge (Center for National Monuments) for providing accommodation for researchers. We also recognize the MITI of CNRS for funding the equipment used in this study. Finally, our appreciation goes to Nicoletta BIANCHI from the Musée des Merveilles in Tende (Alpes-Maritimes department) for her invaluable assistance in the field. I express my gratitude to the two reviewers whose constructive comments have significantly enhanced the quality of this work.

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Institut Des Sciences de La Terre de Paris (ISTeP), Sorbonne Université, CNRS-INSU, 75005, Paris, France

Raphaël Kerverdo, Sara Lafuerza, Christian Gorini & Alain Rabaute

IFP Energies Nouvelles, Rueil-Malmaison, France

Didier Granjeon & Rémy Deschamps

Laboratoire Médiations, Institut de Géographie, Sorbonne Université, Paris, France

Eric Fouache

Institut Jean Le Rond d’Alembert, Sorbonne Université, Paris, France

Mina Jafari & Pierre-Yves Lagrée

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Correspondence to Raphaël Kerverdo .

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Kerverdo, R., Lafuerza, S., Gorini, C. et al. The impact of Storm Alex on the Vievola catchment: a quantitative analysis of sediment volume and morphological changes in the Roya River tributaries. Landslides (2024). https://doi.org/10.1007/s10346-024-02361-2

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Received : 31 January 2024

Accepted : 23 August 2024

Published : 06 September 2024

DOI : https://doi.org/10.1007/s10346-024-02361-2

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