explain the difference between qualitative and quantitative methods of research

Qualitative vs Quantitative Research Methods & Data Analysis

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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.

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What is the difference between quantitative and qualitative?

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

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 in numerical terms. Quantitative research is often used to test hypotheses, identify patterns, and make predictions.

Qualitative research , on the other hand, collects non-numerical data such as words, images, and sounds. The focus is on exploring subjective experiences, opinions, and attitudes, often through observation and interviews.

Qualitative research aims to produce rich and detailed descriptions of the phenomenon being studied, and to uncover new insights and meanings.

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.

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

• 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

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Quantitative vs. Qualitative Research in Psychology

Anabelle Bernard Fournier is a researcher of sexual and reproductive health at the University of Victoria as well as a freelance writer on various health topics.

Emily is a board-certified science editor who has worked with top digital publishing brands like Voices for Biodiversity, Study.com, GoodTherapy, Vox, and Verywell.

• Key Differences

Quantitative Research Methods

Qualitative research methods.

• How They Relate

In psychology and other social sciences, researchers are faced with an unresolved question: Can we measure concepts like love or racism the same way we can measure temperature or the weight of a star? Social phenomena⁠—things that happen because of and through human behavior⁠—are especially difficult to grasp with typical scientific models.

At a Glance

Psychologists rely on quantitative and quantitative research to better understand human thought and behavior.

• Qualitative research involves collecting and evaluating non-numerical data in order to understand concepts or subjective opinions.
• Quantitative research involves collecting and evaluating numerical data. 

This article discusses what qualitative and quantitative research are, how they are different, and how they are used in psychology research.

Qualitative Research vs. Quantitative Research

In order to understand qualitative and quantitative psychology research, it can be helpful to look at the methods that are used and when each type is most appropriate.

Psychologists rely on a few methods to measure behavior, attitudes, and feelings. These include:

• Self-reports , like surveys or questionnaires
• Observation (often used in experiments or fieldwork)
• Implicit attitude tests that measure timing in responding to prompts

Most of these are quantitative methods. The result is a number that can be used to assess differences between groups.

However, most of these methods are static, inflexible (you can't change a question because a participant doesn't understand it), and provide a "what" answer rather than a "why" answer.

Sometimes, researchers are more interested in the "why" and the "how." That's where qualitative methods come in.

Qualitative research is about speaking to people directly and hearing their words. It is grounded in the philosophy that the social world is ultimately unmeasurable, that no measure is truly ever "objective," and that how humans make meaning is just as important as how much they score on a standardized test.

Used to develop theories

Takes a broad, complex approach

Answers "why" and "how" questions

Explores patterns and themes

Used to test theories

Takes a narrow, specific approach

Answers "what" questions

Explores statistical relationships

Quantitative methods have existed ever since people have been able to count things. But it is only with the positivist philosophy of Auguste Comte (which maintains that factual knowledge obtained by observation is trustworthy) that it became a "scientific method."

The scientific method follows this general process. A researcher must:

• Generate a theory or hypothesis (i.e., predict what might happen in an experiment) and determine the variables needed to answer their question
• Develop instruments to measure the phenomenon (such as a survey, a thermometer, etc.)
• Develop experiments to manipulate the variables
• Collect empirical (measured) data
• Analyze data

Quantitative methods are about measuring phenomena, not explaining them.

Quantitative research compares two groups of people. There are all sorts of variables you could measure, and many kinds of experiments to run using quantitative methods.

These comparisons are generally explained using graphs, pie charts, and other visual representations that give the researcher a sense of how the various data points relate to one another.

Basic Assumptions

Quantitative methods assume:

• That the world is measurable
• That humans can observe objectively
• That we can know things for certain about the world from observation

In some fields, these assumptions hold true. Whether you measure the size of the sun 2000 years ago or now, it will always be the same. But when it comes to human behavior, it is not so simple.

As decades of cultural and social research have shown, people behave differently (and even think differently) based on historical context, cultural context, social context, and even identity-based contexts like gender , social class, or sexual orientation .

Therefore, quantitative methods applied to human behavior (as used in psychology and some areas of sociology) should always be rooted in their particular context. In other words: there are no, or very few, human universals.

Statistical information is the primary form of quantitative data used in human and social quantitative research. Statistics provide lots of information about tendencies across large groups of people, but they can never describe every case or every experience. In other words, there are always outliers.

Correlation and Causation

A basic principle of statistics is that correlation is not causation. Researchers can only claim a cause-and-effect relationship under certain conditions:

• The study was a true experiment.
• The independent variable can be manipulated (for example, researchers cannot manipulate gender, but they can change the primer a study subject sees, such as a picture of nature or of a building).
• The dependent variable can be measured through a ratio or a scale.

So when you read a report that "gender was linked to" something (like a behavior or an attitude), remember that gender is NOT a cause of the behavior or attitude. There is an apparent relationship, but the true cause of the difference is hidden.

Pitfalls of Quantitative Research

Quantitative methods are one way to approach the measurement and understanding of human and social phenomena. But what's missing from this picture?

As noted above, statistics do not tell us about personal, individual experiences and meanings. While surveys can give a general idea, respondents have to choose between only a few responses. This can make it difficult to understand the subtleties of different experiences.

Quantitative methods can be helpful when making objective comparisons between groups or when looking for relationships between variables. They can be analyzed statistically, which can be helpful when looking for patterns and relationships.

Qualitative data are not made out of numbers but rather of descriptions, metaphors, symbols, quotes, analysis, concepts, and characteristics. This approach uses interviews, written texts, art, photos, and other materials to make sense of human experiences and to understand what these experiences mean to people.

While quantitative methods ask "what" and "how much," qualitative methods ask "why" and "how."

Qualitative methods are about describing and analyzing phenomena from a human perspective. There are many different philosophical views on qualitative methods, but in general, they agree that some questions are too complex or impossible to answer with standardized instruments.

These methods also accept that it is impossible to be completely objective in observing phenomena. Researchers have their own thoughts, attitudes, experiences, and beliefs, and these always color how people interpret results.

Qualitative Approaches

There are many different approaches to qualitative research, with their own philosophical bases. Different approaches are best for different kinds of projects. For example:

• Case studies and narrative studies are best for single individuals. These involve studying every aspect of a person's life in great depth.
• Phenomenology aims to explain experiences. This type of work aims to describe and explore different events as they are consciously and subjectively experienced.
• Grounded theory develops models and describes processes. This approach allows researchers to construct a theory based on data that is collected, analyzed, and compared to reach new discoveries.
• Ethnography describes cultural groups. In this approach, researchers immerse themselves in a community or group in order to observe behavior.

Qualitative researchers must be aware of several different methods and know each thoroughly enough to produce valuable research.

Some researchers specialize in a single method, but others specialize in a topic or content area and use many different methods to explore the topic, providing different information and a variety of points of view.

There is not a single model or method that can be used for every qualitative project. Depending on the research question, the people participating, and the kind of information they want to produce, researchers will choose the appropriate approach.

Interpretation

Qualitative research does not look into causal relationships between variables, but rather into themes, values, interpretations, and meanings. As a rule, then, qualitative research is not generalizable (cannot be applied to people outside the research participants).

The insights gained from qualitative research can extend to other groups with proper attention to specific historical and social contexts.

Relationship Between Qualitative and Quantitative Research

It might sound like quantitative and qualitative research do not play well together. They have different philosophies, different data, and different outputs. However, this could not be further from the truth.

These two general methods complement each other. By using both, researchers can gain a fuller, more comprehensive understanding of a phenomenon.

For example, a psychologist wanting to develop a new survey instrument about sexuality might and ask a few dozen people questions about their sexual experiences (this is qualitative research). This gives the researcher some information to begin developing questions for their survey (which is a quantitative method).

After the survey, the same or other researchers might want to dig deeper into issues brought up by its data. Follow-up questions like "how does it feel when...?" or "what does this mean to you?" or "how did you experience this?" can only be answered by qualitative research.

By using both quantitative and qualitative data, researchers have a more holistic, well-rounded understanding of a particular topic or phenomenon.

Qualitative and quantitative methods both play an important role in psychology. Where quantitative methods can help answer questions about what is happening in a group and to what degree, qualitative methods can dig deeper into the reasons behind why it is happening. By using both strategies, psychology researchers can learn more about human thought and behavior.

Gough B, Madill A. Subjectivity in psychological science: From problem to prospect . Psychol Methods . 2012;17(3):374-384. doi:10.1037/a0029313

Pearce T. “Science organized”: Positivism and the metaphysical club, 1865–1875 . J Hist Ideas . 2015;76(3):441-465.

Adams G. Context in person, person in context: A cultural psychology approach to social-personality psychology . In: Deaux K, Snyder M, eds. The Oxford Handbook of Personality and Social Psychology . Oxford University Press; 2012:182-208.

Brady HE. Causation and explanation in social science . In: Goodin RE, ed. The Oxford Handbook of Political Science. Oxford University Press; 2011. doi:10.1093/oxfordhb/9780199604456.013.0049

Chun Tie Y, Birks M, Francis K. Grounded theory research: A design framework for novice researchers .  SAGE Open Med . 2019;7:2050312118822927. doi:10.1177/2050312118822927

Reeves S, Peller J, Goldman J, Kitto S. Ethnography in qualitative educational research: AMEE Guide No. 80 . Medical Teacher . 2013;35(8):e1365-e1379. doi:10.3109/0142159X.2013.804977

Salkind NJ, ed. Encyclopedia of Research Design . Sage Publishing.

Shaughnessy JJ, Zechmeister EB, Zechmeister JS.  Research Methods in Psychology . McGraw Hill Education.

By Anabelle Bernard Fournier Anabelle Bernard Fournier is a researcher of sexual and reproductive health at the University of Victoria as well as a freelance writer on various health topics.

Reference management. Clean and simple.

Qualitative vs. quantitative research - what’s the difference?

What is quantitative research?

What is quantitative research used for, how to collect data for quantitative research, what is qualitative research, what is qualitative research used for, how to collect data for qualitative research, when to use which approach, how to analyze qualitative and quantitative research, analyzing quantitative data, analyzing qualitative data, differences between qualitative and quantitative research, frequently asked questions about qualitative vs. quantitative research, related articles.

Both qualitative and quantitative research are valid and effective approaches to study a particular subject. However, it is important to know that these research approaches serve different purposes and provide different results. This guide will help illustrate quantitative and qualitative research, what they are used for, and the difference between them.

Quantitative research focuses on collecting numerical data and using it to measure variables. As such, quantitative research and data are typically expressed in numbers and graphs. Moreover, this type of research is structured and statistical and the returned results are objective.

The simplest way to describe quantitative research is that it answers the questions " what " or " how much ".

To illustrate what quantitative research is used for, let’s look at a simple example. Let’s assume you want to research the reading habits of a specific part of a population.

With this research, you would like to establish what they read. In other words, do they read fiction, non-fiction, magazines, blogs, and so on? Also, you want to establish what they read about. For example, if they read fiction, is it thrillers, romance novels, or period dramas?

With quantitative research, you can gather concrete data about these reading habits. Your research will then, for example, show that 40% of the audience reads fiction and, of that 40%, 60% prefer romance novels.

In other studies and research projects, quantitative research will work in much the same way. That is, you use it to quantify variables, opinions, behaviors, and more.

Now that we've seen what quantitative research is and what it's used for, let's look at how you'll collect data for it. Because quantitative research is structured and statistical, its data collection methods focus on collecting numerical data.

Some methods to collect this data include:

• Surveys . Surveys are one of the most popular and easiest ways to collect quantitative data. These can include anything from online surveys to paper surveys. It’s important to remember that, to collect quantitative data, you won’t be able to ask open-ended questions.
• Interviews . As is the case with qualitative data, you’ll be able to use interviews to collect quantitative data with the proviso that the data will not be based on open-ended questions.
• Observations . You’ll also be able to use observations to collect quantitative data. However, here you’ll need to make observations in an environment where variables can’t be controlled.
• Website interceptors . With website interceptors, you’ll be able to get real-time insights into a specific product, service, or subject. In most cases, these interceptors take the form of surveys displayed on websites or invitations on the website to complete the survey.
• Longitudinal studies . With these studies, you’ll gather data on the same variables over specified time periods. Longitudinal studies are often used in medical sciences and include, for instance, diet studies. It’s important to remember that, for the results to be reliable, you’ll have to collect data from the same subjects.
• Online polls . Similar to website interceptors, online polls allow you to gather data from websites or social media platforms. These polls are short with only a few options and can give you valuable insights into a very specific question or topic.
• Experiments . With experiments, you’ll manipulate some variables (your independent variables) and gather data on causal relationships between others (your dependent variables). You’ll then measure what effect the manipulation of the independent variables has on the dependent variables.

Qualitative research focuses on collecting and analyzing non-numerical data. As such, it's typically unstructured and non-statistical. The main aim of qualitative research is to get a better understanding and insights into concepts, topics, and subjects.

The easiest way to describe qualitative research is that it answers the question " why ".

Considering that qualitative research aims to provide more profound insights and understanding into specific subjects, we’ll use our example mentioned earlier to explain what qualitative research is used for.

Based on this example, you’ve now established that 40% of the population reads fiction. You’ve probably also discovered in what proportion the population consumes other reading materials.

Qualitative research will now enable you to learn the reasons for these reading habits. For example, it will show you why 40% of the readers prefer fiction, while, for instance, only 10% prefer thrillers. It thus gives you an understanding of your participants’ behaviors and actions.

We've now recapped what qualitative research is and what it's used for. Let's now consider some methods to collect data for this type of research.

Some of these data collection methods include:

• Interviews . These include one-on-one interviews with respondents where you ask open-ended questions. You’ll then record the answers from every respondent and analyze these answers later.
• Open-ended survey questions . Open-ended survey questions give you insights into why respondents feel the way they do about a particular aspect.
• Focus groups . Focus groups allow you to have conversations with small groups of people and record their opinions and views about a specific topic.
• Observations . Observations like ethnography require that you participate in a specific organization or group in order to record their routines and interactions. This will, for instance, be the case where you want to establish how customers use a product in real-life scenarios.
• Literature reviews . With literature reviews, you’ll analyze the published works of other authors to analyze the prevailing view regarding a specific subject.
• Diary studies . Diary studies allow you to collect data about peoples’ habits, activities, and experiences over time. This will, for example, show you how customers use a product, when they use it, and what motivates them.

Now, the immediate question is: When should you use qualitative research, and when should you use quantitative research? As mentioned earlier, in its simplest form:

• Quantitative research allows you to confirm or test a hypothesis or theory or quantify a specific problem or quality.
• Qualitative research allows you to understand concepts or experiences.

Let's look at how you'll use these approaches in a research project a bit closer:

• Formulating a hypothesis . As mentioned earlier, qualitative research gives you a deeper understanding of a topic. Apart from learning more profound insights about your research findings, you can also use it to formulate a hypothesis when you start your research.
• Confirming a hypothesis . Once you’ve formulated a hypothesis, you can test it with quantitative research. As mentioned, you can also use it to quantify trends and behavior.
• Finding general answers . Quantitative research can help you answer broad questions. This is because it uses a larger sample size and thus makes it easier to gather simple binary or numeric data on a specific subject.
• Getting a deeper understanding . Once you have the broad answers mentioned above, qualitative research will help you find reasons for these answers. In other words, quantitative research shows you the motives behind actions or behaviors.

Considering the above, why not consider a mixed approach ? You certainly can because these approaches are not mutually exclusive. In other words, using one does not necessarily exclude the other. Moreover, both these approaches are useful for different reasons.

This means you could use both approaches in one project to achieve different goals. For example, you could use qualitative to formulate a hypothesis. Once formulated, quantitative research will allow you to confirm the hypothesis.

So, to answer the initial question, the approach you use is up to you.  However, when deciding on the right approach, you should consider the specific research project, the data you'll gather, and what you want to achieve.

No matter what approach you choose, you should design your research in such a way that it delivers results that are objective, reliable, and valid.

Both these research approaches are based on data. Once you have this data, however, you need to analyze it to answer your research questions. The method to do this depends on the research approach you use.

To analyze quantitative data, you'll need to use mathematical or statistical analysis. This can involve anything from calculating simple averages to applying complex and advanced methods to calculate the statistical significance of the results. No matter what analysis methods you use, it will enable you to spot trends and patterns in your data.

Considering the above, you can use tools, applications, and programming languages like R to calculate:

• The average of a set of numbers . This could, for instance, be the case where you calculate the average scores students obtained in a test or the average time people spend on a website.
• The frequency of a specific response . This will be the case where you, for example, use open-ended survey questions during qualitative analysis. You could then calculate the frequency of a specific response for deeper insights.
• Any correlation between different variables . Through mathematical analysis, you can calculate whether two or more variables are directly or indirectly correlated. In turn, this could help you identify trends in the data.
• The statistical significance of your results . By analyzing the data and calculating the statistical significance of the results, you'll be able to see whether certain occurrences happen randomly or because of specific factors.

Analyzing qualitative data is more complex than quantitative data. This is simply because it's not based on numerical values but rather text, images, video, and the like. As such, you won't be able to use mathematical analysis to analyze and interpret your results.

Because of this, it relies on a more interpretive analysis style and a strict analytical framework to analyze data and extract insights from it.

Some of the most common ways to analyze qualitative data include:

• Qualitative content analysis . In a content analysis, you'll analyze the language used in a specific piece of text. This allows you to understand the intentions of the author, who the audience is, and find patterns and correlations in how different concepts are communicated. A major benefit of this approach is that it follows a systematic and transparent process that other researchers will be able to replicate. As such, your research will produce highly reliable results. Keep in mind, however, that content analysis can be time-intensive and difficult to automate. ➡️  Learn how to do a content analysis in the guide.
• Thematic analysis . In a thematic analysis, you'll analyze data with a view of extracting themes, topics, and patterns in the data. Although thematic analysis can encompass a range of diverse approaches, it's usually used to analyze a collection of texts like survey responses, focus group discussions, or transcriptions of interviews. One of the main benefits of thematic analysis is that it's flexible in its approach. However, in some cases, thematic analysis can be highly subjective, which, in turn, impacts the reliability of the results. ➡️  Learn how to do a thematic analysis in this guide.
• Discourse analysis . In a discourse analysis, you'll analyze written or spoken language to understand how language is used in real-life social situations. As such, you'll be able to determine how meaning is given to language in different contexts. This is an especially effective approach if you want to gain a deeper understanding of different social groups and how they communicate with each other. As such, it's commonly used in humanities and social science disciplines.

We’ve now given a broad overview of both qualitative and quantitative research. Based on this, we can summarize the differences between these two approaches as follows:

Qualitative research focuses on collecting and analyzing non-numerical data. As such, it's typically unstructured and non-statistical. The main aim of qualitative research is to get a better understanding and insights into concepts, topics, and subjects. Quantitative research focuses on collecting numerical data and using it to measure variables. As such, quantitative research and data are typically expressed in numbers and graphs. Moreover, this type of research is structured and statistical and the returned results are objective.

3 examples of qualitative research would be:

• Interviews . These include one-on-one interviews with respondents with open-ended questions. You’ll then record the answers and analyze them later.
• Observations . Observations require that you participate in a specific organization or group in order to record their routines and interactions.

3 examples of quantitative research include:

• Surveys . Surveys are one of the most popular and easiest ways to collect quantitative data. To collect quantitative data, you won’t be able to ask open-ended questions.
• Longitudinal studies . With these studies, you’ll gather data on the same variables over specified time periods. Longitudinal studies are often used in medical sciences.

The main purpose of qualitative research is to get a better understanding and insights into concepts, topics, and subjects. The easiest way to describe qualitative research is that it answers the question " why ".

The purpose of quantitative research is to collect numerical data and use it to measure variables. As such, quantitative research and data are typically expressed in numbers and graphs. The simplest way to describe quantitative research is that it answers the questions " what " or " how much ".

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Qualitative vs Quantitative Research | Examples & Methods

Published on 4 April 2022 by Raimo Streefkerk . Revised on 8 May 2023.

When collecting and analysing data, quantitative research deals with numbers and statistics, while qualitative research  deals with words and meanings. Both are important for gaining different kinds of knowledge.

Common quantitative methods include experiments, observations recorded as numbers, and surveys with closed-ended questions. Qualitative research Qualitative research is expressed in words . It is used to understand concepts, thoughts or experiences. This type of research enables you to gather in-depth insights on topics that are not well understood.

Table of contents

The differences between quantitative and qualitative research, data collection methods, when to use qualitative vs quantitative research, how to analyse qualitative and quantitative data, frequently asked questions about qualitative and quantitative research.

Quantitative and qualitative research use different research methods to collect and analyse data, and they allow you to answer different kinds of research questions.

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Quantitative and qualitative data can be collected using various methods. It is important to use a data collection method that will help answer your research question(s).

Many data collection methods can be either qualitative or quantitative. For example, in surveys, observations or case studies , your data can be represented as numbers (e.g. using rating scales or counting frequencies) or as words (e.g. with open-ended questions or descriptions of what you observe).

However, some methods are more commonly used in one type or the other.

Quantitative data collection methods

• Surveys :  List of closed or multiple choice questions that is distributed to a sample (online, in person, or over the phone).
• Experiments : Situation in which variables are controlled and manipulated to establish cause-and-effect relationships.
• Observations: Observing subjects in a natural environment where variables can’t be controlled.

Qualitative data collection methods

• Interviews : Asking open-ended questions verbally to respondents.
• Focus groups: Discussion among a group of people about a topic to gather opinions that can be used for further research.
• Ethnography : Participating in a community or organisation for an extended period of time to closely observe culture and behavior.
• Literature review : Survey of published works by other authors.

A rule of thumb for deciding whether to use qualitative or quantitative data is:

• Use quantitative research if you want to confirm or test something (a theory or hypothesis)
• Use qualitative research if you want to understand something (concepts, thoughts, experiences)

For most research topics you can choose a qualitative, quantitative or mixed methods approach . Which type you choose depends on, among other things, whether you’re taking an inductive vs deductive research approach ; your research question(s) ; whether you’re doing experimental , correlational , or descriptive research ; and practical considerations such as time, money, availability of data, and access to respondents.

Quantitative research approach

You survey 300 students at your university and ask them questions such as: ‘on a scale from 1-5, how satisfied are your with your professors?’

You can perform statistical analysis on the data and draw conclusions such as: ‘on average students rated their professors 4.4’.

Qualitative research approach

You conduct in-depth interviews with 15 students and ask them open-ended questions such as: ‘How satisfied are you with your studies?’, ‘What is the most positive aspect of your study program?’ and ‘What can be done to improve the study program?’

Based on the answers you get you can ask follow-up questions to clarify things. You transcribe all interviews using transcription software and try to find commonalities and patterns.

Mixed methods approach

You conduct interviews to find out how satisfied students are with their studies. Through open-ended questions you learn things you never thought about before and gain new insights. Later, you use a survey to test these insights on a larger scale.

It’s also possible to start with a survey to find out the overall trends, followed by interviews to better understand the reasons behind the trends.

Qualitative or quantitative data by itself can’t prove or demonstrate anything, but has to be analysed to show its meaning in relation to the research questions. The method of analysis differs for each type of data.

Analysing quantitative data

Quantitative data is based on numbers. Simple maths or more advanced statistical analysis is used to discover commonalities or patterns in the data. The results are often reported in graphs and tables.

Applications such as Excel, SPSS, or R can be used to calculate things like:

• Average scores
• The number of times a particular answer was given
• The correlation or causation between two or more variables
• The reliability and validity of the results

Analysing qualitative data

Qualitative data is more difficult to analyse than quantitative data. It consists of text, images or videos instead of numbers.

Some common approaches to analysing qualitative data include:

• Qualitative content analysis : Tracking the occurrence, position and meaning of words or phrases
• Thematic analysis : Closely examining the data to identify the main themes and patterns
• Discourse analysis : Studying how communication works in social contexts

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

Quantitative methods allow you to test a hypothesis by systematically collecting and analysing data, while qualitative methods allow you to explore ideas and experiences in depth.

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

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

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

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organisations.

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organise your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

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Qualitative and Quantitative Research: Differences and Similarities

Qualitative research and quantitative research are two complementary approaches for understanding the world around us.

Qualitative research collects non-numerical data , and the results are typically presented as written descriptions, photographs, videos, and/or sound recordings.

In contrast, quantitative research collects numerical data , and the results are typically presented in tables, graphs, and charts.

Debates about whether to use qualitative or quantitative research methods are common in the social sciences (i.e. anthropology, archaeology, economics, geography, history, law, linguistics, politics, psychology, sociology), which aim to understand a broad range of human conditions. Qualitative observations may be used to gain an understanding of unique situations, which may lead to quantitative research that aims to find commonalities.

Within the natural and physical sciences (i.e. physics, chemistry, geology, biology), qualitative observations often lead to a plethora of quantitative studies. For example, unusual observations through a microscope or telescope can immediately lead to counting and measuring. In other situations, meaningful numbers cannot immediately be obtained, and the qualitative research must stand on its own (e.g. The patient presented with an abnormally enlarged spleen (Figure 1), and complained of pain in the left shoulder.)

For both qualitative and quantitative research, the researcher's assumptions shape the direction of the study and thereby influence the results that can be obtained. Let's consider some prominent examples of qualitative and quantitative research, and how these two methods can complement each other.

Qualitative research example

In 1960, Jane Goodall started her decades-long study of chimpanzees in the wild at Gombe Stream National Park in Tanzania. Her work is an example of qualitative research that has fundamentally changed our understanding of non-human primates, and has influenced our understanding of other animals, their abilities, and their social interactions.

Dr. Goodall was by no means the first person to study non-human primates, but she took a highly unusual approach in her research. For example, she named individual chimpanzees instead of numbering them, and used terms such as "childhood", "adolescence", "motivation", "excitement", and "mood". She also described the distinct "personalities" of individual chimpanzees. Dr. Goodall was heavily criticized for describing chimpanzees in ways that are regularly used to describe humans, which perfectly illustrates how the assumptions of the researcher can heavily influence their work.

The quality of qualitative research is largely determined by the researcher's ability, knowledge, creativity, and interpretation of the results. One of the hallmarks of good qualitative research is that nothing is predefined or taken for granted, and that the study subjects teach the researcher about their lives. As a result, qualitative research studies evolve over time, and the focus or techniques used can shift as the study progresses.

Qualitative research methods

Dr. Goodall immersed herself in the chimpanzees' natural surroundings, and used direct observation to learn about their daily life. She used photographs, videos, sound recordings, and written descriptions to present her data. These are all well-established methods of qualitative research, with direct observation within the natural setting considered a gold standard. These methods are time-intensive for the researcher (and therefore monetarily expensive) and limit the number of individuals that can be studied at one time.

When studying humans, a wider variety of research methods are available to understand how people perceive and navigate their world—past or present. These techniques include: in-depth interviews (e.g. Can you discuss your experience of growing up in the Deep South in the 1950s?), open-ended survey questions (e.g. What do you enjoy most about being part of the Church of Latter Day Saints?), focus group discussions, researcher participation (e.g. in military training), review of written documents (e.g. social media accounts, diaries, school records, etc), and analysis of cultural records (e.g. anything left behind including trash, clothing, buildings, etc).

Qualitative research can lead to quantitative research

Qualitative research is largely exploratory. The goal is to gain a better understanding of an unknown situation. Qualitative research in humans may lead to a better understanding of underlying reasons, opinions, motivations, experiences, etc. The information generated through qualitative research can provide new hypotheses to test through quantitative research. Quantitative research studies are typically more focused and less exploratory, involve a larger sample size, and by definition produce numerical data.

Dr. Goodall's qualitative research clearly established periods of childhood and adolescence in chimpanzees. Quantitative studies could better characterize these time periods, for example by recording the amount of time individual chimpanzees spend with their mothers, with peers, or alone each day during childhood compared to adolescence.

For studies involving humans, quantitative data might be collected through a questionnaire with a limited number of answers (e.g. If you were being bullied, what is the likelihood that you would tell at least one parent? A) Very likely, B) Somewhat likely, C) Somewhat unlikely, D) Unlikely).

Quantitative research example

One of the most influential examples of quantitative research began with a simple qualitative observation: Some peas are round, and other peas are wrinkled. Gregor Mendel was not the first to make this observation, but he was the first to carry out rigorous quantitative experiments to better understand this characteristic of garden peas.

As described in his 1865 research paper, Mendel carried out carefully controlled genetic crosses and counted thousands of resulting peas. He discovered that the ratio of round peas to wrinkled peas matched the ratio expected if pea shape were determined by two copies of a gene for pea shape, one inherited from each parent. These experiments and calculations became the foundation of modern genetics, and Mendel's ratios became the default hypothesis for experiments involving thousands of different genes in hundreds of different organisms.

The quality of quantitative research is largely determined by the researcher's ability to design a feasible experiment, that will provide clear evidence to support or refute the working hypothesis. The hallmarks of good quantitative research include: a study that can be replicated by an independent group and produce similar results, a sample population that is representative of the population under study, a sample size that is large enough to reveal any expected statistical significance.

Quantitative research methods

The basic methods of quantitative research involve measuring or counting things (size, weight, distance, offspring, light intensity, participants, number of times a specific phrase is used, etc). In the social sciences especially, responses are often be split into somewhat arbitrary categories (e.g. How much time do you spend on social media during a typical weekday? A) 0-15 min, B) 15-30 min, C) 30-60 min, D) 1-2 hrs, E) more than 2 hrs).

These quantitative data can be displayed in a table, graph, or chart, and grouped in ways that highlight patterns and relationships. The quantitative data should also be subjected to mathematical and statistical analysis. To reveal overall trends, the average (or most common survey answer) and standard deviation can be determined for different groups (e.g. with treatment A and without treatment B).

Typically, the most important result from a quantitative experiment is the test of statistical significance. There are many different methods for determining statistical significance (e.g. t-test, chi square test, ANOVA, etc.), and the appropriate method will depend on the specific experiment.

Statistical significance provides an answer to the question: What is the probably that the difference observed between two groups is due to chance alone, and the two groups are actually the same? For example, your initial results might show that 32% of Friday grocery shoppers buy alcohol, while only 16% of Monday grocery shoppers buy alcohol. If this result reflects a true difference between Friday shoppers and Monday shoppers, grocery store managers might want to offer Friday specials to increase sales.

After the appropriate statistical test is conducted (which incorporates sample size and other variables), the probability that the observed difference is due to chance alone might be more than 5%, or less than 5%. If the probability is less than 5%, the convention is that the result is considered statistically significant. (The researcher is also likely to cheer and have at least a small celebration.) Otherwise, the result is considered statistically insignificant. (If the value is close to 5%, the researcher may try to group the data in different ways to achieve statistical significance. For example, by comparing alcohol sales after 5pm on Friday and Monday.) While it is important to reveal differences that may not be immediately obvious, the desire to manipulate information until it becomes statistically significant can also contribute to bias in research.

So how often do results from two groups that are actually the same give a probability of less than 5%? A bit less than 5% of the time (by definition). This is one of the reasons why it is so important that quantitative research can be replicated by different groups.

Which research method should I choose?

Choose the research methods that will allow you to produce the best results for a meaningful question, while acknowledging any unknowns and controlling for any bias. In many situations, this will involve a mixed methods approach. Qualitative research may allow you to learn about a poorly understood topic, and then quantitative research may allow you to obtain results that can be subjected to rigorous statistical tests to find true and meaningful patterns. Many different approaches are required to understand the complex world around us.

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Qualitative vs Quantitative Research 101

A plain-language explanation (with examples).

By: Kerryn Warren (PhD, MSc, BSc) | June 2020

So, it’s time to decide what type of research approach you’re going to use – qualitative or quantitative . And, chances are, you want to choose the one that fills you with the least amount of dread. The engineers may be keen on quantitative methods because they loathe interacting with human beings and dealing with the “soft” stuff and are far more comfortable with numbers and algorithms. On the other side, the anthropologists are probably more keen on qualitative methods because they literally have the opposite fears.

However, when justifying your research, “being afraid” is not a good basis for decision making. Your methodology needs to be informed by your research aims and objectives , not your comfort zone. Plus, it’s quite common that the approach you feared (whether qualitative or quantitative) is actually not that big a deal. Research methods can be learnt (usually a lot faster than you think) and software reduces a lot of the complexity of both quantitative and qualitative data analysis. Conversely, choosing the wrong approach and trying to fit a square peg into a round hole is going to create a lot more pain.

In this post, I’ll explain the qualitative vs quantitative choice in straightforward, plain language with loads of examples. This won’t make you an expert in either, but it should give you a good enough “big picture” understanding so that you can make the right methodological decision for your research.

Qualitative vs Quantitative: Overview  

• Qualitative analysis 101
• Quantitative analysis 101
• How to choose which one to use
• Data collection and analysis for qualitative and quantitative research
• The pros and cons of both qualitative and quantitative research
• A quick word on mixed methods

Qualitative Research 101: The Basics

The bathwater is hot.

Let us unpack that a bit. What does that sentence mean? And is it useful?

The answer is: well, it depends. If you’re wanting to know the exact temperature of the bath, then you’re out of luck. But, if you’re wanting to know how someone perceives the temperature of the bathwater, then that sentence can tell you quite a bit if you wear your qualitative hat .

Many a husband and wife have never enjoyed a bath together because of their strongly held, relationship-destroying perceptions of water temperature (or, so I’m told). And while divorce rates due to differences in water-temperature perception would belong more comfortably in “quantitative research”, analyses of the inevitable arguments and disagreements around water temperature belong snugly in the domain of “qualitative research”. This is because qualitative research helps you understand people’s perceptions and experiences  by systematically coding and analysing the data .

With qualitative research, those heated disagreements (excuse the pun) may be analysed in several ways. From interviews to focus groups to direct observation (ideally outside the bathroom, of course). You, as the researcher, could be interested in how the disagreement unfolds, or the emotive language used in the exchange. You might not even be interested in the words at all, but in the body language of someone who has been forced one too many times into (what they believe) was scalding hot water during what should have been a romantic evening. All of these “softer” aspects can be better understood with qualitative research.

In this way, qualitative research can be incredibly rich and detailed , and is often used as a basis to formulate theories and identify patterns. In other words, it’s great for exploratory research (for example, where your objective is to explore what people think or feel), as opposed to confirmatory research (for example, where your objective is to test a hypothesis). Qualitative research is used to understand human perception , world view and the way we describe our experiences. It’s about exploring and understanding a broad question, often with very few preconceived ideas as to what we may find.

But that’s not the only way to analyse bathwater, of course…

Quantitative Research 101: The Basics

The bathwater is 45 degrees Celsius.

Now, what does this mean? How can this be used?

I was once told by someone to whom I am definitely not married that he takes regular cold showers. As a person who is terrified of anything that isn’t body temperature or above, this seemed outright ludicrous. But this raises a question: what is the perfect temperature for a bath? Or at least, what is the temperature of people’s baths more broadly? (Assuming, of course, that they are bathing in water that is ideal to them). To answer this question, you need to now put on your quantitative hat .

If we were to ask 100 people to measure the temperature of their bathwater over the course of a week, we could get the average temperature for each person. Say, for instance, that Jane averages at around 46.3°C. And Billy averages around 42°C. A couple of people may like the unnatural chill of 30°C on the average weekday. And there will be a few of those striving for the 48°C that is apparently the legal limit in England (now, there’s a useless fact for you).

With a quantitative approach, this data can be analysed in heaps of ways. We could, for example, analyse these numbers to find the average temperature, or look to see how much these temperatures vary. We could see if there are significant differences in ideal water temperature between the sexes, or if there is some relationship between ideal bath water temperature and age! We could pop this information onto colourful, vibrant graphs , and use fancy words like “significant”, “correlation” and “eigenvalues”. The opportunities for nerding out are endless…

In this way, quantitative research often involves coming into your research with some level of understanding or expectation regarding the outcome, usually in the form of a hypothesis that you want to test. For example:

Hypothesis: Men prefer bathing in lower temperature water than women do.

This hypothesis can then be tested using statistical analysis. The data may suggest that the hypothesis is sound, or it may reveal that there are some nuances regarding people’s preferences. For example, men may enjoy a hotter bath on certain days.

So, as you can see, qualitative and quantitative research each have their own purpose and function. They are, quite simply, different tools for different jobs .

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Qualitative vs Quantitative Research: Which one should you use?

And here I become annoyingly vague again. The answer: it depends. As I alluded to earlier, your choice of research approach depends on what you’re trying to achieve with your research. 

If you want to understand a situation with richness and depth , and you don’t have firm expectations regarding what you might find, you’ll likely adopt a qualitative research approach. In other words, if you’re starting on a clean slate and trying to build up a theory (which might later be tested), qualitative research probably makes sense for you.

On the other hand, if you need to test an already-theorised hypothesis , or want to measure and describe something numerically, a quantitative approach will probably be best. For example, you may want to quantitatively test a theory (or even just a hypothesis) that was developed using qualitative research.

Basically, this means that your research approach should be chosen based on your broader research aims , objectives and research questions . If your research is exploratory and you’re unsure what findings may emerge, qualitative research allows you to have open-ended questions and lets people and subjects speak, in some ways, for themselves. Quantitative questions, on the other hand, will not. They’ll often be pre-categorised, or allow you to insert a numeric response. Anything that requires measurement , using a scale, machine or… a thermometer… is going to need a quantitative method.

Let’s look at an example.

Say you want to ask people about their bath water temperature preferences. There are many ways you can do this, using a survey or a questionnaire – here are 3 potential options:

• How do you feel about your spouse’s bath water temperature preference? (Qualitative. This open-ended question leaves a lot of space so that the respondent can rant in an adequate manner).
• What is your preferred bath water temperature? (This one’s tricky because most people don’t know or won’t have a thermometer, but this is a quantitative question with a directly numerical answer).
• Most people who have commented on your bath water temperature have said the following (choose most relevant): It’s too hot. It’s just right. It’s too cold. (Quantitative, because you can add up the number of people who responded in each way and compare them).

The answers provided can be used in a myriad of ways, but, while quantitative responses are easily summarised through counting or calculations, categorised and visualised, qualitative responses need a lot of thought and are re-packaged in a way that tries not to lose too much meaning.

Qualitative vs Quantitative Research: Data collection and analysis

The approach to collecting and analysing data differs quite a bit between qualitative and quantitative research.

A qualitative research approach often has a small sample size (i.e. a small number of people researched) since each respondent will provide you with pages and pages of information in the form of interview answers or observations. In our water perception analysis, it would be super tedious to watch the arguments of 50 couples unfold in front of us! But 6-10 would be manageable and would likely provide us with interesting insight into the great bathwater debate.

To sum it up, data collection in qualitative research involves relatively small sample sizes but rich and detailed data.

On the other side, quantitative research relies heavily on the ability to gather data from a large sample and use it to explain a far larger population (this is called “generalisability”). In our bathwater analysis, we would need data from hundreds of people for us to be able to make a universal statement (i.e. to generalise), and at least a few dozen to be able to identify a potential pattern. In terms of data collection, we’d probably use a more scalable tool such as an online survey to gather comparatively basic data.

So, compared to qualitative research, data collection for quantitative research involves large sample sizes but relatively basic data.

Both research approaches use analyses that allow you to explain, describe and compare the things that you are interested in. While qualitative research does this through an analysis of words, texts and explanations, quantitative research does this through reducing your data into numerical form or into graphs.

There are dozens of potential analyses which each uses. For example, qualitative analysis might look at the narration (the lamenting story of love lost through irreconcilable water toleration differences), or the content directly (the words of blame, heat and irritation used in an interview). Quantitative analysis  may involve simple calculations for averages , or it might involve more sophisticated analysis that assesses the relationships between two or more variables (for example, personality type and likelihood to commit a hot water-induced crime). We discuss the many analysis options other blog posts, so I won’t bore you with the details here.

Qualitative vs Quantitative Research: The pros & cons on both sides

Quantitative and qualitative research fundamentally ask different kinds of questions and often have different broader research intentions. As I said earlier, they are different tools for different jobs – so we can’t really pit them off against each other. Regardless, they still each have their pros and cons.

Let’s start with qualitative “pros”

Qualitative research allows for richer , more insightful (and sometimes unexpected) results. This is often what’s needed when we want to dive deeper into a research question . When we want to find out what and how people are thinking and feeling , qualitative is the tool for the job. It’s also important research when it comes to discovery and exploration when you don’t quite know what you are looking for. Qualitative research adds meat to our understanding of the world and is what you’ll use when trying to develop theories.

Qualitative research can be used to explain previously observed phenomena , providing insights that are outside of the bounds of quantitative research, and explaining what is being or has been previously observed. For example, interviewing someone on their cold-bath-induced rage can help flesh out some of the finer (and often lost) details of a research area. We might, for example, learn that some respondents link their bath time experience to childhood memories where hot water was an out of reach luxury. This is something that would never get picked up using a quantitative approach.

There are also a bunch of practical pros to qualitative research. A small sample size means that the researcher can be more selective about who they are approaching. Linked to this is affordability . Unless you have to fork out huge expenses to observe the hunting strategies of the Hadza in Tanzania, then qualitative research often requires less sophisticated and expensive equipment for data collection and analysis.

Qualitative research also has its “cons”:

A small sample size means that the observations made might not be more broadly applicable. This makes it difficult to repeat a study and get similar results. For instance, what if the people you initially interviewed just happened to be those who are especially passionate about bathwater. What if one of your eight interviews was with someone so enraged by a previous experience of being run a cold bath that she dedicated an entire blog post to using this obscure and ridiculous example?

But sample is only one caveat to this research. A researcher’s bias in analysing the data can have a profound effect on the interpretation of said data. In this way, the researcher themselves can limit their own research. For instance, what if they didn’t think to ask a very important or cornerstone question because of previously held prejudices against the person they are interviewing?

Adding to this, researcher inexperience is an additional limitation . Interviewing and observing are skills honed in over time. If the qualitative researcher is not aware of their own biases and limitations, both in the data collection and analysis phase, this could make their research very difficult to replicate, and the theories or frameworks they use highly problematic.

Qualitative research takes a long time to collect and analyse data from a single source. This is often one of the reasons sample sizes are pretty small. That one hour interview? You are probably going to need to listen to it a half a dozen times. And read the recorded transcript of it a half a dozen more. Then take bits and pieces of the interview and reformulate and categorize it, along with the rest of the interviews.

Now let’s turn to quantitative “pros”:

Even simple quantitative techniques can visually and descriptively support or reject assumptions or hypotheses . Want to know the percentage of women who are tired of cold water baths? Boom! Here is the percentage, and a pie chart. And the pie chart is a picture of a real pie in order to placate the hungry, angry mob of cold-water haters.

Quantitative research is respected as being objective and viable . This is useful for supporting or enforcing public opinion and national policy. And if the analytical route doesn’t work, the remainder of the pie can be thrown at politicians who try to enforce maximum bath water temperature standards. Clear, simple, and universally acknowledged. Adding to this, large sample sizes, calculations of significance and half-eaten pies, don’t only tell you WHAT is happening in your data, but the likelihood that what you are seeing is real and repeatable in future research. This is an important cornerstone of the scientific method.

Quantitative research can be pretty fast . The method of data collection is faster on average: for instance, a quantitative survey is far quicker for the subject than a qualitative interview. The method of data analysis is also faster on average. In fact, if you are really fancy, you can code and automate your analyses as your data comes in! This means that you don’t necessarily have to worry about including a long analysis period into your research time.

Lastly – sometimes, not always, quantitative research may ensure a greater level of anonymity , which is an important ethical consideration . A survey may seem less personally invasive than an interview, for instance, and this could potentially also lead to greater honesty. Of course, this isn’t always the case. Without a sufficient sample size, respondents can still worry about anonymity – for example, a survey within a small department.

But there are also quantitative “cons”:

Quantitative research can be comparatively reductive – in other words, it can lead to an oversimplification of a situation. Because quantitative analysis often focuses on the averages and the general relationships between variables, it tends to ignore the outliers. Why is that one person having an ice bath once a week? With quantitative research, you might never know…

It requires large sample sizes to be used meaningfully. In order to claim that your data and results are meaningful regarding the population you are studying, you need to have a pretty chunky dataset. You need large numbers to achieve “statistical power” and “statistically significant” results – often those large sample sizes are difficult to achieve, especially for budgetless or self-funded research such as a Masters dissertation or thesis.

Quantitative techniques require a bit of practice and understanding (often more understanding than most people who use them have). And not just to do, but also to read and interpret what others have done, and spot the potential flaws in their research design (and your own). If you come from a statistics background, this won’t be a problem – but most students don’t have this luxury.

Finally, because of the assumption of objectivity (“it must be true because its numbers”), quantitative researchers are less likely to interrogate and be explicit about their own biases in their research. Sample selection, the kinds of questions asked, and the method of analysis are all incredibly important choices, but they tend to not be given as much attention by researchers, exactly because of the assumption of objectivity.

Mixed methods: a happy medium?

Some of the richest research I’ve seen involved a mix of qualitative and quantitative research. Quantitative research allowed the researcher to paint “birds-eye view” of the issue or topic, while qualitative research enabled a richer understanding. This is the essence of mixed-methods research – it tries to achieve the best of both worlds .

In practical terms, this can take place by having open-ended questions as a part of your research survey. It can happen by having a qualitative separate section (like several interviews) to your otherwise quantitative research (an initial survey, from which, you could invite specific interviewees). Maybe it requires observations: some of which you expect to see, and can easily record, classify and quantify, and some of which are novel, and require deeper description.

A word of warning – just like with choosing a qualitative or quantitative research project, mixed methods should be chosen purposefully , where the research aims, objectives and research questions drive the method chosen. Don’t choose a mixed-methods approach just because you’re unsure of whether to use quantitative or qualitative research. Pulling off mixed methods research well is not an easy task, so approach with caution!

Recap: Qualitative vs Quantitative Research

So, just to recap what we have learned in this post about the great qual vs quant debate:

• Qualitative research is ideal for research which is exploratory in nature (e.g. formulating a theory or hypothesis), whereas quantitative research lends itself to research which is more confirmatory (e.g. hypothesis testing)
• Qualitative research uses data in the form of words, phrases, descriptions or ideas. It is time-consuming and therefore only has a small sample size .
• Quantitative research uses data in the form of numbers and can be visualised in the form of graphs. It requires large sample sizes to be meaningful.
• Your choice in methodology should have more to do with the kind of question you are asking than your fears or previously-held assumptions.
• Mixed methods can be a happy medium, but should be used purposefully.
• Bathwater temperature is a contentious and severely under-studied research topic.

Psst... there’s more!

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

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Qualitative vs. Quantitative Research: Comparing the Methods and Strategies for Education Research

No matter the field of study, all research can be divided into two distinct methodologies: qualitative and quantitative research. Both methodologies offer education researchers important insights.

Education research assesses problems in policy, practices, and curriculum design, and it helps administrators identify solutions. Researchers can conduct small-scale studies to learn more about topics related to instruction or larger-scale ones to gain insight into school systems and investigate how to improve student outcomes.

Education research often relies on the quantitative methodology. Quantitative research in education provides numerical data that can prove or disprove a theory, and administrators can easily share the number-based results with other schools and districts. And while the research may speak to a relatively small sample size, educators and researchers can scale the results from quantifiable data to predict outcomes in larger student populations and groups.

Qualitative vs. Quantitative Research in Education: Definitions

Although there are many overlaps in the objectives of qualitative and quantitative research in education, researchers must understand the fundamental functions of each methodology in order to design and carry out an impactful research study. In addition, they must understand the differences that set qualitative and quantitative research apart in order to determine which methodology is better suited to specific education research topics.

Generate Hypotheses with Qualitative Research

Qualitative research focuses on thoughts, concepts, or experiences. The data collected often comes in narrative form and concentrates on unearthing insights that can lead to testable hypotheses. Educators use qualitative research in a study’s exploratory stages to uncover patterns or new angles.

Form Strong Conclusions with Quantitative Research

Quantitative research in education and other fields of inquiry is expressed in numbers and measurements. This type of research aims to find data to confirm or test a hypothesis.

Differences in Data Collection Methods

Keeping in mind the main distinction in qualitative vs. quantitative research—gathering descriptive information as opposed to numerical data—it stands to reason that there are different ways to acquire data for each research methodology. While certain approaches do overlap, the way researchers apply these collection techniques depends on their goal.

Interviews, for example, are common in both modes of research. An interview with students that features open-ended questions intended to reveal ideas and beliefs around attendance will provide qualitative data. This data may reveal a problem among students, such as a lack of access to transportation, that schools can help address.

An interview can also include questions posed to receive numerical answers. A case in point: how many days a week do students have trouble getting to school, and of those days, how often is a transportation-related issue the cause? In this example, qualitative and quantitative methodologies can lead to similar conclusions, but the research will differ in intent, design, and form.

Taking a look at behavioral observation, another common method used for both qualitative and quantitative research, qualitative data may consider a variety of factors, such as facial expressions, verbal responses, and body language.

On the other hand, a quantitative approach will create a coding scheme for certain predetermined behaviors and observe these in a quantifiable manner.

Qualitative Research Methods

• Case Studies : Researchers conduct in-depth investigations into an individual, group, event, or community, typically gathering data through observation and interviews.
• Focus Groups : A moderator (or researcher) guides conversation around a specific topic among a group of participants.
• Ethnography : Researchers interact with and observe a specific societal or ethnic group in their real-life environment.
• Interviews : Researchers ask participants questions to learn about their perspectives on a particular subject.

Quantitative Research Methods

• Questionnaires and Surveys : Participants receive a list of questions, either closed-ended or multiple choice, which are directed around a particular topic.
• Experiments : Researchers control and test variables to demonstrate cause-and-effect relationships.
• Observations : Researchers look at quantifiable patterns and behavior.
• Structured Interviews : Using a predetermined structure, researchers ask participants a fixed set of questions to acquire numerical data.

Choosing a Research Strategy

When choosing which research strategy to employ for a project or study, a number of considerations apply. One key piece of information to help determine whether to use a qualitative vs. quantitative research method is which phase of development the study is in.

For example, if a project is in its early stages and requires more research to find a testable hypothesis, qualitative research methods might prove most helpful. On the other hand, if the research team has already established a hypothesis or theory, quantitative research methods will provide data that can validate the theory or refine it for further testing.

It’s also important to understand a project’s research goals. For instance, do researchers aim to produce findings that reveal how to best encourage student engagement in math? Or is the goal to determine how many students are passing geometry? These two scenarios require distinct sets of data, which will determine the best methodology to employ.

In some situations, studies will benefit from a mixed-methods approach. Using the goals in the above example, one set of data could find the percentage of students passing geometry, which would be quantitative. The research team could also lead a focus group with the students achieving success to discuss which techniques and teaching practices they find most helpful, which would produce qualitative data.

Learn How to Put Education Research into Action

Those with an interest in learning how to harness research to develop innovative ideas to improve education systems may want to consider pursuing a doctoral degree. American University’s School of Education online offers a Doctor of Education (EdD) in Education Policy and Leadership that prepares future educators, school administrators, and other education professionals to become leaders who effect positive changes in schools. Courses such as Applied Research Methods I: Enacting Critical Research provides students with the techniques and research skills needed to begin conducting research exploring new ways to enhance education. Learn more about American’ University’s EdD in Education Policy and Leadership .

What’s the Difference Between Educational Equity and Equality?

EdD vs. PhD in Education: Requirements, Career Outlook, and Salary

Top Education Technology Jobs for Doctorate in Education Graduates

American University, EdD in Education Policy and Leadership

Edutopia, “2019 Education Research Highlights”

Formplus, “Qualitative vs. Quantitative Data: 15 Key Differences and Similarities”

iMotion, “Qualitative vs. Quantitative Research: What Is What?”

Scribbr, “Qualitative vs. Quantitative Research”

Simply Psychology, “What’s the Difference Between Quantitative and Qualitative Research?”

Typeform, “A Simple Guide to Qualitative and Quantitative Research”

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Qualitative vs Quantitative Research: Differences and Examples

Understanding the differences between qualitative vs quantitative research is essential when conducting a research project, as both methods underpin the two key approaches in conducting a study.

In recent blogs, we elaborately discussed quantitative and qualitative research methods b ut what is the difference between the two? Which one is the best? Let’s find out.

Qualitative Research In a nutshell

Qualitative research is a research methodology where “quality” or opinion based research is conducted to derive research conclusions. This type of research is often conversational in nature rather than being quantifiable through empirical research and measurements.

Qualitative research: Methods & Characteristics

1. Conversation : A conversation takes place between the researcher and the respondent. This can be in the form of focus groups , in-depth interviews using telephonic / video / face-to-face conversations.

However, with the rise of online platforms, a bulk of steps in qualitative research involves creating and maintaining online community portals for a more quantifiable and recordable qualitative study.

LEARN ABOUT: Qualitative Interview

2. Conclusions : Research conclusions are subjective in nature when conducting qualitative research. The researcher may derive conclusions based on in-depth analysis of respondent attitude, reason behind responses and understanding of psychological motivations.

Quantitative Research In a nutshell

Quantitative research is a research methodology which uses questions and questionnaires to gather quantifiable data and perform statistical analysis to derive meaningful research conclusions.

Quantitative research: Methods & Characteristics

1. Questions : Quantitative research method uses surveys and polls to gather information on a given subject. There are a variety of question types used based on a nature of the research study.

For Example: If you want to conduct a customer satisfaction quantitative research, the Net Promoter Score is one of the critically acclaimed survey questions for this purpose.

2. Distribution : Quantitative research uses email surveys as the primary mode of gathering responses to questions. Alternatively, technology has given rise to offline distribution methods for relatively remote locations using offline mobile data capture apps. For social sciences and psychological quantitative research, social media surveys are also used to gather data.

3. Statistical Analysis : Quantitative research uses a wide range of data analysis techniques such as Conjoint Analysis , Cross Tabulation and Trend Analysis .

Qualitative vs Quantitative Research

Now let’s compare the qualitative and quantitative research methods in different aspects so that you can choose the right one in your next investigation.:

1. Objective and flow of research

Quantitative research is used in data-oriented research where the objective of research design is to derive “measurable empirical evidence” based on fixed and pre-determined questions. The flow of research, is therefore, decided before the research is conducted.

Where as, qualitative research is used where the objective is research is to keep probing the respondents based on previous answers under the complete discretion of the interviewer. The flow of research is not determined and the researcher / interviewer has the liberty to frame and ask new questions.

2. Respondent sample size

Respondents or sample of a particular panel is much larger for quantitative research such that enough verifiable information is gather to reach a conclusion without opinion bias. In large scale quantitative research, sample size can be in thousands.

Where as, qualitative research inherently uses less sample size because a large sample size makes it difficult of the research to probe respondents. For instance, a typical political focus group study evaluating election candidates involves no more than 5-10 panelists.

3. Information gathering

Quantitative research uses information gathering methods that can be quantified and processed for statistical analysis techniques. Simply put – quantitative research is heavily dependent on “numbers”, data and stats.

LEARN ABOUT: Research Process Steps

Where as, qualitative research uses conversational methods to gather relevant information on a given subject.

4. Post-research response analysis and conclusions

Quantitative research uses a variety of statistical analysis methods to derive quantifiable research conclusions. These are based on mathematical processes applied on the gather data.

Where as, qualitative researc h depends on the interviewer to derive research conclusions based on qualitative conversations held with the respondents. This conclusion is effectively subjective in nature. This is why quantitative research recordings are often reviewed by senior researchers before the final research conclusion is drawn.

Differences between qualitative vs quantitative research

We hope that this information helps you choose your next research method and achieve your goals.

If you want to carry out any qualitative or qualitative research questions , ask about the tools that QuestionPro has available to help you with the qualitative data collection of the data you need. We have functions for all types of research!.

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Quantitative and Qualitative Research

• Quantitative vs. Qualitative Research
• Find quantitative or qualitative research in CINAHL
• Find quantitative or qualitative research in PsycINFO
• Relevant book titles

Mixed Methods Research

As its name suggests, mixed methods research involves using elements of both quantitative and qualitative research methods. Using mixed methods, a researcher can more fully explore a research question and provide greater insight. 

What is Empirical Research?

Empirical research is based on observed  and measured phenomena. Knowledge is extracted from real lived experience rather than from theory or belief. 

IMRaD: Scholarly journals sometimes use the "IMRaD" format to communicate empirical research findings.

Introduction:  explains why this research is important or necessary. Provides context ("literature review").

Methodology:  explains how the research was conducted ("research design").

Results: presents what was learned through the study ("findings").

Discussion:  explains or comments upon the findings including why the study is important and connecting to other research ("conclusion").

What is Quantitative Research?

Quantitative research gathers data that can be measured numerically and analyzed mathematically. Quantitative research attempts to answer research questions through the quantification of data. 

Indicators of quantitative research include:

contains statistical analysis 

large sample size 

objective - little room to argue with the numbers 

types of research: descriptive studies, exploratory studies, experimental studies, explanatory studies, predictive studies, clinical trials 

What is Qualitative Research?

Qualitative research is based upon data that is gathered by observation. Qualitative research articles will attempt to answer questions that cannot be measured by numbers but rather by perceived meaning. Qualitative research will likely include interviews, case studies, ethnography, or focus groups. 

Indicators of qualitative research include:

interviews or focus groups 

small sample size 

subjective - researchers are often interpreting meaning 

methods used: phenomenology, ethnography, grounded theory, historical method, case study 

Video: Empirical Studies: Qualitative vs. Quantitative

This video from usu libraries walks you through the differences between quantitative and qualitative research methods. (5:51 minutes) creative commons attribution license (reuse allowed)  https://youtu.be/rzcfma1l6ce.

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• Key Differences

Know the Differences & Comparisons

Difference Between Qualitative and Quantitative Research

In a qualitative research, there are only a few non-representative cases are used as a sample to develop an initial understanding. Unlike, quantitative research in which a sufficient number of representative cases are taken to consideration to recommend a final course of action.

There is a never-ending debate on, which research is better than the other, so in this article, we are going to shed light on the difference between qualitative and quantitative research.

Content: Qualitative Research Vs Quantitative Research

Comparison chart, definition of qualitative research.

Qualitative research is one which provides insights and understanding of the problem setting. It is an unstructured, exploratory research method that studies highly complex phenomena that are impossible to elucidate with the quantitative research. Although, it generates ideas or hypothesis for later quantitative research.

Qualitative research is used to gain an in-depth understanding of human behaviour, experience, attitudes, intentions, and motivations, on the basis of observation and interpretation, to find out the way people think and feel. It is a form of research in which the researcher gives more weight to the views of the participants. Case study, grounded theory, ethnography, historical and phenomenology are the types of qualitative research.

Definition of Quantitative Research

Quantitative research is a form of research that relies on the methods of natural sciences, which produces numerical data and hard facts. It aims at establishing cause and effect relationship between two variables by using mathematical, computational and statistical methods. The research is also known as empirical research as it can be accurately and precisely measured.

The data collected by the researcher can be divided into categories or put into rank, or it can be measured in terms of units of measurement. Graphs and tables of raw data can be constructed with the help quantitative research, making it easier for the researcher to analyse the results.

Key Differences Between Qualitative And Quantitative Research

The differences between qualitative and quantitative research are provided can be drawn clearly on the following grounds:

• Qualitative research is a method of inquiry that develops understanding on human and social sciences, to find the way people think and feel. A scientific and empirical research method that is used to generate numerical data, by employing statistical, logical and mathematical technique is called quantitative research.
• Qualitative research is holistic in nature while quantitative research is particularistic.
• The qualitative research follows a subjective approach as the researcher is intimately involved, whereas the approach of quantitative research is objective, as the researcher is uninvolved and attempts to precise the observations and analysis on the topic to answer the inquiry.
• Qualitative research is exploratory. As opposed to quantitative research which is conclusive.
• The reasoning used to synthesise data in qualitative research is inductive whereas in the case of quantitative research the reasoning is deductive.
• Qualitative research is based on purposive sampling, where a small sample size is selected with a view to get a thorough understanding of the target concept. On the other hand, quantitative research relies on random sampling; wherein a large representative sample is chosen in order to extrapolate the results to the whole population.
• Verbal data are collected in qualitative research. Conversely, in quantitative research measurable data is gathered.
• Inquiry in qualitative research is a process-oriented, which is not in the case of quantitative research.
• Elements used in the analysis of qualitative research are words, pictures, and objects while that of quantitative research is numerical data.
• Qualitative Research is conducted with the aim of exploring and discovering ideas used in the ongoing processes. As opposed to quantitative research the purpose is to examine cause and effect relationship between variables.
• Lastly, the methods used in qualitative research are in-depth interviews, focus groups, etc. In contrast, the methods of conducting quantitative research are structured interviews and observations.
• Qualitative Research develops the initial understanding whereas quantitative research recommends a final course of action.

Video: Qualitative Vs Quantitative Research

An ideal research is one, which is conducted by considering both the methods, together. Although, there are some particular areas which require, only one type of research which mainly depends on the information required by the researcher.  While qualitative research tends to be interpretative, quantitative research is concrete.

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Quantitative and Qualitative Research

• I NEED TO . . .
• What is Quantitative Research?
• What is Qualitative Research?
• Quantitative vs Qualitative
• Step 1: Accessing CINAHL
• Step 2: Create a Keyword Search
• Step 3: Create a Subject Heading Search
• Step 4: Repeat Steps 1-3 for Second Concept
• Step 5: Repeat Steps 1-3 for Quantitative Terms
• Step 6: Combining All Searches
• Step 7: Adding Limiters
• Step 8: Save Your Search!
• What Kind of Article is This?
• More Research Help This link opens in a new window

Adapted from  https://www.orau.gov/cdcynergy/soc2web/Content/phase05/phase05_step03_deeper_qualitative_and_quantitative.htm

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Qualitative and Quantitative Research

In general, quantitative research seeks to understand the causal or correlational relationship between variables through testing hypotheses, whereas qualitative research seeks to understand a phenomenon within a real-world context through the use of interviews and observation. Both types of research are valid, and certain research topics are better suited to one approach or the other. However, it is important to understand the differences between qualitative and quantitative research so that you will be able to conduct an informed critique and analysis of any articles that you read, because you will understand the different advantages, disadvantages, and influencing factors for each approach. 

The table below illustrates the main differences between qualitative and quantitative research. Be aware that these are generalizations, and that not every research study or article will fit neatly into these categories. 

Systematic reviews, meta-analyses, and integrative reviews are not exactly designs, but they synthesize, analyze, and compare the results from many research studies and are somewhat quantitative in nature. However, they are not truly quantitative or qualitative studies.

References:

LoBiondo-Wood, G., & Haber, J. (2010). Nursing research: Methods and critical appraisal for evidence-based practice (7 th ed.). St. Louis, MO: Mosby Elsevier

Mertens, D. M. (2010). Research and evaluation in education and psychology (3 rd ed.). Los Angeles: SAGE

Quick Overview

This 2-minute video provides a simplified overview of the primary distinctions between quantitative and qualitative research.

It's Not Always One or the Other!

It's important to keep in mind that research studies and articles are not always 100% qualitative or 100% quantitative. A mixed methods study involves both qualitative and quantitative approaches. If you need to find articles that are purely qualitative or purely quanititative, be sure to look carefully at the methodology sections to make sure the studies did not utilize both methods. 

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Qualitative vs Quantitative research: Similarities, differences, pros, and cons

Amirah Khan • 2023-05-15

Qualitative and quantitative research are two popular approaches to data collection and analysis. Both are essential research approaches that are utilised across disciplines, including psychology, business, user research, computer science, and more. In this article, we’ll share the key features, research methods, pros and cons, and use cases of qualitative and quantitative research.

What is Qualitative Research?

Qualitative research aims to use non-numerical data to understand, explore, and interpret the way people think, behaviour, and feel. This includes examining experiences, attitudes, and beliefs that exist in our subjective social reality. Qualitative research uses descriptive data to draw rich, in-depth insights into problems, topics, and phenomena. This kind of research focuses on making sense of the subjective, dynamic, and evolving nature of real life. Using this research approach, it is possible to generate new ideas for research, including hypotheses and theories that are rooted in natural settings. 

Key Features

Non-Numerical Data: Qualitative data focuses on rich, subjective sources of information including images, videos, text, and audio. This could be documents, observation notes, interview transcripts, audio recordings, video interviews, diaries, personal logs, photographs, and many more descriptive data sources. 

Inductive Reasoning: Rather than test existing theories and hypotheses, qualitative research aims to generate new ideas for research. The goal is to take a bottom-up approach and extract rich, in-depth meaning from a specific dataset. Researchers examine unique experiences and aim to draw out common themes or categories to make sense of the topic at hand. 

Flexible Research Design: Qualitative research studies have a flexible and emergent design that is data-driven. The research design, including the methods of data collection and analysis, can change throughout the study as findings emerge. This allows the design to develop alongside the study, as long as the research question is answered. 

Qualitative Researchers: Due to the subjective nature of qualitative research, the qualitative researchers are considered instruments in the process. This is because their beliefs, attitudes, personal characteristics, and experiences can influence the interpretive data collection and analysis process. 

Small Scale: Qualitative research methods can be time-consuming, and the subject matter can sometimes be very specific to a certain group of people. This means qualitative research often features a small sample of participants to be observed, interviewed, or given questionnaires. 

Open-Ended Questions: To gather the rich, in-depth data needed for qualitative research, open-ended questions are used throughout the research methods. These kinds of questions allow participants to answer how they want in detail, rather than having to select from a limited range of pre-determined answers. 

Qualitative Research Methods

For qualitative research, there are five common research methods used for data collection. Researchers often use multiple methods collect data and this depends on their chosen research approach:

Surveys can often be a time-saving, complementary method of data collection. Researchers can collect data using questionnaires with open-ended questions. These can be distributed online or in-person and allows participants to provide detailed responses in their own time. 

In-depth interviews are used to collect in-depth insights into a person’s perspective on a problem, event, or topic. Researchers ask open-ended questions in a one-to-one conversation, and can deep-dive into the participants' answers with follow-up questions. 

Focus groups are ideal for collecting data from multiple participants in the form of a group discussion. Researchers generate and facilitate discussion using open-ended questions. This research method is good for understanding complex social topics, and examining beliefs and opinions. 

Observations occur when researchers go out into natural settings of interest to create records of what they saw, heard, or encountered. This is documented in detailed field notes, and focuses on understanding how people behave. 

Secondary data involves using existing data, such as documents, photos, and videos to conduct qualitative research. This can be a more efficient way to approach a research topic, rather than collecting new data. 

Pros and Cons of Qualitative Research 

Qualitative research produces rich, in-depth insights into problems, issues, and phenomena. The research findings are often full of meaning that explore the ‘why’, ‘how’, and ‘what’ behind processes, behaviours, thoughts, feelings, attitudes, and experiences. This is something that can be hard to obtain from quantitative research. Qualitative research also focuses on real-life settings and people, which can provide a more accurate representation than laboratory based experiments. Finally, the inductive approach of qualitative research allows for new possibilities to be discovered and explored. 

However, the subjective nature of qualitative research makes it hard to replicate. Researchers are also key instruments in the process which further reduces replicability. This limits how reliable qualitative findings are, Qualitative research can also be time-consuming, especially during data analysis. Despite using a small sample, there’s often large amounts of data to prepare and analyse. These smaller samples can also make it harder for researchers to generalise their findings beyond their current participants.  

When to use Qualitative Research?

Qualitative research is ideal if you want to:

• Extract rich, in-depth, and meaningful insights into problems and topics
• Understand how people perceive their own experiences
• Explore a person’s thoughts, feelings, and behaviours
• Gain insight into social realities of specific individuals, groups, and cultures 
• Examine controversial social issues and topics 
• Generate new research ideas and possibilities 
• Learn about attitudes, beliefs, and opinions 

Qualitative Research Questions 

• Why are customers unsatisfied with their new product?
• How do teachers feel about students using artificial intelligence?
• What are teenagers' experiences of para-social relationships with influencers? 

What is Quantitative Research?

Quantitative research focuses on testing hypotheses and theories using numerical data. The aim is to use maths, statistics, and deductive logic to establish facts about behaviour or a phenomena of interest. This type of research aims to understand and measure the causal or correlational relationships between quantifiable variables. Quantitative research data can be transformed into useful graphs and tables using statistics. 

Specifically, descriptive statistics are used to summarise data, and describe the relationships or connections between variables. Inferential statistics establish the statistical significance of the given groups of data. For this reason, quantitative research requires a large sample of participants, and a carefully planned research design. This is important for conducting statistical analyses that are reliable and generalisable.  

Here are the key features of quantitative research that contrast with the features of qualitative research: 

Numerical Data : Quantitative data focuses on variables that can be quantified, measured, and analysed through statistics. This data, which is rooted in numbers and maths, can be displayed using graphs and tables. 

Deductive Reasoning: Quantitative research aims to test whether existing theories, hypotheses, or observations can hold up in specific conditions. This allows researchers to determine whether a theory or hypotheses should be confirmed or rejected for that particular condition. 

Fixed Research Design: Quantitative research follows a structured process that is well-established. The research design, including the research questions, research methods, and data analysis techniques are often decided at the beginning and rarely changed during the study. 

Quantitative Researchers: For quantitative researchers, their approach to the world is objective, and focuses on the quantifiable, measurable aspects of reality. Their goal is to remain as objective as possible and produce results that can be generalised beyond the specific environment of the study. 

Large Scale: Statistical analyses require a large amount of data to produce significant and reliable results. For this reason, quantitative research often involves a large sample of participants. This larger sample allows results to be generalised and enables researchers to account for erroneous data. 

Close-ended Questions: Quantitative data collection methods use close-ended questions to collect quantifiable, measurable data. Close-ended questions have predetermined responses for people to pick from. This can include yes/no questions, multiple-choice answers, and rating scales of all kinds. 

Quantitative Research Methods

Experiments involve manipulating an independent variable and measuring a dependent variable. This is to examine how changes to the independent variable affect the dependent variable. Researchers can use experiments to identify cause and effect relationships between variables. 

Observations are used to watch, understand, and investigate quantifiable variables. Instead of manipulating variables, this method focuses on measuring variables. For example, weight, size, and noting the number of times something occurs are measurements. Observations are used for descriptive and correlational research designs . 

Surveys are a common and popular research method, also used for descriptive and correlational research designs. This method uses close-ended questions, such as multiple choice, or rating scales to collect data. Surveys can be used to understand how something changes over time, or to get a snapshot of the current moment. 

Pros and Cons of Quantitative Research 

Quantitative research follows structured, unambiguous, standardised processes that can be easily replicated. This improves the reliability of the study, allowing it to be replicated and proven using the same approach. Unlike qualitative research, quantitative research can be both quick and scientifically objective. Researchers can study phenomena in a timely manner, and utilise sophisticated softwares for rapid, statistical analyses. This allows researchers to process large amounts of data in an efficient way, and produce findings that are generalisable. 

If researchers are unable to obtain an adequate sample size, or end up with data that cannot be used, this limits the accuracy and generalisability of the findings. Researchers also require statistical expertise in order to conduct statistical analyses in an accurate manner. Finally, quantitative research can lack meaning and be subject to confirmation bias. That is, researchers can miss emerging phenomena because they are focused on testing a theory of hypothesis. 

When to use Quantitative Research?

Quantitative research is best used when you want to:

• Measure or quantify data 
• Establish trends and relationships between variables
• Test existing hypotheses and theories 
• Describe and predict casual relationships
• Investigate correlational relationships
• Understand the characteristics of a population or phenomena 
• Produce visual displays of information, such as graphs or tables 

Quantitative Research Questions 

• What are the demographics of my target audience on social media?
• How satisfied are customers with my products and services?
• Can mindfulness improve a student's ability to recall information?

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What’s the Difference: Qualitative vs Quantitative Research?

Discover the differences between qualitative vs quantitative research. Learn how to choose the right methodology for your research project.

There are several methodologies for conducting research, with qualitative and quantitative research being two of the most prominent. Qualitative research focuses on understanding an individual’s experiences and points of view through observation and interviews, whereas quantitative research analyses and draws conclusions based on numerical data. Both strategies have benefits as well as drawbacks, and selecting the appropriate methodology can have a considerable influence on the outcome of a study. 

In this article, we will look at the differences between qualitative vs quantitative research, their benefits and drawbacks, and how to analyze based on each method. By the end of this article, you will have a better grasp of these two research methods and will be better prepared to select the best one for your research.

What is Qualitative Research?

Qualitative research is a research method that focuses on understanding individuals’ experiences, perspectives, and behaviors in their natural environment. This method is frequently used to investigate complex phenomena that are difficult to quantify, such as beliefs, attitudes, and feelings. Data for qualitative research is often gathered through methods such as observation, interviews, and focus groups. The information gathered is frequently non-numerical and might comprise text, audio, and visual records.

One of the distinctive features of qualitative research is the emphasis on context and the subjective interpretation of data. Rather than attempting to generalize the findings to a broader population, qualitative researchers strive to grasp the meaning and relevance of the data acquired by evaluating it in its context. 

This strategy helps researchers to obtain a better understanding of the experiences and points of view of the individuals being examined, as well as find patterns and themes that may not have been obvious using other research methods. 

What is Quantitative Research?

Quantitative research is a research method that focuses on the systematic collecting and analysis of numerical data. This strategy is frequently used to investigate correlations between variables and to make predictions or generalizations about a wider population based on a sample. Quantitative research often entails gathering data using methods such as surveys, experiments, and structured observations, and then evaluating the data using statistical techniques.

One of the distinctive features of quantitative research is its emphasis on impartiality and the use of standardized measurements. Quantitative researchers use rigorous methods for gathering and analyzing information to reduce the effect of personal bias and subjectivity. 

This method enables researchers to test hypotheses, identify cause-and-effect correlations, and draw statistical inferences about a wider population.

Advantages and Disadvantages of Qualitative Research

When deciding on the methodology to use, researchers should examine the advantages and disadvantages of qualitative research, as follows:

• Data richness and depth: Qualitative research enables researchers to collect rich, detailed data about participants’ experiences, attitudes, and points of view, which can offer a more complete picture of the phenomena under investigation.
• Flexibility: Qualitative research is adaptive and flexible, allowing researchers to change their method in response to new or unexpected discoveries.
• Understanding participants: Since qualitative research frequently involves direct involvement with individuals, researchers can get a better grasp of their personal experiences and points of view.
• Contextualization: Qualitative research stresses the relevance of context and subjective data interpretation, which can give insights into how individuals make meaning of their experiences in their specific settings.
• Hypothesis generation: By recognizing patterns and themes in the data, qualitative research may be utilized to develop hypotheses for additional research. 

Disadvantages

• Limited generalizability: Since qualitative research occasionally relies on small sample size, it may not be representative of the wider population, its generalizability is limited.
• Subjectivity: Qualitative research entails subjective data interpretation, which may be impacted by the researcher’s bias or personal perspective.
• Time-consuming: As qualitative research includes in-depth data collecting and processing, it may be time-consuming.
• Difficulties in analysis: Qualitative data can be complicated and difficult to analyze, especially when non-textual material such as photos or audio recordings are included.
• Data saturation: Qualitative research might reach a point where new information does not yield significant insights, limiting the relevance of further data collection.

Advantages and Disadvantages of Quantitative Research

Quantitative research, like qualitative research, has advantages and disadvantages that researchers should consider when selecting this method for their study.

• Generalizability: Since quantitative research is frequently based on a larger sample size, it can yield statistically valid findings that can be generalized to a broader population.
• Objectivity: Quantitative research places an emphasis on objectivity and standardized measurements, which reduces the impact of personal bias and subjectivity.
• Replicability: Quantitative research provides an established method and standardized measurements, allowing other researchers to replicate the research. 
• Statistical analysis: Statistical analysis is possible in quantitative research, which may assist researchers evaluate hypotheses and discover cause-and-effect connections. 
• Effective data analysis: Quantitative research frequently involves numerical data, which may be quickly examined using statistical tools.

Disadvantages 

• Lack of depth: As quantitative research frequently depends on standardized measurements, it may overlook the intricacies of participants’ experiences and points of view. 
• Limited comprehension: Quantitative research frequently focuses on specific aspects of the phenomenon being examined, it may not give an in-depth understanding of the entire phenomenon being studied.
• Inflexibility: Since quantitative research relies on a set methodology and established measurements, it is frequently inflexible. 
• Limited context: Quantitative research may fail to recognize the significance of context and may neglect the impact of subjective data interpretation.
• Measurement error: Quantitative research is based on numerical data, which might be prone to measurement errors or inaccuracies. 

Data Collection Methods: Qualitative vs Quantitative Research

The collection of data methods differs in different ways between qualitative vs quantitative research.

Qualitative research often employs data-collecting methods such as interviews, focus groups, observation, and document analysis. Using these methods, researchers may acquire extensive, detailed data about participants’ experiences, perspectives, and points of view. 

Interviews and focus groups, for example, allow researchers to interact directly with participants and delve deeper into their personal experiences and points of view. Researchers can use observation to study participants’ behavior in their natural environment and capture their experiences in real-time. 

They might investigate written or visual materials such as diaries, letters, or photos to get insights into participants’ experiences and points of view through document analysis.

Quantitative research often uses methods for collecting data such as surveys, experiments, and structured observations. These methods enable researchers to acquire numerical data that can then be examined statistically. 

Surveys entail asking individuals to answer a series of standardized questions, usually in writing or online. Experiments entail tinkering with one or more variables in order to test hypotheses and quantify the effect on a dependent variable. Structured observations entail gathering data in a methodical manner, frequently utilizing pre-determined categories or checklists.

Overall, data-collecting methods are employed in both qualitative and quantitative research, although the methods utilized change based on the research’s methodology and the type of data being gathered. Quantitative research focuses on numerical data that can be evaluated using statistical tools, whereas qualitative research emphasizes rich, detailed data that can give insights into participants’ experiences and points of view. 

How to Analyze Qualitative vs Quantitative Data

Due to the nature of the data, analyzing qualitative and quantitative data requires different methodologies.

The identification of patterns, themes, and categories in collected data is the primary objective of qualitative data analysis . This method frequently includes the following steps:

• Transcribing or turning recorded data into text is usually the first stage in assessing qualitative data.
• Data coding entails the researcher reading and rereading the data in order to uncover patterns, themes, and categories. To aid in this process, the researcher may employ software programs. 
• The researcher then generates themes and sub-themes that arise from the data once the data has been coded.
• Finally, depending on the data collected, the researcher evaluates the themes and sub-themes, drawing conclusions and providing suggestions.

When it comes to quantitative data analysis , statistical approaches are used to examine the numerical data obtained. The process frequently includes the following steps:

• Cleaning the data is the first step in interpreting quantitative data since it removes errors, inconsistencies, and outliers.
• The researcher then arranges the data into an analyzable format, such as a spreadsheet or database.
• To summarize the data, the researcher may use descriptive statistics such as mean, median, or mode.
• Finally, the researcher may employ inferential statistics such as t-tests or ANOVA to test hypotheses and evaluate if the results are statistically significant.

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Qualitative vs Quantitative Research – What Is the difference?

Bryn Farnsworth

In the world of research, across many different research fields, there are two main approaches that researchers employ: qualitative and quantitative research. In this article, we will explore the key differences between qualitative and quantitative research and look at what the main differences are, including their respective strengths and weaknesses, as well as some common methods used in each approach. By the end of this article, readers will have a better understanding of the two approaches and be able to choose the best approach for their research question.

Table of Contents

The core difference between qualitative and quantitative research.

Once you get started with human behavior research you soon find yourself running into the question of whether your research project is qualitative or quantitative in nature . There are inherent differences between qualitative and quantitative research methods, although their objectives and applications overlap in many ways.

In a nutshell, qualitative research generates “textual data” (non-numerical). Quantitative research, on the contrary, produces “numerical data” or information that can be converted into numbers.

In analogous terms, qualitative research is like exploring a new city. You walk around, talk to people, and soak up the atmosphere to understand the culture and the vibe. It’s all about getting a feel for the place and the people who live there.

Quantitative research, on the other hand, is more like counting cars on a busy street. You collect data, run some calculations, and analyze the numbers to understand how things work and what factors might be influencing them.

So, while qualitative research is about understanding the deeper meaning and nuances of human experiences, quantitative research is about measuring and analyzing data in a systematic way.

Both methodologies have their place in science and research in terms of understanding the world around us, but it is important to know how and when to implement them.

What is Quantitative Research?

What is qualitative research and why is it important.

Qualitative research is considered to be particularly suitable for exploratory research (e.g. during the pilot stage of a research project, for example). It is primarily used to discover and gain an in-depth understanding of individual experiences, thoughts, opinions, and trends, and to dig deeper into the problem at hand.

The data collection toolkit of a qualitative researcher is quite versatile, ranging from completely unstructured to semi-structured techniques.

Most common applied Qualitative Methods:

• Individual interviews
• Group discussions
• Focus groups
• Behavioral observations

Check out: How to measure Human Behavior: Survey vs. Focus Groups vs. Biometric

In addition, eye tracking or automatic facial expressions can be collected and analyzed qualitatively, for example in usability research, where gaze patterns (such as with heatmaps) or moments of expressions of frustration/confusion can be used to track the journey of an individual respondent within a software interface.

Check out: What is Eye Tracking and How Does it Work?

Typically, qualitative research focuses on individual cases and their subjective impressions. This requires an iterative study design – data collection and research questions are adjusted according to what is learned.

Often, qualitative projects are done with few respondents and are supposed to provide insights into the setting of a problem, serving as a source of inspiration to generate hypotheses for subsequent quantitative projects.

Advantages of qualitative research

Qualitative research methods are the tool of choice when a researcher wants to gain in-depth data from a small sample size, such as a local community, closed demographic, or situations where in-depth data lets you extrapolate responses to a broader perspective. Some of the main strengths of qualitative research are;

• Provides in-depth understanding: Qualitative research allows researchers to gain a detailed and comprehensive understanding of a particular phenomenon or concept. By gathering rich and detailed data, qualitative research can provide insights into complex and multifaceted issues.
• Flexibility: By working with much smaller sample sizes, qualitative research is flexible, adaptable, and agile. It can be adjusted as the research progresses to explore emerging themes and ideas that were not initially anticipated.
• Emphasis on context: Qualitative research emphasizes the importance of understanding the social and cultural context in which a phenomenon occurs. This allows for a more nuanced interpretation of the data and a deeper understanding of the factors that influence behavior.
• Participant perspectives: Qualitative research often involves direct engagement with participants, which allows researchers to explore their perspectives, experiences, and attitudes toward the phenomenon being studied.
• Generates new theories: Qualitative research is often used in exploratory studies and can generate new theories and hypotheses. It can be particularly useful in situations where little is known about a phenomenon and more research is needed to generate insights and understanding.

Limitations of qualitative research

• Subjectivity: Qualitative research can be more subjective than is advisable, as it relies heavily on the researcher’s interpretation and understanding of the data. This can lead to bias and affect the validity and reliability of the findings.
• Limited generalizability: Qualitative research is typically based on a small sample size and is context-specific, which means the findings may not be generalizable to other populations or settings.
• Time-consuming: Qualitative research can be time-consuming and require significant resources. Collecting and analyzing data can take a long time, and the data may be difficult to analyze due to its complexity.
• Lack of standardization: Qualitative research methods are often not standardized, which can make it difficult to compare and replicate studies. This can also affect the reliability and validity of the findings.
• Difficulty in data analysis: Qualitative research often involves the analysis of large amounts of data, which can be challenging and time-consuming. It can be difficult to identify patterns and themes in the data, and the findings may be difficult to interpret.

What is quantitative research and how to measure it

Simply put, quantitative research is all about numbers and figures. It is used to quantify opinions, attitudes, behaviors, and other defined variables with the goal to support or refute hypotheses about a specific phenomenon, and potentially contextualize the results from the study sample in a wider population (or specific groups).

As quantitative research explicitly specifies what is measured and how it is measured in order to uncover patterns in – for example – behavior, motivation, emotion, and cognition, quantitative data collection is considered to be much more structured than qualitative methods.

Check out: How To Do Behavioral Coding in iMotions

Advantages of quantitative research

As should be apparent by now, quantitative research should be your method of choice if you are looking to work with a large amount of data. Spotting trends, fluctuations across demographics, and objective generalizations in responses is where the quantitative research methods shine. Here is a list of the best reasons why you should choose quantitative research as your method.

• Objectivity: Quantitative research is often viewed as more objective and reliable than qualitative research due to its focus on numerical data and statistical analysis. In short, if you have done your data collection properly, the data will not lie.
• Generalizability: Quantitative research allows for the generalization of results to larger populations because it relies on representative samples and statistical techniques to draw conclusions and make predictions about a larger group.
• Replication: The use of standardized and objective measures in quantitative research allows for easy replication of studies, enabling other researchers to verify and build upon existing findings.
• Easy to analyze: Quantitative data is often straightforward to analyze using statistical software , allowing researchers to quickly and efficiently identify trends and patterns in the data.

Limitations of quantitative research

Research and data collection methods focused on quantity rather than quality, will inevitably come with certain drawbacks and limitations. These are dependent on the research scope, but at the very least they should be considered when building a study design. Following here is a list of the main limitations or considerations of using quantitative research as a method.

• Limited scope: Quantitative research is focused on data and statistical analysis, which can be limiting in terms of the range of topics it can explore and the depth of insights it can obtain.
• Lack of context : Quantitative research may not provide the depth of information and context that qualitative research can offer, as the main focus is on statistics rather than the experiences and perceptions of participants.
• Difficulty in measuring complex constructs : It can be challenging to measure complex constructs such as emotions, attitudes, and beliefs using quantitative methods, as they are often difficult to define and measure precisely when dealing with large datasets.
• Potential for researcher bias : The collection and analysis of numerical data can be influenced by researcher bias, leading to inaccurate or incomplete results.
• Limited ability to capture individual experiences : Quantitative research may not be able to capture the unique experiences and perspectives of individual participants, as it typically focuses on group-level trends and patterns.

Quantitative research techniques

Quantitative techniques typically comprise various forms of questionnaires and surveys, structured interviews as well as a behavioral observation based on explicit coding and categorization schemes.

In addition to these traditional techniques, biosensor recordings such as eye tracking , EEG , EDA / GSR , EMG , and ECG , as well as computer-guided automatic facial expression analysis procedures, are used.

Check out: What Is Facial Expression Analysis? (And How Does It Work?)

All of these quantify the behavioral processes in such a way that numerical results can be obtained – for example, fixation duration from eye tracking (representing the amount of visual attention), the number of GSR peaks (indicating the amount of physiological arousal) or the power of a specific EEG band.

After data collection, quantitative analysis techniques and statistics can be applied, such as t-tests and ANOVAs, to non-parametric methods. This often necessitates much bigger sample sizes compared to qualitative research but allows you to make more solid conclusions, that are backed up with data.

Check Out: How to perform a qualitative research interview

Qualitative or quantitative research study design?

Ultimately, whether to pursue a qualitative or a quantitative study approach is up to you – however, be sure to base your decision on the nature of your project and the kind of information you seek in the context of your study, and the resources available to you. Qualitative will offer you an in-depth understanding of your research problem and hopefully help answer your hypothesis. Quantitative will allow you to scale your research to provide larger sets of data for reliability and validity. A combination of the two provides you with objectivity.

Measurements to avoid bias

This is generally described with respect to the following criteria:

Objectivity

Objectivity is the most general requirement and reflects the fact that measures should come to the same result no matter who is using them. Also, they should generate the same outcomes independent of outside influences. For example, a multiple-choice personality questionnaire or survey is objective if it returns the same score irrelevant of whether the participant responds verbally or in written form. Further, the result should be independent of the knowledge or attitude of the experimenter, so that the results are purely driven by the performance of the respondent.

Reliability

A measure is said to have a high reliability if it returns the same value under consistent conditions. There are several sub-categories of reliability. For example, “retest reliability” describes the stability of a measure over time, “inter-rater reliability” reflects the amount to which different experimenters give consistent estimates of the same behavior, while “split-half reliability” breaks a test into two and examines to what extent the two halves generate identical results.

This is the final and most crucial criterion. It reflects the extent to which a measure collects what it is supposed to collect. Imagine an experiment where body size is collected to measure its relationship with happiness. Obviously, the measure is both objective and reliable (body size measures are quite consistent irrespective of the person taking the measurement) but it is truly a poor measure with respect to its construct validity (i.e., its capability to truly capture the underlying variable) for happiness.

If you would like to learn more about qualitative and quantitative research designs, contact our experts at iMotions. We’re happy to help!

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Qualitative vs. quantitative research: what's the difference?

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While the topic of qualitative vs. quantitative research sounds intimidating, they’re easy concepts to understand, and they represent things that you’re probably doing already. Most business professionals want to get customer feedback and know their audience , whether you call it research or something else.

For one, suggesting that qualitative and quantitative are at odds with one another is misleading. While quantitative research is the method that most people are familiar with (and the one that gets all the credit), the two complement each other fundamentally. Together, they can give a more holistic view of a problem or situation. Having one without the other means that you’re only getting half a story. Both play a valuable role in measuring your customer experience . 

After a sizeable European car rental company spent time and money developing a car rental subscription membership on a hunch, they were stumped by its poor performance. First, analytics showed that users saw the ads but didn’t sign up. Then, after getting feedback from a handful of users, it became painfully clear why no one wanted to belong to the exclusive club. Watch Kate Margolis, UX/UI Design Lead at Thirdfort, tell the story. 

While there are significant differences between qualitative and quantitative research methods, it’s essential to understand the benefits and blind spots. So let’s start with quantitative. 

What is quantitative research?

Quantitative research is the process of collecting and analyzing numerical data. It aims to find patterns and averages, make predictions, test causal relationships, and generalize results to broader populations by representing data expressed as numbers.

Quantitative research is unlike qualitative research in one critical aspect—it’s numerical. This is because the output of quantitative research is numbers and statistics. 

Quantitative research methods

Some popular ways of conducting quantitative research include: 

• Surveys (ratings, ranking, scales, and closed-ended questions)
• A/B testing
• Benchmarking
• Observational or listening methods
• Web analytics

Advantages and disadvantages of quantitative data 

What’s excellent about quantitative data is that you can easily replicate it. Quantitative data collection is relatively easy to do, and so is analysis. Since you’re dealing with numbers, it’s typically easier to interpret quantitative data and present your findings to others in a less subjective way.  Advantages: Larger sample sizes, quicker, easier, less expensive, can uncover patterns and correlations, traditionally easier to automate, offers continuous information, data interpretation is more straightforward Disadvantages: Less flexibility, can’t follow up, may not reflect actual feelings, lacks context It’s human nature to trust numbers. We tend to believe they’re concrete. More importantly, quantitative methods get more attention because it’s easier to tie quantitative measurements to performance metrics and ROI. But unfortunately, there are many ways numbers can be unreliable . While numerical data can tell you that there’s a problem, it seldom tells you why. Plus, by focusing on numbers only, there’s a risk of missing something. Here are some examples where quantitative data isn’t enough information to make an informed decision: 

• An e-commerce agency notices that her client’s shoppers are dropping off on one of their biggest channels before the checkout, but they don’t know why. 
• A product manager is getting survey data showing that new customers are not satisfied with the onboarding process. While she has an idea of what it could be, she’s not sure where to start. 
• A marketing team spent weeks developing and rolling out a campaign that flopped. While the team believes they’re on the right track, the President of the company, who never liked the idea, tells them to abandon it altogether. 

Lastly, a significant drawback to quantitative research is that numbers don’t convey stories well. So while it’s easy to share a table of data points with an audience, it’s harder to get them to absorb the information and remember it later.

What is qualitative research?

Qualitative research is a behavioral research method that relies on non-numerical data derived from observations and recordings that approximate and characterizes phenomena. It’s collecting, analyzing, and interpreting non-numerical data, such as language. It sometimes seeks to understand how an individual subjectively perceives and gives meaning to their social reality. 

Instead of numbers, qualitative data comes from studying subjects in their natural environment and focusing on understanding the why and how of human behavior in a given situation. It’s especially effective in obtaining information about people's values, opinions, and behaviors. Data is collected through participant observation and interviews. 

Qualitative research methods

There are three common qualitative research methods: 

• Participant observation
• In-depth or unstructured interviews
• Focus groups

1. Participant observation

Use participant observation to collect data on naturally-occurring behaviors in their usual contexts.

2. In-depth or unstructured interviews 

In-depth interviews are optimal for collecting data on individuals’ personal histories, perspectives, and experiences, mainly when exploring sensitive topics or follow-up questions are likely necessary. When asking open questions, the interviewer can get a real sense of the person’s understanding of a situation. For example, they might say one thing, but their body language says something else. 

3. Focus groups

Focus groups effectively gather information from multiple subjects at once and generate broad overviews of issues or concerns related to the demographics represented.

Advantages and disadvantages of qualitative data

The most significant advantage to qualitative data is that it’s easy to present your data as a story to your audience. In this way, qualitative data has both staying power and the ability to persuade others. People remember stories and how they make them feel. While charts and numbers can convince others to change, they won’t always translate into action. Instead, qualitative data offers rich, in-depth insights that allow you to explore new contexts and deeper understandings.  Advantages: Allows for context, empathy, ambiguities and contradictions, deeper insights  Cons: Traditionally time-consuming and expensive, impossible to replicate, challenging to interpret raw data, analysis is subjective The cons of qualitative research are that it’s often not a statistically representative form of data collection, and it can require multiple data sessions, which can lead to varying analyses.  

Examples of qualitative vs. quantitative research questions

When planning research, you want to be strategic with your test questions. Here are some examples of qualitative vs. quantitative questions to give you an idea of how they work. 

Quantitative research questions

Quantitative research questions are typically set up so that the answer is numerical or statistical or so that the answer is objective. Typically this process is automated and answers can’t be followed up. 

• How long have been a customer of our organization?
• On a scale of 1-5, how likely are you to purchase our products again?
• How often do you drink coffee at home?
• Do you prefer to watch movies at home or in the theatre?

Qualitative research questions

Qualitative research questions are open-ended. The interviewer can react to answers and probe for more detail.

• What does the app need to do to improve your experience?
• Do you have any comments, questions, or concerns about our website?
• What do you like most about your favorite coffee shop?
• What makes a movie good?

Why you need both qualitative and quantitative research

Most importantly, the intersection of quantitative and qualitative data methodologies is where human insights come to life. Both methods can be helpful, but they allow you to see things you may have missed. According to Justin Wei, Former Head of Digital Marketing at Royal Wins, while quantitative data is the black and white picture of a problem or opportunity, qualitative data can color your understanding. 

Quantitative data is 'the what' and qualitative data is 'the why'

Commonly, quantitative data will surface trends that you can use as a springboard for qualitative research. However, it’s important to use qualitative research to drive innovation . Organizations that fall into the habit of only using qualitative research to react to quantitative data run the risk of reducing team efficiency and restricting their ability to optimize. In general, here are some common reasons to use qualitative research or quantitative research: 

• Validate hypotheses: quantitative research will get you the key performance indicators (KPIs) you need when you need objective information to confirm or disprove your theory.
• Find answers: It’s typically easier and less expensive to have people fill out a survey than participate in a focus group. In this way, quantitative methods can help answer questions like: were you satisfied with your experience? Would you recommend us to a friend? On the other hand, qualitative research enables you to respond to open-ended questions like: why were you satisfied with your experience? Why would you recommend us to a friend?
• Uncover emotion: qualitative research is especially good at uncovering the emotions behind data. This can be verbal, body language, or facial expressions caught on video. It helps to hear and see your customers describe wants, needs, concerns, frustrations, etc. Qualitative data will get you that.

Watch Jonathan Greenblatt, User Research and Design Leader, explai n how WarnerMedia uses quantitative and qualitative research to flesh out its user personas. 

How UserTesting can help you conduct qualitative research

Researchers, marketers, product managers, and more conduct qualitative research daily using moderated or unmoderated testing with UserTesting.  While the possibilities are endless, here are some common use cases: 

• Ask your audience to record their behaviors and thoughts while interacting with your website. 
• Create better solutions and experiences by exploring your users' attitudes, preferences, and opinions as they test out designs and prototypes.  
• Optimize in-person experiences by watching your customers record themselves in a new store. 

With UserTesting, business professionals can have access to qualitative data at the speed of quantitative analysis. 

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What’s the difference between qualitative and quantitative research?

• Written by Susan E. DeFranzo

People who undertake a research project are often unaware of the differences between Qualitative Research and Quantitative Research methods, mistakenly thinking that the two terms can be used interchangeably.

However, this is not the case.

So what are the differences between Qualitative Research and Quantitative Research?

Generally speaking, Qualitative Research cannot be statistically analyzed, as it revolves around open-ended feedback. In contrast, Quantitative Research is easier to analyze with a survey platform because it relies on questions with specific answer options that can be quantified.

With this distinction in mind, let’s explore further.

Qualitative Research

Qualitative Research is primarily exploratory research. It is used to gain an understanding of underlying reasons, opinions, and motivations.

It provides insights into the problem or helps to develop ideas or hypotheses for potential quantitative research.

Qualitative Research is also used to uncover trends in thought and opinions, and dive deeper into the problem.

Some common methods of qualitative research includes focus groups (group discussions), individual interviews, and participation/observations.

The sample size is typically small, and respondents are selected to fulfil a given quota.

Qualitative Research key points

Objective/purpose.

• To gain an understanding of underlying reasons and motivations.
• To provide insights into the setting of a problem, generating ideas and/or hypotheses for later quantitative research.
• To uncover prevalent trends in thought and opinion.

Usually a small number of non-representative cases. Respondents selected to fulfil a given quota.

Data Collection / Analysis

Unstructured or semi-structured techniques e.g. individual depth interviews or group discussions.

Non-statistical.

Outcome of Qualitative Research

Exploratory and/or investigative. Findings are not conclusive and cannot be used to make generalizations about the population of interest. Develop an initial understanding and sound base for further decision making.

Quantitative Research

A problem is aimed to be quantified by Quantitative Research through the generation of numerical data or information that can be converted into usable statistics. Within the research, facts are established and patterns are uncovered through the utilization of measurable data.

The data collection methods for Quantitative Research are more structured than those for Qualitative Research, encompassing various forms of surveys such as online, paper, mobile, and kiosk surveys. Other methods include face-to-face and telephone interviews, longitudinal studies, website interceptors, online polls, and systematic observations.

Quantitative Research key points

• To quantify data and generalize results from a sample to the population of interest.
• To measure the incidence of various views and opinions in a chosen sample.
• Sometimes succeeded by qualitative research, further exploration of some findings is facilitated.

Usually a large number of cases representing the population of interest. Randomly selected respondents.

Data Collection & Analysis

Structured techniques such as online questionnaires, on-street or telephone interviews.

Statistical data is usually in the form of tabulations (tabs). Findings are conclusive and usually descriptive in nature.

Outcome of Quantitative Research

Used to recommend a final course of action.

Qualitative vs. Quantitative Research infographic

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• data collection , kiosk surveys , Mobile surveys , Online surveys , Paper surveys , qualitative research , quantifiable data , quantifiable research , quantitative data , quantitative research , survey , survey software , surveys

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Understanding Research Questions: Quantitative vs Qualitative

By Divya Bhansali

Columbia University; Biomedical Engineering PhD candidate

3 minute read

Research is like being a detective, trying to uncover the mysteries of the world. In the world of research, one of the first and most crucial decisions you'll make is whether to ask quantitative or qualitative method questions. But what's the difference between quantitative and qualitative research, and why does it matter? Let's dive in and find out!

Quantitative Research Questions

Quantitative research involves numbers, statistics, and hard data. It's like counting beans in a jar. Quantitative research questions aim to answer "how much," "how many," or "to what extent" questions. When understanding how to write research paper , quantitative research questions can provide clear, measurable data to support your findings.

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Examples of Quantitative Research Questions

1. How many high school students use smartphones for over four hours a day?

This research question can be answered with precise numbers - a certain percentage of students may fall into this category.

2. What is the average GPA of students in our school?

You'll get a specific number, like 3.5, as an answer to this question.

3. How much has the average temperature increased over the last decade?

In this case, you're looking for a specific temperature change in degrees Celsius or Fahrenheit.

Considerations for Quantitative Research

Data Collection Methods : To answer quantitative research questions, you'll often use structured surveys, experiments, or observations with predefined variables. These methods help you collect precise, quantifiable data.

Data Analysis : Quantitative research involves statistical analysis, where you'll use mathematical tools to identify patterns and relationships in the data. Understanding how to write a research paper outline can help you organize these methods effectively.

Generalizability : Quantitative research often aims for generalizability, meaning you can draw conclusions that apply to a larger population.

Qualitative Research Questions

On the other hand, the qualitative research method is more about words, descriptions, and understanding the "whys" and "hows" of a phenomenon. It's like exploring the stories behind the beans in the jar. Qualitative analysis questions aim to answer questions about experiences, feelings, and behaviors.

Examples of Qualitative Research Questions

How do high school students feel about using smartphones for extended periods of time?

This question invites students to share their thoughts, emotions, and personal experiences.

2. What are the main challenges that students face when it comes to maintaining a high GPA?

This question prompts students to talk about their struggles, motivations, and strategies.

3. In what ways has climate change affected the daily lives of people in our community?

This question encourages people to share their stories about how they've been impacted.

Considerations for Qualitative Research

Data Collection Methods : Qualitative research methods often involve open-ended interviews, observations, or content analysis. These methods allow you to collect rich, descriptive data. 

Data Analysis : Qualitative research method requires a more interpretive approach. You'll analyze text or visual data to identify themes, patterns, and any unique insight.

In-Depth Understanding : Qualitative research delves deep into the experiences and perceptions of individuals, providing a nuanced understanding of a specific topic.

Knowing how to write an introduction for a research paper can be particularly important when presenting qualitative research. A compelling introduction sets the stage for the rich, descriptive data that follows.

If your study involves STEM subjects, having a solid stem research paper outline will be beneficial. Additionally, knowing how to write a thesis statement for a research paper is crucial for establishing a clear argument or hypothesis.

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Which One to Choose?

The choice between qualitative and quantitative research questions depends on what you want to discover and the nature of your study. Here are some key factors to consider:

Nature of the Research : Is your research more about numbers and statistical analysis, or is it about having a deeper understanding the human experience? Choose the approach that aligns with your research goals.

Data Collection : Think about how you'll gather information. Surveys and experiments often lead to quantitative data, while interviews and observations typically provide qualitative data.

Time and Resources : Consider the time and resources you have. Quantitative research can often be quicker and require fewer resources than in-depth qualitative studies.

Research Participants : The preferences and characteristics of your research participants matter. Some may prefer answering surveys with numeric options, while others may enjoy sharing their stories.

When you are ready to start your study, make sure to also understand how to write a research paper abstract for summarizing your work effectively.

Whether you choose to ask quantitative or qualitative survey questions, remember that both approaches are valuable and have their unique strengths. The key is to match your research goals with the right approach, ensuring that you gather the most relevant and meaningful data.

So, high school detectives, the choice is yours: will you count the beans or explore the stories behind them? Happy researching!

Qualitative vs Quantitative Data: Definitions, Analysis, Examples

If you are involved in statistics, marketing or data science, it is essential to know what is the difference between qualitative and quantitative data and analysis.

On this page:

• Qualitative vs quantitative data : definition, examples, characteristics, contrast, similarities, and differences.
• What is quantitative data analysis? Steps and types.
• What is qualitative data analysis? Steps and types.
• Comparison chart in PDF (infographic).

What is quantitative data?

Quantitative data seems to be simpler to define and identify.

Quantitative data is data that can be expressed as a number or can be quantified. In other words, quantitative data can be measured by numerical variables.

Quantitative data are easily amenable to statistical manipulation and can be represented with a wide variety of statistical types of graphs and chards such as line, graph, bar graph, scatter plot , box and whisker plot and etc.

Key characteristics of quantitative data:

• It can be quantified and verified.
• Data can be counted.
• Data type: number and statistics.
• It answers questions such as “how many, “how much” and “how often”.

Examples of quantitative data:

• Scores on tests and exams e.g. 85, 67, 90 and etc.
• The weight of a person or a subject.
• The number of hours of study.
• Your shoe size.
• The square feet of an apartment.
• The temperature in a room.
• The volume of a gas and etc.

Types of quantitative data:

There are 2 general types of quantitative data:

• Discrete data – a count that involves integers. Only a limited number of values is possible. The discrete values cannot be subdivided into parts. For example, the number of children in a school is discrete data. You can count whole individuals. You can’t count 1.5 kids.
• Continuous data –  information that could be meaningfully divided into finer levels. It can be measured on a scale or continuum and can have almost any numeric value. For example, you can measure your height at very precise scales — meters, centimeters, millimeters and etc. More on the topic see in our post discrete vs continuous data .

What is qualitative data?

As you might guess qualitative data is information that can’t be expressed as a number and can’t be measured.

Qualitative data consist of words, pictures, observations, and symbols, not numbers. It is about qualities.

Qualitative data is also called categorical data . The reason is that the information can be sorted by category, not by number. Qualitative data is analyzed to look for common themes.

Key characteristics of qualitative data:

• It cannot be quantified and verified.
• Data cannot be counted.
• Data type: words, objects, pictures, observations, and symbols.
• It answers questions such as “how this has happened” or and “why this has happened”.

Examples of qualitative data:

• Your socioeconomic status
• Colors e.g. the color of the sea
• The Smell e.g. aromatic, buttery, camphoric and etc.
• Your favorite holiday destination such as Hawaii, New Zealand and etc.
• Names as John, Patricia,…..
• Sounds like bang and blare.
• Ethnicity such as American Indian, Asian, etc.

Quantitative Data Analysis: Meaning, Steps, and Types

Quantitative data analysis ends with easy to understand and quantifiable results. You can analyze it in many different ways. But before starting the analysis you have to define the level of measurement involved in the quantitative data.

Let’s see the steps in the process of analyzing quantitative variables. It will help us to see better the difference between qualitative and quantitative data analysis.

Step 1: Identify the level of measurement 

There are 4 scales/levels of measurement:

• Nominal –  data scales used simply for labeling variables, without quantitative value. The nominal data just name a thing without applying it to an order. Even though we can use the numbers, they do not denote quantity. Examples of nominal data: hair color (Blonde, Brown, Brunette, etc.).
• Ordinal . Ordinal data is placed into some kind of order by their position on the scale. They often indicate superiority. Example of ordinal data: the first, second and third person in a competition. To understand better see our post nominal vs ordinal data .
• Interval –  numerical scales that show information about an order. In interval scales, the intervals between each data value are the same. A popular example here is the temperature in centigrade, where, for instance, the interval between 930C and 950C is the same as the distance between 1060C and 1080C. However, there isn’t a starting point in the interval scales. See more interval data examples .
• Ratio –  not only show order and have equal intervals, but they can also have a value of zero.

Identifying the levels of measurement where a dataset falls under, will help you decide whether or not the data is useful in making calculations. The scales of measurement are very important because they determine the types of data analysis that can be performed.

The best way of doing that is with specialized data software.

As you have the raw data, you cannot just sit and look at it. You need to take actions to identify some patterns or to visualize what the data is showing.

This is where descriptive statistics and inferential statistics come to play.

Step 2:  Perform descriptive statistics

Descriptive statistics are used to describes and summarizes basic features of a data set. Commonly used descriptive statistics are:

• Central tendency (mean, mode, and median).
• Percentages.
• Dispersion (range, quartiles, variance, and standard deviation)
• Distribution.

Step 3:  Perform inferential statistics

Inferential statistics are used to draw conclusions and trends about a large population based on a sample taken from it. Inferential statistics study the relationships between variables within a sample.

Inferential statistics allow you to test different hypotheses and to generalize the gained results to the population as a whole.

Key inferential techniques, methods, and types of calculations are:

• Linear regression models
• Logistic regression
• Analysis of Variance (ANOVA)
• Analysis of Covariance (ANCOVA)
• Statistical significance (T-Test)
• Correlation analysis

Step 4:  Define statistical significance

Finally, you need to look for statistical significance. Statistical significance is captured through a ‘p-value’, which evaluate the probability that your discovering for the data are reliable results, not a coincidence. The lower the p-value, the more confident you can be that your findings are reliable.

As you see when it comes to quantitative data analysis, there are many techniques and methods you can use.

The next step in our post for the difference between qualitative and quantitative data is to see what qualitative data analysis involves.

Qualitative Data Analysis: Definition, steps, and types

It is harder to perform Qualitative Data Analysis (QDA) in comparison with quantitative one.

QDA includes the processes and methods for analyzing data and providing some level of understanding, explanation, and interpretation of patterns and themes in textual data.

Qualitative data analysis is very important because it allows data sciences and statisticians to form parameters for observing and analyzing larger sets of data.

For example, if a company need to identify the diversity of its personnel, it would look at qualitative data such as ethnicity and race of its employees.

For comparison, quantitative data, in this case, could be the frequency of workers to belong to those ethnicities and races.

In general, the qualitative data analysis has the following steps:

Step 1:  Become familiar with your data

As a data scientist or researcher, you have to read and re-read the data, record detailed notes and impressions, and deciding which pieces of data possess value.

Step 2:  Define the key questions that they need to answer through the QDA

Each QDA has specific questions, problems or topics. Find out which questions do you need to answer.

Step 3:   Reduce and code the data into themes

This means to create categories and subcategories. These categories are very likely to get bigger as you work through your data. The list of build themes represents your first set of codes.

Step 4:   Search for patterns and connections

This step involves looking for the relative importance of data, identifying relationships between data sets or themes, and trying to find explanations from the available data.

Step 5:  Interpret the data and draw conclusions

After you identify the themes, connections, and patterns, now you need to assign meaning and importance to the data.

It is very likely to find out much more than you could need, so you will have to decide what is most significant data and results.

Note: qualitative data do not drive conclusions and generalizations across a population. This is an important difference between qualitative and quantitative data analysis. In contrast, quantitative analysis can lead to conclusions or trends about a large population based on a sample taken from it.

There is a range of approaches to qualitative data analysis. Some of the key approaches are:

• Content analysis – a technique to make inferences by interpreting and coding textual information (e.g., documents, graphics, oral communications).
• Thematic analysis –  a widely-used QDA method that involves grouping the data into themes to define patterned meaning across a dataset.
• Discourse analysis – includes a group of approaches for analyzing written or vocal interactions or any significant semiotic event. The method focuses on the social context in which the communication happened. It seeks to understand how people express themselves.
• Grounded theory – allows you to look for latent social patterns and structures.

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Working in data management area involves familiarizing yourself with the suitable software solutions. There is a wide range of tools, dashboards, and programs that you can use to make the whole data analysis process effective and profitable.

About The Author

Silvia Valcheva

Silvia Valcheva is a digital marketer with over a decade of experience creating content for the tech industry. She has a strong passion for writing about emerging software and technologies such as big data, AI (Artificial Intelligence), IoT (Internet of Things), process automation, etc.

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Trends and Motivations in Critical Quantitative Educational Research: A Multimethod Examination Across Higher Education Scholarship and Author Perspectives

• Open access
• Published: 04 June 2024

Cite this article

You have full access to this open access article

• Christa E. Winkler   ORCID: orcid.org/0000-0002-1700-5444 1 &
• Annie M. Wofford   ORCID: orcid.org/0000-0002-2246-1946 2  

9 Altmetric

To challenge “objective” conventions in quantitative methodology, higher education scholars have increasingly employed critical lenses (e.g., quantitative criticalism, QuantCrit). Yet, specific approaches remain opaque. We use a multimethod design to examine researchers’ use of critical approaches and explore how authors discussed embedding strategies to disrupt dominant quantitative thinking. We draw data from a systematic scoping review of critical quantitative higher education research between 2007 and 2021 ( N  = 34) and semi-structured interviews with 18 manuscript authors. Findings illuminate (in)consistencies across scholars’ incorporation of critical approaches, including within study motivations, theoretical framing, and methodological choices. Additionally, interview data reveal complex layers to authors’ decision-making processes, indicating that decisions about embracing critical quantitative approaches must be asset-based and intentional. Lastly, we discuss findings in the context of their guiding frameworks (e.g., quantitative criticalism, QuantCrit) and offer implications for employing and conducting research about critical quantitative research.

Avoid common mistakes on your manuscript.

Across the field of higher education and within many roles—including policymakers, researchers, and administrators—key leaders and educational partners have historically relied on quantitative methods to inform system-level and student-level changes to policy and practice. This reliance is rooted, in part, on the misconception that quantitative methods depict the objective state of affairs in higher education. This perception is not only inaccurate but also dangerous, as the numbers produced from quantitative methods are “neither objective nor color-blind” (Gillborn et al., 2018 , p. 159). In fact, like all research, quantitative data collection and analysis are informed by theories and beliefs that are susceptible to bias. Further, such bias may come in multiple forms such as researcher bias and bias within the statistical methods themselves (e.g., Bierema et al., 2021 ; Torgerson & Torgerson, 2003 ). Thus, if left unexamined from a critical perspective, quantitative research may inform policies and practices that fuel the engine of cultural and social reproduction in higher education (e.g., Bourdieu, 1977 ).

Largely, critical approaches to higher education research have been dominated by qualitative methods (McCoy & Rodricks, 2015 ). While qualitative approaches are vital, some have argued that a wider conceptualization of critical inquiry may propel our understanding of processes in higher education (Stage & Wells, 2014 ) and that critical research need not be explicitly qualitative (refer to Sablan, 2019 ; Stage, 2007 ). If scholars hope to embrace multiple ways of challenging persistent inequities and structures of oppression in higher education, such as racism, advancing critical quantitative work can help higher education researchers “expose and challenge hidden assumptions that frequently encode racist perspectives beneath the façade of supposed quantitative objectivity” (Gillborn et al., 2018 , p. 158).

Across professional networks in higher education, the perspectives of association leaders (e.g., Association for the Study of Higher Education [ASHE]) have often placed qualitative and quantitative research in opposition to each other, with qualitative research being a primary way to amplify the voices of systemically minoritized students, faculty, and staff (Kimball & Friedensen, 2019 ). Yet, given the vast growth of critical higher education research (e.g., Byrd, 2019 ; Espino, 2012 ; Martínez-Alemán et al., 2015 ), recent ASHE presidents have recognized how prior leaders planted transformative seeds of critical theory and praxis (Renn, 2020 ) and advocated for critical higher education scholarship as a disrupter (Stewart, 2022 ). With this shift in discourse, many members of the higher education research community have also grown their desire to expand upon the legacy of critical research—in both qualitative and quantitative forms.

Critical quantitative approaches hold promise as one avenue for meeting recent calls to embrace equity-mindedness and transform the future of higher education research, yet current structures of training and resources for quantitative methods lack guidance on engaging such approaches. For higher education scholars to advance critical inquiry via quantitative methods, we must first understand the extent to which such approaches have been adopted. Accordingly, this study sheds light on critical quantitative approaches used in higher education literature and provides storied insights from the experiences of scholars who have engaged critical perspectives with quantitative methods. We were guided by the following research questions:

To what extent do higher education scholars incorporate critical perspectives into quantitative research?

How do higher education scholars discuss specific strategies to leverage critical perspectives in quantitative research?

Contextualizing Existing Critical Approaches to Quantitative Research

To foreground our analysis of literature employing critical quantitative lenses to studies about higher education, we first must understand the roots of such framing. Broadly, the foundations of critical quantitative approaches align with many elements of equity-mindedness. Equity-mindedness prompts individuals to question divergent patterns in educational outcomes, recognize that racism is embedded in everyday practices, and invest in un/learning the effects of racial identity and racialized expectations (Bensimon, 2018 ). Yet, researchers’ commitments to critical quantitative approaches stand out as a unique thread in the larger fabric of opportunities to embrace equity-mindedness in higher education research. Below, we discuss three significant publications that have been widely applied as frameworks to engage critical quantitative approaches in higher education. While these publications are not the only ones associated with critical inquiry in quantitative research, their evolution, commonalities, and distinctions offer a robust background of epistemological development in this area of scholarship.

Quantitative Criticalism (Stage, 2007 )

Although some higher education scholars have applied critical perspectives in their research for many years, Stage’s ( 2007 ) introduction of quantitative criticalism was a salient contribution to creating greater discourse related to such perspectives. Quantitative criticalism, as a coined paradigmatic approach for engaging critical questions using quantitative data, was among the first of several crucial publications on this topic in a 2007 edition of New Directions for Institutional Research . Collectively, this special issue advanced perspectives on how higher education scholars may challenge traditional positivist and post-positivist paradigms in quantitative inquiry. Instead, researchers could apply (what Stage referred to as) quantitative criticalism to develop research questions centering on social inequities in educational processes and outcomes as well as challenge widely accepted models, measures, and analytic practices.

Notably, Stage ( 2007 ) grounded the motivation for this new paradigmatic approach in the core concepts of critical inquiry (e.g., Kincheloe & McLaren, 1994 ). Tracing critical inquiry back to the German Frankfurt school, Stage discussed how the principles of critical theory have evolved over time and highlighted Kincheloe and McLaren’s ( 1994 ) definition of critical theory as most relevant to the principles of quantitative criticalism. Kincheloe and McLaren’s definition of critical describes how researchers applying critical paradigms in their scholarship center concepts such as socially and historically created power structures, subjectivity, privilege and oppression, and the reproduction of oppression in traditional research approaches. Perhaps most importantly, Kincheloe and McLaren urge scholars to be self-conscious in their decision making—a tall ask of quantitative scholars operating from positivist and post-positivist vantage points.

In advancing quantitative criticalism, Stage ( 2007 ) first argued that all critical scholars must center their outcomes on equity. To enact this core focus on equity in quantitative criticalism, Stage outlined two tasks for researchers. First, critical quantitative researchers must “use data to represent educational processes and outcomes on a large scale to reveal inequities and to identify social or institutional perpetuation of systematic inequities in such processes and outcomes” (p. 10). Second, Stage advocated for critical quantitative researchers to “question the models, measures, and analytic practices of quantitative research in order to offer competing models, measures, and analytic practices that better describe experiences of those who have not been adequately represented” (p. 10). Stage’s arguments and invitations for criticalism spurred crucial conversations, many of which led to the development of a two-part series on critical quantitative approaches in New Directions for Institutional Research (Stage & Wells, 2014 ; Wells & Stage, 2015 ). With nearly a decade of new perspectives to offer, manuscripts within these subsequent special issues expanded the concepts of quantitative criticalism. Specifically, these new contributions advanced the notion that quantitative criticalism should include all parts of the research process—instead of maintaining a focus on paradigm and research questions alone—and made inroads when it came to challenging the (default, dominant) process of quantitative research. While many scholars offered noteworthy perspectives in these special issues (Stage & Wells, 2014 ; Wells & Stage, 2015 ), we now turn to one specific article within these special issues that offered a conceptual model for critical quantitative inquiry.

Critical Quantitative Inquiry (Rios-Aguilar, 2014 )

Building from and guided by the work of other criticalists (namely, Estela Bensimon, Sara Goldrick-Rab, Frances Stage, and Erin Leahey), Rios-Aguilar ( 2014 ) developed a complementary framework representing the process and application of critical quantitative inquiry in higher education scholarship. At the heart of Rios-Aguilar’s conceptualization lies the acknowledgment that quantitative research is a human activity that requires careful decisions. With this foundation comes the pressing need for quantitative scholars to engage in self-reflection and transparency about the processes and outcomes of their methodological choices—actions that could potentially disrupt traditional notions and deficit assumptions that maintain systems of oppression in higher education.

Rios-Aguilar ( 2014 ) offered greater specificity to build upon many principles from other criticalists. For one, methodologically, Rios-Aguilar challenged the notion of using “fancy” statistical methods just for the sake of applying advanced methods. Instead, she argued that critical quantitative scholars should engage “in a self-reflection of the actual research practices and statistical approaches (i.e., choice of centering approach, type of model estimated, number of control variables, etc.) they use and the various influences that affect those practices” (Rios-Aguilar, 2014 , p. 98). In this purview, scholars should ensure that all methodological choices advance their ability to reveal inequities; such choices may include those that challenge the use of reference groups in coding, the interpretation of statistics in ways that move beyond p -values for statistical significance, or the application and alignment of theoretical and conceptual frameworks that focus on the assets of systemically minoritized students. Rios-Aguilar also noted, in agreement with the foundations of equity-mindedness and critical theory, that quantitative criticalists have an obligation to translate findings into tangible changes in policy and practice that can redress inequities.

Ultimately, Rios-Aguilar’s ( 2014 ) framework focused on “the interplay between research questions, theory, method/research practices, and policy/advocacy” to identify how quantitative criticalists’ scholarship can be “relevant and meaningful” (p. 96). Specifically, Rios-Aguilar called upon quantitative criticalists to ask research questions that center on equity and power, engage in self-reflection about their data sources, analyses, and disaggregation techniques, attend to interpretation with practical/policy-related significance, and expand beyond field-level silos in theory and implications. Without challenging dominant approaches in quantitative higher education research, Rios-Aguilar noted that the field will continue to inaccurately capture the experiences of systemically minoritized students. In college access and success, for example, ignoring this need for evolving approaches and models would continue what Bensimon ( 2007 ) referred to as the Tintonian Dynasty, with scholars widely applying and citing Tinto’s work but failing to acknowledge the unique experiences of systemically minoritized students. These and other concrete recommendations have served as a springboard for quantitative criticalists, prompting scholars to incorporate critical approaches in more cohesive and congruent ways.

QuantCrit (Gillborn et al., 2018 )

As an epistemologically different but related form of critical quantitative scholarship, QuantCrit—quantitative critical race theory—has emerged as a vital stream of inquiry that applies critical race theory to methodological approaches. Given that statistical methods were developed in support of the eugenics movement (Zuberi, 2001 ), QuantCrit researchers must consider how the “norms” of quantitative research support white supremacy (Zuberi & Bonilla-Silva, 2008 ). Fortunately, as Garcia et al. ( 2018 ) noted, “[t]he problems concerning the ahistorical and decontextualized ‘default’ mode and misuse of quantitative research methods are not insurmountable” (p. 154). As such, the goal of QuantCrit is to conduct quantitative research in a way that can contextualize and challenge historical, social, political, and economic power structures that uphold racism (e.g., Garcia et al., 2018 ; Gillborn et al., 2018 ).

In coining the term QuantCrit, Gillborn et al. ( 2018 ) provided five QuantCrit tenets adapted from critical race theory. First, the centrality of racism offers a methodological and political statement about how racism is complex, fluid, and rooted in social dynamics of power. Second, numbers are not neutral demonstrates an imperative for QuantCrit researchers—one that prompts scholars to understand how quantitative data have been collected and analyzed to prioritize interests rooted in white, elite worldviews. As such, QuantCrit researchers must reject numbers as “true” and as presenting a unidimensional truth. Third, categories are neither “natural” nor given prompts researchers to consider how “even the most basic decisions in research design can have fundamental consequences for the re/presentation of race inequity” (Gillborn et al., 2018 , p. 171). Notably, even when race is a focus, scholars must operationalize and interpret findings related to race in the context of racism. Fourth, prioritizing voice and insight advances the notion that data cannot “speak for itself” and numerous interpretations are possible. In QuantCrit, this tenet leverages experiential knowledge among People of Color as an interpretive tool. Finally, the fifth tenet explicates how numbers can be used for social justice but statistical research cannot be placed in a position of greater legitimacy in equity efforts relative to qualitative research. Collectively, although Gillborn et al. ( 2018 ) stated that they expect—much like all epistemological foundations—the tenets of QuantCrit to be expanded, we must first understand how these stated principles arise in critical quantitative research.

Bridging Critical Quantitative Concepts as a Guiding Framework

Guided by these framings (i.e., quantitative criticalism, critical quantitative inquiry, QuantCrit) as a specific stream of inquiry within the larger realm of equity-minded educational research, we explore the extent to which the primary elements of these critical quantitative frameworks are applied in higher education. Across the framings discussed, the commitment to equity-mindedness contributes to a shared underlying essence of critical quantitative approaches. Not only do Stage, Rios-Aguilar, and Gillborn et al. aim for researchers to center on inequities and commit to disrupting “neutral” decisions about and interpretations of statistics, but they also advocate for critical quantitative research (by any name) to serve as a tool for advocacy and praxis—creating structural changes to discriminatory policies and practices, rather than ceasing equity-based commitments with publications alone. Thus, the conceptual framework for the present study brings together alignments and distinctions in scholars’ motivations and actualizations of quantitative research through a critical lens.

Specifically, looking to Stage ( 2007 ), quantitative criticalists must center on inequity in their questions and actions to disrupt traditional models, methods, and practices. Second, extending critical inquiry through all aspects of quantitative research (Rios-Aguilar, 2014 ), researchers must interrogate how critical perspectives can be embedded in every part of research. The embedded nature of critical approaches should consider how study questions, frameworks, analytic practices, and advocacy are developed with intentionality, reflexivity, and the goal of unmasking inequities. Third, centering on the five known tenets of QuantCrit (Gillborn et al., 2018 ), QuantCrit researchers should adapt critical race theory for quantitative research. Although QuantCrit tenets are likely to be expanded in the future, the foundations of such research should continue to acknowledge the centrality of racism, advance critiques of statistical neutrality and categories that serve white racial interests, prioritize the lived experiences of People of Color, and complicate how statistics can be one—but not the lone—part of social justice endeavors.

Over many years, higher education scholars have advanced more critical research, as illustrated through publication trends of critical quantitative manuscripts in higher education (Wofford & Winkler, 2022 ). However, the application of critical quantitative approaches remains laced with tensions among paradigms and analytic strategies. Despite recent systematic examinations of critical quantitative scholarship across educational research broadly (Tabron & Thomas, 2023 ), there has yet to be a comprehensive, systematic review of higher education studies that attempt to apply principles rooted in quantitative criticalism, critical quantitative inquiry, and QuantCrit. Thus, much remains to be learned regarding whether and how higher education researchers have been able to apply the principles previously articulated. In order for researchers to fully (re)imagine possibilities for future critical approaches to quantitative higher education research, we must first understand the landscape of current approaches.

Study Aims and Role of the Researchers

Study aims and scope.

For this study, we examined the extent to which authors adopted critical quantitative approaches in higher education research and the trends in tools and strategies they employed to do so. In other words, we sought to understand to what extent, and in what ways, authors—in their own perspectives—applied critical perspectives to quantitative research. We relied on the nomenclature used by the authors of each manuscript (e.g., whether they operated from the lens of quantitative criticalism, QuantCrit, or another approach determined by the authors). Importantly, our intent was not to evaluate the quality of authors’ applications of critical approaches to quantitative research in higher education.

Researcher Positionality

As with all research, our positions and motivations shape how we conceptualized and executed the present study. We come to this work as early career higher education faculty, drawn to the study of higher education as one way to rectify educational disparities, and thus are both deeply invested in understanding how critical quantitative approaches may advance such efforts. After engaging in initial discussions during an association-sponsored workshop on critical quantitative research in higher education, we were motivated to explore these perspectives, understand trends in our field, and inform our own empirical engagement. Throughout our collaboration, we were also reflexive about the social privileges we hold in the academy and society as white, cisgender women—particularly given how quantitative criticalism and QuantCrit create inroads for systemically minoritized scholars to combat the erasure of perspectives from their communities due to small sample sizes. As we work to understand prior critical quantitative endeavors, with the goal of creating opportunity for this work to flourish in the future, we continually reflect on how we can use our positions of privilege to be co-conspirators in the advancement of quantitative research for social justice in higher education.

This study employed a qualitatively driven multimethod sequential design (Hesse-Biber et al., 2015 ) to illuminate how critical quantitative perspectives and methods have been applied in higher education contexts over 15 years. Anguera et al. ( 2018 ) noted that the hallmark feature of multimethod studies is the coexistence of different methodologies. Unlike mixed-methods studies, which integrate both quantitative and qualitative methods, multimethod studies can be exclusively qualitative, exclusively quantitative, or a combination of qualitative and quantitative methods. A multimethod research design was also appropriate given the distinct research questions in this study—each answered using a different stream of data. Specifically, we conducted a systematic scoping review of existing literature and facilitated follow-up interviews with a subset of corresponding authors from included publications, as detailed below and in Fig.  1 . We employed a systematic scoping review to examine the extent to which higher education scholars incorporated critical perspectives into quantitative research (research question one), and we then conducted follow-up interviews to elucidate how those scholars discussed specific strategies for leveraging critical perspectives in their quantitative research (research question two).

Sequential multimethod approach to data collection and analysis

Given the scope of our work—which examined the extent to which, and in what ways, authors applied critical perspectives to quantitative higher education research—we employed an exploratory approach with a constructivist lens. Using a constructivist paradigm allowed us to explore the many realities of doing critical quantitative research, with the authors themselves constructing truths from their worldviews (Magoon, 1977 ). In what follows, we contextualize both our methodological choices and the limitations of those choices in executing this study.

Data Sources

Systematic scoping review.

First, we employed a systematic scoping review of published higher education literature. Consistent with the purpose of a scoping review, we sought to “examine the extent, range, and nature” of critical quantitative approaches in higher education that integrate quantitative methods and critical inquiry (Arskey & O’Malley, 2005 , p. 6). We used a multi-stage scoping framework (Arskey & O’Malley, 2005 ; Levac et al., 2010 ) to identify studies that were (a) empirical, (b) conducted within a higher education context, and (c) guided by critical quantitative perspectives. We restricted our review to literature published in 2007 or later (i.e., since Stage’s formal introduction of quantitative criticalism in higher education). All studies considered for review were written in the English language.

The literature search spanned multiple databases, including Academic Search Premier, Scopus, ERIC, PsychINFO, Web of Science, SocINDEX , Psychological and Behavioral Sciences Collection, Sociological Abstracts, and JSTOR. To locate relevant works, we used independent and combined keywords that reflected the inclusion criteria, with the initial search resulting in 285 unique records for eligibility screening. All screening was conducted separately by both authors using the CADIMA online platform (Kohl et al., 2018). In total, 285 title/abstract records were screened for eligibility, with 40 full-text records subsequently screened for eligibility. After separately screening all records, we discussed inconsistencies in title/abstract and full-text eligibility ratings to reach consensus. This strategy led us to a sample of 34 manuscripts that met all inclusion criteria (Fig.  2 ).

Identification of systematic scoping review sample via literature search and screening

Systematic scoping reviews are particularly well-suited for initial examinations of emerging approaches in the literature (Munn et al., 2018 ), aligning with our goal to establish an initial understanding of the landscape of critical quantitative research applications in higher education. It also relies heavily on researcher-led qualitative review of the literature, which we viewed as a vital component of our study, as we sought to identify not just what researchers did (e.g., what topics they explored or in what outlets they did so), but also how they articulated their decision-making process in the literature. Alternative methods to examining the literature, such as bibliometric analysis, supervised topic modeling, and network analysis, may reveal additional insights regarding the scope and structure of critical quantitative research in higher education not addressed in the current study. As noted by Munn et al. ( 2018 ), systematic scoping reviews can serve as a useful precursor to more advanced approaches of research synthesis.

Semi-structured Interviews

To understand how scholars navigated the opportunities and tensions of critical quantitative inquiry in their research, we then conducted semi-structured interviews with authors whose work was identified in the scoping review. For each article meeting the review criteria ( N  = 34), we compiled information about the corresponding author and their contact information as our sample universe (Robinson, 2014 ). Each corresponding author was contacted via email for participation in a semi-structured interview. There were 32 distinct corresponding authors for the 34 manuscripts, as two corresponding authors led two manuscripts each within our corpus of data. In the recruitment email, we provided corresponding authors with a link to a Qualtrics intake survey; this survey confirmed potential participants’ role as corresponding author on the identified manuscript, collected information about their professional roles and social identities, and provided information about informed consent in the study. Twenty-five authors responded to the Qualtrics survey, with 18 corresponding authors ultimately participating in an interview.

Individual semi-structured interviews were conducted via Zoom and lasted approximately 45–60 min. The interview protocol began with questions about corresponding authors’ backgrounds, then moving into questions regarding their motivations for engaging in critical approaches to quantitative methods, navigation of the epistemological and methodological tensions that may arise when doing quantitative research with a critical lens, approaches to research design, frameworks, and methods that challenged quantitative norms, and experiences with the publication process for their manuscript included in the scoping review. In other words, we asked that corresponding authors explicitly relay the thought processes underlying their methodological choices in the article(s) from our scoping review. Importantly, given the semi-structured nature of these interviews, conversations also reflected participants’ broader trajectory to and through critical quantitative thinking as well as their general reflections about how the field of higher education has grappled with critical approaches to quantitative scholarship. To increase consistency in our data collection and the nature of these conversations, the first author conducted all interviews. With participants’ consent, we recorded each interview, had interviews professionally transcribed, and then de-identified data for subsequent analysis. All interview participants were compensated for their time and contributions with a $50 Amazon gift card.

At the conclusion of each interview, participants were given the opportunity to select their own pseudonym. A profile of interview participants, along with their self-selected pseudonyms, is provided in Table  1 . Although we invited all corresponding authors to participate in interviews, our sample may reflect some self-selection bias, as authors had to opt in to be represented in the interview data. Further, interview insights do not represent all perspectives from participants’ co-authors, some of which may diverge based on lived experiences, history with quantitative research, or engagement with critical quantitative approaches.

Data Analysis

After identifying the sample of 34 publications, we began data analysis for the scoping review by uploading manuscripts to Dedoose. Both researchers then independently applied a priori codes (Saldaña, 2015 ) from Stage’s ( 2007 ) conceptualization of quantitative criticalism, Rios-Aguilar’s ( 2014 ) framework for quantitative critical inquiry, and Gillborn et al.’s ( 2018 ) QuantCrit tenets (Table  2 ). While we applied codes in accordance with Stage’s and Rios-Aguilar’s conceptualizations to each article, codes relevant to Gillborn et al.’s tenets of QuantCrit were only applied to manuscripts where authors self-identified as explicitly employing QuantCrit. Given the distinct epistemological origin of QuantCrit from broader forms of critical quantitative scholarship, codes representing the tenets of QuantCrit reflect its origins in critical race theory and may not be appropriate to apply to broader streams of critical quantitative scholarship that do not center on racism (e.g., scholarship related to (dis)ability, gender identity, sexual identity and orientation). After individually completing a priori coding, we met to reconcile discrepancies and engage in peer debriefing (Creswell & Miller, 2000 ). Data synthesis involved tabulating and reporting findings to explore how each manuscript component aligned with critical quantitative frameworks in higher education research to date.

We analyzed interview data through a multiphase process that engaged deductive and inductive coding strategies. After interviews were transcribed and redacted, we uploaded the transcripts to Dedoose for collaborative qualitative coding. The second author read each transcript in full to holistically understand participants’ insights about generating critical quantitative research. During this initial read, the second author noted quotes that were salient to our question regarding the strategies that scholars use to employ critical quantitative approaches.

Then, using the a priori codes drawn from Stage’s ( 2007 ), Rios-Aguilar’s ( 2014 ) and Gillborn et al.’s ( 2018 ) conceptualizations relevant to quantitative criticalism, critical quantitative inquiry, and QuantCrit, we collaboratively established a working codebook for deductive coding by defining the a priori codes in ways that could capture how participants discussed their work. Although these a priori codes had been previously applied to the manuscripts in the scoping review, definitions and applications of the same codes for interview analysis were noticeably broader (to align with the nature of conversations during interviews). For example, we originally applied the code “policy/advocacy”—established from Rios-Aguilar's work—to components from the implications section of scoping review manuscripts. When (re)developed for deductive coding of interview data, however, we expanded the definition of “policy/advocacy” to include participants’ policy- and advocacy-related actions (beyond writing) that advanced critical inquiry and equity for their educational communities.

In the final phase of analysis, each research team member engaged in inductive coding of the interview data. Specifically, we relied on open coding (Saldaña, 2015 ) to analyze excerpts pertaining to participants’ strategies for employing critical quantitative approaches that were not previously captured by deductive codes. Through open coding, we used successive analysis to work in sequence from a single case to multiple cases (Miles et al., 2014 ). Then, as suggested by Saldaña ( 2015 ), we collapsed our initial codes into broader categories that allowed us insight regarding how participants’ strategies in critical quantitative research expanded beyond those which have been previously articulated. Finally, to draw cohesive interpretations from these data, we independently drafted analytic memos for each interview participant’s transcript, later bridging examples from the scoping review that mapped onto qualitative codes as a form of establishing greater confidence and trustworthiness in our multimethod design.

In introducing study findings through a synthesized lens that heeds our multimethod design, we organize the sections below to draw from both scoping review and interview data. Specifically, we organize findings into two primary areas that address authors’ (1) articulated motivations to adopt critical approaches to quantitative higher education research, and (2) methodological choices that they perceive to align with critical approaches to quantitative higher education research. Within these sections, we discuss several coherent areas where authors collectively grappled with tensions in motivation (i.e., broad motivations, using coined names of critical approaches, conveying positionality, leveraging asset-based frameworks) and method (i.e., using data sources and choosing variables, challenging coding norms, interpreting statistical results), all of which signal authors’ efforts to embody criticality in quantitative research about higher education. Given our sequential research questions, which first examined the landscape of critical quantitative higher education research and then asked authors to elucidate their thought processes and strategies underlying their approaches to these manuscripts, our findings primarily focus on areas of convergence across data sources; we do, however, highlight challenges and tensions authors faced in conducting such work.

Articulated Motivations in Critical Approaches to Quantitative Research

To date, critical quantitative researchers in higher education have heeded Stage’s ( 2007 ) call to use data to reveal the large-scale perpetuation of inequities in educational processes and outcomes. This emerged as a defining aspect of higher education scholars’ critical quantitative work, as all manuscripts ( N  = 34) in the scoping review articulated underlying motivations to identify and/or address inequities.

Often, these motivations were reflected in the articulated research questions ( n  = 31; 91.2%). For example, one manuscript sought to “critically examine […] whether students were differentially impacted” by an educational policy based on intersecting race/ethnicity, gender, and income (Article 29, p. 39). Others sought to challenge notions of homogeneity across groups of systemically minoritized individuals by “explor[ing] within-group heterogeneity” of constructs such as sense of belonging among Asian American students (Article 32, p. iii) and “challenging the assumption that [economically and educationally challenged] students are a monolithic group with the same values and concerns” (Article 31, p. 5). These underlying motivations for conducting critical quantitative research emerged most clearly in the named approaches, positionality statements, and asset-based frameworks articulated in manuscripts.

Adopting the Coined Names of Quantitative Criticalism, QuantCrit, and Related Approaches

Based on the inclusion criteria applied in the scoping review, we anticipated that all manuscripts would employ approaches that were explicitly critical and quantitative in nature. Accordingly, all manuscripts ( N  = 34; 100%) adopted approaches that were coined as quantitative criticalism , QuantCrit , critical policy analysis (CPA), critical quantitative intersectionality (CQI) , or some combination of those terms. Twenty-one manuscripts (61.8%) identified their approach as quantitative criticalism, nine manuscripts (26.5%) identified their approach as QuantCrit, two manuscripts (5.9%) identified their approach as CPA, and two manuscripts (5.9%) identified their approach as CQI.

One of the manuscripts that applied quantitative criticalism broadly described it as an approach that “seeks to quantitatively understand the predictors contributing to completion for a specific population of minority students” (Article 34, p. 62), noting that researchers have historically “attempted to explain the experiences of [minority] students using theories, concepts, and approaches that were initially designed for white, middle and upper class students” (Article 34, p. 62). Although this example speaks only to the limited context and outcomes of one study, it highlights a broader theme found across articles; that is, quantitative criticalism was often leveraged to challenge dominant theories, concepts, and approaches that failed to represent systemically minoritized individuals’ experiences. In challenging dominant theories, QuantCrit applications were most explicitly associated with critical race theory and issues of racism. One manuscript noted that “QuantCrit recognizes the limitations of quantitative data as it cannot fully capture individual experiences and the impact of racism” (Article 29, p. 9). However, these authors subsequently noted that “quantitative methodology can support CRT work by measuring and highlighting inequities” (Article 29, p. 9). Several scholars who employed QuantCrit explicitly identified tenets of QuantCrit that they aimed to address, with several authors making clear how they aligned decisions with two tenets establishing that categories are not given and numbers are not neutral.

Despite broadly applying several of the coined names for critical realms of quantitative research, interview data revealed that several authors felt a palpable tension in labeling. Some participants, like Nathan, questioned the surface-level engagement that may come with coined names: “I don’t know, I think it’s the thinking and the thought processes and the intentionality that matters. How invested should we be in the label?” Nathan elaborated by noting how he has shied away from labeling some of his work as quantitative criticalist , given that he did not have a clear answer about “what would set it apart from the equity-minded, inequality-focused, structurally and systematically-oriented kind of work.” Similarly, Leo stated how labels could (un)intentionally stop short of the true mission for the research, recalling that he felt “more inclined to say that I’m employing critical quantitative leanings or influences from critical quant” because a true application of critical epistemology should be apparent in each part of the research process. Although most interview participants remained comfortable with labeling, we also note that—within both interview data and the articles themselves—authors sometimes presented varied source attributions for labels and conflated some of the coined names, representing the messiness of this emerging body of research.

Challenging Objectivity by Conveying Researcher Positionality

Positionality statements acknowledge the influence of scholars’ identities and social positions on research decisions. Quantitative research has historically been viewed as an objective, value-neutral endeavor, with some researchers deeming positionality statements as unnecessary and inconsistent with the positivist paradigm from which such work is often conducted. Several interviewed authors noted that positivist or post-positivist roots of quantitative research characterized their doctoral training, which often meant that their “original thinking around statistics and research was very post-positivist” (Carter) or that “there really wasn’t much of a discussion, as far as I can remember as a doc student, about epistemology or ontology” (Randall). Although positionality statements have been generally rare in quantitative research studies, half of the manuscripts in our sample ( n  = 17; 50.0%) included statements of researcher positionality. One interview participant, Gabrielle, discussed the importance of positionality statements as one way to challenge norms of quantitative research in saying:

It’s not objective, right? I think having more space to say, “This is why I chose the measures I chose. This is how I’m coming to this work. This is why it matters to me. This is my positioning, right?” I think that’s really important in quantitative work…that raises that level of consciousness to say these are not just passive, like every decision you make in your research is an active decision.

While Gabrielle, as well as Carter and Randall, all came to be advocates of positionality statements in quantitative scholarship through different pathways, it became clear through these and other interviews that positionality statements were one way to bring greater transparency to a traditionally value-neutral space.

As an additional source of contextual data, we reviewed submission guidelines for the peer-reviewed journals in which manuscripts were published. Not one of the 15 peer-reviewed outlets represented in our scoping review sample required that authors include positionality statements. One outlet, Journal of Diversity in Higher Education (where two scoping review articles were printed), offered “inclusive reporting standards” where they recommended that authors include reflexivity and positionality statements in their submitted manuscripts (American Psychological Association, 2024 ). Another outlet, Teachers College Record (where one scoping review article was printed), mentioned positionality statements in their author instructions. Yet, Teachers College Record did not require nor recommend the inclusion of author positionality statements; rather, they offered recommendations if authors chose to include them. Specifically, they suggested that if authors chose to include a positionality statement, it should be “more than demographic information or abstract statements” (Sage Journals, 2024 ). The remaining 13 peer-reviewed outlets from the scoping review data made no mention of author reflexivity or positionality in their author guidelines.

When present, the scoping review revealed that positionality statements varied in form and content. Some positionality statements were embedded in manuscript narratives, while others existed as separate tables with each author’s positionality represented as a separate row. In content, it was most common for authors to identify how their identities and experiences motivated their work. For example, one author noted their shared identity with their research participants as a low-income, first-generation Latina college student (Article 2, p. 25). Another author discussed the identity that they and their co-author shared as AAPI faculty, making the research “personally relevant for [them]” (Article 11, p. 344),

In interviews, participants recalled how the relationship between their identities, lived experiences, and motivations for critical approaches to quantitative research were all intertwined. Leo mentioned, “naming who we are in a study helps us be very forthright with the pieces that we’re more likely to attend to.” Yet, Leo went on to say that “one of the most cosmetic choices that people see in critically oriented quantitative research is our positionality statements,” which other participants noted about how information in positionality statements is presented. In several interviews, authors’ reflections on whether these statements should appear as lists of identities or deeper statements about reflexivity presented a clear tension. For some, positionality statements were places to “identify ourselves and our social locations” (David) or “brand yourself” as a critical quantitative scholar to meet “trendy” writing standards in this area (Michelle). Yet, others felt such statements fall short in revealing “how this study was shaped by their background identities and perspectives” (Junco) or appear to “be written in response to the context of the research or people participating” (Ginger). Ultimately, many participants felt that shaping honest positionality statements that better convey “the assumptions, and the biases and experiences we’ve all had” (Randall) was one area where quantitative higher education scholars could significantly improve their writing to reflect a critical lens.

Some manuscripts also clarified how authors’ identities and social positions reshaped the research process and product. For instance, authors of one manuscript reported being “guided by [their] cultural intuition” throughout the research (Article 17, p. 218). Alternatively, another author described the narrative style of their manuscript as intentionally “autobiographical and personally reflexive” in order “to represent the connections [they] made between [their] own experiences and findings that emerged” from their work (Article 28, p. 56). Taken together, among the manuscripts that explicitly included positionality statements, these remarks make clear that authors had widely varying approaches to their reflexivity and writing processes.

Actualizing Asset-Based Frameworks

Notably, conceptual and theoretical frameworks emerged as a common way for critical quantitative scholars to pursue equitable educational processes and outcomes in higher education research. Nearly all ( n  = 32; 94.1%) manuscripts explicitly challenged dominant conceptual and theoretical models. Some authors enacted this challenge by countering canonical constructs and theories in the framing of their study. For example, several manuscripts addressed critiques of theoretical concepts such as integration and sense of belonging in building the conceptual framework for their own studies. Other manuscripts were constructed with the underlying goal to problematize and redefine frameworks, such as engagement for Latina/e/o/x students or the “leaky pipeline” discourse related to broadening participation in the sciences.

Across interviews, participants challenged deficit framings or “traditional” theoretical and conceptual approaches in many ways. Some frameworks are taken as a “truism in higher ed” (Leo), such as sense of belonging and Astin’s ( 1984 ) I-E-O model, and these frameworks were sometimes purposefully used to disrupt their normative assumptions. Randall, for one, recalled using a more normative higher education framework but opted to think about this framework “as more culturalized” than had previously been done. Further, Carter noted that “thinking about the findings in an anti-deficit lens” comprised a large portion of critical quantitative approaches. Using frameworks for asset-based interpretation was further exemplified by Caroline stating, “We found that Black students don’t do as well, but it’s not the fault of Black students.” Instead, Caroline challenged deficit understandings through the selected framework and implications for institutional policy. Collectively, challenging normative theoretical underpinnings in higher education was widely favored among participants, and Jackie hoped that “the field continues to turn a critical lens onto itself, to grow and incorporate new knowledges and even older forms of knowledge that maybe it hasn’t yet.”

Alternatively, some participants discussed rejecting widely used frameworks in higher education research in favor of adapting frameworks from other disciplines. For example, QuantCrit researchers drew from critical race theory (and related frameworks, such as intersectionality) to quantitatively examine higher education topics in ways that value the knowledge of People of Color. In using these frameworks, which have origins in critical legal and Black feminist theorization, interview participants noted how important it was “to put yourself out there with talking about race and racism” (Isabel) and connect the statistics “back to systems related to power, privilege, and oppression [because] it’s about connecting [results] to these systemic factors that shape experience, opportunities, barriers, all of that kind of stuff” (Jackie). Further, several authors related pulling theoretical lenses from sociology, gender studies, feminist studies, and queer studies to explore asset-based theorization in higher education contexts and potentially (re)build culturally relevant concepts for quantitative measurement in higher education.

Embodying Criticality in Methodological Sources, Approaches, and Interpretations

Moving beyond underlying motivations of critical quantitative higher education research, scoping review authors also frequently actualized the task of questioning and reconstructing “models, measures, and analytic practices [to] better describe experiences of those who have not been adequately represented” (Stage, 2007 , p. 10). Common across all manuscripts ( N  = 34) was the discussion of specific ways in which authors’ critical quantitative approaches informed their analytic decisions. In fact, “analytic practices” was by far the most prevalent code applied to the manuscripts in our dataset, with 342 total references across the 34 manuscripts. This amounted to 20.8% of the excerpts in the scoping review dataset being coded as reflecting critical quantitative approaches to analytic practices, specifically.

Interestingly, many analytic approaches reflected what some would consider “standard” quantitative methodological tools. For example, manuscripts employed factor analysis to assess measures, t-tests to examine differences between groups, and hierarchical linear regression to examine relationships in specific contexts. Some more advanced, though less commonly applied, methods included measurement invariance testing and latent class analysis. Thus, applying a critical quantitative lens tended not to involve applying a separate set of analytic tools; rather, the critical lens was reflected in authors’ selection of data sources and variables, approaches to data coding and (dis)aggregation, and interpretation of statistical results.

Selecting Data Sources and Variables

Although scholars were explicit in their underlying motivations and approaches to critical quantitative research, this did not often translate into explicitly critical data collection endeavors. Most manuscripts ( n  = 29; 85.3%) leveraged existing measures and data sources for quantitative analysis. Existing data sources included many national, large-scale datasets including the Educational Longitudinal Study (NCES), National Survey of Recent College Graduates (NSF), and the Current Population Survey (U.S. Census Bureau). Other large-scale data sources reflecting specific higher education contexts and populations included the HEDS Diversity and Equity Campus Climate Survey, Learning About STEM Student Outcomes (LASSO) platform, and National Longitudinal Survey of Freshmen. Only five manuscripts (14.7%) conducted analysis using original data collected and/or with newly designed measures.

It was apparent, however, that many authors grappled with challenges related to using existing data and measures. Interview participants’ stories crystallized the strengths and limitations of secondary data. Over half of the interview participants in our study spoke about their choices regarding quantitative data sources. Some participants noted that surveys “weren’t really designed to ask critical questions” (Sarah) and discussed the issues with survey data collected around sex and gender (Jessica). Still, Sarah and Jessica drew from existing survey data to complicate the higher education experiences they aimed to understand and tried to leverage critical framing to question “traditional” definitions of social constructs. In another discussion about data sources and the design of such sources, Carter expanded by saying:

I came in without [being] able to think through the sampling or data collection portion, but rather “this is what I have, how do I use it in a way that is applying critical frameworks but also staying true to the data themselves.” That is something that looks different for each study.

In discussing quantitative data source design, more broadly, Tyler added: “In a lot of ways, all quantitative methods are mixed methods. All of our measures should be developed with a qualitative component to them.” In the scoping review articles, one example of this qualitative component is evident within the cognitive interviews that Sablan ( 2019 ) employed to validate survey items. Finally, several participants noted how crucial it is to “just be honest and acknowledge the [limitations of secondary data] in the paper” (Caroline) and “not try to hide [the limitations]” (Alexis), illustrating the value of increased transparency when it comes to the selection and use of existing quantitative data in manuscripts advancing critical perspectives.

Regardless of data source, attention to power, oppression, and systemic inequities was apparent in the selection of variables across manuscripts. Many variables, and thus the associated models, captured institutional contexts and conditions. The multilevel nature of variables, which extended beyond individual experiences, aligned with authors’ articulated motivations to disrupt inequitable educational processes and outcomes, which are often systemic and institutionalized in nature. For one, David explained key motivations behind his analytic process: “We could have controlled for various effects, but we really wanted to see how are [the outcomes] differing by these different life experiences?” David’s focus on moving past “controlling” for different effects shows a deep level of intentionality that was reflected among many participants. Carter expanded on this notion by recalling how variable selection required, “thinking through how I can account for systemic oppression in my model even though it’s not included in the survey…I’ve never seen it measured.” Further, Leo discussed how reflexivity shaped variable selection and shared: “Ultimately, it’s thinking about how do these environments not function in value-neutral ways, right? It’s not just selecting X, Y, and Z variable to include. It’s being able to interrogate [how] these variables represent environments that are not power neutral.” The process of selecting quantitative data sources and variables was perhaps best summed up by Nick, who concisely shared, “it’s been very iterative.” Indeed, most participants recalled how their methodological processes necessitated reflexivity—an iterative process of continually revisiting assumptions one brings to the quantitative research process (Jamieson et al., 2023 )—and a willingness to lean into innovative ways of operationalizing data for critical purposes.

Challenging the Norms of Coding

An especially common way of enacting critical principles in quantitative research was to challenge traditional norms of coding. This emerged in three primary ways: (1) disaggregation of categories to reflect heterogeneity in individuals’ experiences, (2) alternative approaches to identifying reference groups, and (3) efforts to capture individuals’ intersecting identities. Across manuscripts, authors often intentionally disaggregated identity subgroups (e.g., race/ethnicity, gender) and ran distinct analytical models for each subgroup separately. In interviews, Junco expressed that running separate models was one way that analyses could cultivate a different way of thinking about racial equity. Specifically, Junco challenged colleagues’ analytic processes by asking whether their research questions “really need to focus on racial comparison?” Junco then pushed her colleagues by asking, “can we make a different story when we look at just the Black groups? Or when we look at only Asian groups, can we make a different story that people have not really heard?” Isabel added that focusing on measurement for People of Color allowed for them (Isabel and her research collaborators) to “apply our knowledge and understanding about minoritized students to understand what the nuances were.” In nearly one third of the manuscripts ( n  = 11; 32.4%), focusing on single group analyses emerged as one way that QuantCrit scholars disrupted the perceived neutrality of numbers and how categories have previously been established to serve white, elite interests. Five of those manuscripts (14.7%) explicitly focused on understanding heterogeneity within systemically minoritized subpopulations, including Asian American, Latina/e/o/x, and Black students.

It was not the case, however, that authors avoided group comparisons altogether. For example, one team of authors used separate principal components analysis (PCA) models for Indigenous and non-Indigenous students with the explicit intent of comparing models between groups. The authors noted that “[t]ypically, monolithic comparisons between racial groups perpetuate deficit thinking and marginalization.” However, they sought to “highlight the nuance in belonging for Indigenous community college students as it differs from the White-centric or normative standards” by comparing groups from an asset-driven perspective (Article 5, p. 7). Thus, in cases where critical quantitative scholars included group comparisons, the intentionality underlying those choices as a mechanism to highlight inequities and/or contribute to asset-based narratives was apparent.

Four manuscripts (11.8%) were explicit in their efforts to identify alternative analytic methods to normative reference groups. Reference groups are often required when building quantitative models with categorical variables such as racial/ethnic and gender identity. Often, dominant identities (e.g., respondents who are white and/or men) comprise the largest portion of a research sample and are selected as the comparison group, typifying experiences of individuals with those dominant identities. To counter the traditional practice of reference groups, some manuscript authors stated using effect coding, often referencing the work of Mayhew and Simonoff ( 2015 ), and dynamic centering as two alternatives. Effect coding (used in three manuscripts) removes the need for a reference group; instead, all groups are compared to the overall sample mean. Dynamic centering (used in one manuscript), on the other hand, uses a reference group but one that is intentionally selected based on the construct in question, as opposed to relying on sample size or dominant identities.

Interview participants also discussed navigating alternative coding practices, with several authors raising key points about their exposure to and capacity building for effect coding. As Angela described, effect coding necessitates that “you don’t choose a specific group as your benchmark to do the comparison. And you instead compare to the group.” Angela then stated that this approach made more sense than choosing benchmarks, as she felt uncomfortable identifying one group as a comparison group. Junco, however, noted that “effect coding was much more complicated than what I thought,” as she reflected on unlearning positivist strategies in favor of equity-focused approaches that could elucidate greater nuance. Importantly, using alternative coding practices was not universal among manuscripts or interview participants. One manuscript utilized traditional dummy coding for race in regression models, with white students as the reference group to which all other groups were compared. The authors explicated that “using white students as the reference [was] not a result of ‘privileging’ them or maintaining the patterns of power related to racial categorizations” (Article 8, p. 1282). Instead, they argued that the comparison was a deliberate choice to “reveal patterns of racial or ethnic educational inequality compared to the privileged racial group” (Article 8, p. 1282). Another author maintained the use of reference groups purely for ease of interpretation. David shared, “it’s easier for the person to just look at it and compare magnitudes.” However, by prioritizing the benefit of easy interpretation with traditional reference groups, authors may incur other costs (such as sustaining unnecessary comparisons to white students). Additionally, several manuscripts ( n  = 13; 38.2%) employed analytic coding practices that aimed to account for intersectionality. While authors identified these practices by various names (e.g., interaction terms, mediating variables, conditional effects) they all afforded similar opportunities. The most common practice among authors in our sample ( n  = 8; 23.5%) was computing interaction terms to account for intersecting identities, such as race and gender. Specifically pertaining to intersectionality, Alexis summarized many researchers’ tensions well in sharing, “I know what Kimberlé Crenshaw says. But how do I operationalize that mathematically into something that’s relevant?” In offering one way that intersectionality could be realized with quantitative data, Tyler stated that “being able to keep in these variables that are interacting [via interaction terms] and showing differences” may align with the core ideas of intersectionality. Yet, participants also recognized that statistics would inherently always fall short of representing respondents’ lived experiences, as discussed by Nick: “We disaggregate as far as we can, but you could only go so far, and like, how do we deal with tension.” Several other participants reflected on bringing in open-text response data about individuals’ social identities, categorizing racial and ethnic groups according to continent (while also recognizing that this did not necessarily attend to the complexities of diasporas), or making decisions about groups that qualify as “minoritized” based on disciplinary and social movements. Collectively, the disparate approaches that authors used and discussed directly speak to critical higher education scholars’ movement away from normative comparisons that did not meaningfully answer questions related to (in)equity and/or intersectionality in higher education.

Interpreting Statistical Results

One notable, albeit less common, way higher education scholars enacted critical quantitative approaches through analytic methods was by challenging traditional ways of reporting and interpreting statistical results. The dominant approach to statistical methods aligns with a null hypothesis significance testing (NHST) approach, whereby p -values—used as indicators of statistically significant effects—serve to identify meaningful results. NHST practices were prevalent in nearly all scoping review manuscripts; yet, there were some exceptions. For example, three manuscripts (8.8%) cautioned against reliance on statistical significance due to its dependence on large sample size (i.e., statistical power), which is often at odds with centering research on systemically minoritized populations. One of those manuscripts (2.9%) even chose to interpret nonsignificant results from their quantitative analyses. In a similar vein, two manuscripts (5.9%) also questioned and adapted common statistical practices related to model selection (e.g., using corrected Akaike information criteria (AIC) instead of p -values) and variable selection (e.g., avoiding use of variance explained so as not to “[exclude] marginalized students from groups with small representations in the data” (Article 23, p. 7). Meanwhile, others attended to raw numeric data and uncertainty associated with quantitative results. The resources to enact these alternative methodological practices were briefly discussed by Tyler through his interview, in which he shared: “The use of p -values is so poorly done that the American Statistical Association has released a statement on p -values, an entire special collection [and people in my field] don’t know those things exist.” Tyler went on to share that this knowledge barrier was tied to the siloed nature of academia, and that such siloes may inhibit the generation of critical quantitative research that draws from different disciplinary origins.

Among interviewed authors, many also viewed interpretation as a stage of quantitative research that required a high level of responsibility and awareness of worldview. Nick related that using a QuantCrit approach changed how he was interpreting results, in “talking about educational debts instead of gaps, talking about racism instead of race.” As demonstrated by Nick, critical interpretations of statistics necessitate congruence with theoretical or conceptual framing, as well, given the explicit call to interrogate structures of inequity and power in research adopting a critical lens. Leo described this responsibility as a necessary challenge:

It’s very easy to look at results and interpret them—I don’t wanna say ‘as is’ because I don’t think that there is an ‘as is’—but interpret them in ways that they’re traditionally interpreted and to keep them there. But, if we’re truly trying to accomplish these critical quantitative themes, then we need to be able to reference these larger structures to make meaning of the results that are put in front of us.

Nick, Leo, and several other participants all emphasized how crucial interpretation is in critical quantitative research in ways that expanded beyond statistical practices; ultimately, the perspective that “behind every number is a human” served as a primary motivation for many authors in fulfilling the call toward ethical and intentional interpretation of statistics.

Leveraging a multimethod approach with 15 years of published manuscripts ( N  = 34) and 18 semi-structured interviews with corresponding authors, this study identifies the extent to which principles of quantitative criticalism, critical quantitative inquiry, and QuantCrit have been applied in higher education research. While scholars are continuing to develop strategies to enact a critical quantitative lens in their studies—a path we hope will continue, as continued questioning, creativity, and exploration of new possibilities underscore the foundations of critical theory (Bronner, 2017 )—our findings do suggest that higher education researchers may benefit from intentional conversations regarding specific analytic practices they use to advance critical quantitative research (e.g., confidence intervals versus p -values, finite mixture models versus homogeneous distribution models).

Our interviews with higher education scholars who produced such work also fills a need for guidance on strategies to enact critical perspectives in quantitative research, addressing an absence of such from most quantitative training and resources. By drawing on the work and insights of higher education researchers engaging critical quantitative approaches, we provide a foundation on which future scholars can imagine and implement a fuller range of possibilities for critical inquiry via quantitative methods in higher education. In what follows, we discuss the findings of this study alongside the frameworks from which they drew inspiration. Then, we offer implications for research and practice to catalyze continued exploration and application of critical quantitative approaches in higher education scholarship.

Synthesizing Key Takeaways

First, scoping review data revealed several commonalities across manuscripts regarding authors’ underlying motivations to identify and/or address inequities for systemically minoritized populations—speaking to how critical quantitative approaches can fall within the larger umbrella of equity-mindedness in higher education research. Such motivations were reflected in authors’ research questions and frameworks (consistent with Stage’s ( 2007 ) initial guidance). Most manuscripts identified their approach as quantitative criticalism broadly, although there were sometimes blurred boundaries between approaches termed quantitative criticalism, QuantCrit, critical policy analysis, and critical quantitative intersectionality. Notably, authors’ decisions about which framing their work invoked also determined how scholars enacted a specified critical quantitative approach. For example, the tenets of QuantCrit, offered by Gillborn et al. ( 2018 ), were specifically heeded by researchers seeking to take up a QuantCrit lens. Scholars who noted inspiration from Rios-Aguilar ( 2014 ) often drew specifically from the framework for critical quantitative inquiry. While the key ingredients of these critical quantitative approaches were offered in the foundational framings we introduced, the field has lacked understanding on how scholars take up these considerations. Thus, the present findings create inroads to a conversation about applying and extending the articulated components associated with critical quantitative higher education research.

Second, our multimethod approach illuminated general agreement (in manuscripts and interviews) that quantitative research in higher education—whether explicitly critical or not—is not neutral nor objective. However, despite positionality being a key part of Rios-Aguilar’s ( 2014 ) critical quantitative inquiry framework, only half of the manuscripts included researcher positionality. Thus, while educational researchers may agree that, without challenging objectivity, quantitative methods serve to uphold inequity (e.g., Arellano, 2022 ; Castillo & Babb, 2024 ), higher education scholars may not have yet established consensus on how these principles materialize. To be clear, consensus need not be the goal of critical quantitative approaches, given that critical theory demands constant questioning for new ways of thinking and being (Bronner, 2017 ); yet, greater solidarity among critical quantitative higher education researchers may be beneficial, so that community-based discussions can drive the actualization of equity-minded motivations. Interview data also revealed complications in how scholars choose if, and how, to define and label critical quantitative approaches. Some participants struggled with whether their work was “critical enough” to be labeled as such. Those conversations raise concerns that critical quantitative research in higher education could—or potentially has—become an exclusionary space where level of criticality is measured by an arbitrary barometer (refer to Garvey & Huynh, 2024 ). Meanwhile, other participants worried that attaching such a label to their work was irrelevant (i.e., that it was the motivations and intentionality underlying the work that mattered, not the label). Although the field remains in disagreement regarding if/how labeling should be implemented for critical quantitative approaches, “it is the naming of experience and ideologies of power that initiates the process [of transformation] in its critical form” (Hanley, 2004 , p. 55). As such, we argue that naming critical quantitative approaches can serve as a lever for transforming quantitative higher education research and create power in related dialogue.

Implications for Future Studies on Critical Quantitative Higher Education Research

As with any empirical approach, and especially those that are gaining traction (as critical quantitative approaches are in higher education; Wofford & Winkler, 2022 ), there is utility in conducting research about the research . First, in the context of higher education as a broad field of applied research, there is a need to illustrate what critical quantitative scholars focus on when they conceptualize higher education in the first place. For example, is higher education viewed as a possibility for social mobility? Or are critical quantitative scholars viewing postsecondary institutions as engines of inequity? Second, it was notable that—among the manuscripts including positionality statements—it was common for such statements to read as biographies (i.e., lists of social identities) rather than as reflexive accounts about the roles/commitments of the researcher(s). Future research would benefit from a deeper understanding of the enactment of positionality in critical quantitative higher education research. Third, given the productive tensions associated with naming and understanding the (dis)agreed upon ingredients between quantitative criticalism, critical quantitative inquiry, QuantCrit, as well as additional known and unknown conceptualizations, further research regarding how higher education scholars grapple with definitions, distinctions, and adaptations of these related approaches will clarify how scholars can advance their critical commitments with quantitative postsecondary data.

Implications for Employing Critical Quantitative Higher Education Research

Emerging analytical tools for critical quantitative research.

In terms of employing critical quantitative approaches in higher education research, there is significant room for scholars to explore emerging quantitative methodological tools. We agree with López et al.’s ( 2018 ) assessment that critical quantitative work tends to remain demographic and/or descriptive in its methodological nature, and there is great potential for more advanced inferential quantitative methods to serve critical aims. While there are some examples in the literature—for example, Sablan’s ( 2019 ) work in the realm of quantitative measurement and Malcom-Piqueux’s (2015) work related to latent class analysis and other person-centered modeling approaches—additional examples of advanced and innovative analytical tools were limited in our findings. Thus, integrating more advanced quantitative methodological tools into critical quantitative higher education research, such as finite mixture modeling (as noted by Malcom-Piqueux, 2015), measurement invariance testing, and multi-group structural equation modeling, may advance the ways in which scholars address questions related to heterogeneity in the experiences and outcomes of college students, faculty, and staff.

Traditional quantitative analytical tools have historically highlighted between-group differences that perpetuate deficit narratives for systemically minoritized students, faculty, and staff on college campuses; for example, comparing the educational outcomes of Black students to white students. Emerging approaches such as finite mixture modeling hold promise in unearthing more nuanced understandings. Of growing interest to many critical quantitative scholars is heterogeneity within minoritized populations; finite mixture modeling approaches such as growth mixture modeling, latent class analysis, and latent profile analysis are particularly well suited to reveal within-group differences that are otherwise obfuscated in most quantitative analyses. Although we found a few examples in our scoping review of authors who leveraged more traditional group comparisons for equity-minded aims, these emerging analytical approaches may be better suited for the questions asked by future critical quantitative scholars.

One Size Does Not Fit All

Many emerging analytical tools demonstrate promise in advancing conversations about inequity, particularly related to heterogeneity in subpopulations on college and university campuses. As noted previously, however, Rios-Aguilar ( 2014 ) noted that critical quantitative research need not rely solely on “fancy” or advanced analytical tools; in fact, our findings did not lead us to conclude that higher education scholars have established a set of analytical approaches that are explicitly critical in nature. Rather, our results revealed a common theme: that critical quantitative scholarship in higher education necessitates an elevated degree of intentionality in selection, application, and interpretation of whichever analytical approaches—advanced or not—scholars choose.

As noted, there were several instances in our data where commonly critiqued analytical approaches were still applied in the critical quantitative literature. For example, we found manuscripts that conducted a monolithic comparison of Indigenous and non-Indigenous students and the utilization of traditional dummy coding with white students as a normative reference group. What made these manuscripts distinct from more non-critical quantitative research was the thoughtfulness and intentionality with which those approaches were selected to serve equity-minded goals—an intentionality that was explicitly communicated to readers in the methods section of manuscripts. Just as the inclusion of positionality statements in half of the manuscripts suggests that researcher objectivity was generally not assumed by higher education scholars conducting critical quantitative scholarship, choices that often otherwise go unquestioned were interrogated and discussed in manuscripts.

Cokley and Awad ( 2013 ) share several recommendations for advancing social justice research via quantitative methods. One of their recommendations addresses the utilization of racial group comparisons in quantitative analyses. They do not suggest that researchers avoid comparisons between groups altogether, but rather they avoid “unnecessary” comparisons between groups (p. 35). They elaborate that, “[t]here should be a clear research questions that necessitates the use of the comparison” if utilized in quantitative research with critical aims (Cokley & Awad, 2013 , p. 35). Our findings suggested that—in the current state of critical quantitate scholarship in higher education—it is not so much about a specific set of approaches deeming scholarship as critical (or not), but rather about asking critical questions (as Stage initially called us to do in 2007) and then selecting methods that align with those goals.

Opportunities for Training and Collaboration

Notably, many of the emerging analytical approaches mentioned require a significant degree of methodological training. The limited use of such tools, which are otherwise well-suited for critical quantitative applications, points to a potential disconnect in training of higher education scholars. Some structured opportunities for partnership between disciplinary and methodological scholars have emerged via training programs such as the Quantitative Research Methods (QRM) for STEM Education Scholars Program (funded by the National Science Foundation Award 1937745) and the Institute on Mixture Modeling for Equity-Oriented Researchers, Scholars and Educators (IMMERSE) fellowship (funded by the Institute for Education Sciences Award R305B220021). These grant-funded training opportunities connect quantitative methodological experts with applied researchers across educational contexts.

We must consider additional ways, both formal and informal, to expand training opportunities for higher education scholars with interest in both advanced quantitative methods and equity-focused research; until then, expertise in quantitative methods and critical frameworks will likely inhabit two distinct communities of scholars. For higher education scholars to fully embrace the potential of critical quantitative research, we will be well served by intentional partnerships across methodological (e.g., quantitative and qualitative) and disciplinary (e.g., higher education scholars and methodologists) boundaries. In addition to expanding applied researchers’ analytical skillsets, training and collaboration opportunities also prepare potential critical quantitative scholars in higher education to select methodological approaches, whether introductory or advanced, that most closely align with their research aims.

Historically, critical inquiry has been viewed primarily as an endeavor for qualitative research. Recently, educational scholars have begun considering the possibilities for quantitative research to be leveraged in support of critical inquiry. However, there remains limited work evaluating whether and to what extent principles from quantitative criticalism, critical quantitative inquiry, and QuantCrit have been applied in higher education research. By drawing on the work and insights of scholars engaging in critical quantitative work, we provide a foundation on which future scholars can imagine and implement a vast range of possibilities for critical inquiry via quantitative methods in higher education. Ultimately, this work will allow scholars to realize the potential for research methodologies to directly support critical aims.

Data Availability

The list of manuscripts generated from the scoping review analysis is available via the Online Supplemental Materials Information link. Given the nature of our sample and topics discussed, interview data will not be shared publicly to protect participant anonymity.

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Acknowledgements

This research was supported by a grant from the American Educational Research Association, Division D. The authors gratefully thank Dr. Jason (Jay) Garvey for his support as an early thought partner with regard to this project, and Dr. Christopher Sewell for his helpful feedback on an earlier version of this manuscript, which was presented at the 2022 Association for the Study of Higher Education meeting.

This research was supported by a grant from the American Educational Research Association, Division D.

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Winkler, C.E., Wofford, A.M. Trends and Motivations in Critical Quantitative Educational Research: A Multimethod Examination Across Higher Education Scholarship and Author Perspectives. Res High Educ (2024). https://doi.org/10.1007/s11162-024-09802-w

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Unlocking Insights: A Guide to Data Analysis Methods

The data collected already in this information age are what makes advancement possible. But by itself, raw data is a confused mess. We employ the performance of data analysis to clear this confusion, extracting valuable insights from the muck that’s gradually forming the base for key decisions and innovation. This article plunges into the methods used in data analysis, arming one with know-how for the dynamic field.

Table of Content

Understanding Data Analysis

Types of data analysis, quantitative data analysis methods, quantitative data analysis methods: when to use, advantages and disadvantages, qualitative data analysis methods, qualitative data analysis methods: when to use, advantages and disadvantages, data analysis mixed methods ( quantitative and qualitative), data analysis mixed methods : when to use, advantages and disadvantages.

Data analysis is the process of inspecting, cleaning, transforming, and modeling data to answer questions, make conclusions, and support decision-making. It is a multi-disciplinary field of study that involves deriving knowledge from raw data. Data analysis is used by companies in order to outcompete and get that cutting edge in understanding customer behaviors, optimizing campaigns for marketing, and predicting trends in the market.

Data analytic techniques have wide-ranging methodologies, roughly placed under two main approaches: quantitative analysis and qualitative analysis.

• Quantitative Analysis: This is where one begins to work with numbers and to use the power of statistics and mathematical models in order to determine patterns, trends, and relationships from which data could be drawn. It’s quite like using a ruler to measure and compare data points. Techniques under this level include regression analysis, hypothesis testing, and time series analysis. Just try and imagine using regression analysis in trying to understand how changes in the advertising budget are reflected in the sales numbers.
• Qualitative Analysis: This method should be reserved for non-numeric data, or data that does not easily translate into numbers. This refers to data such as customer reviews ; images, such as those contained within social media posts; and, in some cases, even the audio recording of responses to questions during a focus group. Some techniques used in qualitative analysis include but are not limited to content analysis, thematic analysis, and sentiment analysis to truly understand the meaning of the data and all the emotions and underlying concepts derived from it. For example, sentiment analysis is done on customer reviews to see overall levels of customer satisfaction.
• Mixed Methods: Research involves the integration of both quantitative and qualitative data collection and analysis techniques within a single study. This approach allows researchers to capitalize on the strengths of both methods while compensating for their weaknesses. By counting numerical data and analyzing descriptive data, researchers can achieve a more comprehensive understanding of the research problem. Mixed Methods is beneficial for exploring complex phenomena, providing both breadth and depth, and is widely used in fields like education, health sciences, and social sciences.

1. Descriptive Analysis

Descriptive analysis involves summarizing and organizing data to understand its basic features. It provides simple summaries about the sample and the measures. This can include measures of central tendency (mean, median, mode), measures of variability (standard deviation, range), and frequency distributions. Visual tools like histograms, pie charts, and box plots are often used. Descriptive analysis helps to identify patterns and trends within the data, offering a foundation for further statistical analysis.

2. Inferential Analysis

Inferential analysis allows researchers to make predictions or inferences about a population based on a sample of data. Techniques include hypothesis testing, confidence intervals, and analysis of variance (ANOVA). This method helps in determining the probability that an observed difference or relationship exists in the larger population. It goes beyond the data at hand, enabling generalizations and predictions about the broader group.

3. Regression Analysis

Regression analysis is used to understand the relationship between dependent and independent variables. The primary goal is to model the relationship and make predictions. Simple linear regression deals with one independent variable, while multiple regression involves several independent variables. The method quantifies the strength of the impact of the variables and can highlight significant predictors of the outcome variable.

4. Time Series Analysis

Time series analysis involves analyzing data points collected or recorded at specific time intervals. It focuses on identifying trends, seasonal patterns, and cyclical behaviors in data over time. Techniques include moving averages, exponential smoothing, and ARIMA models. Time series analysis is crucial for forecasting future values based on past observations, often used in economic forecasting, stock market analysis, and demand planning.

5. Factor Analysis

Factor analysis is a technique used to reduce data dimensionality by identifying underlying factors or constructs. It simplifies data by modeling the observed variables as linear combinations of potential factors. There are two main types: exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). This method is widely used in psychology, social sciences, and market research to identify latent variables that explain observed correlations.

6. Cluster Analysis

Cluster analysis groups a set of objects in such a way that objects in the same group (or cluster) are more similar to each other than to those in other groups. It is an unsupervised learning technique used in pattern recognition, image analysis, and market segmentation. Methods include k-means, hierarchical clustering, and DBSCAN. Cluster analysis helps in identifying distinct subgroups within a dataset, enhancing understanding of the data structure.

7. Classification Analysis

Classification analysis is a supervised learning technique used to assign data into predefined categories. It uses algorithms such as decision trees, support vector machines, and neural networks to classify data based on training datasets. Commonly applied in spam detection, credit scoring, and medical diagnosis, classification analysis aims to accurately predict the category to which new data points belong.

8. Predictive Analysis

Predictive analysis utilizes statistical techniques and machine learning algorithms to forecast future outcomes based on historical data. It includes methods like regression, time series analysis, and classification. Predictive analysis is used in various fields, such as finance for risk management, marketing for customer behavior prediction, and healthcare for predicting disease outbreaks. It helps organizations make informed decisions by anticipating future trends and behaviors.

9. Prescriptive Analysis

Prescriptive analysis goes beyond predicting future outcomes by recommending actions to achieve desired results. It uses optimization and simulation algorithms to suggest the best course of action among various alternatives. Techniques often involve a combination of data analytics, operations research, and decision science. Prescriptive analysis is used in supply chain management, financial planning, and resource allocation to improve decision-making and optimize outcomes.

10. Diagnostic Analysis

Diagnostic analysis examines data to understand the causes of past outcomes. It delves into historical data to identify patterns and correlations that explain why something happened. Techniques include drill-down, data mining, and correlation analysis. Diagnostic analysis is crucial for root cause analysis in various industries, helping organizations to understand underlying issues and improve processes and performance.

11. Statistical Analysis

Statistical analysis involves collecting, exploring, and presenting large amounts of data to discover underlying patterns and trends. It includes descriptive statistics, inferential statistics, and multivariate techniques. Statistical analysis is fundamental in hypothesis testing, estimating population parameters, and making data-driven decisions. It is widely used across disciplines, including economics, psychology, medicine, and engineering, to validate research findings and support evidence-based practices.

1. Content Analysis

Content Analysis is a systematic, quantitative approach to analyzing the presence, meanings, and relationships of certain words, themes, or concepts within qualitative data. This method involves counting and coding the content into manageable categories, which can then be used to draw inferences about the data. By counting the frequency and context of words or phrases, researchers can identify patterns, trends, and biases. Content Analysis is widely used in media studies, psychology, and social sciences to examine communication patterns, such as speeches, interviews, and social media posts.

2. Thematic Analysis

Thematic Analysis is a method for identifying, analyzing, and reporting patterns (themes) within qualitative data. It involves counting, coding the data, and organizing codes into themes, which are then reviewed and refined. This approach provides a flexible and accessible way to understand data, allowing researchers to interpret various aspects of the research topic. Thematic Analysis is particularly useful for exploring participants’ perspectives, experiences, and social contexts, making it popular in psychology, health studies, and social research.

3. Narrative Analysis

Narrative Analysis focuses on the stories people tell and the ways they tell them. It involves examining the structure, content, and context of narratives to understand how individuals make sense of their experiences and convey meaning. This method includes counting and paying attention to the sequencing and coherence of narratives, as well as the socio-cultural factors influencing them. Narrative Analysis is often used in fields such as sociology, psychology, and education to explore identity, culture, and human behavior through personal stories and biographies.

4. Grounded Theory

Grounded Theory is a systematic methodology in social science research for constructing theory from data. It involves iterative data collection and analysis, where the researcher counts instances, develops concepts, and theories through continuous comparison of data. This method emphasizes inductive reasoning, allowing theories to emerge directly from the data rather than being imposed by pre-existing frameworks. Grounded Theory is widely used in sociology, nursing, education, and other fields to generate substantive or formal theories that are deeply rooted in empirical evidence.

5. Discourse Analysis

Discourse Analysis examines how language is used in texts and contexts to construct meaning and social reality. It involves counting and analyzing written, spoken, or signed language to understand how discourse shapes and is shaped by social, political, and cultural contexts. This method explores power dynamics, ideologies, and identities embedded in language. Discourse Analysis is commonly applied in linguistics, sociology, media studies, and communication studies to study everything from political speeches and media content to everyday conversations.

6. Interpretive Phenomenological Analysis (IPA)

Interpretive Phenomenological Analysis (IPA) is a qualitative research approach focused on exploring how individuals make sense of their personal and social experiences. It involves detailed examination and counting of participants’ lived experiences, emphasizing their perceptions and interpretations. IPA is idiographic, meaning it aims to provide in-depth insights into individual cases before identifying broader patterns. This method is popular in psychology, health, and social sciences, particularly for studying complex, sensitive, or deeply personal phenomena.

7. Case Study Analysis

Case Study Analysis is an in-depth examination of a single case or a small number of cases within a real-life context. This method involves counting and analyzing various types of data, such as interviews, observations, and documents, to gain a comprehensive understanding of the case(s). Case Study Analysis allows for detailed exploration of complex issues, processes, and relationships, providing rich insights that can inform theory and practice. It is widely used in fields like business, education, social sciences, and medicine.

8. Ethnographic Analysis

Ethnographic Analysis involves the systematic study of people and cultures through immersive observation and participation. Researchers spend extended periods in the field, counting and collecting data through participant observation, interviews, and other qualitative methods. The goal is to understand the social dynamics, behaviors, and meanings from the insider’s perspective. Ethnographic Analysis provides detailed, context-rich insights into cultural practices, making it a valuable method in anthropology, sociology, and other social sciences.

1. Triangulation

Triangulation is a strategy used in research to enhance the validity and reliability of the findings by combining multiple methodologies, data sources, theories, or investigators. By counting and comparing different data points or perspectives, researchers can cross-verify the consistency of their results. This method reduces biases and increases the robustness of the conclusions. Triangulation is commonly employed in qualitative research, mixed methods studies, and evaluation research to corroborate findings and provide a fuller picture of the phenomenon under study.

2. Convergent Parallel Design

Convergent Parallel Design is a type of Mixed Methods design where quantitative and qualitative data are collected simultaneously but analyzed separately. After the independent analysis, the results are merged to see how they corroborate, diverge, or complement each other. This design involves counting and coding quantitative data and thematic analysis of qualitative data concurrently. The purpose is to provide a comprehensive understanding by comparing and relating both sets of results. It is often used in social sciences, education, and health research to address complex research questions from multiple angles.

3. Explanatory Sequential Design

Explanatory Sequential Design is a Mixed Methods approach that begins with the collection and analysis of quantitative data, followed by the collection and analysis of qualitative data to explain or build upon the initial results. This sequential process involves first counting numerical data and identifying significant patterns, then exploring these findings in-depth through qualitative methods. This design is useful for studies where the researcher seeks to explain quantitative results in more detail. It is commonly used in educational research, program evaluation, and health studies.

4. Exploratory Sequential Design

Exploratory Sequential Design is a Mixed Methods approach that starts with qualitative data collection and analysis, followed by quantitative data collection and analysis. The initial qualitative phase involves thematic analysis to uncover patterns and generate hypotheses, which are then tested through quantitative methods. This sequential process involves coding qualitative data and then counting and analyzing numerical data to validate or expand on the initial findings. Exploratory Sequential Design is particularly useful for developing new theories, instruments, or interventions and is frequently used in social sciences, education, and health research.

Data analysis is crucial for transforming raw data into actionable insights. Each method, whether quantitative, qualitative, or mixed, has its specific applications, advantages, and disadvantages. By understanding and applying these methods, one can effectively navigate the vast amounts of data available today, fostering innovation and informed decision-making.

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Exploring the use of body worn cameras in acute mental health wards: a mixed-method evaluation of a pilot intervention

• Una Foye 1 , 2 ,
• Keiran Wilson 1 , 2 ,
• Jessica Jepps 1 , 2 ,
• James Blease 1 ,
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Body worn cameras (BWC) are mobile audio and video capture devices that can be secured to clothing allowing the wearer to record some of what they see and hear. This technology is being introduced in a range of healthcare settings as part of larger violence reduction strategies aimed at reducing incidents of aggression and violence on inpatient wards, however limited evidence exists to understand if this technology achieves such goals.

This study aimed to evaluate the implementation of BWCs on two inpatient mental health wards, including the impact on incidents, the acceptability to staff and patients, the sustainability of the resource use and ability to manage the use of BWCs on these wards.

The study used a mixed-methods design comparing quantitative measures including ward activity and routinely collected incident data at three time-points before during and after the pilot implementation of BWCs on one acute ward and one psychiatric intensive care unit, alongside pre and post pilot qualitative interviews with patients and staff, analysed using a framework based on the Consolidated Framework for Implementation Research.

Results showed no clear relationship between the use of BWCs and rates or severity of incidents on either ward, with limited impact of using BWCs on levels of incidents. Qualitative findings noted mixed perceptions about the use of BWCs and highlighted the complexity of implementing such technology as a violence reduction method within a busy healthcare setting Furthermore, the qualitative data collected during this pilot period highlighted the potential systemic and contextual factors such as low staffing that may impact on the incident data presented.

This study sheds light on the complexities of using such BWCs as a tool for ‘maximising safety’ on mental health settings. The findings suggest that BWCs have a limited impact on levels of incidents on wards, something that is likely to be largely influenced by the process of implementation as well as a range of contextual factors. As a result, it is likely that while BWCs may see successes in one hospital site this is not guaranteed for another site as such factors will have a considerable impact on efficacy, acceptability, and feasibility.

Peer Review reports

Body worn cameras (BWC) are mobile audio and video capture devices that can be secured to clothing allowing the wearer to record some of what they see and hear. In England, these have been introduced in the National Health Service (NHS) as part of a violence reduction strategy [ 1 ] which emphasises the reduction of aggression and violence against staff. The NHS Staff Survey 2022 found that 14.7% of NHS staff had experienced at least one incident of physical violence from patients, relatives or other members of the public in the previous 12 months. Violent attacks on staff were found to contribute to almost half of staff illness [ 2 ]. Levels of violence against staff working in mental health trusts remain much higher than other types of healthcare providers [ 3 ]. Numerous reports internationally highlight the increased risks faced by staff working in psychiatric care [ 4 ], though studies have reported that both ward staff and mental health patients experience violence and feeling unsafe on inpatient wards [ 5 , 6 ].

Body worn cameras have been in use for over a decade within law enforcement, where they hoped to provide transparency and accountability within use-of-force incidents and in the event of citizen complaints against police [ 7 ]. It was believed that video surveillance would help identify integral problems within the organisation, improve documentation of evidence, reduce use-of-force incidents, improve police-community relations, and provide training opportunities for officers [ 8 ]. However, a recent extensive international systematic review by Lum et al. [ 9 ], found that despite the successes noted in early evaluations, the way BWCs are currently used by police may not substantially affect most officer or citizen behaviours. Irrespective of these findings, other public services such as train operators have been implementing BWCs for security purposes, with reductions reported in the number of assaults on railway staff [ 10 ].

A recent systematic review of BWC use in public sector services established that there is a poor evidence base supporting the use of BWCs in the reduction of violence and aggression [ 11 ]. Yet, we are seeing a swift increase in the use of BWCs in mental health settings with that aim, with few studies conducted on the use of BWC technology in inpatient mental health wards, and even fewer studies exploring staff or patients’ views. Two evaluations conducted in England reported mixed results with both increases and decreases in violence and aggression found, and variation between types of wards. There is some suggestion of a reduction in more serious incidents and the use of restraint, but quality of evidence is low [ 12 , 13 ].

The use of BWCs in mental healthcare settings for safety and security remains a contentious topic due to the lack of evidence regarding the influence that such technology has on preventing violence and aggression and the complex philosophical and ethical issues raised, particularly where many patients may lack capacity and/or are detained under mental health legislation [ 14 ]. Additionally, there are concerns that BWCs may be used as a ‘quick fix’ for staff shortages rather than addressing the wider systemic and resourcing issues facing services [ 15 ]. With little independent evaluation of body-worn cameras in mental health settings, many of these concerns remain unanswered. There is also limited understanding of this technology from an implementation perspective. Therefore, in this study we aimed to conduct an independent evaluation of the introduction of BWCs as a violence reduction intervention on two inpatient mental health wards during a six-month pilot period to explore the impact of using the technology, alongside an exploration of the facilitators and barriers to implementation.

Research aim(s)

To evaluate the implementation of BWCs on two inpatient mental health wards, including the impact on incidents, the acceptability to staff and patients, the sustainability of the resource use and ability to manage the use of BWCs on these wards.

Patient and public involvement

The research team included a researcher and independent consultant, each with lived experience of mental health inpatient care. In addition, we recruited and facilitated a six member Lived Experience Advisory Panel (LEAP). This group was made up of patients and carers, some of whom had experienced the use of BWCs. Members were of diverse ethnic backgrounds and included four women and two men. The LEAP provided guidance and support for the research team in developing an understanding of the various potential impacts of the use of BWCs on inpatient mental health wards. Members contributed to the design of the study, development of the interview schedule, practice interviews prior to data collection on the wards, and supported the analysis and interpretation of the data, taking part in coding sessions to identify themes in the interview transcripts. The LEAP met once a month for two hours and was chaired by the Lived Experience Research Assistant and Lived Experience Consultant. Participants in the LEAP were provided with training and paid for their time.

The pilot introduction of the body worn cameras was conducted within a London mental health Trust consisting of four hospital sites with 17 acute wards. The research team were made aware of extensive preparatory work and planning that was conducted at a directorate and senior management level prior to camera implementation, including lived experience involvement and consultation, and the development of relevant policies and protocols inclusive of a human rights assessment and legal consultation.

The pilot period ran from 25th April to 25th October 2022. Reveal (a company who supply BWCs nationally across the UK) provided the Trust with 12 Calla BWCs for a flat fee that covered use of the cameras, cloud-based storage of footage, management software, and any support/maintenance required during the pilot period. Cameras were introduced to two wards based on two hospital sites, with six cameras provided to each of the wards on the same date. Training on using the BWCs was provided by the BWC company to staff working on both wards prior to starting the pilot period. Ward one was a 20-bed male acute inpatient ward, representing the most common ward setting where cameras have been introduced. Ward two was a ten-bed male Psychiatric Intensive Care Unit (PICU), representing smaller and more secure wards in which patients are likely to present as more unwell and where there are higher staff to patient ratios.

To answer our research questions, we used a mixed-methods design [ 16 ]. Using this design allowed us to investigate the impact of implementing BWCs in mental health settings on a range of quantitative and qualitative outcomes. This mixed methods design allows the study to statistically evaluate the effectiveness of using BWCs in these settings on key dependent variables (i.e., rates of violence and aggression, and incidents of conflict and containment) alongside qualitatively exploring the impact that the implementation of such technology has on patients and staff.

To ensure that the study was able to capture the impact and effect of implementation of the cameras, a repeated measures design was utilised to capture data at three phases on these wards:

Pre-pilot data: data prior of the implementation of the BWCs (quantitative and qualitative data).

Pilot period data: data collected during the six-month pilot period when BWCs were implemented on the wards (quantitative and qualitative data).

Post-pilot: data collected after the pilot period ended and cameras had been removed from the wards (quantitative data only).

Quantitative methods

Quantitative data was collected at all three data collection periods:

Pre-period: Data spanning six months prior to the implementation of BWCs (Nov 21 to May 22).

Pilot period: Data spanning the six months of the Trusts pilot period of using BWCs on the wards (June 22 to Nov 22).

Post-pilot: Data spanning the six months following the pilot period, when BWCs had been removed (Dec 22 to May 23).

Quantitative measures

To analyse the impact of BWC implementation, we collected two types of incident data related to violence and aggression and use of containment measures, including BWCs. Combined, these data provide a view of a wide range of incidents and events happening across the wards prior to, during, and after the implementation and removal of the BWCs.

The patient-staff conflict checklist

The Patient-staff Conflict Checklist (PCC-SR) [ 17 ] is an end of shift report that is completed by nurses to collate the frequency of conflict and containment events. This measure has been used successfully in several studies on inpatient wards [ 18 , 19 , 20 ].The checklist consists of 21 conflict behaviour items, including physical and verbal aggression, general rule breaking (e.g., smoking, refusing to attend to personal hygiene), eight containment measures (e.g., special observation, seclusion, physical restraint, time out), and staffing levels. In tests based on use with case note material, the PCC-SR has demonstrated an interrater reliability of 0.69 [ 21 ] and has shown a significant association with rates of officially reported incidents [ 22 ].

The checklist was revised for this study to include questions related to the use of BWCs ( e.g., how many uses of BWCs happened during the shift when a warning was given and the BWC was not used; when a warning was given and the BWC was used; when the BWC was switched on with no warning given ) in order to provide insight into how the cameras were being used on each ward (see appendix 1). Ward staff were asked to complete the checklist online at the end of each shift.

Routinely collected incident data (via datix system)

To supplement the PCC-SR-R, we also used routinely collected incident data from both wards for all three data collection phases. This data is gathered as part of routine practice by ward staff members via the Datix system Datix [ 23 ] is a risk management system used widely across mental health wards and Trusts in the UK to gather information on processes and errors. Previous studies have utilised routinely collect data via this system [ 24 , 25 ]. Incidents recorded in various Datix categories were included in this study (see Table  1 ). Incidents were anonymised before being provided to the research team to ensure confidentiality.

Routinely collected data included:

Recorded incidents of violence and aggression.

Recorded use of restrictive practices including seclusion, restraint, and intra-muscular medication/rapid tranquilisations.

Patient numbers.

Staffing levels.

Numbers of staff attending BWC training.

Quantitative data analysis

Incident reports.

Incident reports retrieved from Datix were binary coded into aggregate variables to examine violence and aggression, self-harm, and other conflict as outlined in Table  1 . Multivariate analyses of variance (MANOVA) were used to identify differences in type of incident (violence against person, violence against object, verbal aggression, self-harm, conflict) for each ward. MANOVA was also used to examine differences in incident outcomes (severity, use of restrictive practice, police involvement) across pre-trial, trial, and post-trial periods for each ward. Incident severity was scored by ward staff on a four-point scale (1 = No adverse outcome, 2 = Low severity, 3 = Moderate severity, 4 = Severe). Use of restrictive practice and police involvement were binary coded for presence or absence. Analyses were conducted using SPSS [ 26 ].

Patient-staff conflict checklist shift-report – revised (PCC-SR-R; )

Data were condensed into weeks for analysis rather than shifts to account for variability in PCC-SR-R submission by shift. Linear regressions assessed the relationship between BWC use and incident outcome (severity, use of restrictive practice, police involvement).

Qualitative methods

We used semi-structured qualitative interviews to explore participants’ experiences of BWCs on the ward to understand the impact of their use as well as to identify any salient issues for patients, staff and visitors that align with the measures utilised within the quantitative aspect of this study. These interviews were conducted at two time points: pre-pilot and at the end of the six-month pilot period.

Sample selection, eligibility, and recruitment

Convenience sampling was used to recruit staff and patients on wards. Researchers approached ward managers to distribute information sheets to staff, who shared that information with patients. Staff self-selected to participate in the study by liaising directly with the research team. Patients that were identified as close to discharge and having capacity to consent were approached by a clinical member of the team who was briefed on the study inclusion criteria (see Table  2 ). The staff member spoke with the patient about the study and provided them with a copy of the information sheet to consider. If patients consented, a member of the research team approached the participant to provide more information on the study and answer questions. After initial contact with the research team, participants were given a 24-hour period to consider whether they wanted to participate before being invited for an interview.

Participants were invited to take part in an interview within a private space on the ward. Interviews were scheduled for one hour with an additional 15 min before and after to obtain informed consent and answer any questions. Participation was voluntary and participants were free to withdraw at any time. To thank patients for their time, we offered a £10 voucher following the interview. Interviews were audio-recorded and saved to an encrypted server. Interview recordings were transcribed by an external company, and the research team checked the transcripts for accuracy and pseudonymised all participants. All transcripts were allocated a unique ID number and imported to MicroSoft Excel [ 27 ] for analysis.

Qualitative data analysis

Qualitative data were analysed using a framework analysis [ 28 ] informed by implementation science frameworks. Our coding framework used the Consolidated Framework for Implementation Research (CFIR) [ 29 ], which is comprised of five major domains including: Intervention Characteristics, Implementation Processes, Outer Setting, Inner Setting, and Characteristics of the Individual. Each domain consists of several constructs that reflect the evidence base of the types of factors that are most likely to influence implementation of interventions. The CFIR is frequently used to design and conduct implementation evaluations and is commonly used for complex health care delivery interventions to understand barriers and facilitators to implementation. Based on its description, the CFIR is an effective model to address our research question, particularly given the complexity of the implementation of surveillance technology such as BWCs in this acute care setting.

The initial analytic stage was undertaken by eight members of the study team with each researcher charting data summaries onto the framework for each of the interviews they had conducted on MicroSoft Excel [ 27 ]. Sub-themes within each broad deductive theme from our initial framework were then derived inductively through further coding and collaborative discussion within the research team, inclusive of Lived Experience Researcher colleagues. Pseudonyms were assigned to each participant during the anonymisation of transcripts along with key identifiers to provide context for illustrative quotes (e.g., P = patient, S = staff, A = acute ward, I = Intensive Care, Pre = pre-BWC implementation interview, Post = Post BWC implementation interview).

All participants gave their informed consent for inclusion before they participated in the study. The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Health Research Authority: London - Camden & Kings Cross Research Ethics Committee (IRAS Project ID 322,268, REC Reference 23/LO/0337).

Quantitative results

Exploring how body worn cameras were used during the pilot period.

Analysis of the PCC-SR-R provides information about how the BWCs were used on a day-to-day basis during the pilot period. Out of 543 total shift reports completed, BWC use was reported 50 times, indicating that BWCs were used on less than 10% of shifts overall; 78% of those deployments were on the Acute ward (see Figure 1 ). Overall, the majority of deployments happened as activations without a warning being given ( n  = 30, 60% of activations), 19 times the BWC was deployed with a warning but the camera was not activated (38%), and only one was the camera activated without a warning being given (2%).

BWC use by ward per week of pilot (no data available before week 6 on Ward 1)

According to the PCC-SR-R, a total of 227 incidents of aggression occurred during the pilot period across both wards (see Table  3 ). Overall, there were small statistically significant correlations between BWC usage and certain types of conflict, aggression, and restrictive practice. Results found that BWC use was positively correlated with verbal aggression and use of physical restraint. BWC use was moderately positively correlated with verbal aggression ( r  = .37, p  < .001). This indicates that BWCs were more likely to be used in incidents involving verbal aggression, which do not tend to be documented in Datix. Similarly, BWC use was moderately positively correlated with physical restraint ( r  = .31, p  < .001) indicating that they were also more likely to be used alongside physical restraint.

Exploring the impact of BWCs utilising routinely collected ward data

Acute ward results.

Routine data collected via Datix records were used to examine differences in frequency of conflict and aggression, incident severity, and use of containment measures before, during, and after introduction of BWCs on each trial ward (see Table  4 ).

There was no effect of trial period on incident type ( F (10, 592) = 1.703, p  = .077, Wilk’s Λ = 0.945), meaning there was no discernible difference in the type of incidents that occurred (E.g., verbal aggression, physical aggression) before, during, and after the pilot phase.

Incident outcomes

There was an effect of trial period on incident outcomes ( F (6, 596) = 10.900, p  < .001, Wilk’s Λ = 0.812). Incident severity was statistically significantly higher in the trial and post-trial periods compared to the pre-trial period. Use of restrictive practice was significantly lower in the post-trial period compared to the pre-trial and trial period. Police involvement was also lower in the post-trial period compared to the pre-trial and trial periods (see Table  5 ).

Results for the psychiatric intensive care unit

There was an effect of trial period on incident type ( F (10, 490) = 4.252, p  < .001, Wilk’s Λ = 0.847). Verbal aggression was statistically significantly higher in the post-trial period compared to the pre and trial periods. Self-harm was statistically significantly higher in the trial period compared to the pre-trial and post-trial periods. There were no differences in violence against a person ( p  = .162), violence against an object or conflict behaviour (see Table  4 ).

There was a statistically significant difference in incident outcome across the trial periods ( F (6, 494) = 12.907, p  < .001, Wilk’s Λ = 0.747). There was no difference in incident severity or police involvement. However, use of restrictive practice was statistically significantly higher in the pre-trial period, reducing in the test period, and reducing further in the post-trial period (see Table  5 ).

Qualitative findings

A total of 22 participants took part in interviews: five patients and 16 staff members. During the pre-pilot interviews a total of nine staff took part (five in the acute ward, four in the PICU ward) and two patients (both from the acute ward). After the pilot period, a total of eight staff took part (four from each ward) and three patients (all from the acute ward). Table  6 includes a full description of participants.

Below we have presented the key themes aligning to the five core CFIR categories of Intervention Characteristics, Characteristics of Individuals, The Process of Implementation, the Inner Setting, and The Outer Setting (see Table  7 ).

Intervention characteristics

Design and usability of wearing a bwc on the ward.

When discussing the use of the BWCs, staff noted a range of design issues related to the cameras that they said impacted on their use and acceptance of the cameras. This included the nature of the camera pulling on clothing necklines (a particular issue for female staff working on male wards), and overheating causing discomfort and irritation to skin, challenges with infection control, as well as the issue of cameras in a mental health setting where they can be easily grabbed, thrown and broken during an incident. Staff often cited these design issues as related to the lack of proactive use of the cameras on the wards.

There were issues around the devices getting overheated or about it going on your clothing, it pulls down the top… we had one person who was leading on it, whenever he was around, of course, the camera was being used, but if he wasn’t there, people weren’t as proactive in using the camera. Petra (f), Staff, A, Post.

There were also issues with staff forgetting to wear the cameras, forgetting to switch them on during incidents, and forgetting to charge them at the end of the shift, reducing the potential use of the cameras by other staff. These were perceived as key logistical issues prior to the pilot and were reported as issues at the end of the pilot by several staff on the wards.

The practicalities of will they actually turn it on in those sorts of incidents, I don’t know. Just little stuff as well, like if they don’t put it back on the docking station, so you think you’re charging it for next shift but then it’s not charged and the battery is dead, that’s one less camera to use, so little stuff. Jamal (m), Staff, A, Pre.

In relation to usability, staff noted that the cameras were small and easy to use given their simple single switch interface. It was felt that not having to upload and manage the data themselves made cameras more user friendly and usable by staff members. Protocols put into place such as signing the cameras in and out, and allocation for use during shifts were likened to procedures in place for other security measures therefore the implementation of this for the BWCs was viewed as easy for many staff.

It’s just like the ASCOM alarms that we wear. There’s a system to sign in and sign out, and that’s it. Alice (f), Staff, A, Pre.

While staff were generally positive about the usability of the cameras, some were cautious of with concerns for those less confident with technology.

… you have to be conscious that there’s some people – it’s quite easy to use, but I can say that because I’m alright using devices and all that but there’s some that are older age or not that familiar with using devices that may struggle with using it… they’re feeling a bit anxious and a bit scared, if they’re not familiar with it then they won’t use it. Jamal (m), Staff, A, Pre.

Evidence strength and quality: do BWCs change anything?

There were conflicting reports regarding the potential benefits of using BWCs on the wards, with both staff and patients reporting mixed perceptions as to whether the cameras might reduce violence and aggression. In the pre-pilot interviews, some staff reported feeling that the BWCs may have a positive impact on reducing physical violence.

I think it’s going to reduce violence and aggression on the ward…I don’t think they’ll want to punch you…they might be verbally abusive but in terms of physical that might reduce. Sarah (f), Staff, I, Pre.

Patients however noted that the cameras might hold staff to account of their own behaviours and therefore may improve care, however they felt that this impact would wear off after the first few months after which people might forget about the cameras being there.

Now they’ve got the body cams, it’s going to be a lot of changes. They’ll think, ‘Ooh well he’s on tape’. So, it might do something to their conscience, they actually start to listen to patients… until the novelty wears off and it might go back to square one again. Ian (m), Patient, A, Pre.

One staff member suggested that incident rates had reduced following introduction of the BWCs, but they remained unsure as to whether this was due to the cameras, reflecting that violence and aggression on wards can be related to many factors.

I know our violence and aggression has reduced significantly since the start of the cameras pilot… I don’t know, because obviously wearing the camera’s one thing, but if they weren’t in use, I don’t know maybe just the presence of the camera made a difference. But yeah, it’s hard to tell. Petra (f), Staff, A, Post.

In contrast, several staff reported that they had seen limited evidence for such changes.

I used it yesterday. He was aggressive and I used it, but he even when I was using [it] he doesn’t care about the camera… it didn’t make any difference… It doesn’t stop them to do anything, this camera does not stop them to do anything. Abraham (m), Staff, I, Post.

Some staff suggested that in some circumstances the cameras increased patient agitation and created incidents, so there was a need to consider whether the BWCs were going to instigate aggression in some circumstances.

There has been with a few patients because they will threaten you. They will tell you, ‘if you turn it on, I’m gonna smash your head in’. So incidents like that, I will not turn it on… Yeah, or some of them will just tell you, ‘if you come close by, I’m going to pull that off your chest’. So things like that, I just stay back. Ada (f), Staff, A, Post.

One rationale for a potential lack of effectiveness was noted by both staff and patients and was related to the levels of acute illness being experienced by patients which meant that for many they were too unwell to have insight into their own actions or those of staff switching on the cameras.

We’ve had instances where patients are so unwell that they just don’t care. You switch on the camera, whether you switch it on or not, it doesn’t really change the behaviour. ‘All right, okay, whatever switch it on’. They’re so unwell, they’re not really understanding. Petra (f), Staff, A, Post. It might make [staff] feel safer as a placebo effect, but I don’t think it would necessarily make them safer… I think the people that are likely to attack a member of staff are crazy enough that they’re not gonna even consider the camera as a factor. Harry (m), Patient, A, Pre.

This lack of evidence that the cameras were necessarily effective in reducing incident rates or severity of incidents may have had an impact on staff buy-in and the use of the cameras as a result. One staff member reflected that having feedback from senior management about the impact and evidence would have been useful during the pilot period to inform ward staff whether the cameras were influencing things or not.

Staff want feedback. I don’t think we’ve had any since we’ve had the cameras… it would be nice to get feedback from, I don’t know, whoever is watching it, and stuff like that. Ada (f), Staff, A, Post.

Relative advantage: are BWCs effective and efficient for the ward?

Due to a combination of personal beliefs related to BWCs, the lack of evidence of their impact on violence and aggression, and other elements of care and culture on the wards, a number of staff and patients explored alternative interventions and approaches that may be more beneficial than BWCs. Both staff and patients suggested that Closed Circuit Television (CCTV) as an intervention that provided the transparency of using cameras and video footage but with an independent perspective. This was felt by many to remove the bias that could be introduced in BWC use as the video capture didn’t require staff control of the filming.

I feel like [BWCs] puts all the power and trust into the hands of the staff and I feel that it would be better to have CCTV on the ward because CCTV is neutral. Harry (m), Patient, A, Pre. I have control over that [BWC recording] … It kind of gives that split as well between staff and patients. You can tell me or I can tell you when to switch it on. Whereas I feel like a CCTV camera is there all the time. Nobody’s asking to switch it on. It’s there. If you wanted to review the footage you can request it, anyone can request to view the footage for a legitimate reason. Whereas the camera can come across as if you’re threatening. Petra (f), Staff, A, Post.

In addition, some participants reflected that the nature and design of BWCs meant that unless staff were present for an incident it wouldn’t be captured, whereas CCTV has the advantage of being always present.

If there’s CCTV, then it’s the same thing, you get me. Like, if its body worn cameras that people can always do things away from staff. They can always go down to that corridor to have their fight or go to the side where staff ain’t gonna see them to have their fight, but with CCTV you can’t do that. Elijah (m), Patient, A, Post.

In addition to exploring technological and video-based interventions, many staff noted that the key tool to violence reduction had to be the use of de-escalation skills, noting that the use of communication and positive relationships had to be the primary tool before other interventions such as BWCs or CCTV.

We do a lot of verbal de-escalation. So we got our destress room now still open. That has a punch bag, and it’s got sensory tiles, and the aim and hope is that when people do get frustrated, because we’re all human. We all get annoyed at anything or many little things in life. There is the aim that they go into that room and start punching the bag instead of property and damaging furniture. But we also are working really hard on verbal de-escalation and actually trying to listen to patients and talk to them before anything else. And that’s helped a lot. And between this kind of shared, or role modelling, where while we’re showing staff, actually even spending an extra 20 min is okay. If it means you’re not going to end up having to restrain a patient. Petra (f), Staff, A, Post.

By using communication skills and de-escalation techniques skilfully, some staff felt there was no need to utilise the BWCs. One concern with the introduction of the BWCs for staff was that the use of this technology may negatively impact on trust and relationships and the use of de-escalation.

Some situations I feel like it can make a situation worse sometimes… I think a lot of situations can be avoided if you just talk with people…. Trying to find out why they’re angry, trying to just kind of see it from their point of view, understand them… I think maybe additional training for verbal de-escalation is needed first. Patrick (m), Staff, A, Post.

Characteristics of individuals

Staff and patients’ knowledge and beliefs about the intervention.

Overall, there were mixed views among both staff and patients as to whether cameras would reduce incidents, prior to and after the pilot period. When considering the possible impact on violence and aggressive incidents there was a view among staff that there was the need for a nuanced and person-centred view.

All the patients that come in, they’re different you know. They have different perceptions; they like different things… everyone is different. So, it just depends. We might go live, and then we have good feedback because the patients they are open and the understand why we have it, and then as they get discharged and new patients come in it might not go as well. It just depends. Serene (f), Staff, A, Pre.

As a result of the desire to be person-centred in the use of such interventions, one staff member noted that they weighed-up such consequences for the patient before using the BWC and would make decisions not to use the camera where they thought it may have a negative impact.

Actually, with this body worn camera, as I did mention, if a patient is unwell, that doesn’t, the patient will not have the capacity to I mean, say yes, you cannot just put it on like that. Yeah, I know it’s for evidence, but when something happens, you first have to attend to the patient. You first have to attend to the patient before this camera is, for me. Ruby (f), Staff, I, Post.

Some staff questioned the existing evidence and theories as to why BWCs work to reduce incidents, and instead noted that for some people it will instigate an incident, while others may be triggered by a camera.

I’m on the fence of how that is going to work because I know the evidence is that by telling a patient ‘look if you keep escalating I’m gonna have to turn this on’, but I know several of our patients would kind of take that as a dare and escalate just to spite so that you would turn it on. Diana (f), Staff, A, Pre.

In contrast, some staff felt the cameras helped them feel safer on wards due to transparency of footage as evidence for both staff and patients.

They [staff] need to use it for protection, for recording evidence, that type of thing… They can record instances for later evidence. Yeah, for them as well. Safer for them and for patients because you can also have the right to get them to record, because a patient might be in the wrong but sometimes it may be the staff is in the wrong position. And that’s achieving safety for patients as well. Yeah, I think it works both ways. Dylan (m), Patient, A, Post.

Positive buy-in was also related to the potential use of the intervention as a training, learning or reflective tool for staff to improve practice and care and promote positive staff behaviour.

If you know that your actions might be filmed one way or the other, that would make me to step up your behaviour to patients… if you know that your actions can be viewed, if the authority wants to, then you behave properly with patients so I think that will improve the quality of the care to patient. Davide (m), Staff, I, Pre.

While there were some positive attitudes towards the cameras, there remained considerable concerns among participants regarding the transparency of camera use to collate evidence in relation to incidents as it was widely noted that the cameras remain in staff control therefore there is an issue in relation to bias and power.

I do think my gut would say that it wouldn’t necessarily be well received. Because also I think people feel like prisoners in here, that’s how some of the patients have described their experience, so in terms of the power dynamic and also just – I think that can make one feel a bit, even worse, basically, you know? Leslie (m), Staff, A, Pre.

These issues lead to staff reporting they didn’t want to wear the camera.

I’d feel quite uncomfortable wearing one to be honest. Leslie (m), Staff, A, Pre.

The staff control of the cameras had a particular impact on patient acceptability of the intervention as it led to some patients viewing BWCs as being an intervention for staff advantage and staff safety, thus increasing a ‘them and us’ culture and leading to patient resistance to the cameras. This was particularly salient for those with prior negative experiences of police use of cameras or mistrust in staff.

I feel like the fact that the body worn cameras is gonna be similar to how the police use them, if a staff member has negative intent toward a patient, they would be able to instigate an incident and then turn the camera on and use the consequences of what they’ve instigated to expect restraint or injection or whatever else might happen. So, I feel like it would be putting all the power and trust into the hands of the staff and I feel that it would be better to have CCTV on the ward because CCTV is neutral. Whereas, the body worn camera, especially with some of the personality conflicts/bad attitudes, impressions I’ve had from certain members of staff since I’ve been here, I feel like body worn cameras might be abused in that way possible. Harry (m), Patient, A, Pre.

Perceived unintended consequences and impact on care

Prior to the implementation there were concerns from staff that the introduction of BWCs could have consequences beyond the intended use of reducing violence and aggression, unintentionally affecting a range of factors that may impact on the overall delivery of care. There was a key concern regarding the potential negative impact that cameras may have for patients who have paranoia or psychosis as well as for those who may have prior traumatic experiences of being filmed.

It might have negative impacts on these patients because I’m thinking about kind of patients with schizophrenia and things like that who already have paranoid delusions, thinking that people are after them, thinking that people are spying on them, people are watching them, and then seeing kind of cameras around. It might have negative impacts on them. Tayla (f), Staff, I, Pre. When I was admitted I was going through psychosis… I don’t want to be filmed and things like that. So you just see a camera, a guy with a camera on, you are like, are you filming me? Elijah (m), Patient, A, Post.

There was also a considerable concern among both staff and patients that the use of cameras would have a negative impact on the therapeutic relationship between staff and patients. This was felt to be related to the implication that the cameras enhanced a ‘them and us’ dynamic due to the power differential that staff controlling the cameras can create, likened to policing and criminalisation of patients. With the potential of a negative impact on relationships between staff and patients, staff suggested they may be disinclined to use BWCs if it would stop patients speaking to them or approaching them if they needed support.

Yeah, I think it would probably damage [the therapeutic relationship] because I think what’s probably quite helpful is things that maybe create less of a power difference. I think to some extent, [the BWC] might hinder that ability. Like for example imagine going to a therapist and them just like ‘I’ve got this camera in the corner of the room and it’s gonna be filming our session and just in case – or like, just in case I feel that you might get aggressive with me’. Um, I don’t think that’s going to help the therapeutic relationship! Jamal (m), Staff, A, Pre. When you get body worn cameras on there, the relationship as well between staff and patients, is just gonna instantly change because you’re looking like police! Elijah (m), Patient, A, Post.

In contrast, a minority of staff felt that the presence of cameras may improve relationships as they provide transparency of staff behaviour and would encourage staff to behave well and provide high quality care for patients.

It will also help how, improve the way we look at the patients… because if you know that your actions might be filmed one way or the other, that would make me to step up your behaviour you know… you behave properly with patients so I think that will improve the quality of the care to patient. More efficiently, more caring to patient. Davide (m), Staff, I, Pre.

The process of implementation

Planning: top-down implementation.

Staff perceived that BWC implementation directives had been given by senior management or policy stakeholders whom they felt viewed the process from a position of limited understanding due to a lack of ‘frontline’ mental health service experience. This led to a lack of faith amongst staff, and a perception that funds were being misspent.

They sit up there, they just roll it out and see how it works, how it goes. They waste a whole lot of money, millions or whatever, thousands of pounds in it, and then they see that ‘Oh, it’s not gonna work’. They take it back and all of that. Before coming out with it, you need to come speak to us… they just sit up there drinking tea and coffee, and then they’re just like, Oh, yeah, well, let’s do it this way…come stay with these people, work with them, for just I give you a 12 h shift, stay with them. Richard (m), Staff, I, Post.

This was exacerbated when staff felt there was a lack of consultation or explanation.

we don’t always get the ins and outs of certain things…We know that the cameras are coming in and stuff like that, but you know, and obviously it’s gone through every avenue to make sure that it’s fine. But then sometimes we don’t always know the ins and outs to then explain to people why we have the cameras. Patrick (m), Staff, A, Post.

It was also highlighted that due to multiple initiatives being implemented and directives handed down in parallel, staff felt negative towards interventions more widely, with the BWCs being ‘ just another thing to do’ , adding to workload for staff and reducing enthusiasm to use the cameras.

it’s not just to do with the camera, I just think there’s lots of changes happening at once, and there’s loads of new things being constantly introduced that people are just thinking oh it’s another thing. I think that’s what it is more than the camera itself. Alice (f), Staff, A, Pre.

Execution: training, Use and Ward Visibility

Overall, there was a lack of consistency amongst staff in their understanding of the purpose and processes of using the BWCs on the wards.

What do you do, do you record every single thing or, I don’t know. Do you record like, if a patient said, I want to talk to you, confidential, you go sit in a room, do you record things like those or is it just violence and aggression? Ada (f), Staff, A, Post.

The lack of clarity regarding the purpose of the intervention and the appropriate use of the cameras was felt to impact staffs’ attitudes and acceptance of using them and contributed to a lack of transparency or perhaps trust regarding the use of any subsequent video footage.

I think if the importance of the recording was explained a bit more…and how it would improve things, I think people would use it more… that’s why I don’t think it’s always used sometimes… if you’re not sure why some of it’s important, then you’re not going to see the value…I think if you’re gonna keep with them, it’s about updating the training, teaching staff when to use it, then where does that information go? How does that look in terms of improving? Just a bit of transparency, I think. But when you don’t know certain things it’s a bit hard to get behind something or back it, you know? Patrick (m), Staff, A, Post.

The lack of information about the purpose and processes related to the intervention was also seen among patients, with most patients noting that they hadn’t received information about the cameras during their admissions.

No information at all. I don’t think any of the patients know about it. Toby (m), Patient, A, Post.

While training was provided it was widely felt that it was insufficient to provide understanding about the purpose of the cameras or the more in-depth processes beyond operational aspects such as charging and docking. Several staff interviewed were unaware of the training, while others noted that they had an informal run-through by colleagues rather than anything formal.

What training are you talking about?… I wasn’t here, so I was taught by my colleague. I mean, from what I was taught, to operate the camera, and to give a warning to the patient that you’re going to use the camera. Nevis (f), Staff, A, Post.

Longer training with further details beyond operational use was felt to be needed by staff.

I think the training should have to be longer, even if it’s like an hour or something… Like what situations deem the camera to be… more information on the cameras, when to use it, why it’s used, and I think if the importance of the recording was explained a bit more and what it was doing and how that recording would go and how it would improve things. Patrick (m), Staff, A, Post.

Furthermore, there was a need for training to be on a rolling basis given the use of bank staff who were not trained to use the cameras or to understand the proper processes or purpose of using the BWCs, which could leave them vulnerable to misuse or abuse.

We have bank staff [who aren’t trained] so they say ‘I don’t know how to use that camera you are giving me’. Nevis (f), Staff, A, Post.

The inner setting

Ward context: acceptance of violence and aggression is part of the job.

It was widely believed by staff that the nature of working on a mental health ward included accepting that violence and aggression was part of the job. This was not seen as an acceptance of violence but more that the job was providing care for individuals who are mentally unwell, and confusion, fear, frustration and aggression can be part of that. As a result, there was an ambivalence among some staff that the introduction of cameras would change this.

I think like in this line of work, there’s always that potential for like risky behaviours to happen. I’m not sure if putting the camera on will make much difference. Patrick (m), Staff, A, Post.

Staff noted that because of the nature of the job, staff are used to managing these situations and they understood that it was part of the job; therefore, it was unlikely that they would record everything that on paper might be considered an incident.

There’s also enough things that happen here, so I don’t think they would record [the incidents] because it’s just another day here. You know what I’m saying… [staff] can just say, ‘Stop, go back to your room and leave it at that and that kind of be the end of it’. Dylan (m), Patient, A, Post. We are trained for it. Eveline (f), Staff, I, Pre.

This acceptance that incidents are a hazard of mental healthcare was linked to staff’s acknowledgment that many factors make up the complexity of violence and aggression including the nature of individual patients, acuity levels, ward atmosphere, staffing levels, access to activities, leave and outside space. The interplay of multiple factors creates a context in which frustrations and incidents are likely, thus become part of the everyday and ‘normal’ life on the ward for staff and patients alike.

I feel like, you know, how in GP services you say, zero tolerance to abusive language, or any kind of harassment. I don’t think there is that on a psychiatric ward you are kind of expected to take all the abuse and just get on with it. Petra (f), Staff, A, Post.

With staff reported having a higher threshold for these behaviours it was perceived that this was likely to impact on the efficiency of the intervention as staff would be less likely to consider a situation as violent but more ‘ part of the job’ .

Reactive nature of the ward and incidents

Most participants noted that the ward context is always changing with people being admitted and discharged, with daily staff changes and wider turnover of staff, so things are never static and can change at any point. This reflects the dynamic nature of the ward which creates a complex moving picture that staff need to consider and react to.

[the atmosphere] it’s very good at the moment. If you had asked me this two weeks ago, I would say, ‘Oh, my gosh’. But it changes… The type of patient can make your whole ward change… it depends on the client group we have at the time. Nevis (f), Staff, A, Post.

Staff noted that a key limitation of using the cameras to reduce incidents was the reactive nature of the environment and care being provided. This was felt to impact on the feasibility and use of the cameras as staff noted that they often react to what is happening rather than thinking to ‘ put the camera on first ’. It was felt by staff with experience of reacting to incidents that the failure to use BWCs during these processes were linked to staff’s instincts and training to focus on patients as a priority.

Say for instance, you’re in the office, and two patients start fighting, or a patient attacks someone and, all you’re thinking about is to go there to stop the person. You’re not thinking about putting on any camera. You understand? So sometimes it’s halfway through it, somebody might say, ‘Has anybody switched the camera on’? And that’s the time you start recording… If something happens immediately, you’re not thinking about the camera at that time, you’re just thinking to just go, so yeah. Nevis (f), Staff, A, Post.

Incidents happen quickly and often surprise staff, therefore staff react instantly so are not thinking about new processes such as recording on the cameras as this would slow things down or is not in the reactive nature needed by staff during such incidents.

When you’re in the middle of an incident and your adrenaline’s high, you’re focusing on the incident itself. It’s very difficult for you to now remember, remind yourself to switch on the camera because you’re thinking, patient safety, staff safety, who’s coming to relieve you? What’s going on? Who’s at the door? Petra (f), Staff, A, Post.

In addition, the need for an immediate response meant that it was felt that by the time staff remember to, or have the chance to, switch the camera on it was often too late.

Sometimes in the heat of moments and stuff like that, or if the situation’s happening, sometimes you don’t always think to, you know, put your camera on. Patrick (m), Staff, A, Post.

Outer setting

Resources: staffing.

Issues related to staffing were highlighted by several participants as a key problem facing mental health wards thus leading to staff having higher workloads, and higher rates of bank and agency staff being used on shift and feeling burnt-out.

Out of all the wards I’ve been on I’d say this is the worst. It’s primarily because the staff are overworked…it seems like they spend more time doing paperwork than they do interacting with the patients. Harry (m), Patient, A, Pre. We’re in a bit of a crisis at the minute, we’re really, really understaffed. We’re struggling to cover shifts, so the staff are generally quite burnt out. We’ve had a number of people that have just left all at once, so that had an impact… Staff do get frustrated if they’re burnt out from lack of staff and what have you. Alice (f), Staff, A, Pre.

It was noted by one participant that the link of a new intervention with extra workload was likely to have a negative impact on its acceptability due to these increasing demands.

People automatically link the camera to then the additional paperwork that goes alongside it. It’s like, ‘Oh god, if we do this, we’ve got to do that’, and that could play a part. Petra (f), Staff, A, Post.

One staff member noted that the staffing issue meant there were more likely to be bank staff on wards so the care of patients may be affected as temporary staff may be less able to build meaningful therapeutic relationships.

So obviously there is the basic impact on safety of not having adequate staffing, but then there’s the impact of having a lot of bank staff. So obviously when you have permanent staff they get to know the patients more, we’re able to give them the more individualised care that we ideally should be giving them, but we can’t do that with bank staff. Diana (f), Staff, A, Pre.

It was also suggested that staffing levels and mix often made it more difficult to provide activities or facilitate escorted leave which can lead to patients feeling frustrated and becoming more aggressive.

So you know there is enough staff to facilitate the actual shift, so you know when there’s less staff like you say you’ve got people knocking at the door, but then you don’t have staff to take people out on leave straight away, that all has a rippling effect! Serene (f), Staff, A, Pre.

Wider systemic issues

Overall, there was a concern that the introduction of BWCs would not impact on wider, underlying factors that may contribute to frustration, aggression and incidents on wards. Providing a more enhanced level of care and better addressing the needs of patients was felt to be central to helping people but also reducing the frustration that patients feel when on the ward.

… for violence and aggression, [focus on] the mental health side of things like therapy and psychology should be compulsory. It shouldn’t be something you apply for and have to wait three or four weeks for. I think every person should, more than three or four weeks even, months even… we need psychology and therapists. That’s what will stop most violence, because psychologists and a therapist can edit the way that they speak to people because they’ve been given that skill depending on the way the person behaves. So that’s what we need regularly… not like all this dancing therapy, yoga therapy. That’s a person, that you come and you actually sit down and talk through your shit with them. That will help! Elijah (m), Patient, A, Post. There’s a lack of routine and I think there’s a lack of positive interaction between the patient and the staff as well. The only time you interact with a member of staff is if you’re hassling them for something, you have to hassle for every little thing, and it becomes a sort of, frustration inducing and like I’m a very calm person, but I found myself getting very fucking angry, to be honest, on this ward just because out of pure frustration… there’s bigger problems than body worn cameras going on. Harry (m), Patient, A, Pre.

Staff agreed that there was a need to invest in staff and training rather than new technologies or innovations as it is staff and their skills behind the camera.

It’s not the camera that will do all of that. It’s not making the difference. It’s a very good, very beautiful device, probably doing its job in its own way. But it’s more about investing in the staff, giving them that training and making them reflect on every day-to-day shift. Richard (m), Staff, I, Post.

There was felt to be a need to support staff more in delivering care within wards that can be challenging and where patients are unwell to ensure that staff feel safe. While in some circumstances the cameras made some staff feel safer, greater support from management would be more beneficial in making staff feel valued.

In this study exploring the implementation and use of body-worn cameras on mental health wards, we employed two methods for collecting and comparing data on incidents and use of containment measures, including BWCs, on one acute ward and one psychiatric intensive care unit. We found no clear relationship between the use of BWCs and rates or severity of incidents on either ward. While BWCs may be used when there are incidents of both physical and verbal aggression, results indicate that they may also provoke verbal aggression, as was suggested during some interviews within this study. This should be a concern, as strong evidence that being repeatedly subject to verbal aggression and abuse can lead to burnout and withdrawal of care by staff [ 30 ]. These mixed findings reflect results that were reported in two earlier studies of BWCs on mental health wards [ 12 , 13 ]. However, the very low use of the cameras, on just 10 per cent of the shifts where data was obtained, makes it even more difficult to draw any conclusions.

While the data shows limited impact of using BWCs on levels of incidents, we did find that during the pilot period BWC use tended to occur alongside physical restraint, but the direction of relationship is unclear as staff were asked to use BWCs when planning an intervention such as restraint. This relationship with restraint reflected the findings on several wards in a previous study [ 13 ], while contrasting with those reported in a second study that found reductions in incidents involving restraint during the evaluation period [ 12 ]. Such a mix of findings highlights the complexity of using BWCs as a violence reduction method within a busy healthcare setting in which several interacting components and contextual factors, and behaviours by staff and patients can affect outcomes [ 31 ]. The qualitative data collected during this pilot period highlighted the potential systemic and contextual factors such as low staffing that may have a confounding impact on the incident data presented in this simple form.

The findings presented within this evaluation provide some insights into the process of implementing BWCs as a safety intervention in mental health services and highlight some of the challenges and barriers faced. The use of implementation science to evaluate the piloting of BWCs on wards helps to demonstrate how multiple elements including a variety of contextual and systemic factors can have a considerable impact and thus change how a technology may vary not only between hospitals, but even across wards in the same hospital. By understanding the elements that may and do occur during the process of implementing such interventions, we can better understand if and how BWCs might be used in the future.

Within this pilot, extensive preparatory work conducted at a directorate and senior management level did not translate during the process of implementation at a ward level, which appeared to impact on the use of BWCs by individuals on the wards. This highlights that there is a need to utilise implementation science approaches in planning the implementation of new technologies or interventions and to investigate elements related to behavioural change and context rather than just the desired and actual effects of the intervention itself.

While ward staff and patients identified the potential for BWCs to enhance safety on the wards, participants distrusted their deployment and expressed concerns about ethical issues and possible harmful consequences of their use on therapeutic relationships, care provided and patient wellbeing. These themes reflect previous findings from a national interview study of patient and staff perspectives and experiences of BWCs in inpatient mental health wards [ 14 ]. Given these issues, alternatives such as increasing de-escalation skills were identified by staff as possible routes that may be more beneficial in these settings. Furthermore, other approaches such as safety huddles have also been highlighted within the literature as potential means to improve patient safety by looking ahead at what can be attended to or averted [ 32 ].

Furthermore, it is important to consider that the presence of power imbalances and the pre-existing culture on the ward have considerable implications for safety approaches and must be considered, as exemplified by the preferences by both staff and patients in this evaluation for more perceived ‘impartial’ interventions such as CCTV. As identified within previous studies [ 14 ], BWCs can have different implications for psychological safety, particularly for vulnerable patients who already feel criminalised in an environment with asymmetrical power imbalances between staff and patients. This is particularly salient when considering aspects of identity such as race, ethnicity, and gender both in terms of the identities of the patient group but also in terms of the staff/patient relationship.

While preferences in this study note CCTV as more ‘impartial’, work by Desai [ 33 ] draws on the literature about the use of surveillance cameras in other settings (such as public streets) as well as on psychiatric wards and concludes that CCTV monitoring is fraught with difficulties and challenges, and that ‘watching’ patients and staff through the lens of a camera can distort the reality of what is happening within a ward environment. In her recently published book, Desai [ 34 ] develops this theme to explore the impacts of being watched on both patients and staff through her ethnographic research in psychiatric intensive care units. She highlights concerns over the criminalisation of patient behaviour, safeguarding concerns in relation to the way women’s bodies and behaviours are viewed and judged, and the undermining by CCTV of ethical mental health practice by staff who attempt to engage in thoughtful, constructive, therapeutic interactions with patients in face-to-face encounters. Appenzeller et al.’s [ 35 ] review found that whilst the presence of CCTV appeared to increase subjective feelings of safety amongst patients and visitors, there was no objective evidence that video surveillance increases security, and that staff may develop an over-reliance on the technology.

In addition, our findings add to the existing literature which notes that alternative interventions and approaches that address underlying contextual and systemic issues related to improving care on inpatient wards require attention to address the underlying factors related to incidents, e.g., flashpoints [ 36 ]. Evidence suggests that factors leading to incidents can be predicted; therefore, there is a need to enable staff to work in a proactive way to anticipate and prevent incidents rather than view incidents as purely reactive [ 37 , 38 , 39 ]. Such skills-based and relational approaches are likely to impact more on improving safety and reducing incidents by addressing the complex and multi-faceted issue of incidents on inpatient mental health wards [ 40 ].

These findings highlight that interventions such as BWCs are not used within a vacuum, and that hospitals are complex contexts in which there are a range of unique populations, processes, and microsystems that are multi-faceted [ 41 ]. As a result, interventions will encounter both universal, specific, and local barriers that will impact on its functioning in the real world. This is salient because research suggests that camera use inside mental health wards is based on a perception of the violent nature of the mental health patient, a perception that not only influences practice but also impacts how patients experience the ward [ 33 ]. As a result, there needs to be careful consideration of the use of any new and innovative intervention aimed at improving safety within mental health settings that have limited research supporting their efficacy.

Limitations

While the study provides important insights into the efficacy and acceptability of introducing BWCs onto inpatient mental health wards, there were several limitations. Firstly, the analysis of incident data is limited in its nature as it only presents surface level information about incidents without wider contextual information. Results using such data should be cautiously interpreted as they do not account for confounding factors, such as staffing, acuity, ward culture or ward atmosphere, that are likely to contribute to incidents of violence and aggression. For example, while there was a statistically significant decrease in restrictive practice on the PICU across the study period, we know that BWCs were not widely used on that ward, so this is likely due to a confounding variable that was not accounted for in the study design.

Secondly, the study faced limitations in relation to recruitment, particularly with patients. Researchers’ access to wards was challenging due to high staff turnover and high rates of acuity, meaning many patients were not deemed well enough to be able to consent to take part in the study. In addition, the low use of the cameras on wards meant that many patients, and some staff, had not seen the BWCs in use. Similarly, patients had been provided limited information about the pilot, so their ability to engage in the research and describe their own experiences with BWCs was restricted.

Thirdly, analysis captures the active use of the BWC, however it does not fully capture the impact of staff wearing the cameras even where they do not actively use them. While our qualitative analysis provides insight into the limitation of such passive use, it is likely that the presence of the cameras being worn by staff, even when turned off, may have an impact on both staff and patient behaviours. This may explain trends in the data that did not reach significance but warrant further investigation in relation to the presence of BWCs, nonetheless.

Finally, researchers had planned to collect quantitative surveys from staff and patients in relation to their experiences of the ward atmosphere and climate, views related to therapeutic relationships on the ward, levels of burnout among staff, views on care, and attitudes to containment measures. Due to issues related to staff time, patient acuity, and poor engagement from staff leading to challenges accessing the wards, the collection of such survey data was unfeasible, and this element of the study was discontinued. As a result, we have not reported this aspect in our paper. This limitation reflects the busy nature of inpatient mental health wards with pressures on staff and high levels of ill health among patients. As such, traditional methodologies for evaluation are unlikely to elicit data that is comprehensive and meaningful. Alternative approaches may need to be considered.

Future directions

With BWCs being increasingly used across inpatient mental health services [ 14 ], it is important that further research and evaluation is conducted. To date, there is limited data regarding the effectiveness of this technology in relation to violence reduction; however, there may be other beneficial uses in relation to safeguarding and training [ 13 ]. Future research should consider alternative methods that ensure contextual factors are accounted for and that patient voices can be maximised. For example, focus groups with patients currently admitted to a mental health ward or interviews with those who have recently been on a ward that has used the cameras, would bypass problems encountered with capacity to consent in the present study. Furthermore, ethnographic approaches may provide a deeper understanding of the implementation, deployment and impact that BWCs have on wards.

Overall, this research sheds light on the complexities of using BWCs as a tool for ‘maximising safety’ in mental health settings. The findings suggest that BWCs have a limited impact on levels of incidents on wards, something that is likely to be largely influenced by the process of implementation as well as a range of contextual factors, including the staff and patient populations on the wards. As a result, it is likely that while BWCs may see successes in one hospital site this is not guaranteed for another site as such factors will have a considerable impact on efficacy, acceptability, and feasibility. Furthermore, the findings point towards the need for more consideration to be placed on processes of implementation and the complex ethical discussions regarding BWC use from both a patient and a staff perspective.

In conclusion, while there have been advances in digital applications and immersive technologies showing promise of therapeutic benefits for patients and staff more widely, whether BWCs and other surveillance approaches are to be part of that picture remains to be seen and needs to be informed by high-quality, co-produced research that focuses on wider therapeutic aspects of mental healthcare.

Data availability

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

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Acknowledgements

We would like to thank The Burdett Trust for Nursing for funding this work. We would also like to acknowledge our wider Lived Experience Advisory Panel and Project Advisory Panel for their contributions and support and would like to thank the staff and service users on the wards we attended for their warmth and participation.

Funding was provided by The Burdett Trust of Nursing. Funders were independent of the research and did not impact findings.

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All authors have read and approved the manuscript. Authors AS, UF, KW, GB created the protocol for the study. KW, JJ, UF conducted the recruitment for the study, and conducted the interviews. UF, JJ, JB, LMA, LU, SMK, KB, ET coded data, and contributed to the analysis. All authors supported drafting and development of the manuscript.

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Foye, U., Wilson, K., Jepps, J. et al. Exploring the use of body worn cameras in acute mental health wards: a mixed-method evaluation of a pilot intervention. BMC Health Serv Res 24 , 681 (2024). https://doi.org/10.1186/s12913-024-11085-x

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DOI : https://doi.org/10.1186/s12913-024-11085-x

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