is quantitative research easier than qualitative

Qualitative vs Quantitative Research Methods & Data Analysis

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

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

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

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

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

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

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

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

On This Page:

What Is Qualitative Research?

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

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

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

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

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

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

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

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

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

Qualitative Methods

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

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

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

Here are some examples of qualitative data:

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

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

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

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

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

Qualitative Data Analysis

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

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

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

Key Features

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

Limitations of Qualitative Research

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

Advantages of Qualitative Research

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

What Is Quantitative Research?

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

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

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

Quantitative Methods

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

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

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

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

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

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

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

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

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

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

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

Quantitative Data Analysis

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

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

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

Limitations of Quantitative Research

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

Advantages of Quantitative Research

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

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

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

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

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

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

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

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

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

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

Further Information

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

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

• Knowledge Base

Methodology

• Qualitative vs. Quantitative Research | Differences, Examples & Methods

Qualitative vs. Quantitative Research | Differences, Examples & Methods

Published on April 12, 2019 by Raimo Streefkerk . Revised on June 22, 2023.

When collecting and analyzing 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.

Quantitative research is at risk for research biases including information bias , omitted variable bias , sampling bias , or selection bias . 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.

Common qualitative methods include interviews with open-ended questions, observations described in words, and literature reviews that explore concepts and theories.

Table of contents

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

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

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, observational studies 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 different types of 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 organization 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 analyzed to show its meaning in relation to the research questions. The method of analysis differs for each type of data.

Analyzing quantitative data

Quantitative data is based on numbers. Simple math 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 ( means )
• 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

Analyzing qualitative data

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

Some common approaches to analyzing 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

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

• Chi square goodness of fit test
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Inclusion and exclusion criteria

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

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

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

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

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

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

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 organizations.

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

• Prepare and organize 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 .

A research project is an academic, scientific, or professional undertaking to answer a research question . Research projects can take many forms, such as qualitative or quantitative , descriptive , longitudinal , experimental , or correlational . What kind of research approach you choose will depend on your topic.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

Streefkerk, R. (2023, June 22). Qualitative vs. Quantitative Research | Differences, Examples & Methods. Scribbr. Retrieved August 27, 2024, from https://www.scribbr.com/methodology/qualitative-quantitative-research/

Is this article helpful?

Raimo Streefkerk

Other students also liked, what is quantitative research | definition, uses & methods, what is qualitative research | methods & examples, mixed methods research | definition, guide & examples, get unlimited documents corrected.

✔ Free APA citation check included ✔ Unlimited document corrections ✔ Specialized in correcting academic texts

Critically Thinking About Qualitative Versus Quantitative Research

What should we do regarding our research questions and methodology.

Posted January 26, 2022 | Reviewed by Davia Sills

• Neither a quantitative nor a qualitative methodology is the right way to approach every scientific question.
• Rather, the nature of the question determines which methodology is best suited to address it.
• Often, researchers benefit from a mixed approach that incorporates both quantitative and qualitative methodologies.

As a researcher who has used a wide variety of methodologies, I understand the importance of acknowledging that we, as researchers, do not pick the methodology; rather, the research question dictates it. So, you can only imagine how annoyed I get when I hear of undergraduates designing their research projects based on preconceived notions, like "quantitative is more straightforward," or "qualitative is easier." Apart from the fact that neither of these assertions is actually the case, these young researchers are blatantly missing one of the foundational steps of good research: If you are interested in researching a particular area, you must get to know the area (i.e., through reading) and then develop a question based on that reading.

The nature of the question will dictate the most appropriate methodological approach.

I’ve debated with researchers in the past who are "exclusively" qualitative or "exclusively" quantitative. Depending on the rationale for their exclusivity, I might question a little deeper, learn something, and move on, or I might debate further. Sometimes, I throw some contentious statements out to see what the responses are like. For example, "Qualitative research, in isolation, is nothing but glorified journalism . " This one might not be new to you. Yes, qualitative is flawed, but so, too, is quantitative.

Let's try this one: "Numbers don’t lie, just the researchers who interpret them." If researchers are going to have a pop at qual for subjectivity, why don’t they recognize the same issues in quant? The numbers in a results section may be objectively correct, but their meaningfulness is only made clear through the interpretation of the human reporting them. This is not a criticism but is an important observation for those who believe in the absolute objectivity of quantitative reporting. The subjectivity associated with this interpretation may miss something crucial in the interpretation of the numbers because, hey, we’re only human.

With that, I love quantitative research, but I’m not unreasonable about it. Let’s say we’ve evaluated a three-arm RCT—the new therapeutic intervention is significantly efficacious, with a large effect, for enhancing "x" in people living with "y." One might conclude that this intervention works and that we must conduct further research on it to further support its efficacy—this is, of course, a fine suggestion, consistent with good research practice and epistemological understanding.

However, blindly recommending the intervention based on the interpretation of numbers alone might be suspect—think of all the variables that could be involved in a 4-, 8-, 12-, or 52-week intervention with human participants. It would be foolish to believe that all variables were considered—so, here is a fantastic example of where a qualitative methodology might be useful. At the end of the intervention, a researcher might decide to interview a random 20 percent of the cohort who participated in the intervention group about their experience and the program’s strengths and weaknesses. The findings from this qualitative element might help further explain the effects, aid the initial interpretation, and bring to life new ideas and concepts that had been missing from the initial interpretation. In this respect, infusing a qualitative approach at the end of quantitative analysis has shown its benefits—a mixed approach to intervention evaluation is very useful.

What about before that? Well, let’s say I want to develop another intervention to enhance "z," but there’s little research on it, and that which has been conducted isn’t of the highest quality; furthermore, we don’t know about people’s experiences with "z" or even other variables associated with it.

To design an intervention around "z" would be ‘jumping the gun’ at best (and a waste of funds). It seems that an exploration of some sort is necessary. This is where qualitative again shines—giving us an opportunity to explore what "z" is from the perspective of a relevant cohort(s).

Of course, we cannot generalize the findings; we cannot draw a definitive conclusion as to what "z" is. But what the findings facilitate is providing a foundation from which to work; for example, we still cannot say that "z" is this, that, or the other, but it appears that it might be associated with "a," "b" and "c." Thus, future research should investigate the nature of "z" as a particular concept, in relation to "a," "b" and "c." Again, a qualitative methodology shows its worth. In the previous examples, a qualitative method was used because the research questions warranted it.

Through considering the potentially controversial statements about qual and quant above, we are pushed into examining the strengths and weaknesses of research methodologies (regardless of our exclusivity with a particular approach). This is useful if we’re going to think critically about finding answers to our research questions. But simply considering these does not let poor research practice off the hook.

For example, credible qualitative researchers acknowledge that generalizability is not the point of their research; however, that doesn’t stop some less-than-credible researchers from presenting their "findings" as generalizable as possible, without actually using the word. Such practices should be frowned upon—so should making a career out of strictly using qualitative methodology in an attempt to find answers core to the human condition. All these researchers are really doing is spending a career exploring, yet never really finding anything (despite arguing to the contrary, albeit avoiding the word "generalize").

The solution to this problem, again, is to truly listen to what your research question is telling you. Eventually, it’s going to recommend a quantitative approach. Likewise, a "numbers person" will be recommended a qualitative approach from time to time—flip around the example above, and there’s a similar criticism. Again, embrace a mixed approach.

What's the point of this argument?

I conduct both research methodologies. Which do I prefer? Simple—whichever one helps me most appropriately answer my research question.

Do I have problems with qualitative methodologies? Absolutely—but I have issues with quantitative methods as well. Having these issues is good—it means that you recognize the limitations of your tools, which increases the chances of you "fixing," "sharpening" or "changing out" your tools when necessary.

So, the next time someone speaks with you about labeling researchers as one type or another, ask them why they think that way, ask them which they think you are, and then reflect on the responses alongside your own views of methodology and epistemology. It might just help you become a better researcher.

Christopher Dwyer, Ph.D., is a lecturer at the Technological University of the Shannon in Athlone, Ireland.

• Find a Therapist
• Find a Treatment Center
• Find a Psychiatrist
• Find a Support Group
• Find Online Therapy
• United States
• Brooklyn, NY
• Chicago, IL
• Houston, TX
• Los Angeles, CA
• New York, NY
• Portland, OR
• San Diego, CA
• San Francisco, CA
• Seattle, WA
• Washington, DC
• Asperger's
• Bipolar Disorder
• Chronic Pain
• Eating Disorders
• Passive Aggression
• Personality
• Goal Setting
• Positive Psychology
• Stopping Smoking
• Low Sexual Desire
• Relationships
• Child Development
• Self Tests NEW
• Therapy Center
• Diagnosis Dictionary
• Types of Therapy

Sticking up for yourself is no easy task. But there are concrete skills you can use to hone your assertiveness and advocate for yourself.

• Emotional Intelligence
• Gaslighting
• Affective Forecasting
• Neuroscience

• 15 Reasons to Choose Quantitative over Qualitative Research

Researchers often have issues choosing which research method to go with: quantitative or qualitative research methods? Many incorrectly think the two terms can be used interchangeably.

Qualitative research is regarded as exploratory and is used to uncover trends in thoughts and opinions, while quantitative research is used to quantify the problem by way of generating numerical data or data that can be transformed into usable statistics.

At the end of this article, you will understand why you should consider using quantitative research instead of qualitative method in your research surveys.

Sign up on Formplus Builder to create your preferred online surveys for qualitative and quantitative research. You don’t need any special coding experience! Start now to create research survey questions with Formplus. 

What is Qualitative Research?

Qualitative research is a process of real-life inquiry that aims to understand social phenomena. It focuses on the “why” and “how” rather than the “what” of social phenomena and depends on the direct experiences of human beings as meaning-making agents in their everyday lives.

It is a scientific research method used to gather non-numerical data. Qualitative research focuses on human behavior from a participant’s point of view.

The three major focus areas are individuals, societies and cultures, and language and communication – employed across academic disciplines, qualitative market research, journalism, business, and so on.

Qualitative researchers use varying methods of inquiry for the study of human phenomena including biography, case study, historical analysis, discourse analysis, ethnography, grounded theory and phenomenology.

The common assumptions are that knowledge is subjective rather than objective and that the researcher learns from the participants in order to understand the meaning of their lives.

Types of Qualitative Research

Just as quantitative, there are varieties of qualitative research methods. We shall look at five types of qualitative research that are widely used in business, education and government organizational models.

• Narrative Research

This method occurs over extended periods of time and garners information as it happens. It laces a sequence of events, usually from just one or two individuals to form a consistent story.

Narrative research can be considered both a research method in itself but also the phenomenon under study.

Businesses use the narrative method to define buyer personas and use them to identify innovations that appeal to a target market.

• Ethnographic Research

This method is one of the most popular and widely recognized methods of qualitative research, as it immerses samples in cultures unfamiliar to them. The researcher is also often immersed as a subject for extended periods of time.

The objective is to understand and describe characteristics of cultures the same way anthropologists observe cultural variations among humans.

“ Ethnographic research allows us to regard and represent the actors as creators and execute their own meanings. The very way in which they tell us about what they do, tells the researcher a great deal about what is meaningful for and in the research. It adds richness and texture to the experience of conducting research .” (Stuart Hannabuss).

The ethnographic method looks at people in their cultural setting; their behavior as well as their words; their interactions with one another and with their social and cultural environment; their language and its symbols; rituals etc. to produce a narrative account of that culture.

Read Also: Ethnographic Research: Types, Methods + [Question Examples]

• Historical Research

This method investigates past events in order to learn present patterns and anticipate future choices. It enables the researcher to explore and explain the meanings, phases and characteristics of a phenomenon or process at a particular point of time in the past.

It is not simply the accumulation of dates and facts or even just a description of past happenings but is a flowing and dynamic explanation or description of past events which include an interpretation of these events in an effort to recapture implications, personalities and ideas that have influenced these events (ibid).

The purpose of historical research is to authenticate and explicate the history of any area of human activities, subjects or events by means of scientific processes (Špiláčková, 2012).

Businesses can use historical data of previous ad campaigns alongside their targeted demographic to split-test new campaigns. This would help determine the more effective campaign.

• Grounded Theory

The grounded theory research method looks at large subject matters and attempts to explain why a course of action progresses the way it did.

Simply put, it seeks to provide an explanation or theory behind the events. Sample sizes are often larger to better establish a theory.

Grounded theory can help inform design decisions by better understanding how a community of users currently use a product or perform tasks. For example, a grounded theory study could involve understanding how software developers use portals to communicate and write code.

Businesses use grounded theory when conducting user or satisfaction surveys that target why consumers use company products or services.

This involves deep understanding through multiple data sources. Case studies can be explanatory, exploratory, or descriptive. 

Unlike grounded theory, the case study method provides an in-depth look at one test subject. The subject can be a person or family, business or organization, or a town or city.

Businesses often use case studies when marketing to new clients to show how their business solutions solve a problem for the subject.

What is quantitative research?

Quantitative research is used to quantify behaviors, opinions, attitudes, and other variables and make generalizations from a larger population. quantitative research uses quantifiable data to articulate facts and reveal patterns in research. This type of research method involves the use of statistical, mathematical tools to derive results.

When trying to quantify a problem, quantitative data will conclude on its purpose and understand how dominant it is by looking for results that can be projected to a larger population.

This data collection method includes various forms of online, paper, mobile, kiosk surveys; online polls; systematic observations; face-to-face interviews, phone interviews and so on.

Researchers who use quantitative research method are typically looking to quantify the degree and accentuate objective measurements through polls, questionnaires, and surveys, or by manipulating an existing statistical data using computational techniques. 

Summarily, the goal in quantitative research is to understand the relationship between an independent and dependent variable in a population.

5 Types of Quantitative Research

There are four main types of quantitative research designs: correlational, descriptive, experimental and quasi-experimental. But there’s another one; survey research.

• Descriptive Research

Descriptive research method is more focused on the ‘what’ of the subject matter rather than the ‘why’.i.e. it aims to describe the current status of a variable or phenomenon.  Descriptive research is pretty much as it sounds – it describes circumstances. It can be used to define respondent characteristics, organize comparisons, measure data trends, validate existing conditions.

Data collection is mostly by observation and the researcher does not begin with a hypothesis but, creates one after the data is collected. Albeit very useful, this method cannot draw conclusions from received data and cannot determine cause and effect. 

• Correlational Research

Correlational research is a non-experimental research method, where the researcher measures two variables, and studies the statistical relationship i.e. the correlation between variables. The researcher ultimately assesses that relationship without influence from any peripheral variable.

Let’s take this example, without classroom teaching, our minds relate to the fact that the ‘louder the jingle of an ice cream truck is, the closer it is to use. We also memorize the jingle that comes from the speakers of the truck. And if there are multiple ice cream trucks in the area with different jingles, we would be able to memorize all of it and relate particular jingles to particular trucks. This is how the correlational method works.

The most prominent feature of correlational research is that the two variables are measured – neither is manipulated.

A correlation has direction and can be either positive or negative. It can also differ in the degree or strength of the relationship.

Read Also: Correlational Research Designs: Types, Examples & Methods

• Experimental Research

Often referred to as ‘true experimentation’, this type of research method uses a scientific method to establish a cause-effect relationship among a group of variables.

It is commonly defined as a type of research where the scientist actively influences something to observe the consequences.

It is a systematic and scientific approach to research in which the researcher manipulates one or more variables, and controls/randomizes any change in other variables.

Experimental research is commonly used in sciences such as sociology and psychology, physics, chemistry, biology and medicine and so on.

• Quasi-experimental Research

The prefix quasi means “resembling”. Quasi-experimental research resembles experimental research but is not a true experimental research. It is often referred to as ‘Causal-Comparative’.

In this type of research, the researcher seeks to establish a cause-effect relationship between two variables and manipulates the independent variable.

Although the independent variable is manipulated, participants are not randomly assigned to conditions or orders of conditions (Cook & Campbell, 1979).

Abraham & MacDonald (2011) states:

“ Quasi-experimental research is similar to experimental research in that there is manipulation of an independent variable. It differs from experimental research because either there is no control group, no random selection, no random assignment, and/or no active manipulation .”

Quasi-experimental involves ‘comparison.’ The study of two or more groups is done without focusing on their relationship.

• Survey Research

Survey Research uses interviews, questionnaires, and sampling polls to get a sense of behavior with concentrated precision. Researchers are able to judge behavior and then present the findings in an accurate way.

Survey research can be conducted around one group specifically or used to compare several groups. When conducting survey research, it is imperative that the researcher samples random people. This allows for more accurate findings across a greater number of respondents.

This kind of research can be done in person, over the phone, or through email. They can be self-administered.

Sign up to use Formplus Builder to create your preferred online surveys for qualitative and quantitative research. You don’t need any special coding experience! Start now to create research survey questions with Formplus. 

Why choose Quantitative Research over Qualitative Research?

Quantitative research is more preferred over qualitative research because it is more scientific, objective, fast, focused and acceptable. However, qualitative research is used when the researcher has no idea what to expect. It is used to define the problem or develop and approach to the problem.

• More scientific : A large amount of data is gathered and then analyzed statistically. This almost erases bias, and if more researchers ran the analysis on the data, they would always end up with the same numbers at the end of it.
• Control-sensitive : The researcher has more control over how the data is gathered and is more distant from the experiment. An outside perspective is gained using this method.
• Less biased/objective : The research aims for objectivity i.e. without bias, and is separated from the data. Researcher has clearly defined research questions to which objective answers are sought.
• Focused : The design of the study is determined before it begins and research is used to test a theory and ultimately support or reject it.
• Deals with larger samples : The results are based on larger sample sizes that are representative of the population. The large sample size is used to gain statistically valid results in customer insight.
• Repeatable : The research study can usually be replicated or repeated, given its high reliability.
• Arranged in simple analytical methods : Received data are in the form of numbers and statistics, often arranged in tables, charts, figures, or other non-textual forms.
• Generalizable : Project can be used to generalize concepts more widely, predict future results, or investigate causal relationships. Findings can be generalized if selection process is well-designed and sample is representative of a study population.
• Relatable : Quantitative research aims to make predictions, establish facts and test hypotheses that have already been stated. It aims to find evidence which supports or does not support an existing hypothesis. It tests and validates already constructed theories about how and why phenomena occur.
• More structured : Researcher uses tools, such as questionnaires or equipment to collect numerical data.
• Pertinent in later stages of research : Quantitative research is usually recommended in later stages of research because it produces more reliable results.
• Consistent with data : With quantitative research, you may be getting data that is precise, reliable and consistent, quantitative and numerical.
• More acceptable : It may have higher credibility among many influential people (e.g., administrators, politicians, sponsors, donors)
• Fast : Data collection using quantitative methods is relatively quick (e.g., telephone interviews). Also, data analysis is relatively less time consuming (using statistical software).
• Useful for decision making : Data from quantitative research—such as market size, demographics, and user preferences—provides important information for business decisions.

“ There’s no such thing as qualitative data. Everything is either 1 or 0 ” – Fred Kerlinger

When to use Quantitative Research Method

Quantitative research ends with conclusions/recommendations, as it tries to quantify a problem and understand how prevalent it is by looking for results that can be projected to a larger population. It can help you see the big picture.

A researcher may want to determine the link between income and whether or not more people pay taxes. This is a question that asks “how many” and seeks to confirm a hypothesis.

The method will be structured and consistent during data collection, most likely using a questionnaire with closed-ended questions. The data can be used to look for cause and effect relationships and therefore, can be used to make predictions.

The results will provide numerical data that can be analyzed statistically as the researcher looks for a correlation between income and tax payers. Quantitative methodology would best apply to this research problem.

Use quantitative research methods such as A/B testing for validating or choosing a design based on user satisfaction scores, perceived usability measures, and/or task performance. The data received is statistically valid and can be generalized to the entire user population.

Basically, quantitative research is helpful when you get feedback from more than a handful of participants; need to present a more convincing case to an audience; you want to gather feedback from a diverse population of users NOT all located in the same place; you have a limited budget.

When to use Qualitative Research

Qualitative research is explanatory and is used when the researcher has no idea what to expect. It is used to define the problem or develop and approach to the problem.

It is used to delve deeper into issues of interest. Qualitative data adds the details and can also give a human voice to your results.

Use this type of research method if you want to do in-depth interviews, want to analyze issues affecting focus groups, want uninterrupted observation and ethnographic participation.

You can use it to initiate your research by discovering the problems or opportunities people are thinking about. Those ideas can later become hypotheses.

Quotes from open-ended questions in qualitative research can put a human voice to the objective numbers and trends in your results. Many times, it helps to hear your customers describe your organization honestly which helps point out blind spots.

Choose qualitative research if you want to capture the language and imagery customers use to describe and can easily relate with a brand, product, service and so on.

How to Interpret Qualitative Research Data

Qualitative data consists of words, observations, pictures, and symbols. Analyzing received data typically occurs simultaneously with the data collection.

See qualitative research can be analysed and interpreted with the following steps:

• Data familiarity : As a researcher, you should read and understand the data, noting impressions, look for meaning and weed out unnecessary data.
• Identify key questions you want to answer through the analysis. One way to focus the analysis is to examine the data as it relates to a case, an individual, or a particular group.
• Code and index the data by identifying themes and patterns that may consist of ideas, concepts, behaviors, interactions, phrases and so on. Then, assign a code to pieces of data to label the data and make it easier to manage.
• After that, you should identify patterns and make connections. Identify the themes, look for relative importance of responses received and try to find explanations from the data.
• The last thing to do is to interpret the data and explain findings. You can develop a list of key ideas or use models to explain the findings.

How to Interpret Quantitative Research Data

Quantitative research methods result in data that provides quantifiable, objective, and easy to interpret results. Quantitative data can be analyzed in several ways.

The first thing to do for quantitative data is to identify the scales of measurement. There are four levels of measurement: nominal, ordinal, interval, ratio (scale).

Identifying the scale of measurement helps determine how best to organize the data. It can be entered into a spreadsheet and managed in a way that gives meaning to the data.

The next thing to do is to use some of the quantitative data analysis procedures – data tabulation, descriptive data, data disaggregation, moderate and advanced analytics.

Case Study of Quantitative Research

Geramian et al considered the prevalent problem of drug abuse in Iran especially in adolescents and youth, and conducted a study to assess the status of drug abuse among high school students in Isfahan Province, Iran.

The study was conducted through a questionnaire in 2009 in 20 cities. Study population was high school students aged 14–18 years. The required sample size (considering α = 0.05) was calculated as 6489 students, which was increased to 7137 students with consideration of the dropout rate of 10%.

The study identified the degree of drug abuse according to age, gender and cities. There was also an assessment of the type of drugs used, the most common causes of drug abuse for the first time, the most important cause of drug abuse, mean age of abusers and mean age at first abuse, knowledge about short and long-term complications of narcotics and stimulants, common time and locations of drug abuse, and the most common routes of drug abuse according to gender as well as urban and rural areas of Isfahan Province.

Using the results of the research, the knowledge, attitude, and practice of students toward drug abuse were identified.

Case study of Qualitative Research

A good example of qualitative research is Alan Peshkin’s 1986 book God’s Choice: The Total World of a Fundamentalist Christian School published by the University of Chicago Press.

Peshkin examines the culture of Bethany Baptist Academy by interviewing the students, parents, teachers, and members of the community; and observing for eighteen months – to provide a comprehensive and in-depth analysis of Christian schooling as an alternative to public education.

Peshkin’s work represents qualitative research as it is an in-depth study using tools such as observations and unstructured interviews, aimed at securing descriptive or non-quantifiable data on Bethany Baptist Academy specifically, without attempting to generalize the findings to other schools.

Peshkin describes Bethany Baptist Academy as having institutional unity of purpose, a dedicated faculty, an administration that backs teachers in enforcing classroom disciplines, cheerful students, rigorous homework, committed parents, and above all grounded in positive moral values and a character-building environment.

According to Peshkin, the school focuses on providing ‘wholesome’ lives for students, separate from a secular world, however interacting with the same world.

He adds that there is a lack of cultural diversity in the Academy and the counterproductive method of training students in one-dimensional thought, where students are not allowed to question the viewpoints of their teacher’s biblical interpretations; not forgetting the presence of a heavily censored library.

The school also ignores state regulations for schools, such as state assessments, certification and minimum wages for teachers, while enforcing compulsory volunteer tasks for teachers. Peshkin however paints the school in a positive light and holds that public schools have much to learn from such schools.

What is the best Data Collection tool?

Formplus! This is a unique online form tool that lets you collect and manage all the data you need. With Formplus builder, you can create surveys, questionnaires or polls that will help you gather data for your qualitative or quantitative research 

Formplus gives you an easy-to-use form builder with a variety of options including customization to beautify the form in your way.

Signup on Formplus Builder to create your preferred online surveys for qualitative and quantitative research.

Why Formplus is the Best Data Collection Tool for Quantitative & Qualitative Data

Notwithstanding the kind of research you have chosen to do, Formplus offers you amazing features to make your experience simple and easy.

• Collect Data Online

The world is more digital than ever and will become even more digital. Formplus understands this and is giving you a platform to collect store data received from your research, without having to look beyond your shoulders, worrying whether your data is safe or not.

On Formplus, you can create forms for any type of qualitative or quantitative research and you know what? There’s no limit to the amount of online forms you can create.

You can collect all types and sizes of data including typed documents, images, videos and so on.

• Email Invitation

After you have created the online form, you definitely will want to get it to more people so data collection is not restricted.

Use the email invitation feature on Formplus online form to invite people to fill the research form. You can add the emails one after the other, upload a CSV file or populate from an existing database.

• Geolocation

You want to know where responses are coming from? Or concentration of responses from a particular location? Use the geolocation feature, so when responses are submitted, you see the longitude and latitude of the said response.

This will come in handy when you are doing qualitative research for a particular area and want to weed out data coming from other areas.

• Social Media/Website Popup Sharing

It does not end with email invitations, you could share your online forms to Facebook, Twitter or LinkedIn for more responses.

Embed on your website as a popup to make it easy for respondents to click and fill forms right away without leaving your website.

• Export/Data Interpretation

Export received data into another format – PDF or Microsoft Word – make information easy to digest.

Use the exported data to review responses for the research or make comparisons.

On your dashboard, you can view live analytics of responses including abandonment rate, total visits, average time spent and more.

• Storage Integration

Researches always come in with a lot of data but we got you covered. Formplus allows you store unlimited file types and sizes. Added to that are cloud storage integrations to give you options to choose from.

With Formplus, you can decide to use either Google Drive, OneDrive or Dropbox to store and share received data without hassles. All you need to do is connect an existing account you have with either of those three options and you are on your way. You can easily create an account with any of them, if you do not have in easy steps.

• Team & Collaboration

Manage teams for your research to delegate duties to departments or specializations. Add team members and assign roles to them. Restrict their access, also monitor their activities on your account.

Basically, Formplus allows you collaborate with members of the research team to ensure the data is well managed and positive results maintained.

One more thing, even if you give admin access to a team member, you are still in control of your account.

As much as qualitative data adds humanity to data, quantitative data usually comes at the end to use numerical data to make conclusions.

Both qualitative and quantitative research methods have their flaws. However, it is imperative to note that quantitative research method deals with a larger population and quantifiable data and will, therefore, produce a more reliable result than qualitative research.

We provided 15 reasons quantitative research outsmarts qualitative research but you still have doubts? Let’s talk about it. 

• Cook, T. D., & Campbell, D. T. (1979). Quasi-experimentation: Design & analysis issues in field settings . Boston, MA: Houghton Mifflin.
• Abraham, I. and MacDonald, K. (2011) Encyclopedia Of Nursing Research: Quasi-Experimental Research. Springer Publishing Company. Available here
• Stuart Hannabuss,”Being there: ethnographic research and autobiography”, Library Management, Vol. 21 No. 2.
• Jovita J. Tan (2015), Historical Research: A Qualitative Research Method.

Connect to Formplus, Get Started Now - It's Free!

• abstract in research papers
• accuracy surveys
• Applied research methods
• qualitative data
• qualitative research
• qualitative research data
• quantitative data
• busayo.longe

You may also like:

How to Write a Problem Statement for your Research

Learn how to write problem statements before commencing any research effort. Learn about its structure and explore examples

User Research: Definition, Methods, Tools and Guide

In this article, you’ll learn to provide value to your target market with user research. As a bonus, we’ve added user research tools and...

How to Write An Abstract For Research Papers: Tips & Examples

In this article, we will share some tips for writing an effective abstract, plus samples you can learn from.

21 Chrome Extensions for Academic Researchers in 2022

In this article, we will discuss a number of chrome extensions you can use to make your research process even seamless

Formplus - For Seamless Data Collection

Collect data the right way with a versatile data collection tool. try formplus and transform your work productivity today..

• How it works

Qualitative Vs Quantitative Research – A Comprehensive Guide

Published by Carmen Troy at August 13th, 2021 , Revised On September 20, 2023

What is Quantitative Research?

Quantitative research is associated with numerical data or data that can be measured. It is used to study a large group of population. The information is gathered by performing statistical, mathematical, or computational techniques.

Quantitative research isn’t simply based on  statistical analysis or quantitative techniques but rather uses a certain approach to theory to address research hypotheses or questions, establish an appropriate research methodology, and draw findings & conclusions .

Characteristics of Quantitative Research

Some most commonly employed quantitative research strategies include data-driven dissertations, theory-driven studies, and reflection-driven research. Regardless of the chosen approach, there are some common quantitative research features as listed below.

• Quantitative research tests or builds on other researchers’ existing theories whilst taking a reflective or extensive route.
• Quantitative research aims to test the research hypothesis or answer established research questions.
• It is primarily justified by positivist or post-positivist research paradigms.
• The  research design can be relationship-based, quasi-experimental, experimental, or descriptive.
• It draws on a small sample to make generalisations to a wider population using probability sampling techniques.
• Quantitative data is gathered according to the established research questions using research vehicles such as structured observation, structured interviews, surveys, questionnaires, and laboratory results.
• The researcher uses  statistical analysis tools and techniques to measure variables and gather inferential or descriptive data. In some cases, your tutor or dissertation committee members might find it easier to verify your study results with numbers and statistical analysis.
• The study results’ accuracy is based on external and internal validity and authenticity of the data used.
• Quantitative research answers research questions or tests the hypothesis using charts, graphs, tables, data, and statements.
• It underpins  research questions or hypotheses and findings to make conclusions.
• The researcher can provide recommendations for future research and expand or test existing theories.

What is Qualitative Research?

Qualitative research is a type of scientific research where a researcher collects evidence to seek answers to a  question . It is associated with studying human behavior from an informative perspective. It aims at obtaining in-depth details of the problem.

As the term suggests,  qualitative research  is based on qualitative research methods, including participants’ observations, focus groups, and unstructured interviews.

Qualitative research is very different in nature when compared to quantitative research. It takes an established path towards the  research process , how  research questions  are set up, how existing theories are built upon, what research methods are employed, and how the  findings  are unveiled to the readers.

You may adopt conventional methods, including phenomenological research, narrative-based research, grounded theory research, ethnographies, case studies, and auto-ethnographies.

Does your Research Methodology Have the Following?

• Great Research/Sources
• Perfect Language
• Accurate Sources

If not, we can help. Our panel of experts makes sure to keep the 3 pillars of Research Methodology strong.

Characteristics of Qualitative Research

Again, regardless of the chosen approach to qualitative research, your dissertation will have unique key features as listed below.

• The research questions that you aim to answer will expand or even change as the  dissertation writing process continues . This aspect of the research is typically known as an emergent design where the research objectives evolve with time.
• Qualitative research may use existing theories to cultivate new theoretical understandings or fall back on existing theories to support the research process. However, the original goal of testing a certain theoretical understanding remains the same.
• It can be based on various research models, such as critical theory, constructivism, and interpretivism.
• The chosen research design largely influences the analysis and discussion of results and the choices you make . Research design depends on the adopted research path: phenomenological research, narrative-based research, grounded theory-based research, ethnography, case study-based research, or auto-ethnography.
• Qualitative research answers research questions with theoretical sampling, where data gathered from the organisation or people are studied.
• It involves various research methods to gather qualitative data from participants belonging to the field of study. As indicated previously, some of the most notable qualitative research methods include participant observation, focus groups, and unstructured interviews.
• It incorporates an  inductive process where the researcher analyses and understands the data through his own eyes and judgments to identify concepts and themes that comprehensively depict the researched material.
• The key quality characteristics of qualitative research are transferability, conformity, confirmability, and reliability.
• Results and discussions are largely based on narratives, case study and personal experiences, which help detect inconsistencies, observations, processes, and ideas.
• Qualitative research discusses theoretical concepts obtained from the results whilst taking research questions and/or hypotheses to  draw general  conclusions .

Confused between qualitative and quantitative methods of data analysis? No idea what discourse and content analysis are?

We hear you.

• Whether you want a full dissertation written or need help forming a dissertation proposal, we can help you with both.
• Get different dissertation services at ResearchProspect and score amazing grades!

When to Use Qualitative and Quantitative Research Model?

• The research  title, research questions,  hypothesis , objectives, and study area generally determine the dissertation’s best research method.
• If the primary aim of your research is to test a hypothesis, validate an existing theory or perhaps measure some variables, then the quantitative research model will be the more appropriate choice because it might be easier for you to convince your supervisor or members of the dissertation committee with the use of statistics and numbers.
• On the other hand, oftentimes, statistics and a collection of numbers are not the answer, especially where there is a need to understand meanings, experiences, and beliefs.
• If your research questions or hypothesis can be better addressed through people’s observations and experiences, you should consider qualitative data.
• If you select an inappropriate research method, you will not prove your findings’ accuracy, and your dissertation will be pretty much meaningless. To prove that your research is authentic and reliable, choose a research method that best suits your study’s requirements.
• In the sections that follow, we explain the most commonly employed research methods for the dissertation, including quantitative, qualitative, and mixed research methods.

Now that you know the unique differences between quantitative and qualitative research methods, you may want to learn a bit about primary and secondary research methods.

Here is an article that will help you  distinguish between primary and secondary research  and decide whether you need to use quantitative and/or qualitative methods of primary research in your dissertation.

Alternatively, you can base your dissertation on secondary research, which is descriptive and explanatory.

Limitations of Quantitative and Qualitative Research

What is quantitative research?

What is qualitative research.

Qualitative research is a type of scientific research where a researcher collects evidence to seek answers to a question . It is associated with studying human behavior from an informative perspective. It aims at obtaining in-depth details of the problem.

Qualitative or quantitative, which research type should I use?

The research title, research questions, hypothesis , objectives, and study area generally determine the dissertation’s best research method.

You May Also Like

Thematic analysis is commonly used for qualitative data. Researchers give preference to thematic analysis when analysing audio or video transcripts.

Baffled by the concept of reliability and validity? Reliability refers to the consistency of measurement. Validity refers to the accuracy of measurement.

This article presents the key advantages and disadvantages of secondary research so you can select the most appropriate research approach for your study.

USEFUL LINKS

LEARNING RESOURCES

COMPANY DETAILS

• How It Works

Educational resources and simple solutions for your research journey

Qualitative vs Quantitative Research: Differences, Examples, and Methods

There are two broad kinds of research approaches: qualitative and quantitative research that are used to study and analyze phenomena in various fields such as natural sciences, social sciences, and humanities. Whether you have realized it or not, your research must have followed either or both research types. In this article we will discuss what qualitative vs quantitative research is, their applications, pros and cons, and when to use qualitative vs quantitative research . Before we get into the details, it is important to understand the differences between the qualitative and quantitative research.     

Table of Contents

Qualitative v s Quantitative Research  

Quantitative research deals with quantity, hence, this research type is concerned with numbers and statistics to prove or disapprove theories or hypothesis. In contrast, qualitative research is all about quality – characteristics, unquantifiable features, and meanings to seek deeper understanding of behavior and phenomenon. These two methodologies serve complementary roles in the research process, each offering unique insights and methods suited to different research questions and objectives.    

Qualitative and quantitative research approaches have their own unique characteristics, drawbacks, advantages, and uses. Where quantitative research is mostly employed to validate theories or assumptions with the goal of generalizing facts to the larger population, qualitative research is used to study concepts, thoughts, or experiences for the purpose of gaining the underlying reasons, motivations, and meanings behind human behavior .   

What Are the Differences Between Qualitative and Quantitative Research  

Qualitative and quantitative research differs in terms of the methods they employ to conduct, collect, and analyze data. For example, qualitative research usually relies on interviews, observations, and textual analysis to explore subjective experiences and diverse perspectives. While quantitative data collection methods include surveys, experiments, and statistical analysis to gather and analyze numerical data. The differences between the two research approaches across various aspects are listed in the table below.    

Data Collection Methods  

There are differences between qualitative and quantitative research when it comes to data collection as they deal with different types of data. Qualitative research is concerned with personal or descriptive accounts to understand human behavior within society. Quantitative research deals with numerical or measurable data to delineate relations among variables. Hence, the qualitative data collection methods differ significantly from quantitative data collection methods due to the nature of data being collected and the research objectives. Below is the list of data collection methods for each research approach:    

Qualitative Research Data Collection  

• Interviews  
• Focus g roups  
• Content a nalysis  
• Literature review  
• Observation  
• Ethnography  

Qualitative research data collection can involve one-on-one group interviews to capture in-depth perspectives of participants using open-ended questions. These interviews could be structured, semi-structured or unstructured depending upon the nature of the study. Focus groups can be used to explore specific topics and generate rich data through discussions among participants. Another qualitative data collection method is content analysis, which involves systematically analyzing text documents, audio, and video files or visual content to uncover patterns, themes, and meanings. This can be done through coding and categorization of raw data to draw meaningful insights. Data can be collected through observation studies where the goal is to simply observe and document behaviors, interaction, and phenomena in natural settings without interference. Lastly, ethnography allows one to immerse themselves in the culture or environment under study for a prolonged period to gain a deep understanding of the social phenomena.   

Quantitative Research Data Collection  

• Surveys/ q uestionnaires  
• Experiments
• Secondary data analysis  
• Structured o bservations  
• Case studies   
• Tests and a ssessments  

Quantitative research data collection approaches comprise of fundamental methods for generating numerical data that can be analyzed using statistical or mathematical tools. The most common quantitative data collection approach is the usage of structured surveys with close-ended questions to collect quantifiable data from a large sample of participants. These can be conducted online, over the phone, or in person.   

Performing experiments is another important data collection approach, in which variables are manipulated under controlled conditions to observe their effects on dependent variables. This often involves random assignment of participants to different conditions or groups. Such experimental settings are employed to gauge cause-and-effect relationships and understand a complex phenomenon. At times, instead of acquiring original data, researchers may deal with secondary data, which is the dataset curated by others, such as government agencies, research organizations, or academic institute. With structured observations, subjects in a natural environment can be studied by controlling the variables which aids in understanding the relationship among various variables. The secondary data is then analyzed to identify patterns and relationships among variables. Observational studies provide a means to systematically observe and record behaviors or phenomena as they occur in controlled environments. Case studies form an interesting study methodology in which a researcher studies a single entity or a small number of entities (individuals or organizations) in detail to understand complex phenomena within a specific context.   

Qualitative vs Quantitative Research Outcomes  

Qualitative research and quantitative research lead to varied research outcomes, each with its own strengths and limitations. For example, qualitative research outcomes provide deep descriptive accounts of human experiences, motivations, and perspectives that allow us to identify themes or narratives and context in which behavior, attitudes, or phenomena occurs.  Quantitative research outcomes on the other hand produce numerical data that is analyzed statistically to establish patterns and relationships objectively, to form generalizations about the larger population and make predictions. This numerical data can be presented in the form of graphs, tables, or charts. Both approaches offer valuable perspectives on complex phenomena, with qualitative research focusing on depth and interpretation, while quantitative research emphasizes numerical analysis and objectivity.  

When to Use Qualitative vs Quantitative Research Approach  

The decision to choose between qualitative and quantitative research depends on various factors, such as the research question, objectives, whether you are taking an inductive or deductive approach, available resources, practical considerations such as time and money, and the nature of the phenomenon under investigation. To simplify, quantitative research can be used if the aim of the research is to prove or test a hypothesis, while qualitative research should be used if the research question is more exploratory and an in-depth understanding of the concepts, behavior, or experiences is needed.     

Qualitative research approach  

Qualitative research approach is used under following scenarios:   

• To study complex phenomena: When the research requires understanding the depth, complexity, and context of a phenomenon.  
• Collecting participant perspectives: When the goal is to understand the why behind a certain behavior, and a need to capture subjective experiences and perceptions of participants.  
• Generating hypotheses or theories: When generating hypotheses, theories, or conceptual frameworks based on exploratory research.  

Example: If you have a research question “What obstacles do expatriate students encounter when acquiring a new language in their host country?”  

This research question can be addressed using the qualitative research approach by conducting in-depth interviews with 15-25 expatriate university students. Ask open-ended questions such as “What are the major challenges you face while attempting to learn the new language?”, “Do you find it difficult to learn the language as an adult?”, and “Do you feel practicing with a native friend or colleague helps the learning process”?  

Based on the findings of these answers, a follow-up questionnaire can be planned to clarify things. Next step will be to transcribe all interviews using transcription software and identify themes and patterns.   

Quantitative research approach  

Quantitative research approach is used under following scenarios:   

• Testing hypotheses or proving theories: When aiming to test hypotheses, establish relationships, or examine cause-and-effect relationships.   
• Generalizability: When needing findings that can be generalized to broader populations using large, representative samples.  
• Statistical analysis: When requiring rigorous statistical analysis to quantify relationships, patterns, or trends in data.   

Example : Considering the above example, you can conduct a survey of 200-300 expatriate university students and ask them specific questions such as: “On a scale of 1-10 how difficult is it to learn a new language?”  

Next, statistical analysis can be performed on the responses to draw conclusions like, on an average expatriate students rated the difficulty of learning a language 6.5 on the scale of 10.    

Mixed methods approach  

In many cases, researchers may opt for a mixed methods approach , combining qualitative and quantitative methods to leverage the strengths of both approaches. Researchers may use qualitative data to explore phenomena in-depth and generate hypotheses, while quantitative data can be used to test these hypotheses and generalize findings to broader populations.  

Example: Both qualitative and quantitative research methods can be used in combination to address the above research question. Through open-ended questions you can gain insights about different perspectives and experiences while quantitative research allows you to test that knowledge and prove/disprove your hypothesis.   

How to Analyze Qualitative and Quantitative Data  

When it comes to analyzing qualitative and quantitative data, the focus is on identifying patterns in the data to highlight the relationship between elements. The best research method for any given study should be chosen based on the study aim. A few methods to analyze qualitative and quantitative data are listed below.  

Analyzing qualitative data  

Qualitative data analysis is challenging as it is not expressed in numbers and consists majorly of texts, images, or videos. Hence, care must be taken while using any analytical approach. Some common approaches to analyze qualitative data include:  

• Organization: The first step is data (transcripts or notes) organization into different categories with similar concepts, themes, and patterns to find inter-relationships.  
• Coding: Data can be arranged in categories based on themes/concepts using coding.  
• Theme development: Utilize higher-level organization to group related codes into broader themes.  
• Interpretation: Explore the meaning behind different emerging themes to understand connections. Use different perspectives like culture, environment, and status to evaluate emerging themes.  
• Reporting: Present findings with quotes or excerpts to illustrate key themes.   

Analyzing quantitative data  

Quantitative data analysis is more direct compared to qualitative data as it primarily deals with numbers. Data can be evaluated using simple math or advanced statistics (descriptive or inferential). Some common approaches to analyze quantitative data include:  

• Processing raw data: Check missing values, outliers, or inconsistencies in raw data.  
• Descriptive statistics: Summarize data with means, standard deviations, or standard error using programs such as Excel, SPSS, or R language.  
• Exploratory data analysis: Usage of visuals to deduce patterns and trends.  
• Hypothesis testing: Apply statistical tests to find significance and test hypothesis (Student’s t-test or ANOVA).  
• Interpretation: Analyze results considering significance and practical implications.  
• Validation: Data validation through replication or literature review.  
• Reporting: Present findings by means of tables, figures, or graphs.   

Benefits and limitations of qualitative vs quantitative research  

There are significant differences between qualitative and quantitative research; we have listed the benefits and limitations of both methods below:  

Benefits of qualitative research  

• Rich insights: As qualitative research often produces information-rich data, it aids in gaining in-depth insights into complex phenomena, allowing researchers to explore nuances and meanings of the topic of study.  
• Flexibility: One of the most important benefits of qualitative research is flexibility in acquiring and analyzing data that allows researchers to adapt to the context and explore more unconventional aspects.  
• Contextual understanding: With descriptive and comprehensive data, understanding the context in which behaviors or phenomena occur becomes accessible.   
• Capturing different perspectives: Qualitative research allows for capturing different participant perspectives with open-ended question formats that further enrich data.   
• Hypothesis/theory generation: Qualitative research is often the first step in generating theory/hypothesis, which leads to future investigation thereby contributing to the field of research.

Limitations of qualitative research  

• Subjectivity: It is difficult to have objective interpretation with qualitative research, as research findings might be influenced by the expertise of researchers. The risk of researcher bias or interpretations affects the reliability and validity of the results.   
• Limited generalizability: Due to the presence of small, non-representative samples, the qualitative data cannot be used to make generalizations to a broader population.  
• Cost and time intensive: Qualitative data collection can be time-consuming and resource-intensive, therefore, it requires strategic planning and commitment.   
• Complex analysis: Analyzing qualitative data needs specialized skills and techniques, hence, it’s challenging for researchers without sufficient training or experience.   
• Potential misinterpretation: There is a risk of sampling bias and misinterpretation in data collection and analysis if researchers lack cultural or contextual understanding.   

Benefits of quantitative research  

• Objectivity: A key benefit of quantitative research approach, this objectivity reduces researcher bias and subjectivity, enhancing the reliability and validity of findings.   
• Generalizability: For quantitative research, the sample size must be large and representative enough to allow for generalization to broader populations.   
• Statistical analysis: Quantitative research enables rigorous statistical analysis (increasing power of the analysis), aiding hypothesis testing and finding patterns or relationship among variables.   
• Efficiency: Quantitative data collection and analysis is usually more efficient compared to the qualitative methods, especially when dealing with large datasets.   
• Clarity and Precision: The findings are usually clear and precise, making it easier to present them as graphs, tables, and figures to convey them to a larger audience.  

Limitations of quantitative research  

• Lacks depth and details: Due to its objective nature, quantitative research might lack the depth and richness of qualitative approaches, potentially overlooking important contextual factors or nuances.   
• Limited exploration: By not considering the subjective experiences of participants in depth , there’s a limited chance to study complex phenomenon in detail.   
• Potential oversimplification: Quantitative research may oversimplify complex phenomena by boiling them down to numbers, which might ignore key nuances.   
• Inflexibility: Quantitative research deals with predecided varibales and measures , which limits the ability of researchers to explore unexpected findings or adjust the research design as new findings become available .  
• Ethical consideration: Quantitative research may raise ethical concerns especially regarding privacy, informed consent, and the potential for harm, when dealing with sensitive topics or vulnerable populations.   

Frequently asked questions  

• What is the difference between qualitative and quantitative research? 

Quantitative methods use numerical data and statistical analysis for objective measurement and hypothesis testing, emphasizing generalizability. Qualitative methods gather non-numerical data to explore subjective experiences and contexts, providing rich, nuanced insights.  

• What are the types of qualitative research? 

Qualitative research methods include interviews, observations, focus groups, and case studies. They provide rich insights into participants’ perspectives and behaviors within their contexts, enabling exploration of complex phenomena.  

• What are the types of quantitative research? 

Quantitative research methods include surveys, experiments, observations, correlational studies, and longitudinal research. They gather numerical data for statistical analysis, aiming for objectivity and generalizability.  

• Can you give me examples for qualitative and quantitative research? 

Qualitative Research Example: 

Research Question: What are the experiences of parents with autistic children in accessing support services?  

Method: Conducting in-depth interviews with parents to explore their perspectives, challenges, and needs.  

Quantitative Research Example: 

Research Question: What is the correlation between sleep duration and academic performance in college students?  

Method: Distributing surveys to a large sample of college students to collect data on their sleep habits and academic performance, then analyzing the data statistically to determine any correlations.  

Editage All Access is a subscription-based platform that unifies the best AI tools and services designed to speed up, simplify, and streamline every step of a researcher’s journey. The Editage All Access Pack is a one-of-a-kind subscription that unlocks full access to an AI writing assistant, literature recommender, journal finder, scientific illustration tool, and exclusive discounts on professional publication services from Editage.  

Based on 22+ years of experience in academia, Editage All Access empowers researchers to put their best research forward and move closer to success. Explore our top AI Tools pack, AI Tools + Publication Services pack, or Build Your Own Plan. Find everything a researcher needs to succeed, all in one place –  Get All Access now starting at just $14 a month !    

Related Posts

What is Convenience Sampling: Definition, Method, and Examples 

What are the Best Research Funding Sources

Root out friction in every digital experience, super-charge conversion rates, and optimize digital self-service

Uncover insights from any interaction, deliver AI-powered agent coaching, and reduce cost to serve

Increase revenue and loyalty with real-time insights and recommendations delivered to teams on the ground

Know how your people feel and empower managers to improve employee engagement, productivity, and retention

Take action in the moments that matter most along the employee journey and drive bottom line growth

Whatever they’re are saying, wherever they’re saying it, know exactly what’s going on with your people

Get faster, richer insights with qual and quant tools that make powerful market research available to everyone

Run concept tests, pricing studies, prototyping + more with fast, powerful studies designed by UX research experts

Track your brand performance 24/7 and act quickly to respond to opportunities and challenges in your market

Explore the platform powering Experience Management

• Free Account
• Product Demos
• For Digital
• For Customer Care
• For Human Resources
• For Researchers
• Financial Services
• All Industries

Popular Use Cases

• Customer Experience
• Employee Experience
• Net Promoter Score
• Voice of Customer
• Customer Success Hub
• Product Documentation
• Training & Certification
• XM Institute
• Popular Resources
• Customer Stories
• Artificial Intelligence

Market Research

• Partnerships
• Marketplace

The annual gathering of the experience leaders at the world’s iconic brands building breakthrough business results, live in Salt Lake City.

• English/AU & NZ
• Español/Europa
• Español/América Latina
• Português Brasileiro
• REQUEST DEMO
• Experience Management
• Qualitative Vs Quantitative Research

Try Qualtrics for free

Qualitative vs quantitative research.

13 min read You’ll use both quantitative and qualitative research methods to gather data in your research projects. So what do qualitative and quantitative mean exactly, and how can you best use them to gain the most accurate insights?

What is qualitative research?

Qualitative research is all about language, expression, body language and other forms of human communication. That covers words, meanings and understanding. Qualitative research is used to describe WHY. Why do people feel the way they do, why do they act in a certain way, what opinions do they have and what motivates them?

Qualitative data is used to understand phenomena – things that happen, situations that exist, and most importantly the meanings associated with them. It can help add a ‘why’ element to factual, objective data.

Qualitative research gives breadth, depth and context to questions, although its linguistic subtleties and subjectivity can mean that results are trickier to analyze than quantitative data.

This qualitative data is called unstructured data by researchers. This is because it has not traditionally had the type of structure that can be processed by computers, until today. It has, until recently at least, been exclusively accessible to human brains. And although our brains are highly sophisticated, they have limited processing power. What can help analyze this structured data to assist computers and the human brain?

Free eBook: Quantitative and qualitative research design

What is quantitative research?

Quantitative data refers to numerical information. Quantitative research gathers information that can be counted, measured, or rated numerically – AKA quantitative data. Scores, measurements, financial records, temperature charts and receipts or ledgers are all examples of quantitative data.

Quantitative data is often structured data, because it follows a consistent, predictable pattern that computers and calculating devices are able to process with ease. Humans can process it too, although we are now able to pass it over to machines to process on our behalf. This is partly what has made quantitative data so important historically, and why quantitative data – sometimes called ‘hard data’ – has dominated over qualitative data in fields like business, finance and economics.

It’s easy to ‘crunch the numbers’ of quantitative data and produce results visually in graphs, tables and on data analysis dashboards. Thanks to today’s abundance and accessibility of processing power, combined with our ability to store huge amounts of information, quantitative data has fuelled the Big Data phenomenon, putting quantitative methods and vast amounts of quantitative data at our fingertips.

As we’ve indicated, quantitative and qualitative data are entirely different and mutually exclusive categories. Here are a few of the differences between them.

1. Data collection

Data collection methods for quantitative data and qualitative data vary, but there are also some places where they overlap.

2. Data analysis

Quantitative data suits statistical analysis techniques like linear regression, T-tests and ANOVA. These are quite easy to automate, and large quantities of quantitative data can be analyzed quickly.

Analyzing qualitative data needs a higher degree of human judgement, since unlike quantitative data, non numerical data of a subjective nature has certain characteristics that inferential statistics can’t perceive. Working at a human scale has historically meant that qualitative data is lower in volume – although it can be richer in insights.

3. Strengths and weaknesses

When weighing up qualitative vs quantitative research, it’s largely a matter of choosing the method appropriate to your research goals. If you’re in the position of having to choose one method over another, it’s worth knowing the strengths and limitations of each, so that you know what to expect from your results.

Qualitative vs quantitative – the role of research questions

How do you know whether you need qualitative or quantitative research techniques? By finding out what kind of data you’re going to be collecting.

You’ll do this as you develop your research question, one of the first steps to any research program. It’s a single sentence that sums up the purpose of your research, who you’re going to gather data from, and what results you’re looking for.

As you formulate your question, you’ll get a sense of the sort of answer you’re working towards, and whether it will be expressed in numerical data or qualitative data.

For example, your research question might be “How often does a poor customer experience cause shoppers to abandon their shopping carts?” – this is a quantitative topic, as you’re looking for numerical values.

Or it might be “What is the emotional impact of a poor customer experience on regular customers in our supermarket?” This is a qualitative topic, concerned with thoughts and feelings and answered in personal, subjective ways that vary between respondents.

Here’s how to evaluate your research question and decide which method to use:

• Qualitative research:

Use this if your goal is to understand something – experiences, problems, ideas.

For example, you may want to understand how poor experiences in a supermarket make your customers feel. You might carry out this research through focus groups or in depth interviews (IDI’s). For a larger scale research method you could start  by surveying supermarket loyalty card holders, asking open text questions, like “How would you describe your experience today?” or “What could be improved about your experience?” This research will provide context and understanding that quantitative research will not.

• Quantitative research:

Use this if your goal is to test or confirm a hypothesis, or to study cause and effect relationships. For example, you want to find out what percentage of your returning customers are happy with the customer experience at your store. You can collect data to answer this via a survey.

For example, you could recruit 1,000 loyalty card holders as participants, asking them, “On a scale of 1-5, how happy are you with our store?” You can then make simple mathematical calculations to find the average score. The larger sample size will help make sure your results aren’t skewed by anomalous data or outliers, so you can draw conclusions with confidence.

Qualitative and quantitative research combined?

Do you always have to choose between qualitative or quantitative data?

In some cases you can get the best of both worlds by combining both quantitative and qualitative data.You could use pre quantitative data to understand the landscape of your research. Here you can gain insights around a topic and propose a hypothesis. Then adopt a quantitative research method to test it out. Here you’ll discover where to focus your survey appropriately or to pre-test your survey, to ensure your questions are understood as you intended. Finally, using a round of qualitative research methods to bring your insights and story to life. This mixed methods approach is becoming increasingly popular with businesses who are looking for in depth insights.

For example, in the supermarket scenario we’ve described, you could start out with a qualitative data collection phase where you use focus groups and conduct interviews with customers. You might find suggestions in your qualitative data that customers would like to be able to buy children’s clothes in the store.

In response, the supermarket might pilot a children’s clothing range. Targeted quantitative research could then reveal whether or not those stores selling children’s clothes achieve higher customer satisfaction scores and a rise in profits for clothing.

Together, qualitative and quantitative data, combined with statistical analysis, have provided important insights about customer experience, and have proven the effectiveness of a solution to business problems.

Qualitative vs quantitative question types

As we’ve noted, surveys are one of the data collection methods suitable for both quantitative and qualitative research. Depending on the types of questions you choose to include, you can generate qualitative and quantitative data. Here we have summarized some of the survey question types you can use for each purpose.

Qualitative data survey questions

There are fewer survey question options for collecting qualitative data, since they all essentially do the same thing – provide the respondent with space to enter information in their own words. Qualitative research is not typically done with surveys alone, and researchers may use a mix of qualitative methods. As well as a survey, they might conduct in depth interviews, use observational studies or hold focus groups.

Open text ‘Other’ box (can be used with multiple choice questions)

Text box (space for short written answer)

Essay box (space for longer, more detailed written answers)

Quantitative data survey questions

These questions will yield quantitative data – i.e. a numerical value.

Net Promoter Score (NPS)

Likert Scale

Radio buttons (respondents choose just one option)

Check boxes (respondents can choose multiple options)

Sliding scale

Star rating

Analyzing data (quantitative or qualitative) using technology

We are currently at an exciting point in the history of qualitative analysis. Digital analysis and other methods that were formerly exclusively used for quantitative data are now used for interpreting non numerical data too.

A rtificial intelligence programs can now be used to analyze open text, and turn qualitative data into structured and semi structured quantitative data that relates to qualitative data topics such as emotion and sentiment, opinion and experience.

Research that in the past would have meant qualitative researchers conducting time-intensive studies using analysis methods like thematic analysis can now be done in a very short space of time. This not only saves time and money, but opens up qualitative data analysis to a much wider range of businesses and organizations.

The most advanced tools can even be used for real-time statistical analysis, forecasting and prediction, making them a powerful asset for businesses.

Qualitative or quantitative – which is better for data analysis?

Historically, quantitative data was much easier to analyze than qualitative data. But as we’ve seen, modern technology is helping qualitative analysis to catch up, making it quicker and less labor-intensive than before.

That means the choice between qualitative and quantitative studies no longer needs to factor in ease of analysis, provided you have the right tools at your disposal. With an integrated platform like Qualtrics, which incorporates data collection, data cleaning, data coding and a powerful suite of analysis tools for both qualitative and quantitative data, you have a wide range of options at your fingertips.

Related resources

Qualitative research questions 11 min read, qualitative research design 12 min read, primary vs secondary research 14 min read, business research methods 12 min read, qualitative research interviews 11 min read, market intelligence 10 min read, marketing insights 11 min read, request demo.

Ready to learn more about Qualtrics?

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 .

Need a helping hand?

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 ...

It was helpful

thanks much it has given me an inside on research. i still have issue coming out with my methodology from the topic below: strategies for the improvement of infastructure resilience to natural phenomena

Waoo! Simplifies language. I have read this several times and had probs. Today it is very clear. Bravo

Trackbacks/Pingbacks

• What Is Research Methodology? Simple Definition (With Examples) - Grad Coach - […] Qualitative, quantitative and mixed-methods are different types of methodologies, distinguished by whether they focus on words, numbers or both. This…

Submit a Comment Cancel reply

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.

• Print Friendly

• Bipolar Disorder
• Therapy Center
• When To See a Therapist
• Types of Therapy
• Best Online Therapy
• Best Couples Therapy
• Managing Stress
• Sleep and Dreaming
• Understanding Emotions
• Self-Improvement
• Healthy Relationships
• Student Resources
• Personality Types
• Sweepstakes
• Guided Meditations
• Verywell Mind Insights
• 2024 Verywell Mind 25
• Mental Health in the Classroom
• Editorial Process
• Meet Our Review Board
• Crisis Support

Quantitative vs. Qualitative Research in Psychology

• 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.

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, automatically generate references for free.

• Knowledge Base
• Methodology
• Qualitative vs Quantitative Research | Examples & Methods

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.

Prevent plagiarism, run a free check.

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 .

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the ‘Cite this Scribbr article’ button to automatically add the citation to our free Reference Generator.

Streefkerk, R. (2023, May 08). Qualitative vs Quantitative Research | Examples & Methods. Scribbr. Retrieved 26 August 2024, from https://www.scribbr.co.uk/research-methods/quantitative-qualitative-research/

Is this article helpful?

Raimo Streefkerk

• Qualitative vs Quantitative Research: When to Use Each

User research is crucial for understanding the needs, preferences, and behaviours of your users. By directly engaging with and observing real users, you gain invaluable insights that can inform the design and development of your product or service.

There are two main approaches to conducting user research: qualitative and quantitative.

This article will provide an overview of qualitative vs quantitative research. I’ll define what each method is, walk through example scenarios of when you might use one versus the other, highlight the benefits of each, and offer guidelines on when qualitative or quantitative user research is most appropriate.

With a foundational understanding of these two complementary research approaches, you’ll be equipped to choose the right user research method(s) for gaining the insights you need.

Let’s get started.

Table of Contents

What is user research.

User research is the study of target users and their needs, goals, and behaviours. It provides critical insights that inform the design and development of products, services, and experiences.

The goal of user research is to understand users’ motivations and thought processes so that solutions can be crafted to meaningfully address their pain points and desires. Researchers utilize various qualitative and quantitative techniques to uncover users’ attitudes, perceptions, and needs.

The findings from user research drive design decisions, product strategy, and business objectives. By grounding designs in real user data, teams can create solutions that delight users by meeting their needs. User research provides a profound understanding of the problem space so that products resonate with users’ mental models and workflows.

Qualitative User Research

Qualitative user research is a set of exploratory research techniques focused on developing a deep understanding of why and how people behave, think, feel, and make decisions. 

It typically involves open-ended observations, interviews, and analysis based on small sample sizes. 

The goal is to uncover insights into human motivations, attitudes and needs through immersive and conversational research methods. 

Rather than focusing on quantitative metrics or measurements, qualitative user research aims to understand the nuanced human context surrounding products, services, and experiences.

Key characteristics of qualitative research include:

Asking open-ended questions – 

Qualitative research utilizes flexible, open-ended questions that allow users to provide thoughtful and descriptive responses. Questions focus on the “why” and “how” behind bbehaviours not just surface-level preferences. For example, researchers may ask “Can you walk me through how you accomplished that task?” rather than “Did you find that task easy or difficult?”. Open questions lead to deeper psychological insights.

Small but focused sample sizes – 

Qualitative studies recruit a smaller number of users, but they represent the target audience segment. For example, rather than 500 broadly targeted surveys, qualitative research may study 8-12 users who match the persona. Smaller samples enable more time spent discovering each user’s nuanced perspectives.

Naturalistic observations – 

Qualitative research observes users interacting in real environments, like their homes or workplaces. This naturalistic approach reveals authentic behaviours versus what people say. Researchers can shadow users and see real-world contexts.

Immersive techniques – 

Qualitative research utilizes ethnography-inspired techniques. Researchers embed themselves alongside users to empathize with their worldview. In-depth interviews, diary studies, and field visits all facilitate first-hand experience of the user’s journey – Through open and natural dialogue, qualitative research uncovers emotional and social insights difficult to extract via surveys or analytics. The human-to-human approach highlights feelings, relationships, and unarticulated needs.

Common Qualitative Research Methods

1. one-on-one interviews.

Conducting a one-on-one user interview involves an in-depth, conversational session between the researcher and a single user representative of the target audience. The interviewer guides the discussion using flexible, open-ended questions to elicit deep insights into the user’s perspectives, bebehavioursand needs.

One-on-one interviews shine when:

• Granular insights are needed from individuals based on their distinct circumstances and backgrounds.
• Understanding nuanced personal contexts, thought processes, pain points and emotions is critical.
• Users may be more forthcoming when peaking alone versus groups.
• The order and wording of questions benefit from real-time adaptation to the dialogue flow.
• Non-verbal cues and body language provide additional context to verbal answers.

Effective one-on-one interview tips include:

• Establishing rapport helps the user open up honestly. Avoid an interrogation vibe.
• Adapt questions based on responses, probing for richer details. Don’t just stick to a rigid script.
• Remain neutral and avoid leading questions that influence the user’s answers.
• Listen fully not just for what’s said but also what’s unspoken. Note emotions and inconsistencies.
• Thank the user for generously providing their time and perspectives. They feel valued.

One-on-one engagement allows deep discovery of individual motivations and contexts. It requires planning, active listening, and interpreting both verbal and non-verbal cues.

2. Focus Groups

A focus group brings together 6-12 users from the target audience for a moderated, interactive discussion focused on a product, service, or topic. Participants share perspectives and build on each other’s ideas in a conversational setting.

Focus groups are advantageous when:

• Real-time user interaction and feedback on concepts is desired.
• Sparking new ideas across users with different attitudes and behaviors is the goal.
• Observing how users influence each other reveals social dynamics and norms.
• A wider range of feedback is needed in the time available versus 1-on-1 interviews.

Tips for productive focus groups include:

• Recruit users who offer diverse perspectives but fit the target audience.
• Use a skilled, neutral moderator to facilitate constructive discussion and keep it on track.
• Explain ground rules upfront so all participants engage respectfully.
• Guide the flow from general to specific questions, leaving time for open discussion.
• Change up activities and stimuli (images, prototype demos) to sustain energy.
• Send recordings for further analysis of responses, interactions, and nonverbal behaviors.

3. User Diaries

User diaries involve having target audience members self-document and reflect on their experiences related to a product or service over time in an ongoing journal. Diary studies provide rich, longitudinal insights from the user’s perspective.

Diary studies are advantageous when:

• Capturing detailed, nuanced accounts of user journeys, motivations, pain points, and perceptions in a real-world context is needed.
• Users are geographically dispersed making direct observations or interviews impractical.
• Revealing changes over time rather than one-off interactions is the research goal.
• Users can clearly articulate their experiences through written or multimedia diaries.

Tips for productive diary studies include:

• Provide clear instructions and templates detailing what details to capture in diary entries over the study duration. Offer tools like written journals, audio recorders, or online forms.
• Set reasonable time commitments per day/week and study length based on depth required and user willingness.
• Check-in throughout the process to maintain participation, answer questions, and fix issues.
• Incentivize participation by compensating users for time spent journaling.
• Regularly review entries to identify compelling patterns and follow up for more context.
• Analyze entries to uncover key themes, insights, and opportunities related to the research aims.

Well-designed diary studies generate rich qualitative data by tapping into users’ direct experiences in their own words over time.

4. Ethnographic Studies

This involves immersing in users’ real-world environments to observe behaviors, understand contexts, and uncover unarticulated needs. Researchers embed directly in the user experience.

Ethnographies excel when:

• Deep insight into “unsaid” user behaviors, motivations, and pain points is needed.
• Directly observing users interacting in real environments provides more authenticity than interviews.
• Longer-term immersion reveals ingrained habits, rituals, and relationships.
• Users cannot fully or accurately articulate their own behaviors and motivations.

Tips for effective ethnographies:

• Clearly define the cultural/environmental scope for observations. Get necessary access.
• Utilize fly-on-the-wall observation techniques to avoid disrupting natural behaviors.
• Take comprehensive notes on user activities, interactions, tools, and environmental factors.
• Look for patterns in activities, conversations, rituals, artifacts, and relationships.
• Balance active observation with informal interview discussions to add context.
• Keep the human perspective; focus on empathy not just data gathering.

5. User Testing

User testing involves directly observing representative users interact with a product or prototype to identify usability issues and collect feedback. Participants work through realistic scenarios while researchers analyze successes, pain points, emotions, and verbal commentary.

User testing shines when:

• Feedback is needed on whether designs meet user expectations and needs.
• Identifying issues in workflows, navigation, learnability, and comprehension is important.
• Directly observing user behavior provides more reliable insights than what they self-report.
• Testing with iterations is built into the product development process.

Tips for effective user testing:

• Develop realistic usage scenarios and test scripts tailored to key research questions. Avoid bias.
• Recruit users matching target demographics and familiarity with the product domain.
• Set up comfortable testing spaces and moderation that put users at ease.
• Record sessions to capture insights from body language, tones, facial expressions etc.
• Analyze results for trends and outliers in behaviors, problems, emotions. Focus on learning.
• Iterate on solutions based on insights. Retest with new users to validate improvements.

6. Think-Aloud-Protocol

The think-aloud protocol method asks users to continuously verbalize their thoughts, feelings, and opinions while completing tasks with a product or prototype. Researchers observe and listen as users express in-the-moment reactions.

Think-aloud testing is ideal when:

• Understanding users’ in-the-moment decision making process and emotional responses is invaluable.
• Insights into points of confusion, frustration, delight can rapidly inform design iterations.
• Users can competently complete tasks while articulating their thinking concurrently.
• Limited time is available compared to extensive ethnographies or diary studies.

Effective think-aloud tips include:

• Provide clear instructions to share thoughts continuously throughout the session. Reassure users.
• Use open-ended prompts like “Tell me what you’re thinking” to encourage articulation without leading.
• Avoid interfering with the user’s process so their commentary feels natural.
• Have users complete realistic, task-based scenarios representative of the product experience.
• Capture direct quotes and time stamp compelling reactions to inform development priorities.

Think-aloud testing efficiently provides a window into users’ in-the-moment perceptions and decision making during hands-on product experiences

Applications Of Qualitative Research

Early product development stages:.

Qualitative user research is invaluable in the early ideation and discovery phases of product development when the problem space is still being explored.

Methods like interviews, ethnographies, and diary studies help researchers deeply understand user needs even before product ideas exist. Qualitative data informs initial user personas, journeys, and use cases so product concepts address real user problems.

Early qualitative insights ensure the end solution resonates with user contexts, attitudes, behaviors and motivations. This upfront user-centricity pays dividends across the entire product lifecycle.

Understanding user needs:

Qualitative techniques directly engage with end users to reveal not just what they do, but why they do it. Immersive interviews unveil users’ unstated needs because researchers can ask follow-up questions on the spot.

Observational studies capture nuanced behaviors that users themselves may not consciously realize or find important to mention. The qualitative emphasis on unlocking the “why” behind user actions is crucial for identifying needs that statistics alone miss. The human-centered discoveries spark innovation opportunities.

Problem identification:

The flexible and exploratory nature of qualitative research allows people to openly share the frustrations, anxieties, and pain points they experience.

Their candid words and emotions convey the meaning behind problems far better than numbers alone. For example, ethnographies and diaries may reveal users’ biggest problems stem not from one specific functionality issue but from misaligned workflows overall.

Qualitative techniques dig into the impacts of problems. The human perspectives guide better solutions.

Understanding context of use:

Well-designed qualitative studies meet users in their natural environments and daily lives. This enables researchers to observe how products and services integrate within existing ecosystems, habits, relationships, and workflows.

Key contextual insights are revealed that surveys alone could miss. For example, home interviews may show a smart speaker’s role in family dynamics. Contextual understanding ensures products fit seamlessly into users’ worlds.

Benefits Of Qualitative Research

Gaining deep insights:.

Qualitative techniques like long-form interviews, think-aloud protocol, and diary studies uncover not just surface-level behaviors and preferences, but the deeper meaning, motivations and emotions behind users’ actions.

Asking probing open-ended questions during in-depth conversations reveals nuanced perspectives on needs, thought processes, pain points, and ecosystems.

Immersive ethnographic observation also provides a holistic view of ingrained user habits and contexts. The richness of these qualitative findings informs truly human-centered innovation opportunities in a way quantitative data alone cannot.

Understanding user emotions:

Qualitative research effectively captures the wide range of emotional aspects of the user experience. Through ethnographic observation, researchers directly see moments of delight during usability testing or frustration while completing a task.

Diary studies provide outlets for users to express perceptions in their own words over time.

In interviews, asking follow-up questions on reactions and feelings provides more color than rating scales. This emotional intelligence helps designers move beyond functional requirements to empathetically address felt needs like enjoyment, trust, accomplishment, and belonging.

Exploring new ideas:

The flexible, conversational nature of qualitative research facilitates creative ideation.

Interactive sessions like focus groups or participatory design workshops allow people to organically share, build on, and iterate on ideas together.

Moderators can probe concepts through clarifying, non-leading questions to draw out nuance and have participants riff on each other’s thoughts. This process efficiently fosters new directions and uncovers latent needs that traditional surveys may never have identified.

Uncovering underlying reasons:

Asking “why” is fundamental to qualitative inquiry. Researchers go beyond documenting surface patterns to uncover the deeper motivations, contextual influences, ingrained habits, and thought processes driving user behaviours.

Observations combined with follow-up interviews provide well-rounded explanations for why people act as they do. For example, apparent routines may be based on social norms versus personal preferences. Qualitative findings explain behavior in a way quantitative data alone often cannot.

Facilitating empathy:

Approaches like ethnography facilitate stepping into the user’s shoes to immerse in their worldview.

Two-way dialogue through long-form interviews allows candid exchange as fellow humans, not detached research subjects. Insights derived from conversations and observations in real-world contexts inspire greater empathy among researchers for users’ needs, frustrations, delights, and realities. Teams feel connected to the people they aim to understand and serve.

Quantitative User Research

Quantitative research seeks to quantify user behaviors, preferences, and attitudes through numerical and statistical analysis. It emphasizes objective measurements and large sample sizes to uncover insights that can be generalized to the broader population.

Key characteristics of quantitative research include:

Structured methodology: 

Quantitative studies utilize highly structured data collection methods like surveys, structured user observation, and user metrics tracking. Surveys rely on closed-ended questions with predefined response options. Observation uses systematic checklists to tally predefined behaviors. This standardization allows mathematical analysis across all participants.

Numerical and statistical analysis: 

The numerical data gathered through quantitative research is analyzed using statistics, aggregates, regressions, and predictive modeling to draw conclusions. Researchers can analyze response frequencies, statistical relationships between variables, segmentation analyses, and predictive models based on the quantitative data.

Large representative samples: 

Quantitative research prioritizes large sample sizes that aim to be representative of the target population. For surveys, sufficient sample sizes are determined using power analyses to ensure findings are generalizable. Some common samples can be in the hundreds to thousands. This is in contrast to smaller qualitative samples aimed at diving deep into individual experiences.

Rating scales: 

Surveys and questionnaires rely heavily on numerical rating scales to quantify subjective attributes like satisfaction, ease-of-use, urgency, importance etc. Respondents rank options or choose numbers that correspond to stances. This assigns discrete values for comparison and statistical testing.

Objectivity : 

Quantitative research focuses on uncovering factual, observable and measurable truths about user behaviors, needs or perceptions. There is less emphasis on gathering subjective viewpoints, contexts, and detailed narratives which are hallmarks of qualitative research. The goal is objective, generalizable insights.

Common Quantitative Research Methods

1. online surveys.

Online surveys involve asking a sample of users to respond to a standardized set of questions delivered through web forms or email. Surveys gather self-reported data on attitudes, preferences, needs and behaviors that can be statistically analyzed.

Online surveys are ideal when:

• A large sample size is needed to gain representative insights from a population.
• Standardized, quantitative data on usages, perceptions, features etc. is desired.
• Users have the literacy level to understand and thoughtfully complete surveys.
• Stakeholders want quantitative metrics, benchmarks and models based on user data.

Effective online survey tips:

• Limit survey length and design clear, focused questions to maintain engagement.
• Structure questions and response options to enable statistical analysis for trends and relationships.
• Use rating scales to quantify subjective attributes like satisfaction, urgency, importance etc.
• Write simple, unambiguous statements users can assess consistently. Avoid leading or loaded language.
• Test surveys before deployment to refine questions and ensure technical functionality.
• Analyze results with statistics and visualizations to glean actionable, user-centered insights.

2. Usability Benchmarking

Usability benchmarking involves assessing a product’s ease-of-use against quantified performance standards and metrics. Researchers conduct structured usability tests to gather performance data that is compared to benchmarks.

Usability benchmarking is ideal when:

• Quantitative goals exist for critical usability metrics like task completion rate, errors, time-on-task, perceived ease-of-use.
• Comparing usability data to other products, previous versions, or industry standards is desired.
• There is a focus on improving usability measured through standardized objectives versus qualitative insights.

Effective usability benchmarking tips:

• Identify key usage tasks and scenarios that align to business goals to standardize testing.
• Leverage established usability metrics like System Usability Scale (SUS) to enable benchmarking.
• Conduct structured tests with representative users on targeted tasks.
• Analyze metrics using statistical methods to surface enhancements tied to benchmarks.
• Set incremental usability goals and continue testing post-launch to drive improvements.

3. Analytics

Analytics involves collecting and analyzing usage data from products to uncover patterns, metrics, and insights about real customer behaviors. Sources like web analytics, app metrics, and usage logs are common.

Analytics excel when:

• Objective data on how customers are actually using a product is needed to optimize features and workflows.
• Large volumes of real customer usage data are available for analysis.
• Revealing segments based on behavioral differences can inform personalized experiences.
• Improving key performance indicators and quantifying impact is a priority.

Effective analytics tips:

• Identify key questions and metrics aligned to business goals to focus analysis. Common metrics are conversions, engagement, retention etc.
• Leverage tools like Google Analytics to collect event and behavioral data at scale.
• Analyze trends, run statistical tests, and build models to surface insights from noise.
• Make insights actionable by tying to opportunities like improving at-risk customer retention.
• Continuously analyze data over time and across updates to optimize ongoing enhancements.

Applications of Quantitative Research

Validating hypotheses:.

Quantitative studies provide statistically robust methods to validate assumptions and confirm hypotheses related to user behaviors or preferences.

After initial qualitative research like interviews raise theories about user needs or pain points, quantitative experiments can verify if those hypotheses hold true at a larger scale.

For example, A/B testing can validate if a new checkout flow improves conversion rates as hypothesized based on earlier usability studies. Statistical validation boosts confidence that recommended changes will have the expected impact on business goals.

Generalizing findings:

The large, representative sample sizes and standardized methodologies in quantitative studies allow findings to be generalized to the full target population with known confidence intervals.

Proper sampling methods ensure data reflects the intended audience demographics, attitudes, and behaviours.

If certain usability benchmarks hold true across hundreds of participants, they are assumed to apply to similar users across that segment. This enables product improvements to be made for broad groups based on generalizable data.

Tracking granular changes:

Quantitative data enables even subtle changes over time, iterative tweaks, or segmented differences to be precisely tracked using consistent metrics.

Longitudinal surveys can pinpoint if customer satisfaction trends upward or downward month-to-month based on new features.

Web analytics continuously monitor click-through rates over years to optimize paths. Controlled A/B tests discern the isolated impact of iterative enhancements. The reliability of quantitative metrics ensures changes are spotted quickly.

Quantifying problem severity:

Statistical analysis in quantitative research can accurately define the frequency and severity of user problems.

For example, an eye-tracking study might uncover 60% of users miss a key navigation element. Surveys can determine what percentage of customers are highly frustrated by unclear documentation.

Quantifying the scope and business impact of issues in this way allows product teams to confidently prioritize the problems with greatest value to solve first.

Benefits of Quantitative Research

Quantifying user behaviours:.

Quantitative methods like analytics, surveys, and usability metrics capture concrete, observable data on how users interact with products.

Usage metrics quantify engagement levels, conversion rates, task completion times, feature adoption, and more. The numerical data enables statistical analysis to uncover trends, model outcomes, and optimize products based on revealed behaviours versus subjective hunches. Quantification also facilitates benchmarking and goal-setting.

Validating hypotheses rigorously:

Quantitative experiments like A/B tests and controlled usability studies allow assumptions and theories about user behaviors to be validated with statistical rigour.

Significant results provide confidence that patterns are real and not due to chance alone. Teams can test hypotheses raised in past qualitative research to prevent high-risk decisions based on false premises. Statistical validation lends credibility to recommended changes expected to impact key metrics.

Precisely tracking granular trends:

The consistent, standardized metrics in quantitative studies powerfully track usage trends over time, across releases, and between user segments. For example, longitudinal surveys can monitor how satisfaction ratings shift month-to-month based on new features.

Web analytics uncover how click-through rates trend up or down over years as needs evolve. Controlled tests isolate the impact of each iteration. Quantitative data spots subtle changes.

Informed decision-making:

Quantitative data provides concrete, measurable evidence of user behaviours, needs, and pain points for informed decision-making.

Metrics on usage, conversions, completion rates, satisfaction, and more enable teams to identify and prioritize issues based on representative data versus hunches. Leaders can justify decisions using statistical significance, projected optimization gains, and benchmark comparisons.

Mitigating biases:

The focus on objective, observable metrics can reduce biases that may inadvertently influence qualitative findings.

Proper sampling methods, significance testing, and controlled experiments also minimize distortions from individual perspectives. While no research is assumption-free, quantitative techniques substantially limit bias through rigorous design and large sample sizes.

Comparing Qualitative and Quantitative User Research

Here is a comparison of qualitative and quantitative user research in a table format:

When to Use Each Method

When to use qualitative research:.

• Early in the product development lifecycle during the fuzzy front-end stages. Open-ended qualitative research is critical for discovering user needs, pain points, and behaviors when the problems are unclear. Qualitative data provides the rich contextual insights required to guide initial solution ideation and design before quantifying anything. Methods like in-depth interviews and contextual inquiries reveal pain points that pure quantitative data often overlooks.
• When research questions are ambiguous, expansive, or nuanced at the start. Qualitative methods can flexibly follow where the data leads to uncover unexpected themes. The fluid approach adapts to capture unforeseen insights, especially on subjective topics like emotions and motivations that require deep probing. Qualitative approaches excel at understanding complex “why” and “how” aspects behind behaviors.
• If seeking highly vivid, detailed narratives of user motivations, ecosystems, thought processes, and needs. Qualitative data maintains all the situational nuance and color intact, not condensed statistically. User stories and perspectives come through with empathy and emotion versus sterile numbers. This level of detail informs truly human-centered solutions.
• During discovery of new market opportunities, expanding into new segments, or exploringnew capabilities with many unknowns. Flexible qualitative digging uncovers fresh territories before attempting to quantify anything. Fuzzy front-end exploration is suited to qualitative exploration.

When to use quantitative research:

• To validate assumptions, theories, and qualitative insights at scale using statistical rigor. Quantitative data provides the confidence that patterns seen are significant and not just anecdotal findings. Surveys, controlled experiments, and metrics test hypotheses raised during qualitative discovery. The statistics offer credibility.
• If research questions aim to precisely quantify target audience behaviors, attitudes, and preferences. Quantitative methods objectively measure “what” users do without room for fuzzy interpretation. The numerical data acts as a precise compass for decision-making.
• When clear metrics and benchmarks are required to set optimization goals, compare design solutions, and tightly track progress. Quantitative data delivers concrete KPIs to orient teams and chart enhancement impact.
• To isolate the precise impact of changes over time or between design solutions by tracking standardized metrics. Controlled A/B tests discern what improvements unequivocally moved key metrics versus speculation.

Frequently Asked Questions (FAQs)

1. What is the main difference between qualitative and quantitative user research?

The main difference is that qualitative research aims to uncover the “why” behind user behaviors through subjective, non-numerical data like interviews and observations. Quantitative research focuses on quantifying the “what” through objective, numerical data like metrics and statistics.

2. Can qualitative and quantitative user research be used together?

Absolutely. Many researchers use a mixed methods approach that combines both qualitative and quantitative techniques to get comprehensive insights. Qualitative research can uncover problems to quantify, while quantitative testing can validate qualitative theories.

3. How do I choose between qualitative and quantitative user research?

Choose based on your current product stage, questions, timeline, and resources. Qualitative research is best for exploratory discovery, while quantitative confirms hypotheses. Use qualitative first, then quantitative or a mix of both.

4. What are some common tools for conducting qualitative and quantitative user research?

Qualitative tools include interviews, focus groups, surveys, user testing and more. Quantitative tools include web analytics, App store metrics, usability metrics, controlled experiments and surveys.

5. What are the limitations of qualitative and quantitative user research?

Qualitative findings are not statistically representative. Quantitative data lacks rich behavioral details. Using both offsets the weaknesses.

• A/B Testing
• Company News
• Conversion Rate Optimisation
• CRO Tools and Resources
• Experimentation Articles
• Google Analytics
• Personalization
• Usability Testing
• User Research
• The Ultimate Guide To Integrating Voice of the Customer into CRO
• Scaling User Research For Enterprise CRO
• Understanding the Core Concepts of CRO Personalization
• Dynamic Content Personalization: Tips and Best Practices
• The Impact of AB Testing on User Retention
• The 7Ps Of Marketing
• What Is A CRO Test? Definition, Types & Examples
• Close-Ended Questions - A Complete Guide
• Introduction To MultiVariate Testing: A Complete Guide (2023)
• The Complete Guide To Personalization In CRO
• Top 19 Best CRO Books (recommended by experts)
• The surprising truth about Amazon and Booking.com's culture of experimentation
• A Guide To The Ten Usability Heuristics
• Explore vs Exploit: Finding the Balance in CRO

Is your CRO programme delivering the impact you hoped for ?

Benchmark your CRO now for immediate, free report packed with ACTIONABLE insights you and your team can implement today to increase conversion.

Takes only two minutes

If your CRO programme is not delivering the highest ROI of all of your marketing spend, then we should talk.

Before you go...

“The world’s leading companies are using experimentation to generate better results for the whole business.

Download our latest ebook to discover how businesses have made the shift from CRO to experimentation and how you can too.

Join the next one!

Be the first to know about future events with top speakers from the CRO industry.

Cookie consent

We use our own and third-party cookies to show you more relevant content based on your browsing and navigation history. Please accept or manage your cookie settings below. Here's our   cookie policy

• Product overview What is Typeform?
• Form builder Signups and orders
• Survey maker Research and feedback
• Quiz maker Trivia and product match
• Typeform for Growth For B2B marketers
• Product updates Latest feature releases
• HubSpot for Typeform Our new partner

• Human resources
• Customer success
• Acquire customers
• Get feedback
• Do research

• Help center Find quick answers
• Community Share and interact
• Contact us Speak to our team
• Partners Browse or join
• Careers Join our team

• → Qualitative vs. quantitative research...

Qualitative vs. quantitative research: A simple guide

Quantitative research deals with numbers and statistics, while qualitative research involves pulling information from experiences and stories.

Latest posts on Tips

Typeform    |    08.2024

Typeform    |    07.2024

From Tesla to Tushy, every successful brand is built on a foundation of both quantitative and qualitative research. Marketers and product developers use this zero-party data to frame their advertising strategies, product positioning, and brand voice—basically, everything that goes into designing and selling a product or service.

When it comes to qualitative vs. quantitative research, both methods have their benefits and drawbacks in certain applications. We break down what you need to know before running your next round of market research. 

Qualitative vs. quantitative research: What’s the difference?

Quantitative research counts and measures numbers to find statistical patterns, while qualitative research is a deep dive into understanding people’s thoughts and experiences. They're similar in that they both aim to uncover valuable insights, but they use different tools and approaches to do so.

But don’t be fooled into thinking that one research method is better than the other—both require systematically applied research methods and analysis.

What is qualitative research and data?

Qualitative research is like the Sherlock Holmes of the research world—it seeks to uncover the hidden stories, motivations, and intricacies that numbers can't reveal. Instead of crunching data, it dives deep into people's experiences, thoughts, and feelings to help explain certain behaviors and patterns. 

In qualitative research, it's not about numbers but rather words, pictures, and observations. You'll collect rich, unstructured data via interviews, focus group discussions, or open-ended surveys. 

Say you're a marketing rep keen on understanding how people perceive your smartphone brand. 

First, you organize a series of in-depth interviews with smartphone users, asking open-ended questions about their experiences with the brand. Participants share stories about their interactions, likes, dislikes, and emotional connections with the product. You also delve into social media posts, online reviews, and forum discussions to gauge the brand's online reputation.

As you analyze this data, patterns begin to emerge. You find that users consistently describe the brand as "innovative" and "user-friendly." However, you also discover a recurring frustration with battery life and customer support. Qualitative research not only provides you with insights into how people perceive the brand but also dives into the emotional nuances behind their perceptions. Armed with this knowledge, you can fine-tune your advertising campaigns and product improvements to align with your target audience's genuine feelings and experiences.

Pros and cons of qualitative research

Qualitative research is your go-to when you want to explore the human side of data. It's like having a heart-to-heart conversation with your research subjects. Just keep in mind that, like any detective work, it comes with its own quirks and challenges.

Deep insights: It's great at uncovering the "whys" and "hows" behind human behavior, providing rich insights that quantitative data can miss.

Flexible and exploratory: Qualitative research allows for flexibility, so you can adapt your questions and approach when you face the unexpected.

Humanizing data: Unlike numbers, qualitative research humanizes data by bringing stories and personal experiences to the forefront. It's perfect for capturing human nuances and emotions.

Subjectivity: Different researchers might draw different conclusions from the same data based on their own personal feelings, experiences, or opinions, so it's crucial to stay aware of potential bias.

Resource-intensive: Qualitative research demands time and effort. Conducting interviews, transcribing, and analyzing data is a labor-intensive process, which might not suit all budgets or timelines.

Smaller samples: Your pool of participants tends to be smaller compared to quantitative research, making it challenging to generalize findings to a larger population. It's like diving deep into a few personal stories rather than looking at the bigger picture.

Can’t always be automated: Unlike quantitative research, where you can automate data collection and analysis with software, qualitative research relies heavily on human interaction and interpretation. You can, however, create a survey with open-ended questions to collect qualitative data. Better yet, try our VideoAsk feature, which allows you to ask questions via pre-recorded video and lets respondents answer in video, voice, or text format, preserving that ever-important human element that defines qualitative data. 

What is quantitative research and data?

Quantitative research is all about numbers, statistics, and cold, hard data. It’s more structured and objective and helps reduce researcher biases . It gets at the “what” of a person’s behavior by answering questions like how many, how often, and to what extent?

Let’s look at quantitative research in action. Imagine you're trying to pinpoint the target market for your new fitness app. You survey the app's users, collecting data on their age, gender, location, and fitness habits. The data reveals that 75% of your target users are ages 18-34, with a nearly even split between men and women. You also notice that users in urban areas are 20% more likely to use your app regularly than those in rural areas.

Quantitative research doesn't stop at just counting, though. It's also about analyzing data to spot trends and differences. In this case, it's clear that your core audience consists of younger adults in urban settings, and you can tailor your marketing strategies and app features to better cater to this demographic. So, if you're a number-crunching, stats-loving kind of researcher, quantitative research is your jam.

Pros and cons of quantitative research

In a nutshell, quantitative research is your go-to when you want solid, numerical answers. But remember, it won't tell you the whole story, and sometimes, life's questions are a bit too complex for a numbers-only approach. Keep these pros and cons in mind when running your next quantitative study:

Precision with numbers: Quantitative research is like a laser-guided missile for numbers. It offers precise measurements and statistical analysis, which is great when you need concrete answers.

Reproducibility: It's a cookie-cutter approach—your methods and results can be replicated by others, making it a cornerstone of scientific rigor.

Generalizability: You can often apply findings to a larger population—if it works for one group, it might work for a similar one.

Limited bias: Quantitative research can be a bias-buster. With structured surveys, standardized data collection methods, and statistical analysis, it's easier to minimize researcher bias and keep the study objective. 

Fewer resources: If you're watching your budget, quantitative research may give you more bang for your buck. It often requires fewer resources in terms of time, personnel, and money, making it a practical choice, especially for smaller-scale research projects.

Limited depth: While it's king of numbers, quantitative research can be a bit shallow in understanding. It's like knowing the “what” but not the “why.”

Context ignored: Sometimes context gets lost in a sea of numbers, and you might miss the bigger picture.

Inflexibility: If your research question isn't easily quantifiable, you might end up with results that are difficult to decipher. Not everything can be counted or measured.

Which is better: Qualitative or quantitative research?

It’s a trick question. We’re not pitting qualitative and quantitative research against each other. However, one may prove more useful than the other, depending on your research goals. 

For example, it’s best to stick with qualitative research when:

You want to explore in-depth: Choose qualitative research when you need a deep understanding of a complex phenomenon, like customer perceptions or human behavior. It's like peeling back the layers of an onion to uncover the core.

You need to generate hypotheses: Qualitative research is fantastic for generating ideas or hypotheses that you can later test with quantitative research. 

You value the human perspective: If you want to capture emotions, stories, and personal experiences, opt for qualitative research. It's your go-to when you're interested in "the why" rather than just "the what."

On the other hand, quantitative research may prove more valuable if:

You need to measure and quantify: If you're after hard numbers, like percentages, averages, or correlations, quantitative research is your go-to.

You want to generalize to a larger population: Quantitative research allows you to make statistically valid generalizations to a broader audience. If you plan to reach a wide market, this is your best bet.

You prefer structured and standardized data collection: When consistency and minimizing bias are critical, quantitative research methods like surveys and online tests provide a structured and uniform approach. 

However, you aren’t limited to just one type of research method. You can use both qualitative and quantitative data to give you the most insightful information when:

You need a comprehensive understanding: Sometimes, using both qualitative and quantitative research sequentially is the ideal approach. Start with qualitative research to explore a topic, identify key variables, and generate hypotheses. Then, use quantitative research to test those hypotheses on a larger scale, ensuring a more comprehensive understanding.

You want to validate findings: When you've conducted qualitative research and want to make sure your findings are not just anecdotal, quantitative research can validate and generalize your insights to a broader population.

You're tackling a complex problem: For multifaceted issues, using both approaches can provide a well-rounded view. Qualitative research can uncover the depth and nuances, while quantitative research can quantify the extent of the issue and help prioritize actions.

Quantitative research provides evidence and predictions. Qualitative research provides context and explanations. So which one is best for you? That depends on the questions you need answered.

Research methods

Quantitative and qualitative research methods are systematic ways of collecting data and testing hypotheses. And guess what? It’s something you already do all the time.

We constantly take in information from our surroundings to figure out how to interact with the people around us.

The same goes for market research . A company tries to learn more about their customers and the market. Why? To develop an effective marketing plan or tweak one they already have. The method you use to do this depends on the data that will answer your key questions.

Qualitative research methods

Here are some of the most common qualitative research methods:

In-depth interviews: Known as IDI in market research circles, in-depth interviews are ideal for digging into people’s attitudes and experiences. 

Case studies: In-depth analysis of a single case or a few cases are best suited for investigating unique or complex cases in depth

Focus groups: These are effective for getting several opinions in a conversational format. Participants lead the discussion, while a facilitator guides the conversation through a list of topics, questions, or projective exercises.

Participant observation: Simply engaging and observing your audience day-to-day provides a firsthand view of how people interact in real-life situations.

Historical research: Exploring historical documents and records helps you examine the past through primary and secondary sources, contributing to our understanding of historical events and trends and how they may relate to the current scenario.

Qualitative surveys: Surveys comprised of open-ended questions provide an automated way to receive qualitative data through a quantitative approach..

Ethnography: Ethnography is a broad market research approach that involves all of the methods above in order to gain a comprehensive understanding of the culture or community being studied. 

Quantitative research methods

Here are some of the most common quantitative research methods:

Surveys: Surveys conducted online, over the phone, and even in person with structured interview questionnaires are an efficient way of collecting data from a large pool of participants. 

Polls: Polls are one- or two-question surveys that are often used to gauge public opinion on an important matter (or a frivolous matter—it’s your poll). Because polls are only one or two questions, analysis is pretty much immediate.

Structured observation: This is a structured form of ethnography used to measure certain actions or behaviors, such as tracking how many boxes of cereal people pick up before choosing one to purchase.

Experiment: Market researchers conduct controlled, manipulated, or randomized experiments to understand how specific variables influence outcomes through methods like A/B testing or pilot testing.

Quizzes: Answering a few general questions to find out which Harry Potter character you are may seem like fun and games, but interactive quizzes are a great tool for gathering information while keeping your audience engaged. 

Secondary data analysis: This cost-effective research method taps into big existing datasets like government databases or company records to pull relevant data. 

Mixed research methods

Mixed research methods combine both qualitative and quantitative approaches to provide a comprehensive understanding of the question at hand. Some of the most common mixed research methods include:

User testing: You’ve heard the phrase “Show, don’t tell.” So rather than asking people to explain their experiences, why not have them show you? User testing can tell you where you thrive and fall short, so you can adjust your marketing strategy accordingly.

Help transcripts: Live chat or call transcripts can yield both qualitative and quantitative data. Reading and coding them can help you understand people’s pain points and challenges throughout your conversion funnel.

Customer reviews: Look beyond your own surveys and check sites like Yelp or Google reviews. What are people saying about you? What do they like and dislike? The things people say and how often they say it can yield robust qualitative and quantitative data.

Data analysis

Data analysis is the search for patterns in data, followed by the interpretation of that information to help explain why those patterns are there.

It’s important to keep in mind that quantitative and qualitative data aren't mutually exclusive.

Qualitative data can be translated into quantitative data. For example, you could count the number of times interviewees used a particular word to describe your product to yield quantitative data.

Similarly, quantitative methods of analysis require you to explain what the patterns mean and connect them to other parts of your business—a qualitative exercise!

Qualitative data analysis example

Qualitative data can be difficult to analyze since it’s largely made up of text, images, videos, and open-ended responses instead of numbers. Examples of qualitative data analysis include:

Thematic analysis: Identifying and categorizing recurring themes, patterns, or concepts within the data to uncover the most prevalent and significant themes in your dataset

Content analysis: Examining large amounts of text, visuals, or audio content to identify themes or patterns 

Discourse analysis: Dissecting the language used in the data to understand how individuals or groups construct meaning and social reality through their discourse

Cross-case analysis: Comparing and contrasting multiple cases to identify commonalities and differences, helping to develop broader insights

Quantitative data analysis example

Quantitative data analysis is all about crunching numbers. It can involve presenting data models such as graphs, charts, tables, probabilities, and more.

Tools like Excel, R, and Stata make it easy to track quantitative data like:

Average scores and means

The number of times a specific response is recorded

Connections or potential cause-and-effect relationships between two or more variables

The reliability and validity of results 

Get the right data with Typeform

Congrats—you’ve learned all about the differences between qualitative vs. quantitative research.

Now, the key to successful data collection is iteration.

That doesn’t mean doing the same thing again and again.

It means continually returning to your questions, methods, and data to spark new ideas and insights that'll level up your research —and your business.

Typeform makes it easy to design and automate forms that collect both quantitative and qualitative data—no extensive interviews or focus groups required. With conditional formatting and various question types, you can gather the information you need to get more customers.

About the author

Lydia is a content marketer with experience across both the B2B and B2C landscapes. Besides marketing and content, she's really into her dog Louie.

Liked that? Check these out:

How to increase business through customer feedback

No time to spare? Grow revenue fast by asking clients what they want.

Marc Cinnani    |    03.2016

3 benefits of using customer feedback for product-led growth

Customer feedback can fuel product-led growth by guiding innovation and improving user experiences. Here's how to harness this feedback to stay ahead in a customer-centric market.

Typeform    |    06.2024

Strategies for Zero-Party Data Collection: 9 Proven Methods + Examples

There are all sorts of small ways to collect the data you need from customers. Here are 9 expert strategies to get the data you need by asking your audience directly.

Lydia Kentowski    |    02.2024

The differences between qualitative and quantitative research methods

Last updated

15 January 2023

Reviewed by

Two approaches to this systematic information gathering are qualitative and quantitative research. Each of these has its place in data collection, but each one approaches from a different direction. Here's what you need to know about qualitative and quantitative research.

All your data in one place

Analyze your qualitative and quantitative data together in Dovetail and uncover deeper insights

• The differences between quantitative and qualitative research

The main difference between these two approaches is the type of data you collect and how you interpret it. Qualitative research focuses on word-based data, aiming to define and understand ideas. This study allows researchers to collect information in an open-ended way through interviews, ethnography, and observation. You’ll study this information to determine patterns and the interplay of variables.

On the other hand, quantitative research focuses on numerical data and using it to determine relationships between variables. Researchers use easily quantifiable forms of data collection, such as experiments that measure the effect of one or several variables on one another.

• Qualitative vs. quantitative data collection

Focusing on different types of data means that the data collection methods vary. 

Quantitative data collection methods

As previously stated, quantitative data collection focuses on numbers. You gather information through experiments, database reports, or surveys with multiple-choice answers. The goal is to have data you can use in numerical analysis to determine relationships.

Qualitative data collection methods

On the other hand, the data collected for qualitative research is an exploration of a subject's attributes, thoughts, actions, or viewpoints. Researchers will typically conduct interviews , hold focus groups, or observe behavior in a natural setting to assemble this information. Other options include studying personal accounts or cultural records. 

• Qualitative vs. quantitative outcomes

The two approaches naturally produce different types of outcomes. Qualitative research gains a better understanding of the reason something happens. For example, researchers may comb through feedback and statements to ascertain the reasoning behind certain behaviors or actions.

On the other hand, quantitative research focuses on the numerical analysis of data, which may show cause-and-effect relationships. Put another way, qualitative research investigates why something happens, while quantitative research looks at what happens.

• How to analyze qualitative and quantitative data

Because the two research methods focus on different types of information, analyzing the data you've collected will look different, depending on your approach.

Analyzing quantitative data

As this data is often numerical, you’ll likely use statistical analysis to identify patterns. Researchers may use computer programs to generate data such as averages or rate changes, illustrating the results in tables or graphs.

Analyzing qualitative data

Qualitative data is more complex and time-consuming to process as it may include written texts, videos, or images to study. Finding patterns in thinking, actions, and beliefs is more nuanced and subject to interpretation. 

Researchers may use techniques such as thematic analysis , combing through the data to identify core themes or patterns. Another tool is discourse analysis , which studies how communication functions in different contexts.

• When to use qualitative vs. quantitative research

Choosing between the two approaches comes down to understanding what your goal is with the research.

Qualitative research approach

Qualitative research is useful for understanding a concept, such as what people think about certain experiences or how cultural beliefs affect perceptions of events. It can help you formulate a hypothesis or clarify general questions about the topic.

Quantitative research approach

On the other hand, quantitative research verifies or tests a hypothesis you've developed, or you can use it to find answers to those questions. 

Mixed methods approach

Often, researchers use elements of both types of research to provide complex and targeted information. This may look like a survey with multiple-choice and open-ended questions.

• Benefits and limitations

Of course, each type of research has drawbacks and strengths. It's essential to be aware of the pros and cons.

Qualitative studies: Pros and cons

This approach lets you consider your subject creatively and examine big-picture questions. It can advance your global understanding of topics that are challenging to quantify.

On the other hand, the wide-open possibilities of qualitative research can make it tricky to focus effectively on your subject of inquiry. It makes it easier for researchers to skew the data with social biases and personal assumptions. There’s also the tendency for people to behave differently under observation.

It can also be more difficult to get a large sample size because it's generally more complex and expensive to conduct qualitative research. The process usually takes longer, as well. 

Quantitative studies: Pros and cons

The quantitative methodology produces data you can communicate and present without bias. The methods are direct and generally easier to reproduce on a larger scale, enabling researchers to get accurate results. It can be instrumental in pinning down precise facts about a topic. 

It is also a restrictive form of inquiry. Researchers cannot add context to this type of data collection or expand their focus in a different direction within a single study. They must be alert for biases. Quantitative research is more susceptible to selection bias and omitting or incorrectly measuring variables.

• How to balance qualitative and quantitative research

Although people tend to gravitate to one form of inquiry over another, each has its place in studying a subject. Both approaches can identify patterns illustrating the connection between multiple elements, and they can each advance your understanding of subjects in important ways. 

Understanding how each option will serve you will help you decide how and when to use each. Generally, qualitative research can help you develop and refine questions, while quantitative research helps you get targeted answers to those questions. Which element do you need to advance your study of the subject? Can both of them hone your knowledge?

Open-ended vs. close-ended questions

One way to use techniques from both approaches is with open-ended and close-ended questions in surveys. Because quantitative analysis requires defined sets of data that you can represent numerically, the questions must be close-ended. On the other hand, qualitative inquiry is naturally open-ended, allowing room for complex ideas.

An example of this is a survey on the impact of inflation. You could include both multiple-choice questions and open-response questions:

1. How do you compensate for higher prices at the grocery store? (Select all that apply)

A. Purchase fewer items

B. Opt for less expensive choices

C. Take money from other parts of the budget

D. Use a food bank or other charity to fill the gaps

E. Make more food from scratch

2. How do rising prices affect your grocery shopping habits? (Write your answer)

We need qualitative and quantitative forms of research to advance our understanding of the world. Neither is the "right" way to go, but one may be better for you depending on your needs. 

Learn more about qualitative research data analysis software

Should you be using a customer insights hub.

Do you want to discover previous research faster?

Do you share your research findings with others?

Do you analyze research data?

Start for free today, add your research, and get to key insights faster

Editor’s picks

Last updated: 18 April 2023

Last updated: 27 February 2023

Last updated: 22 August 2024

Last updated: 5 February 2023

Last updated: 16 August 2024

Last updated: 9 March 2023

Last updated: 30 April 2024

Last updated: 12 December 2023

Last updated: 11 March 2024

Last updated: 4 July 2024

Last updated: 6 March 2024

Last updated: 5 March 2024

Last updated: 13 May 2024

Latest articles

Related topics, .css-je19u9{-webkit-align-items:flex-end;-webkit-box-align:flex-end;-ms-flex-align:flex-end;align-items:flex-end;display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-flex-direction:row;-ms-flex-direction:row;flex-direction:row;-webkit-box-flex-wrap:wrap;-webkit-flex-wrap:wrap;-ms-flex-wrap:wrap;flex-wrap:wrap;-webkit-box-pack:center;-ms-flex-pack:center;-webkit-justify-content:center;justify-content:center;row-gap:0;text-align:center;max-width:671px;}@media (max-width: 1079px){.css-je19u9{max-width:400px;}.css-je19u9>span{white-space:pre;}}@media (max-width: 799px){.css-je19u9{max-width:400px;}.css-je19u9>span{white-space:pre;}} decide what to .css-1kiodld{max-height:56px;display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}@media (max-width: 1079px){.css-1kiodld{display:none;}} build next, decide what to build next, log in or sign up.

Get started for free

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”

Request Information

AU Program Helper

This AI chatbot provides automated responses, which may not always be accurate. By continuing with this conversation, you agree that the contents of this chat session may be transcribed and retained. You also consent that this chat session and your interactions, including cookie usage, are subject to our  privacy policy .

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• J Korean Med Sci
• v.37(16); 2022 Apr 25

A Practical Guide to Writing Quantitative and Qualitative Research Questions and Hypotheses in Scholarly Articles

Edward barroga.

1 Department of General Education, Graduate School of Nursing Science, St. Luke’s International University, Tokyo, Japan.

Glafera Janet Matanguihan

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

The development of research questions and the subsequent hypotheses are prerequisites to defining the main research purpose and specific objectives of a study. Consequently, these objectives determine the study design and research outcome. The development of research questions is a process based on knowledge of current trends, cutting-edge studies, and technological advances in the research field. Excellent research questions are focused and require a comprehensive literature search and in-depth understanding of the problem being investigated. Initially, research questions may be written as descriptive questions which could be developed into inferential questions. These questions must be specific and concise to provide a clear foundation for developing hypotheses. Hypotheses are more formal predictions about the research outcomes. These specify the possible results that may or may not be expected regarding the relationship between groups. Thus, research questions and hypotheses clarify the main purpose and specific objectives of the study, which in turn dictate the design of the study, its direction, and outcome. Studies developed from good research questions and hypotheses will have trustworthy outcomes with wide-ranging social and health implications.

INTRODUCTION

Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses. 1 , 2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results. 3 , 4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the inception of novel studies and the ethical testing of ideas. 5 , 6

It is crucial to have knowledge of both quantitative and qualitative research 2 as both types of research involve writing research questions and hypotheses. 7 However, these crucial elements of research are sometimes overlooked; if not overlooked, then framed without the forethought and meticulous attention it needs. Planning and careful consideration are needed when developing quantitative or qualitative research, particularly when conceptualizing research questions and hypotheses. 4

There is a continuing need to support researchers in the creation of innovative research questions and hypotheses, as well as for journal articles that carefully review these elements. 1 When research questions and hypotheses are not carefully thought of, unethical studies and poor outcomes usually ensue. Carefully formulated research questions and hypotheses define well-founded objectives, which in turn determine the appropriate design, course, and outcome of the study. This article then aims to discuss in detail the various aspects of crafting research questions and hypotheses, with the goal of guiding researchers as they develop their own. Examples from the authors and peer-reviewed scientific articles in the healthcare field are provided to illustrate key points.

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

A research question is what a study aims to answer after data analysis and interpretation. The answer is written in length in the discussion section of the paper. Thus, the research question gives a preview of the different parts and variables of the study meant to address the problem posed in the research question. 1 An excellent research question clarifies the research writing while facilitating understanding of the research topic, objective, scope, and limitations of the study. 5

On the other hand, a research hypothesis is an educated statement of an expected outcome. This statement is based on background research and current knowledge. 8 , 9 The research hypothesis makes a specific prediction about a new phenomenon 10 or a formal statement on the expected relationship between an independent variable and a dependent variable. 3 , 11 It provides a tentative answer to the research question to be tested or explored. 4

Hypotheses employ reasoning to predict a theory-based outcome. 10 These can also be developed from theories by focusing on components of theories that have not yet been observed. 10 The validity of hypotheses is often based on the testability of the prediction made in a reproducible experiment. 8

Conversely, hypotheses can also be rephrased as research questions. Several hypotheses based on existing theories and knowledge may be needed to answer a research question. Developing ethical research questions and hypotheses creates a research design that has logical relationships among variables. These relationships serve as a solid foundation for the conduct of the study. 4 , 11 Haphazardly constructed research questions can result in poorly formulated hypotheses and improper study designs, leading to unreliable results. Thus, the formulations of relevant research questions and verifiable hypotheses are crucial when beginning research. 12

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

Excellent research questions are specific and focused. These integrate collective data and observations to confirm or refute the subsequent hypotheses. Well-constructed hypotheses are based on previous reports and verify the research context. These are realistic, in-depth, sufficiently complex, and reproducible. More importantly, these hypotheses can be addressed and tested. 13

There are several characteristics of well-developed hypotheses. Good hypotheses are 1) empirically testable 7 , 10 , 11 , 13 ; 2) backed by preliminary evidence 9 ; 3) testable by ethical research 7 , 9 ; 4) based on original ideas 9 ; 5) have evidenced-based logical reasoning 10 ; and 6) can be predicted. 11 Good hypotheses can infer ethical and positive implications, indicating the presence of a relationship or effect relevant to the research theme. 7 , 11 These are initially developed from a general theory and branch into specific hypotheses by deductive reasoning. In the absence of a theory to base the hypotheses, inductive reasoning based on specific observations or findings form more general hypotheses. 10

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

Research questions and hypotheses are developed according to the type of research, which can be broadly classified into quantitative and qualitative research. We provide a summary of the types of research questions and hypotheses under quantitative and qualitative research categories in Table 1 .

Research questions in quantitative research

In quantitative research, research questions inquire about the relationships among variables being investigated and are usually framed at the start of the study. These are precise and typically linked to the subject population, dependent and independent variables, and research design. 1 Research questions may also attempt to describe the behavior of a population in relation to one or more variables, or describe the characteristics of variables to be measured ( descriptive research questions ). 1 , 5 , 14 These questions may also aim to discover differences between groups within the context of an outcome variable ( comparative research questions ), 1 , 5 , 14 or elucidate trends and interactions among variables ( relationship research questions ). 1 , 5 We provide examples of descriptive, comparative, and relationship research questions in quantitative research in Table 2 .

Hypotheses in quantitative research

In quantitative research, hypotheses predict the expected relationships among variables. 15 Relationships among variables that can be predicted include 1) between a single dependent variable and a single independent variable ( simple hypothesis ) or 2) between two or more independent and dependent variables ( complex hypothesis ). 4 , 11 Hypotheses may also specify the expected direction to be followed and imply an intellectual commitment to a particular outcome ( directional hypothesis ) 4 . On the other hand, hypotheses may not predict the exact direction and are used in the absence of a theory, or when findings contradict previous studies ( non-directional hypothesis ). 4 In addition, hypotheses can 1) define interdependency between variables ( associative hypothesis ), 4 2) propose an effect on the dependent variable from manipulation of the independent variable ( causal hypothesis ), 4 3) state a negative relationship between two variables ( null hypothesis ), 4 , 11 , 15 4) replace the working hypothesis if rejected ( alternative hypothesis ), 15 explain the relationship of phenomena to possibly generate a theory ( working hypothesis ), 11 5) involve quantifiable variables that can be tested statistically ( statistical hypothesis ), 11 6) or express a relationship whose interlinks can be verified logically ( logical hypothesis ). 11 We provide examples of simple, complex, directional, non-directional, associative, causal, null, alternative, working, statistical, and logical hypotheses in quantitative research, as well as the definition of quantitative hypothesis-testing research in Table 3 .

Research questions in qualitative research

Unlike research questions in quantitative research, research questions in qualitative research are usually continuously reviewed and reformulated. The central question and associated subquestions are stated more than the hypotheses. 15 The central question broadly explores a complex set of factors surrounding the central phenomenon, aiming to present the varied perspectives of participants. 15

There are varied goals for which qualitative research questions are developed. These questions can function in several ways, such as to 1) identify and describe existing conditions ( contextual research question s); 2) describe a phenomenon ( descriptive research questions ); 3) assess the effectiveness of existing methods, protocols, theories, or procedures ( evaluation research questions ); 4) examine a phenomenon or analyze the reasons or relationships between subjects or phenomena ( explanatory research questions ); or 5) focus on unknown aspects of a particular topic ( exploratory research questions ). 5 In addition, some qualitative research questions provide new ideas for the development of theories and actions ( generative research questions ) or advance specific ideologies of a position ( ideological research questions ). 1 Other qualitative research questions may build on a body of existing literature and become working guidelines ( ethnographic research questions ). Research questions may also be broadly stated without specific reference to the existing literature or a typology of questions ( phenomenological research questions ), may be directed towards generating a theory of some process ( grounded theory questions ), or may address a description of the case and the emerging themes ( qualitative case study questions ). 15 We provide examples of contextual, descriptive, evaluation, explanatory, exploratory, generative, ideological, ethnographic, phenomenological, grounded theory, and qualitative case study research questions in qualitative research in Table 4 , and the definition of qualitative hypothesis-generating research in Table 5 .

Qualitative studies usually pose at least one central research question and several subquestions starting with How or What . These research questions use exploratory verbs such as explore or describe . These also focus on one central phenomenon of interest, and may mention the participants and research site. 15

Hypotheses in qualitative research

Hypotheses in qualitative research are stated in the form of a clear statement concerning the problem to be investigated. Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes. 2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed-methods research question can be developed. 1

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

Research questions followed by hypotheses should be developed before the start of the study. 1 , 12 , 14 It is crucial to develop feasible research questions on a topic that is interesting to both the researcher and the scientific community. This can be achieved by a meticulous review of previous and current studies to establish a novel topic. Specific areas are subsequently focused on to generate ethical research questions. The relevance of the research questions is evaluated in terms of clarity of the resulting data, specificity of the methodology, objectivity of the outcome, depth of the research, and impact of the study. 1 , 5 These aspects constitute the FINER criteria (i.e., Feasible, Interesting, Novel, Ethical, and Relevant). 1 Clarity and effectiveness are achieved if research questions meet the FINER criteria. In addition to the FINER criteria, Ratan et al. described focus, complexity, novelty, feasibility, and measurability for evaluating the effectiveness of research questions. 14

The PICOT and PEO frameworks are also used when developing research questions. 1 The following elements are addressed in these frameworks, PICOT: P-population/patients/problem, I-intervention or indicator being studied, C-comparison group, O-outcome of interest, and T-timeframe of the study; PEO: P-population being studied, E-exposure to preexisting conditions, and O-outcome of interest. 1 Research questions are also considered good if these meet the “FINERMAPS” framework: Feasible, Interesting, Novel, Ethical, Relevant, Manageable, Appropriate, Potential value/publishable, and Systematic. 14

As we indicated earlier, research questions and hypotheses that are not carefully formulated result in unethical studies or poor outcomes. To illustrate this, we provide some examples of ambiguous research question and hypotheses that result in unclear and weak research objectives in quantitative research ( Table 6 ) 16 and qualitative research ( Table 7 ) 17 , and how to transform these ambiguous research question(s) and hypothesis(es) into clear and good statements.

a These statements were composed for comparison and illustrative purposes only.

b These statements are direct quotes from Higashihara and Horiuchi. 16

a This statement is a direct quote from Shimoda et al. 17

The other statements were composed for comparison and illustrative purposes only.

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

To construct effective research questions and hypotheses, it is very important to 1) clarify the background and 2) identify the research problem at the outset of the research, within a specific timeframe. 9 Then, 3) review or conduct preliminary research to collect all available knowledge about the possible research questions by studying theories and previous studies. 18 Afterwards, 4) construct research questions to investigate the research problem. Identify variables to be accessed from the research questions 4 and make operational definitions of constructs from the research problem and questions. Thereafter, 5) construct specific deductive or inductive predictions in the form of hypotheses. 4 Finally, 6) state the study aims . This general flow for constructing effective research questions and hypotheses prior to conducting research is shown in Fig. 1 .

Research questions are used more frequently in qualitative research than objectives or hypotheses. 3 These questions seek to discover, understand, explore or describe experiences by asking “What” or “How.” The questions are open-ended to elicit a description rather than to relate variables or compare groups. The questions are continually reviewed, reformulated, and changed during the qualitative study. 3 Research questions are also used more frequently in survey projects than hypotheses in experiments in quantitative research to compare variables and their relationships.

Hypotheses are constructed based on the variables identified and as an if-then statement, following the template, ‘If a specific action is taken, then a certain outcome is expected.’ At this stage, some ideas regarding expectations from the research to be conducted must be drawn. 18 Then, the variables to be manipulated (independent) and influenced (dependent) are defined. 4 Thereafter, the hypothesis is stated and refined, and reproducible data tailored to the hypothesis are identified, collected, and analyzed. 4 The hypotheses must be testable and specific, 18 and should describe the variables and their relationships, the specific group being studied, and the predicted research outcome. 18 Hypotheses construction involves a testable proposition to be deduced from theory, and independent and dependent variables to be separated and measured separately. 3 Therefore, good hypotheses must be based on good research questions constructed at the start of a study or trial. 12

In summary, research questions are constructed after establishing the background of the study. Hypotheses are then developed based on the research questions. Thus, it is crucial to have excellent research questions to generate superior hypotheses. In turn, these would determine the research objectives and the design of the study, and ultimately, the outcome of the research. 12 Algorithms for building research questions and hypotheses are shown in Fig. 2 for quantitative research and in Fig. 3 for qualitative research.

EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

• EXAMPLE 1. Descriptive research question (quantitative research)
• - Presents research variables to be assessed (distinct phenotypes and subphenotypes)
• “BACKGROUND: Since COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts.
• RESEARCH QUESTION: Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? ” 19
• EXAMPLE 2. Relationship research question (quantitative research)
• - Shows interactions between dependent variable (static postural control) and independent variable (peripheral visual field loss)
• “Background: Integration of visual, vestibular, and proprioceptive sensations contributes to postural control. People with peripheral visual field loss have serious postural instability. However, the directional specificity of postural stability and sensory reweighting caused by gradual peripheral visual field loss remain unclear.
• Research question: What are the effects of peripheral visual field loss on static postural control ?” 20
• EXAMPLE 3. Comparative research question (quantitative research)
• - Clarifies the difference among groups with an outcome variable (patients enrolled in COMPERA with moderate PH or severe PH in COPD) and another group without the outcome variable (patients with idiopathic pulmonary arterial hypertension (IPAH))
• “BACKGROUND: Pulmonary hypertension (PH) in COPD is a poorly investigated clinical condition.
• RESEARCH QUESTION: Which factors determine the outcome of PH in COPD?
• STUDY DESIGN AND METHODS: We analyzed the characteristics and outcome of patients enrolled in the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) with moderate or severe PH in COPD as defined during the 6th PH World Symposium who received medical therapy for PH and compared them with patients with idiopathic pulmonary arterial hypertension (IPAH) .” 21
• EXAMPLE 4. Exploratory research question (qualitative research)
• - Explores areas that have not been fully investigated (perspectives of families and children who receive care in clinic-based child obesity treatment) to have a deeper understanding of the research problem
• “Problem: Interventions for children with obesity lead to only modest improvements in BMI and long-term outcomes, and data are limited on the perspectives of families of children with obesity in clinic-based treatment. This scoping review seeks to answer the question: What is known about the perspectives of families and children who receive care in clinic-based child obesity treatment? This review aims to explore the scope of perspectives reported by families of children with obesity who have received individualized outpatient clinic-based obesity treatment.” 22
• EXAMPLE 5. Relationship research question (quantitative research)
• - Defines interactions between dependent variable (use of ankle strategies) and independent variable (changes in muscle tone)
• “Background: To maintain an upright standing posture against external disturbances, the human body mainly employs two types of postural control strategies: “ankle strategy” and “hip strategy.” While it has been reported that the magnitude of the disturbance alters the use of postural control strategies, it has not been elucidated how the level of muscle tone, one of the crucial parameters of bodily function, determines the use of each strategy. We have previously confirmed using forward dynamics simulations of human musculoskeletal models that an increased muscle tone promotes the use of ankle strategies. The objective of the present study was to experimentally evaluate a hypothesis: an increased muscle tone promotes the use of ankle strategies. Research question: Do changes in the muscle tone affect the use of ankle strategies ?” 23

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

• EXAMPLE 1. Working hypothesis (quantitative research)
• - A hypothesis that is initially accepted for further research to produce a feasible theory
• “As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness .” 24
• “In conclusion, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response . The difference in perceived safety of these agents in COVID-19 illness could be related to the more potent efficacy to reduce fever with ibuprofen compared to acetaminophen. Compelling data on the benefit of fever warrant further research and review to determine when to treat or withhold ibuprofen for early stage fever for COVID-19 and other related viral illnesses .” 24
• EXAMPLE 2. Exploratory hypothesis (qualitative research)
• - Explores particular areas deeper to clarify subjective experience and develop a formal hypothesis potentially testable in a future quantitative approach
• “We hypothesized that when thinking about a past experience of help-seeking, a self distancing prompt would cause increased help-seeking intentions and more favorable help-seeking outcome expectations .” 25
• “Conclusion
• Although a priori hypotheses were not supported, further research is warranted as results indicate the potential for using self-distancing approaches to increasing help-seeking among some people with depressive symptomatology.” 25
• EXAMPLE 3. Hypothesis-generating research to establish a framework for hypothesis testing (qualitative research)
• “We hypothesize that compassionate care is beneficial for patients (better outcomes), healthcare systems and payers (lower costs), and healthcare providers (lower burnout). ” 26
• Compassionomics is the branch of knowledge and scientific study of the effects of compassionate healthcare. Our main hypotheses are that compassionate healthcare is beneficial for (1) patients, by improving clinical outcomes, (2) healthcare systems and payers, by supporting financial sustainability, and (3) HCPs, by lowering burnout and promoting resilience and well-being. The purpose of this paper is to establish a scientific framework for testing the hypotheses above . If these hypotheses are confirmed through rigorous research, compassionomics will belong in the science of evidence-based medicine, with major implications for all healthcare domains.” 26
• EXAMPLE 4. Statistical hypothesis (quantitative research)
• - An assumption is made about the relationship among several population characteristics ( gender differences in sociodemographic and clinical characteristics of adults with ADHD ). Validity is tested by statistical experiment or analysis ( chi-square test, Students t-test, and logistic regression analysis)
• “Our research investigated gender differences in sociodemographic and clinical characteristics of adults with ADHD in a Japanese clinical sample. Due to unique Japanese cultural ideals and expectations of women's behavior that are in opposition to ADHD symptoms, we hypothesized that women with ADHD experience more difficulties and present more dysfunctions than men . We tested the following hypotheses: first, women with ADHD have more comorbidities than men with ADHD; second, women with ADHD experience more social hardships than men, such as having less full-time employment and being more likely to be divorced.” 27
• “Statistical Analysis
• ( text omitted ) Between-gender comparisons were made using the chi-squared test for categorical variables and Students t-test for continuous variables…( text omitted ). A logistic regression analysis was performed for employment status, marital status, and comorbidity to evaluate the independent effects of gender on these dependent variables.” 27

EXAMPLES OF HYPOTHESIS AS WRITTEN IN PUBLISHED ARTICLES IN RELATION TO OTHER PARTS

• EXAMPLE 1. Background, hypotheses, and aims are provided
• “Pregnant women need skilled care during pregnancy and childbirth, but that skilled care is often delayed in some countries …( text omitted ). The focused antenatal care (FANC) model of WHO recommends that nurses provide information or counseling to all pregnant women …( text omitted ). Job aids are visual support materials that provide the right kind of information using graphics and words in a simple and yet effective manner. When nurses are not highly trained or have many work details to attend to, these job aids can serve as a content reminder for the nurses and can be used for educating their patients (Jennings, Yebadokpo, Affo, & Agbogbe, 2010) ( text omitted ). Importantly, additional evidence is needed to confirm how job aids can further improve the quality of ANC counseling by health workers in maternal care …( text omitted )” 28
• “ This has led us to hypothesize that the quality of ANC counseling would be better if supported by job aids. Consequently, a better quality of ANC counseling is expected to produce higher levels of awareness concerning the danger signs of pregnancy and a more favorable impression of the caring behavior of nurses .” 28
• “This study aimed to examine the differences in the responses of pregnant women to a job aid-supported intervention during ANC visit in terms of 1) their understanding of the danger signs of pregnancy and 2) their impression of the caring behaviors of nurses to pregnant women in rural Tanzania.” 28
• EXAMPLE 2. Background, hypotheses, and aims are provided
• “We conducted a two-arm randomized controlled trial (RCT) to evaluate and compare changes in salivary cortisol and oxytocin levels of first-time pregnant women between experimental and control groups. The women in the experimental group touched and held an infant for 30 min (experimental intervention protocol), whereas those in the control group watched a DVD movie of an infant (control intervention protocol). The primary outcome was salivary cortisol level and the secondary outcome was salivary oxytocin level.” 29
• “ We hypothesize that at 30 min after touching and holding an infant, the salivary cortisol level will significantly decrease and the salivary oxytocin level will increase in the experimental group compared with the control group .” 29
• EXAMPLE 3. Background, aim, and hypothesis are provided
• “In countries where the maternal mortality ratio remains high, antenatal education to increase Birth Preparedness and Complication Readiness (BPCR) is considered one of the top priorities [1]. BPCR includes birth plans during the antenatal period, such as the birthplace, birth attendant, transportation, health facility for complications, expenses, and birth materials, as well as family coordination to achieve such birth plans. In Tanzania, although increasing, only about half of all pregnant women attend an antenatal clinic more than four times [4]. Moreover, the information provided during antenatal care (ANC) is insufficient. In the resource-poor settings, antenatal group education is a potential approach because of the limited time for individual counseling at antenatal clinics.” 30
• “This study aimed to evaluate an antenatal group education program among pregnant women and their families with respect to birth-preparedness and maternal and infant outcomes in rural villages of Tanzania.” 30
• “ The study hypothesis was if Tanzanian pregnant women and their families received a family-oriented antenatal group education, they would (1) have a higher level of BPCR, (2) attend antenatal clinic four or more times, (3) give birth in a health facility, (4) have less complications of women at birth, and (5) have less complications and deaths of infants than those who did not receive the education .” 30

Research questions and hypotheses are crucial components to any type of research, whether quantitative or qualitative. These questions should be developed at the very beginning of the study. Excellent research questions lead to superior hypotheses, which, like a compass, set the direction of research, and can often determine the successful conduct of the study. Many research studies have floundered because the development of research questions and subsequent hypotheses was not given the thought and meticulous attention needed. The development of research questions and hypotheses is an iterative process based on extensive knowledge of the literature and insightful grasp of the knowledge gap. Focused, concise, and specific research questions provide a strong foundation for constructing hypotheses which serve as formal predictions about the research outcomes. Research questions and hypotheses are crucial elements of research that should not be overlooked. They should be carefully thought of and constructed when planning research. This avoids unethical studies and poor outcomes by defining well-founded objectives that determine the design, course, and outcome of the study.

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

Author Contributions:

• Conceptualization: Barroga E, Matanguihan GJ.
• Methodology: Barroga E, Matanguihan GJ.
• Writing - original draft: Barroga E, Matanguihan GJ.
• Writing - review & editing: Barroga E, Matanguihan GJ.

• Qualitative Research

Quantitative Research

Qualitative vs quantitative research: better together.

2 November 2023

By Brynn Harris

Share Post:

I don’t know about you, but I’m super bored. Bored with the same old qualitative vs quantitative research debate. The whole… “I can do anything you can do better. I can do anything better than you. No you can’t! Yes, I can!” …song and dance. 

Here’s how it typically goes:

• Qualitative research is better because you study a smaller group of people and drill deeper into why people think, feel, and do what they do. 
• No, quantitative research is better because it uses larger sample sizes to ensure statistical validity so we can generalize to a larger population. 
• Qualitative research is better because it uses more natural and human data collection techniques, like interviews and focus groups, to explore people’s underlying attitudes and emotions to get to richer insight.
• No, quantitative research is better because it uses structured surveys to help reduce bias and human subjectivity. 
• Qualitative research is better because it can be used at the early stages of research when little is known about a topic to generate hypotheses and refine research questions.
• No, quantitative research is better because it’s confirmatory and can validate or test hypotheses so clients can make data-driven decisions and forecast trends.
• Qualitative research is better because findings are presented in narrative form, using quotes and models, so clients can relate to the story on a more human level.
• No, quantitative research is better because findings are presented in tables, charts, and graphs, so learnings are much easier for clients to interpret and share.

Back and forth… and back and forth… like a superfluous tennis match I am not at all interested in watching. Please tell me this isn’t still happening. It’s nearly 2024. I can’t, I just can’t. It’s exhausting . 

In fact this very topic, “qualitative vs quantitative research”, according to our friend Google, has been referenced about 435,000,000 times. How original. So let’s start where I would guess 99% of these articles started from… and look at what makes qualitative and quantitative research different (cue standard definitions in any marketing research 101 textbook that has ever been published).   

What is qualitative research? 

Traditionally , “qualitative research” is defined as a research method that focuses on exploring and understanding the underlying meanings, motivations, and behaviors of individuals or groups. It focuses on gathering subjective and descriptive (aka non-numerical) data about human behavior, attitudes, and perceptions, related to products, services, brands, or marketing strategies. It aims to understand the “why” and “how” behind people’s choices and behaviors by digging into their lived experiences.

Researchers tend to use methods like in-depth interviews, focus groups, and ethnography to collect qualitative data. These methods encourage people to express themselves freely and provide detailed responses, allowing researchers to explore people’s emotions, motivations, and decision-making processes.

What is quantitative research? 

Again traditionally , “quantitative research” is defined as a systematic empirical investigation that gathers data to provide evidence for understanding, explaining, and predicting human phenomena. It’s primarily concerned with obtaining numeric information, such as numerical values and percentages to understand and measure various aspects of behavior.

Researchers and consultants tend to use structured surveys, and other standardized data collection methods, that aim to gather objective data that allows for numerical analysis and statistical testing. It is often used to answer questions related to “how much,” “how many,” or “to what extent.”” 

If you’ve been in the marketing research world as long as I have, these very standard definitions of qualitative and quantitative research will not be new news to you. Even though the world has changed, clients’ needs have changed, people have changed, the defining characteristics of these two research dinosaurs haven’t changed a heck of a lot within the wider world of research. However, at The Sound, we have a slightly different take. More on that later. 

So if the differences are obvious and the use cases are clear… as an old qualie, naturally my first question is… why the debate? Why qualitative research vs quantitative research? Why do we keep pitting them against each other? Like two kids at the playground fighting for the last swing. Well, when two people (or in this case two research disciplines) continue to battle it out, it often stems from 3 things:

• Insecurity. Like most other industries, the demand for marketing research services fluctuates based on economic conditions. During economic downturns or periods of uncertainty (uh, like now!), businesses are forced to reduce their marketing budgets… because they too are trying to survive the shit storm.
• Scarcity. Smaller marketing budgets leads to less comprehensive research, reducing the number of opportunities available… hence increasing competition between agencies (and methodologies). This competition contributes to a perception of scarcity, particularly for small to medium sized businesses.
• Change. The marketing landscape is constantly evolving, with new channels, technologies (um, hello AI), and market behaviors emerging. Professionals who stay updated on industry trends and adapt to new methodologies are laughing… those who don’t risk falling by the wayside.

So yes, we’ve all been on the struggle bus. But let’s not lose the plot here. A lot of these things we can’t control as research agencies and professionals but what we can control is our reaction to them (you can thank my therapist for that one). So let’s do something different and talk about how they are the same.

What do qualitative research and quantitative research have in common?

The irony of it all is that qualitative research and quantitative research have more in common than one might expect. Let’s take a moment to drill… it… all… the… way… down… to… the… basics.

As researchers and consultants, what do we always start with? Well, it’s typically two things:

• A client with a business objective.
• A group of people to study.   

Let’s start with the first one. A client with a business objective. Clients tend to come to a research partner with a desire to do something. To develop a new product, acquire a new audience, launch a new campaign, whatever it is. And in order to do this thing they want to do they need information to make decisions. Because making uninformed decisions is just… well, not very smart. 

That’s where the people part comes in. In order to gather the information, we need to go straight to the source. People. The people who buy the dish soap, use the food delivery service, or download the dating app. And until Elon Musk invents a sure-fire way to read people’s minds in order to get information from people (scary), we need to ask them questions. Questions about their thoughts, feelings, behaviors, experiences, perceptions, desires, fears, insecurities… all of it.

• How are you feeling about your life right now?
• What is the main idea behind Concept F?
• What grocery stores do you prefer to shop at?
• Why haven’t you purchased the iPhone 15 Pro?

The reality is, regardless of whether you’re running qualitative research or quantitative research, the questions are typically the same. It’s how we ask the question that can differ. Sometimes it’s an open ended question during an in-home ethnography in someone’s living room. And sometimes it’s a closed ended question on the 5-pt likert scale. It doesn’t really matter. It’s still a human being asking a human being a question so that another human being can make a decision that will impact even more human beings. Picking up what I’m putting down?

So whether it’s qualitative research or quantitative research, our intentions don’t change (or at least they shouldn’t change). We are both trying to understand people to help our clients make business decisions. As Simon Sinek says “If you don’t understand people, you don’t understand business.” But what does change is our perspective . The perspective from which we approach understanding people.

From a quantitative research perspective, we tend to look at people from the top down . Quant starts by listening to the stories of 1000 people at the same time… then we break it down, examine the pieces, and put it back together again.

From a qualitative research perspective, we tend to look at people from the ground up. Qual starts by listening to one person’s story, then another, then another… then we analyze for themes and ladder up to insights.

But both lead to the same place. Insight about people . And insight leads to strategic recommendations. And strategic recommendations lead to smart decision making. And smart decision making leads to better products and services for people . Don’t you love it when things come full circle? 

Qualitative research vs quantitative research… So, which is better then?

The answer is, both are better. More specifically, both are better together . Here’s what I mean. You would never suggest someone smell a rose with their eyes closed. Or taste a fresh strawberry while plugging their nose. Because essentially you would be cutting them off from having the full experience of whatever it is they’re doing. 

Similarly, quantitative research and qualitative research offer a unique perspective of the same phenomenon. So by finding ways to employ both quantitative and qualitative research, we are opening ourselves (and our client) up to more stimulus, more data, more insight… and more opportunities to take action. This is what helps all of us become better research partners and marketers. 

In fact, when we look more closely at this “qualitative vs quantitative research debate”… their differences complement each other. Each naturally compensates for what the other doesn’t provide. 

As such, it is important that we don’t get stuck in old research tropes around what qualitative and quantitative research is and isn’t, and what each can and can’t do. Qualitative research is more than just subjective storytelling. It is the rigorous analysis of hundreds of data points. And quantitative research can do more than just tell clients how much or how many. It can reveal insight into why people do the things they do or feel the way they feel. At The Sound we believe that behind every data point (whatever form it comes in… numbers, words, picture, video, voice) is a person , a story to be told. A human story. 

In summary, just like any situation where we use multiple vantage points to examine the same phenomenon… there are huge advantages to using both quantitative and qualitative research . Here’s just a few of them:

• Enhances Understanding: Exploring an audience from two different research perspectives provides us with a more comprehensive and nuanced understanding of their lived experience… which leads to deeper insights for our clients… and greater empathy!
• Improves Problem Solving: Exploring an audience from two different research perspectives can reveal solutions or approaches to solving a client’s business problem we would have never spotted alone. It gives us an opportunity to brainstorm together, weigh out different viewpoints, and help our clients make more informed and well-rounded decisions. 
• Reduces Bias: Exploring an audience from two different research perspectives, we are less likely to be influenced by our personal biases or preconceptions. We can use each other to poke (and fill) holes in our findings and develop a more honest, comprehensive story.  
• Sparks Innovation: Exploring an audience from two different research perspectives can foster innovation and help us come up with novel ideas that cater to a broader audience.
• Increases Adaptability: Exploring an audience from two different research perspectives can make us more adaptable as researchers and consultants. We can adjust your strategies, plans, and approaches based on new information… and most importantly, we can learn from each other. 

So, you see? We both like puzzles. People puzzles. It’s just how qualitative research and quantitative research approach solving them is a bit different. So, let’s drop the pretense. Drop the debate. Drop the drama. And recognize that we need both quantitative research and qualitative research to work together to help our clients achieve their business outcomes. 

Have some research questions you’d like answered? We’re here to help.  

Written By:

Brynn harris, keep reading.

August 26, 2024

From Academia to Industry: Qualitative Research in the Digital Age

Identity, Technology, Qualitative Research - My PhD in Communications enhances my work at The Sound, helping brands understand people in a tech-driven world.

August 18, 2024

Building a Customer Journey that you’ll use

There are few exploratory project requests that come in as complicated to work through as a customer journey. On the surface, it seems like it...

• Brand Communication
• Brand Experience
• Brand Strategy
• Consumer Exploration
• Creative Analytics
• Product Innovation

From the Blog

• Qual vs Quant
• New Product Development
• Market Research Management
• Segmentation Strategy

Stay In Touch

223 West Erie, Suite 5NW Chicago, IL 60654 United States

52-54 Rosebery Avenue, London EC1R 4RP, England, UK

312 Main Street St, #268, Vancouver, BC V6A 2T2, Canada

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

UserTesting

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. 

In this Article

Get started now

Which qualitative method is right for you?

About the author(s).

With UserTesting’s on-demand platform, you uncover ‘the why’ behind customer interactions. In just a few hours, you can capture the critical human insights you need to confidently deliver what your customers want and expect.

Human understanding. Human experiences.

Get the latest news on events, research, and product launches

Oh no! We're unable to display this form.

Please check that you’re not running an adblocker and if you are please whitelist usertesting.com.

If you’re still having problems please drop us an email .

By submitting the form, I agree to the Privacy Policy and Terms of Use .

Qualitative Vs. Quantitative Research — A step-wise guide to conduct research

A research study includes the collection and analysis of data. In quantitative research, the data are analyzed with numbers and statistics, and in qualitative research, the data analyzed are non-numerical and perceive the meaning of social reality.

What Is Qualitative Research?

Qualitative research observes and describes a phenomenon to gain a deeper understanding of a subject. It is also used to generate hypotheses for further studies. In general, qualitative research is explanatory and helps understands how an individual perceives non-numerical data, like video, photographs, or audio recordings. The qualitative data is collected from diary accounts or interviews and analyzed by grounded theory or thematic analysis.

When to Use Qualitative Research?

Qualitative research is used when the outcome of the research study is to disseminate knowledge and understand concepts, thoughts, and experiences. This type of research focuses on creating ideas and formulating theories or hypotheses .

Benefits of Qualitative Research

• Unlike quantitative research, which relies on numerical data, qualitative research relies on data collected from interviews, observations, and written texts.
• It is often used in fields such as sociology and anthropology, where the goal is to understand complex social phenomena.
• Qualitative research is considered to be more flexible and adaptive, as it is used to study a wide range of social aspects.
• Additionally, qualitative research often leads to deeper insights into the research study. This helps researchers and scholars in designing their research methods .

Qualitative Research Example

In research, to understand the culture of a pharma company, one could take an ethnographic approach. With an experience in the company, one could gather data based on the —

• Field notes with observations, and reflections on one’s experiences of the company’s culture
• Open-ended surveys for employees across all the company’s departments via email to find out variations in culture across teams and departments
• Interview sessions with employees and gather information about their experiences and perspectives.

What Is Quantitative Research?

Quantitative research is for testing hypotheses and measuring relationships between variables. It follows the process of objectively collecting data and analyzing it numerically, to determine and control variables of interest. This type of research aims to test causal relationships between variables and provide generalized results. These results determine if the theory proposed for the research study could be accepted or rejected.

When to Use Quantitative Research?

Quantitative research is used when a research study needs to confirm or test a theory or a hypothesis. When a research study is focused on measuring and quantifying data, using a quantitative approach is appropriate. It is often used in fields such as economics, marketing, or biology, where researchers are interested in studying trends and relationships between variables .

Benefits of Quantitative Research

• Quantitative data is interpreted with statistical analysis . The type of statistical study is based on the principles of mathematics and it provides a fast, focused, scientific and relatable approach.
• Quantitative research creates an ability to replicate the test and results of research. This approach makes the data more reliable and less open to argument.
• After collecting the quantitative data, expected results define which statistical tests are applicable and results provide a quantifiable conclusion for the research hypothesis
• Research with complex statistical analysis is considered valuable and impressive. Quantitative research is associated with technical advancements like computer modeling and data-based decisions.

Quantitative Research Example

An organization wishes to conduct a customer satisfaction (CSAT) survey by using a survey template. From the survey, the organization can acquire quantitative data and metrics on the brand or the organization based on the customer’s experience. Various parameters such as product quality, pricing, customer experience, etc. could be used to generate data in the form of numbers that is statistically analyzed.

Data Collection Methods

1. qualitative data collection methods.

Qualitative data is collected from interview sessions, discussions with focus groups, case studies, and ethnography (scientific description of people and cultures with their customs and habits). The collection methods involve understanding and interpreting social interactions.

Qualitative research data also includes respondents’ opinions and feelings, which is conducted face-to-face mostly in focus groups. Respondents are asked open-ended questions either verbally or through discussion among a group of people, related to the research topic implemented to collect opinions for further research.

2. Quantitative Data Collection Methods

Quantitative research data is acquired from surveys, experiments, observations, probability sampling, questionnaire observation, and content review. Surveys usually contain a list of questions with multiple-choice responses relevant to the research topic under study. With the availability of online survey tools, researchers can conduct a web-based survey for quantitative research.

Quantitative data is also assimilated from research experiments. While conducting experiments, researchers focus on exploring one or more independent variables and studying their effect on one or more dependent variables.

A Step-wise Guide to Conduct Qualitative and Quantitative Research

• Understand the difference between types of research — qualitative, quantitative, or mixed-methods-based research.
• Develop a research question or hypothesis. This research approach will define which type of research one could choose.
• Choose a method for data collection. Depending on the process of data collection, the type of research could be determined.
• Analyze and interpret the collected data. Based on the analyzed data, results are reported.
• If observed results are not equivalent to expected results, consider using an unbiased research approach or choose both qualitative and quantitative research methods for preferred results.

Qualitative Vs. Quantitative Research – A Comparison

With an awareness of qualitative vs. quantitative research and the different data collection methods , researchers could use one or both types of research approaches depending on their preferred results. Moreover, to implement unbiased research and acquire meaningful insights from the research study, it is advisable to consider both qualitative and quantitative research methods .

Through this article, you would have understood the comparison between qualitative and quantitative research. However, if you have any queries related to qualitative vs. quantitative research, do comment below or email us.

Well explained and easy to understand.

Rate this article Cancel Reply

Your email address will not be published.

Enago Academy's Most Popular Articles

• Reporting Research

Choosing the Right Analytical Approach: Thematic analysis vs. content analysis for data interpretation

In research, choosing the right approach to understand data is crucial for deriving meaningful insights.…

Demystifying the Role of Confounding Variables in Research

In the realm of scientific research, the pursuit of knowledge often involves complex investigations, meticulous…

Research Interviews: An effective and insightful way of data collection

Research interviews play a pivotal role in collecting data for various academic, scientific, and professional…

Planning Your Data Collection: Designing methods for effective research

Planning your research is very important to obtain desirable results. In research, the relevance of…

• Manuscripts & Grants
• Trending Now

Unraveling Research Population and Sample: Understanding their role in statistical inference

Research population and sample serve as the cornerstones of any scientific inquiry. They hold the…

6 Steps to Evaluate the Effectiveness of Statistical Hypothesis Testing

How to Use Creative Data Visualization Techniques for Easy Comprehension of…

Sign-up to read more

Subscribe for free to get unrestricted access to all our resources on research writing and academic publishing including:

• 2000+ blog articles
• 50+ Webinars
• 10+ Expert podcasts
• 50+ Infographics
• 10+ Checklists
• Research Guides

We hate spam too. We promise to protect your privacy and never spam you.

• Industry News
• Publishing Research
• AI in Academia
• Promoting Research
• Career Corner
• Diversity and Inclusion
• Infographics
• Expert Video Library
• Other Resources
• Enago Learn
• Upcoming & On-Demand Webinars
• Peer Review Week 2024
• Open Access Week 2023
• Conference Videos
• Enago Report
• Journal Finder
• Enago Plagiarism & AI Grammar Check
• Editing Services
• Publication Support Services
• Research Impact
• Translation Services
• Publication solutions
• AI-Based Solutions
• Thought Leadership
• Call for Articles
• Call for Speakers
• Author Training
• Edit Profile

I am looking for Editing/ Proofreading services for my manuscript Tentative date of next journal submission:

In your opinion, what is the most effective way to improve integrity in the peer review process?

When Does a Researcher Choose a Quantitative, Qualitative, or Mixed Research Approach?

• Published: 26 November 2021
• Volume 53 , pages 113–131, ( 2022 )

Cite this article

• Feyisa Mulisa   ORCID: orcid.org/0000-0002-0738-6554 1  

10k Accesses

14 Citations

3 Altmetric

Explore all metrics

In educational studies, the paradigm war over quantitative and qualitative research approaches has raged for more than half a century. The focus in the late twentieth century was on the distinction between the two approaches, and the motivation was to retain one of the approaches’ supremacy. Since the early twenty-first century, there has been a growing interest in situating in the middle position and combining both approaches into a single study or a series of studies. Despite these signs of progress, when it comes to using the appropriate research approach at the right time, beginner educational researchers remain perplexed. This paper, therefore, provides useful guidelines that facilitate the choice of quantitative, qualitative, or mixed research approaches in educational inquiry. To achieve this objective, this article comprises three distinct and underlying areas of interest, which have been structured into three sections. The first section highlights the distinctions between quantitative and qualitative research approaches. The second section discusses the paradigm views that underpin the choice of a particular research approach. Finally, an effort has been made to determine the appropriate time to opt for any of the research approaches that facilitate successful educational investigations. Since truth and the means used to discover it are both dynamic, it is also essential to foresight innovative approaches to research with distinguishing features of applications to educational research.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save.

• Get 10 units per month
• Download Article/Chapter or eBook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Similar content being viewed by others

The Paradigmatic Challenge of Mixed-Methods Research: Positivism, Relativism or Pragmatism?

Research Methods

Trends and Issues in Qualitative Research Methods

Explore related subjects.

• Artificial Intelligence

Åkerblad, L., Seppänen-Järvelä, R., & Haapakoski, K. (2021). Integrative strategies in mixed methods research. Journal of Mixed Methods Research, 15 (2), 152–170. https://doi.org/10.1177/1558689820957125

Article   Google Scholar  

Allwood, C. M. (2012). The distinction between qualitative and quantitative research methods is problematic. Quality and Quantity, 46 (5), 1417–1429. https://doi.org/10.1007/s11135-011-9455-8

Amaratugna, D., Baldry, D., Sarshar, M., & Newton, R. (2002). Quantitative and qualitative research in the built environment: Application of “mixed” research approach. Work Study, 51 (1), 17–31. https://doi.org/10.1108/00438020210415488

Antwi, S. K., & Hamza, K. (2015). Quantitative and qualitative research paradigms in business research: A philosophical reflections. European Journal of Business and Management, 7 (3), 217–225.

Google Scholar  

Bailey, L. F. (2014). The origin and success of qualitative research. International Journal of Market Research, 56 (2), 167–184. https://doi.org/10.2501/IJMR-2014-013

Belk, R. W. (2013). Qualitative versus quantitative research in marketing. Revista de Negócios, 18 (1), 5–9. https://doi.org/10.7867/1980-4431.2013v18n1p5-9

Brinkmann, S., Jacobsen, M. H., Kristiansen, S., Brinkmann, S., Jacobsen, M. H., & Kristiansen, S. (2014). Historical overview of qualitative research in the social sciences. In The Oxford handbook of qualitative research (pp. 16–42). https://doi.org/10.1093/oxfordhb/9780199811755.013.017

Burke Johnson, R., & Onwuegbuzie, A. J. (2004). Mixed methods research: A research paradigm whose time has come. Educational Researcher, 33 (7), 14–26.

Choy, L. T. (2014). The strengths and weaknesses of research methodology: Comparison and complimentary between qualitative and quantitative approaches. IOSR Journal of Humanities and Social Science, 19 (4), 99–104.

Cohen, L., Manion, L., & Morrison, K. (2018). Research methods in education (8th ed.). Routledge.

Creswell, J. W. (2009). Research design: Qualitative, quantitative and mixed methods approaches (3rd ed.). Sage Publications, Inc.

Creswell, J. W. (2013). Steps in conducting a scholarly mixed methods study . http://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1047&context=dberspeakers

Curry, L. A., Nembhard, I. M., & Bradley, E. H. (2009). Qualitative and mixed methods provide unique contributions to outcomes research. Circulation, 119 , 1442–1452. https://doi.org/10.1161/CIRCULATIONAHA.107.742775

Daniel, E. (2016). The usefulness of qualitative and quantitative approaches and methods in researching problem-solving ability in science education curriculum. Journal of Education and Practice, 7 (15), 91–100.

Dawadi, S. (2017). Are quantitative and qualitative approaches to educational research compatible? The Warwick ELT , 3 (6). https://thewarwickeltezine.wordpress.com/2017/05/31/291/

Ejnavarzala, H. (2019). Epistemology–ontology relations in social research: A review. Sociological Bulletin, 68 (1), 94–104. https://doi.org/10.1177/0038022918819369

Fagan, M. B. (2010). Social construction revisited: Epistemology and scientific practice. Philosophy of Science, 77 (1), 92–116.

Farrell, E. (2020). Researching lived experience in education: Misunderstood or missed opportunity? International Journal of Qualitative Methods, 19 , 1–8. https://doi.org/10.1177/1609406920942066

Fassinger, R., & Morrow, S. L. (2013). Toward best practices in quantitative, qualitative, and mixed-method research: A social justice perspective. Journal of Social Action in Counseling Psychology, 5 (2), 69–83.

Greene, J. C., Caracelli, V. J., & Graham, W. F. (1989). Toward a conceptual framework for mixed-method evaluation designs. Educational Evaluation and Policy Analysis, 11 (3), 255–274. https://doi.org/10.3102/01623737011003255

Gunasekare, U. (2015). Mixed research method as the third research paradigm: A literature review. International Journal of Science and Research, 4 (8), 363–367.

Hodkinson, P. (2004). Research as a form of work: Expertise, community and methodological objectivity. British Educational Research Journal, 30 (1), 9–26. https://doi.org/10.1080/01411920310001629947

Howitt, D., & Cramer, D. (2011). Introduction to research methods in psychology (3rd ed.). Pearson Education Limited.

Johnson, R. B., & Christensen, L. (2014). Educational research: Quantitative, qualitative and mixed approaches (5th ed.). Sage Publications, Inc.

Johnson, R. B., & Christensen, L. (2020). Educational research: Quantitative, qualitative, and mixed approaches (7th ed.). Sage Publications, Inc.

Khaldi, K. (2017). Quantitative, qualitative or mixed research: Which research paradigm to use? Journal of Educational and Social Research, 7 (2), 15–24. https://doi.org/10.5901/jesr.2017.v7n2p15

Krivokapic-skoko, B., & O’neill, G. (2011). Beyond the qualitative-quantitative distinction: Some innovative methods for business and management research. International Journal of Multiple Research Approaches, 5 (5), 290–300. https://doi.org/10.5172/mra.2011.5.3.290

Lee, A. S., & Baskerville, R. L. (2003). Generalizing generalizability in information systems research. Information Systems Research . https://doi.org/10.1287/isre.14.3.221.16560

Levers, M. J. D. (2013). Philosophical paradigms, grounded theory, and perspectives on emergence. SAGE Open, 3 (4), 1–6. https://doi.org/10.1177/2158244013517243

Lobe, B., Morgan, D., & Hoffman, K. A. (2020). Qualitative data collection in an era of social distancing. International Journal of Qualitative Methods, 19 , 1–8. https://doi.org/10.1177/1609406920937875

Mack, L. (2010). The philosophical underpinnings of educational research. Polyglossia, 19 , 5–11.

Madill, A., & Gough, B. (2008). Qualitative research and its place in psychological science. Psychological Methods, 13 (3), 254–271. https://doi.org/10.1037/a0013220

Marshall, M. N. (1996). Sampling for qualitative research. Family Practice, 13 (6), 522–525.

Maxwell, J. A., & Reybold, L. E. (2015). Qualitative research. In International encyclopedia of the social & behavioral sciences (2nd ed., Vol. 19, pp. 685–689). Elsevier. https://doi.org/10.1016/B978-0-08-097086-8.10558-6

Mertens, D. M. (2012). What comes first? The paradigm or the approach? Journal of Mixed Methods Research, 6 (4), 255–257. https://doi.org/10.1177/1558689812461574

Meyer, D. K., & Schutz, P. A. (2020). Why talk about qualitative and mixed methods in educational psychology? Introduction to special issue. Educational Psychologist, 55 (4), 193–196. https://doi.org/10.1080/00461520.2020.1796671

Morgan, D. L. (2007). Paradigms lost and pragmatism regained: Methodological implications of combining qualitative and quantitative methods. Journal of Mixed Methods Research, 1 (1), 48–76. https://doi.org/10.1177/2345678906292462

Morgan, D. L., & Nica, A. (2020). Iterative thematic inquiry: A new method for analyzing qualitative data. International Journal of Qualitative Methods, 19 , 1–11. https://doi.org/10.1177/1609406920955118

Östlund, U., Kidd, L., Wengström, Y., & Rowa-Dewar, N. (2011). Combining qualitative and quantitative research within mixed method research designs: A methodological review. International Journal of Nursing Studies, 48 (3), 369–383. https://doi.org/10.1016/j.ijnurstu.2010.10.005

Poli, R. (2018). A note on the classification of future-related methods. European Journal of Futures Research, 6 (1), 1–7. https://doi.org/10.1186/s40309-018-0145-9

Rahman, M. S. (2016). The advantages and disadvantages of using qualitative and quantitative approaches and methods in language “Testing and Assessment” research: A literature review. Journal of Education and Learning, 6 (1), 102. https://doi.org/10.5539/jel.v6n1p102

Sale, J. E. M., Lohfeld, L. H., & Brazil, K. (2002). Revisiting the quantitative-qualitative debate: Implications for mixed methods research. Quality & Quantity, 36 , 43–53. https://doi.org/10.1023/A:1014301607592

Salvador, J. T. (2016). Exploring quantitative and qualitative methodologies: A guide to novice nursing researchers. European Scientific Journal, 12 (18), 107–122. https://doi.org/10.19044/esj.2016.v12n18p107

Shannon-Baker, P. (2016). Making paradigms meaningful in mixed methods research. Journal of Mixed Methods Research, 10 (4), 319–334. https://doi.org/10.1177/1558689815575861

Symonds, J. E., & Gorard, S. (2010). Death of mixed methods? Or the rebirth of research as a craft. Evaluation & Research in Education, 23 (2), 121–136. https://doi.org/10.1080/09500790.2010.483514

Taylor, P., & Kanis, H. (2004). The quantitative-qualitative research dichotomy revisited. Theoretical Issues in Ergonomics Science, 5 (6), 507–516. https://doi.org/10.1080/1463922041233130303418

Techo, V. P. (2016). Research methods-quantitative, qualitative, and mixed methods . Horizons University. https://doi.org/10.13140/RG.2.1.1262.4886

Wohlfart, O. (2020). “Digging Deeper?”: Insights from a novice researcher. International Journal of Qualitative Methods, 19 , 1–5. https://doi.org/10.1177/1609406920963778

Yin, R. K. (2011). Qualitative research from start to finish . The Guilford Press.

Download references

No funding was received for this article.

Author information

Authors and affiliations.

Institute of Education and Behavioral Sciences, Ambo University, Ambo, Ethiopia

Feyisa Mulisa

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Feyisa Mulisa .

Ethics declarations

Conflict of interest.

Not applicable.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Mulisa, F. When Does a Researcher Choose a Quantitative, Qualitative, or Mixed Research Approach?. Interchange 53 , 113–131 (2022). https://doi.org/10.1007/s10780-021-09447-z

Download citation

Received : 29 March 2021

Accepted : 18 November 2021

Published : 26 November 2021

Issue Date : March 2022

DOI : https://doi.org/10.1007/s10780-021-09447-z

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Quantitative research
• Qualitative research
• Mixed research
• Research approaches
• Research paradigm
• Find a journal
• Publish with us
• Track your research

• Open access
• Published: 28 August 2024

Team-based care in specialist practice: a path to improved physician experience in British Columbia

• Eric R. Young 1 ,
• Garth Vatkin 1 ,
• Jason Kur 1 &
• Erin E. Sullivan 2  

BMC Health Services Research volume  24 , Article number:  1000 ( 2024 ) Cite this article

Metrics details

Specialist physicians in the province of British Columbia commonly work on teams in acute care settings such as operating rooms or inpatient hospital units. However, while the implementation of team-based care (TBC) has been supported in primary care clinics, no formal mechanisms have supported specialist physicians in adopting TBC in their private outpatient offices. Adopting TBC models is associated with improving physician experience, efficiency, and patient experience.

The Institute for Healthcare Improvement Breakthrough Series guided a program to support 11 specialist physicians, representing nine different specialties, to develop and implement TBC in outpatient offices. Participants were supported through resources including funding, mentorship, and learning opportunities. To determine whether the program improved physician experience, quantitative data were collected using the validated Mini Z survey and qualitative data were collected through monthly reports, semi-structured interviews, and focus groups. Patient experience data were collected through surveys and follow-up calls.

The fifteen-month program was successful, with 10 of the 11 specialists implementing TBC in their offices. The Mini Z results demonstrated that physician experience improved over the course of the program, with scores on job satisfaction, work pace, and time spent on the electronic medical record improving the most. Interviews with specialists and focus groups with specialists’ team members support these findings, with participants stating that TBC modulates workloads, begins to affect burnout, improves work-life balance, and increases the efficiency of care. Patients reported positive experiences while receiving TBC. Patients were less likely to visit the emergency department after consultations with specialist teams, and providers agreed that their patients would be less likely to seek acute care because of the new practice models.

TBC is a viable model for specialist physicians and their health care teams practicing in British Columbia to foster well-being, job satisfaction, and efficiency, and to improve patient experience. These findings may be of interest to specialists, health care providers, policymakers, and administrators looking to better support and retain specialist practices that are integral to patient care.

Peer Review reports

Introduction

Specialist physicians in British Columbia’s (BC) publicly funded fee-for-service system typically operate as solo practitioners providing services in private, outpatient offices. Physicians are challenged by administrative burdens, particularly related to documentation requirements such as charting and completing forms that detract from direct patient care [ 1 , 2 ]. Specialist physicians acknowledge that medicine is fast-paced and complex, but the increasing volume of non-patient care tasks is overburdening them [ 1 ]. Given the high prevalence of physician burnout [ 3 , 4 , 5 , 6 ], a novel approach to enhancing efficiency, modulating workload, and mitigating further burnout is required to ensure job satisfaction and improve the experience of specialists while delivering quality patient care.

Team-based care (TBC) has proven beneficial in primary care settings, and BC has made strides in enhancing TBC through initiatives such as Primary Care Networks [ 7 , 8 , 9 ]. TBC has been identified as essential for overcoming administrative burdens [ 1 ] and task volume to ensure delivery of high-quality health care [ 10 , 11 , 12 ]. Physician-led TBC involves a collaborative approach where health professionals work together to support a patient’s needs [ 13 ] resulting in decreased workloads, increased efficiency, improved quality of care, improved patient outcomes, and decreased clinician burnout [ 14 , 15 , 16 , 17 ].

While primary care has been supported in implementing TBC initiatives, there remains a gap in support for the 7,257 practicing specialists spanning 43 different specialties across the province [ 18 ]. In BC, patients and their families receive specialized treatment for health concerns from specialist physicians practicing in outpatient offices. The applicability of TBC for specialists who operate in these offices, particularly in terms of enhancing patient and (specialist) provider experience within the Institute for Healthcare Improvement (IHI) Quadruple Aim [ 19 ], has not been extensively studied. The journey toward specialist-led TBC in BC began in 2011 when the Specialist Services Committee (SSC), one of four Joint Collaborative Committees (JCCs) representing a partnership between the Doctors of BC and the Government of BC [ 20 ], collaborated with the section of rheumatology to fund a novel physician-nurse model of care. Early results from this model showed an increased volume of patient encounters and high patient satisfaction [ 21 ].

With funding from the SSC and drawing from the IHI Breakthrough Series (BTS) methodology [ 22 ], the Specialists Team Care (STC) initiative was established. This initiative supported 11 specialists from nine different specialties in implementing a TBC model within their outpatient offices. The purpose of this paper is to describe the results of the program evaluation and report the impact of the STC initiative on physician experience, efficiency, and patient experience.

Study design

The STC initiative ran for 15 months from January 1, 2023 to March 31, 2024 (Fig.  1 ). During this program, various supports (Table  1 ) were offered to participating specialist clinics to implement TBC. Participants were given a Toolkit containing teamwork and leadership frameworks, quality improvement ideas, and a driver diagram (Fig.  2 ) for implementing TBC. This suite of support is commonplace in the IHI BTS methodology and was intended to promote teamwork within specialist clinics over the course of the initiative. A mixed methods program evaluation was used to collect quantitative and qualitative data from specialist physicians, their team members, and patients to assess how TBC implementation affects physician experience, efficiency, and patient experience. Informed consent was not required as this was a quality improvement project and not research involving human subjects per national regulations of Article 2.5 of the Tri-Council Policy Statement: Ethical Conduct for Research Involving Humans [ 23 ]. While all aspects of the IHI Quadruple Aim were evaluated, this paper reports on patient and provider experiences, as these outcomes were more immediate and demonstrable within the timeframe of the study.

Timeline of the Specialists Team Care initiative. This figure presents the timeline for the STC initiative, highlighting key event dates (top) and data collection points (bottom)

Driver Diagram from the STC Toolkit used for implementing TBC. This figure shows the driver diagram and its primary drivers, secondary drivers, and specific change ideas. The diagram offers ideas and suggestions that may prove useful for specialists and their teams to consider when implementing TBC

Initiative participants

Specialists who participated in a series of focus groups to understand their interest in TBC prior to the creation of STC were encouraged to submit an Expression of Interest to become participants. To be eligible, each specialist had to be an actively practicing physician registered with the College of Physicians and Surgeons of British Columbia. Additionally, the specialist had to provide a portion of their care in an outpatient office. Applicants were interviewed and scored against a set of criteria by members of the STC Working Group. The STC Steering Committee selected and approved 11 specialists from 13 applicants to participate (Table  2 ).

Data collection

Specialist and team member experience.

Quantitative data about physician and staff experience were collected anonymously through the STC Mini Z Survey (see Additional File 1), which was administered to specialists and each of their team members 3 months (baseline), 10 months, and 15 months into the initiative. The survey used questions from the validated Mini Z 1.0 survey to assess job satisfaction, stress, and burnout and their risk factors [ 26 , 27 ]. A ‘satisfaction with work-life integration’ item was included as an additional measure and predictor of well-being.

Qualitative data were collected through semi-structured interviews with specialists at 7 months (the midpoint) and 15 months into the initiative; an evaluator that was external to the STC program conducted the interviews. The interview guide asked specialists how their experience changed, using prompts that mirror constructs from the Mini Z, such as job satisfaction, stress, burnout, control over workload, and electronic medical record (EMR) use at home. The interviews were recorded and transcribed.

There were further opportunities for gathering qualitative data from the specialists’ team members through two focus groups conducted at the end of the program. One focus group consisted of 10 individuals including nurses and allied health professionals such as a dietician, a kinesiologist, and a registered clinical counselor. The other focus group consisted of 11 individuals in administrative staff roles, including medical office assistants (MOAs) and office managers. The external evaluator conducted these focus groups, which were also recorded and transcribed.

Patient experience

The patient experience was evaluated through convenience sampling of patients via the STC Patient Survey (See Additional File 2), which was administered by clinic staff shortly after each patient’s appointment with the team. Surveys were completed anonymously either electronically or on paper and then dropped in a ballot box to assure anonymity. Patients were invited to participate in a voluntary follow-up phone call with the STC program coordinator three months after the first interaction with their clinic team. Therefore, a subset of patients who completed the STC Patient Survey were contacted, verbally surveyed, and given an opportunity to comment on their experiences with care at participating specialist clinics.

Long-term outcomes

Indirect measures of patient outcomes (population health) and health care utilization (per capita costs of health care) were collected, as directly observing these long-term outcomes of TBC was not feasible within the 15-month program. Specialist interviewees, patient follow-up call respondents, and focus group participants were asked whether they believed TBC improved patient and utilization outcomes. Patients’ self-reported visits to different types of health care services, including the emergency department (ED), urgent care centre, hospital admissions, family doctor, walk-in clinic, or other, were used as a quantitative proxy to assess whether TBC reduced health care utilization.

Data analysis

The STC Mini Z Survey data were analyzed based on their roles, with the results for specialists and their team members (nursing, allied health, and administrative staff) reported separately. Individual survey questions were assessed by examining the desirable responses (top box of either two or three depending on the item) for each item and then comparing the percentage point change from baseline (3-month) to initiative-end (15-month) results. The interview and focus group data were analyzed thematically by the external evaluator; the qualitative data were used to explain and provide reasons for the findings from the survey’s quantitative results. Role-specific themes were also kept separate and considered in the explanatory analysis.

Quantitative data from the STC Patient Survey and follow-up call were analyzed cross-sectionally by STC administration, pooling all the data collected over the course of the initiative into a single sample. Individual survey questions were assessed by calculating the percentages for each Likert scale response category. Qualitative data were analyzed thematically by the external evaluator and used to provide context on the patient experience.

The percentages of patients making each type of visit were calculated by dividing the number of respondents selecting that type by the total number of survey respondents. Since respondents could select multiple visit types, the percentages may not sum to 100%. The overall care-seeking behaviours were compared for all surveyed patients before and after receiving TBC, using data from the STC Patient Survey (initial interaction with specialists) and the follow-up call conducted three months later. Qualitative comments were also analyzed thematically by the external evaluator.

Specialist and team member Mini Z survey data

At the end of the STC initiative, 10 out of 11 specialists implemented a team care model in their offices. One specialist withdrew from the program because the team care model did not align with their practice setting. The physician Mini Z results (Fig.  3 ) showed that by the end of the initiative, 88% of the specialists reported satisfaction with their current job and 75% reported no symptoms of burnout. TBC improved specialists’ work pace (control over workload, sufficient time for documentation, work atmosphere, and time spent on the EMR at home) and work-life integration. However, the Mini Z indicated that stress worsened for the specialists.

Specialist responses from the STC Mini Z Survey. Small multiple stacked bar charts represent the distribution of responses (by percent) for each of the nine survey items ( A – I ) measured at 3, 10, and 15 months into the STC initiative. Likert scales differ for each question, with descriptions and color coding in the figure keys. The data are ordered from left to right as least to most positive. Positive scores are shown for A – C and I (top-two box), and for D – H (top-three box). Comparator data on physician responses (BC and national) from the Canadian Medical Association (CMA) 2021 National Physician Health Survey are displayed where applicable [ 3 , 4 , 5 , 6 ].

A key part of the STC initiative included providing financial support for specialists to build their team, particularly in terms of adding a nurse or allied health professional. At least one nurse or allied health professional was hired in 10 out of 11 specialist offices during the STC initiative. Mini Z data from team members (Fig.  4 ) demonstrated improvements in job satisfaction, stress, and work atmosphere. The time for documentation worsened for team members. Turnover in specialists’ team members (in at least one role) was observed in 7 of the 10 clinics.

Team member responses from the STC Mini Z Survey. Small multiple stacked bar charts represent the distribution of responses (by percent) for each of the nine survey items ( A – I ) measured at 3, 10, and 15 months into the STC initiative. This figure follows the same format as described in Fig.  2 , with Likert scales differing for each question, descriptions and color coding in the figure keys, and data ordered from least to most positive. Positive scores are shown for A – C and I (top-two box), and for D – H (top-three box). Comparator data on national general population responses from the CMA 2021 National Physician Health Survey are displayed where applicable [ 3 , 4 , 5 , 6 ].

STC Patient Survey data

The STC Patient Survey data collected from June 2023 to March 2024 (Fig.  5 ) showed that a majority of patients reported positive experiences while receiving care from specialists who had implemented a TBC model. On average, across the clinics, more than 95% of the patients said they were treated with courtesy and respect, had confidence in the clinic team, were satisfied with how the clinic team listened, felt the clinic team worked well together, and were satisfied with their care. Of the patients who received a follow-up call three months after their initial appointment, 84% said the specialist clinic provided everything needed to manage their health concerns.

Patient responses from the STC Patient Survey and follow-up call. Small multiple stacked bar charts represent the distribution of patient responses (percent) for each of the questions from the survey ( A – E ) and follow-up call ( F ). Likert scales differ for each question, figure keys contain descriptions and color coding, and data bars are ordered from least to most positive. Positive scores (top-two box) are labeled

Specialist interviews

Multiple specialist interviews provided insight into improvements affecting the specialist experience. Significant improvements were noted in the time allocation on the EMR at home, in the fulfillment of documentation requirements, and in the management of workloads. Some specialists reported that they were able to address waitlists and heavy workloads, and had adequate time for appointments. As one specialist explained,

It is great for the patient because they’re receiving much longer encounters. In the past, I wasn’t spending the time they needed, but now the patient gets as much time as they need because we are using my team members.

The ability to rely on their team rather than be solely responsible for care reduced specialists’ sense of isolation and offloaded administrative burden (while increasing efficiency). While some specialists reported decreased job-related stress, others said it stayed the same or increased due to a variety of factors, such as the pressure of having more patient volume and TBC requiring them to focus on aspects of their practices that were less rewarding or interesting to them personally. Some noted that their burnout stemmed from responsibilities outside of their office, as one physician said,

My office is not the source of my burnout. It comes from provincial things I am working on. I am probably just as burnt out, but my office and team are just a place I can go and enjoy my job.

Overall, the specialists identified facilitators and barriers related to their STC participation. They noted benefitting from the site-to-site learning opportunities, mentorship, and funding to support hiring new team members. Specialists reported, however, that the time and cost involved in training new hires and building team cohesiveness were barriers. Human resource sustainability and turnover among team members are challenges for most clinics. Specialist perceptions related to the turnover of team members included the following: parental leave; administrative staff were not sufficiently qualified or were the ‘right fit’ for TBC (e.g., uncomfortable with ambiguity, change, and pace of work); and the general competitiveness of the job market. Specialists noted struggling with the shift from working independently to operating as a team and recognized that they had to consciously let go of control and respect the training and expertise of other team members. As one specialist said, “The process of making all these changes takes time and dedication. I can see that it will be well worth it once fully established, but you have to be willing to do the work.” Despite these challenges, the majority of specialists were optimistic that their TBC models are financially sustainable and viable in the long term.

Team member focus groups

Focus groups with team members, including nurses, allied health professionals, and MOAs revealed high job satisfaction, and these team members generally enjoyed working with others in the specialist clinic, the ability to make changes quickly, and a focus on improving patient care. Team member attrition was a challenge for 70% of the specialist practices. The MOAs and office managers specifically explained that the implementation of TBC increased their administrative and documentation workload, and subsequently, their feelings of stress and burnout also increased; some noted that the addition of an allied health professional worsened their administrative burden.

Patient follow-up calls

Patient follow-up phone calls provided patients with the opportunity to comment on their experience receiving TBC at participating specialist clinics. Patients generally made positive comments about specific team members or about the team working well together, appreciated their questions being answered, and believed that their needs were addressed or resolved. These remarks serve to emphasize patient satisfaction and positive experiences with the new model of care. As one patient said,

The team-based model is excellent. In particular, I appreciated the nurse specialist. She was very thorough in her history-taking and answered all my questions.

Specialists and their team members highlighted ways in which they believe their patients’ quality of care and health improved as a result of specialist TBC. Nurses and allied health professionals, under physician direction and supervision, were able to take patient histories, offer group education sessions, answer patient questions, and complete patient follow-ups. A majority of providers believe the addition of team members and new ways of delivering care increased their capacity to see patients and reduced waitlists. One medical specialist stated,

It allows a lot of patients sitting at home with their non-urgent issues to be seen sooner and receive longitudinal care for those issues. This is huge for patients. Before being able to see them, many have become quite isolated dealing with life-changing problems that are not urgent.

Quantitative patient survey findings (Fig.  6 ) show that 10.9% (54 out of 495) of patients reported seeking care at the ED within three months prior to their first specialist appointment, compared to 4.5% (6 out of 133) of patients three months after. Patient use of urgent care centres and admissions to hospitals increased slightly, though the volumes remained low. Visits to family doctors and walk-in clinics decreased. A specialist commented that there would be reduction in visits to acute care because patients accessing specialty care in a more timely manner won’t require emergency care. Specialists’ team members agreed, stating that appointments provide preventative care. One surgeon remarked,

We get lots of questions from patients about aftercare and post-surgical things and they don’t know the signs to look for in terms of an infection. Rather than typically going to the emergency room, we can often provide them with the reassurance they need to avoid that visit.

Patient self-reported health care utilization. Clustered bar charts represent the patient responses (percent) to different types of health care or treatment they needed to seek in the last three months (elsewhere than the specialist office). Percents are based on the number of respondents who selected the visit type divided the total number of respondents. In BC, an Urgent Care Centre is an alternative to emergency departments and provides access to same-day, urgent, non-emergency health care

The STC initiative met its stated objective of improving physician experience, increasing efficiency, and improving patient experience. Specialists reported increased job satisfaction, work-life integration, and efficiency, specifically in terms of having more time for documentation and spending less time on their EMR at home. The majority of patients also reported high satisfaction and positive experiences receiving care within a TBC model. While specialists’ team members showed improvement in their overall experience, the increased administrative burden on MOAs and office managers, coupled with team member attrition at 70% of practices is worth noting. This particular finding, which was an unintended consequence of the STC initiative, is aligned with findings in the primary care setting where changes that reduced clinician burnout did not decrease, and in some cases, worsened, burnout among staff [ 28 ]. Sheridan et al. reported that medical assistants (akin to MOAs in Canada), had a greater workload (73%) and greater job satisfaction (86%) when working in team-based primary care models [ 29 ]. This highlights the need for sustainable workloads for all team members in future iterations of STC.

At the start of the STC initiative, 50% of participating specialists reported burnout, similar to that reported by physician peers across Canada (53%) and BC (52%), as measured by the Canadian Medical Association’s (CMA) 2021 National Physician Health Survey [ 3 , 4 , 5 , 6 ]. This survey used the Maslach Burnout Inventory two-item scale [ 30 , 31 ] in addition to the Mini Z questions. This similarity in results indicates the specialists are highly likely to have poor mental health and to reduce or modify their clinical hours. Notable differences were observed between the STC and CMA data. At baseline, STC specialists scored worse than average BC physicians in terms of sufficient time for documentation, time spent on EMR at home, and work-life integration, but responded better on stress [ 4 ]. The CMA survey revealed that BC physicians spend an average of 9.7 h on administrative tasks per week [ 1 , 4 ]. These comparisons suggest that while participating specialists face similar administrative burdens, they may have experienced a greater workload—whether perceived or actual—stemming from documentation and EMR use.

Interestingly, the Mini Z indicated that stress worsened for some specialists over the course of the initiative, but the interview data indicated that some specialists experienced decreased stress or that their stress was related to other parts of their physician role. It is also possible that the stress question in the Mini Z may have been misinterpreted because of the reverse agreement scale, which produced a false signal. Nonetheless, by the end of the STC initiative, improvements in workload and documentation time were noted, both of which are predictors (recognized as part of organizational factors) of burnout [ 6 , 32 ]. We anticipate that these changes directly related to administrative burden will continue to provide protective effects for specialists under the TBC model. Benefits related to burnout, job satisfaction, and professional fulfillment will accrue over time, especially as physicians and their teams continue to work more optimally together, manage workloads, cultivate positive team culture, and achieve professional fulfillment [ 33 , 34 ].

Physicians often lack formal training in leadership and team-building; this is not a core part of their medical education, despite the necessity of these skills in their professional practice where they must frequently work within teams. Essentially, physicians are expected to acquire these abilities on the job [ 35 ]. The STC initiative was designed to support action learning by implementing a TBC model and encouraging specialists to develop these skills through practice. According to the literature, teams progress through distinct phases, and inadequate support during these transitions can lead to unmotivated employees and higher attrition rates [ 36 , 37 ]. This issue was evident in the program, with many teams experiencing the loss of at least one member. This underscores a significant opportunity to better support specialists adopting TBC and enhancing the sustainability of these models. However, simply working in a TBC model is not enough, as teams must also develop and nurture structures (processes) and culture to become effective. Working in tight-knit teams is associated with less clinician exhaustion [ 34 ]. Therefore, establishing a formalized education pathway to equip specialists with the necessary skills for managing transitions and developing sustainable teams would improve overall team effectiveness and the quality of care delivery [ 38 , 39 ].

Early results on improved patient outcomes and reduced health care utilization are promising, demonstrating how TBC can address all aspects of the IHI Quadruple Aim. Participating specialist teams enhanced various aspects of care delivery, including patient education and self-management. These care practices are known to lead to better outcomes, such as improved quality of life, decreased anxiety, fewer complications, adherence to care plans, and patient empowerment [ 40 , 41 , 42 , 43 , 44 ]. TBC also facilitated multidisciplinary care in outpatient specialist practices, which may yield benefits similar to those observed in other care settings, such as cancer clinics, orthopedic rehabilitation centres, and in-hospital units [ 45 , 46 , 47 ]. The participating sites demonstrated positive patient experiences, and a systematic review by Doyle et al. indicated that such experiences are associated with clinical effectiveness, patient safety, better health outcomes (objective and self-rated), health-promoting behaviours, and reduced resource use [ 48 ]. High levels of positive patient experience, self-reported decreases in care visits (to the ED, family doctor, and walk-in clinic), and provider perceptions of preventative care all support the notion that TBC provides patients with the care they need and potentially reduces costs to the health system.

Limitations

This study has both strengths and limitations. A strength of the study is that multiple sources of data were collected across the 15-month program, and the data were obtained from specialists, team members, and patients. A weakness is that this first iteration of STC has a relatively small sample size. We used a validated tool, the Mini Z, for measuring the constructs of burnout, stress, and control over workload; however, our sample size was small. While the results showed a positive trend, it typically takes more time than the length of the STC for these results to decrease significantly [ 49 , 50 , 51 ]. Finally, it is worth acknowledging that this cohort of specialists are early adopters of TBC. It is possible that there are unique pressures on this cohort due to the provincial visibility of this work and additional characteristics in this cohort related to their willingness to innovate that we have not explored. Specialty-specific factors are an area for future study, as specialists are a heterogeneous group in terms of work setting and practice conditions [ 52 ]; thus, there are likely relevant specialty-specific differences that we did not examine in this first iteration of STC. Long-term impacts to patient outcomes, costs to the health care system, and sustainability of specialist TBC will continue to be studied in more direct ways as part of ongoing and future work.

The STC initiative successfully supported specialists in implementing TBC models in their outpatient offices, resulting in reduced administrative burdens and enhanced overall experience in delivering care. The model worked well for those who delivered and received care; physicians and their team members were able to rely on each other to deliver care, and patients were satisfied with the care received from the specialist teams. Specialists highlighted several key factors contributing to their success including financial support, mentoring by program leaders, and the opportunity to learn from fellow participants. The benefits of TBC make it one strategy for bolstering the specialist workforce, which is transferable and applicable for similar health settings across Canada and internationally. Early findings on patient outcomes and costs to the health system also suggest TBC can contribute to a high-quality, sustainable health care system. In BC, the government, in collaboration with specialist leaders, should continue investing in these practice models, with a focus on scaling up to include more specialists and specialties across the province. Future evaluation efforts should consider how TBC models may vary with different specialties, both for physicians and patients, and additional research is needed regarding the impact and importance of mentors in the continued spread of TBC models.

Availability of data and materials

The primary datasets analyzed during the current study are not publicly available due to prior data use agreements. However, the aggregate data used in this paper are available from the corresponding author upon reasonable request.

Doctors of BC. Creating space for doctors to be doctors: A cumulative impact lens on physician demands. Doctors of BC Policy Database. 2022. Available from: https://www.doctorsofbc.ca/file/34710/download?token=KKCUerje  Cited 2024 May 7.

Doctors of BC. Physician burdens. Doctors of BC Policy Database. 2020. Available from: https://www.doctorsofbc.ca/file/23047/download?token=yr1bSJ45  Cited 2024 May 7.

Canadian Medical Association. CMA 2021 National Physician Health Survey report. Ottawa: Canadian Medical Association; 2022. Available from: https://digitallibrary.cma.ca/viewer?file=%2Fmedia%2FDigital_Library_PDF%2F2021%2520NPHS%2520report%2520EN.pdf Cited 2024 May 6.

Canadian Medical Association. CMA 2021 National Physician Health Survey provincial report: British Columbia. Ottawa: Canadian Medical Association; 2022. Cited 2024 May 24.

Canadian Medical Association. CMA 2021 National Physician Health Survey comparison with general population report. Ottawa: Canadian Medical Association; 2023. Available from: https://digitallibrary.cma.ca/viewer?file=%2Fmedia%2FDigital_Library_PDF%2F2021%2520NPHS%2520population%2520comparison%2520EN.pdf  Cited 2024 May 24.

Canadian Medical Association. From data to action: Understanding the drivers of physician wellness: regression analyses - CMA 2021 National Physician Health Survey results. Ottawa: Canadian Medical Association; 2022. Available from: https://digitallibrary.cma.ca/viewer?file=%2Fmedia%2FDigital_Library_PDF%2F2021%2520NPHS%2520regression%2520analysis%2520EN.pdf  Cited 2024 May 15.

Wagner EH. The role of patient care teams in chronic disease management. BMJ. 2000;320(7234):569–72. https://doi.org/10.1136/bmj.320.7234.569 .

Article   CAS   PubMed   PubMed Central   Google Scholar  

Sevin C, Moore G, Shepherd J, Jacobs T, Hupke C. Transforming care teams to provide the best possible patient-centered, collaborative care. J Ambul Care Manage. 2009;32(1):24–31. https://doi.org/10.1097/01.jac.0000343121.07844.e0 .

Article   PubMed   Google Scholar  

Family Practice Services Committee. Primary Care Networks. 2024. Available from: https://fpscbc.ca/what-we-do/system-change/primary-care-networks  Cited 2024 May 6.

Wageman R, Gardner H, Mortensen M. The changing ecology of teams: new directions for teams research. J Organ Behav. 2012;33(3):301–15. https://doi.org/10.1002/job.1775 .

Article   Google Scholar  

Gaboury I, M. Lapierre L, Boon H, Moher D. Interprofessional collaboration within integrative healthcare clinics through the lens of the relationship-centered care model. J Interprof Care. 2010;25(2):124–30. https://doi.org/10.3109/13561820.2010.523654 .

Leonard MW, Frankel AS. Role of effective teamwork and communication in delivering safe, high-quality care. Mount Sinai J Med. 2011;78(6):820–6. https://doi.org/10.1002/msj.20295 .

Health Quality BC. Team-based care. 2024. Available from: https://healthqualitybc.ca/improve-care/team-based-care/  Cited 2024 May 26.

American College of Physicians. Team-based care toolkit. 2022.  Available from: https://www.acponline.org/practice-resources/patient-and-interprofessional-education/team-based-care-toolkit  Cited 2022 Nov 1.

Smith CD, Balatbat C, Corbridge S, Dopp AL, Fried J, Harter R, et al. Implementing optimal team-based care to reduce clinician burnout. NAM Perspect. 2018;8(9):1–13. https://doi.org/10.31478/201809c .

Mitchell PH, Wynia MK, Golden R, McNellis B, Okun S, Webb CE, et al. Core principles & values of effective team-based health care. NAM Perspect. 2012;2(10). https://doi.org/10.31478/201210c

Welp A, Manser T. Integrating teamwork, clinician occupational well-being and patient safety – development of a conceptual framework based on a systematic review. BMC Health Serv Res. 2016;16(1):281. https://doi.org/10.1186/s12913-016-1535-y .

Article   PubMed   PubMed Central   Google Scholar  

College of Physicians and Surgeons of BC. Annual report 2024. 2024.  Available from: https://www.cpsbc.ca/files/pdf/2022-23-Annual-Report.pdf  Cited 2024 May 6.

Institute for Healthcare Improvement. Triple Aim or Quadruple Aim? Four points to help set your strategy. 2017. Available from: https://www.ihi.org/insights/triple-aim-or-quadruple-aim-four-points-help-set-your-strategy  Cited 2024 May 7.

Joint Collaborative Committees. Doctors of BC; 2024.  Available from: https://www.collaborateonhealthbc.ca/  Cited 2024 May 6.

Connell J, Kur J, Gurmin JH. Assessing the impact of a nursing model of care on rheumatology practice patterns and patient satisfaction in British Columbia. BC Med J. 2020;62(3):98–102. Available from:  https://bcmj.org/articles/assessing-impact-nursing-model-care-rheumatology-practice-patterns-and-patient-satisfaction  Cited 2024 May 24.

Google Scholar  

Institute for Healthcare Improvement. The Breakthrough Series: IHI’s Collaborative Model for achieving breakthrough improvement. IHI Innovation Series white paper. Boston: Institute for Healthcare Improvement; 2003. Available from: https://www.ihi.org/resources/white-papers/breakthrough-series-ihis-collaborative-model-achieving-breakthrough  Cited 2024 May 6.

Government of Canada, Interagency Advisory Panel on Research Ethics. Tri-council policy statement: Ethical conduct for research involving humans – TCPS 2 (2022). 2023. Available from: https://ethics.gc.ca/eng/policy-politique_tcps2-eptc2_2022.html  Cited 2024 Aug 20.

Government of British Columbia. Health boundaries. Province of British Columbia; 2023. Available from: https://www2.gov.bc.ca/gov/content/data/geographic-data-services/land-use/administrative-boundaries  Cited 2024 May 25.

HealthLinkBC. Medical specialists. 2023. Available from: https://www.healthlinkbc.ca/health-topics/medical-specialists  Cited 2024 May 24.

Hennepin Healthcare Institute for Professional Worklife. Mini Z survey: Institute for Professional Worklife. Available from: https://www.professionalworklife.com/mini-z-survey  Cited 2024 May 06.

Linzer M, McLoughlin C, Poplau S, Goelz E, Brown R, Sinsky C. The Mini Z Worklife and Burnout Reduction Instrument: psychometrics and clinical implications. J Gen Intern Med. 2022;37(11):2876–8. https://doi.org/10.1007/s11606-021-07278-3 .

Grumbach K, Knox M, Huang B, Hammer H, Kivlahan C, Willard-Grace R. A longitudinal study of trends in burnout during primary care transformation. Ann Fam Med. 2019;17(Suppl 1):S9–16. https://doi.org/10.1370/afm.2406 .

Sheridan B, Chien AT, Peters AS, Rosenthal MB, Brooks JV, Singer SJ. Team-based primary care: the medical assistant perspective. Health Care Manage Rev. 2018;43(2):115–25. https://doi.org/10.1097/hmr.0000000000000136 .

Li-Sauerwine S, Rebillot K, Melamed M, Addo N, Lin M. A 2-question summative score correlates with the Maslach Burnout Inventory. West J Emerg Med. 2020;21(3):610–7. https://doi.org/10.5811/westjem.2020.2.45139 .

Olson K, Sinsky C, Rinne ST, Long T, Vender R, Mukherjee S, et al. Cross-sectional survey of workplace stressors associated with physician burnout measured by the Mini-Z and the Maslach Burnout Inventory. Stress Health. 2019;35(2):157–75. https://doi.org/10.1002/smi.2849 .

West CP, Dyrbye LN, Shanafelt TD. Physician burnout: contributors, consequences and solutions. J Intern Med. 2018;283(6):516–29. https://doi.org/10.1111/joim.12752 .

Article   CAS   PubMed   Google Scholar  

Rathert C, Williams ES, Linhart H. Evidence for the Quadruple Aim: a systematic review of the literature on physician burnout and patient outcomes. Med Care. 2018;56(12):976–84. https://doi.org/10.1097/mlr.0000000000000999 .

Willard-Grace R, Hessler D, Rogers E, Dube K, Bodenheimer T, Grumbach K. Team structure and culture are associated with lower burnout in primary care. J Am Board Fam Med. 2014;27(2):229–38. https://doi.org/10.3122/jabfm.2014.02.130215 .

Porter TH, Stoller JK, Allen SJ. Team development among physician-leaders at the Cleveland Clinic. Leadersh Health Serv (Bradf Engl). 2018;31(2):210–25. https://doi.org/10.1108/lhs-10-2017-0060 .

Kumar S, Deshmukh V, Adhish VS. Building and Leading Teams. Indian J Community Med. 2014;39(4):208–13. https://doi.org/10.4103/0970-0218.143020 .

Iliffe S, Manthorpe J. Job dissatisfaction, ‘burnout’ and alienation of labour: undercurrents in England’s NHS. J R Soc Med. 2019;112(9):370–7. https://doi.org/10.1177/0141076819855956 .

Cohen SG, Bailey DE. What makes teams work: Group effectiveness research from the shop floor to the executive suite. J Manage. 1997;23(3):239–90. https://doi.org/10.1177/014920639702300303 .

Salyers MP, Bonfils KA, Luther L, Firmin RL, White DA, Adams EL, et al. The relationship between professional burnout and quality and safety in healthcare: a meta-analysis. J Gen Intern Med. 2016;32(4):475–82. https://doi.org/10.1007/s11606-016-3886-9 .

Majid N, Lee S, Plummer V. The effectiveness of orthopedic patient education in improving patient outcomes: a systematic review protocol. JBI Database System Rev Implement Rep. 2015;13(1):122–33. https://doi.org/10.11124/jbisrir-2015-1950 .

Kruzik N. Benefits of preoperative education for adult elective surgery patients. AORN J. 2009;90(3):381–7. https://doi.org/10.1016/j.aorn.2009.06.022 .

Rochfort A, Beirne S, Doran G, Patton P, Gensichen J, Kunnamo I, et al. Does patient self-management education of primary care professionals improve patient outcomes: a systematic review. BMC Fam Pract. 2018;19(1):163. https://doi.org/10.1186/s12875-018-0847-x .

Deakin TA, McShane CE, Cade JE, Williams R. Group based self-management strategies in people with type 2 diabetes mellitus. Cochrane Database Syst Rev. 2005;(2). https://doi.org/10.1002/14651858.CD003417.pub2

Parlour R, Slater P. An evaluation of the effectiveness of a self-management programme. Dublin: Health Service Executive, Office of the Nursing and Midwifery Services Director; 2011.

Heinke MY, Vinod SK. A review on the impact of lung cancer multidisciplinary care on patient outcomes. Transl Lung Cancer Res. 2020;9(4):1639–53. https://doi.org/10.21037/tlcr.2019.11.03 .

Feng J, Novikov D, Anoushiravani A, Schwarzkopf R. Total knee arthroplasty: improving outcomes with a multidisciplinary approach. J Multidiscip Healthc. 2018;11:63–73. https://doi.org/10.2147/jmdh.s140550 .

Epstein NE. Multidisciplinary in-hospital teams improve patient outcomes: a review. Surg Neurol Int. 2014;5(8):S295-303. https://doi.org/10.4103/2152-7806.139612 .

Doyle C, Lennox L, Bell D. A systematic review of evidence on the links between patient experience and clinical safety and effectiveness. BMJ Open. 2013;3(1):e001570.

Maslach C, Leiter MP. Understanding the burnout experience: recent research and its implications for psychiatry. World Psychiatry. 2016;15(2):103–11. https://doi.org/10.1002/wps.20311 .

van Dam A. A clinical perspective on burnout: diagnosis, classification, and treatment of clinical burnout. Eur J Work Organ Psychol. 2021;30(5):732–41. https://doi.org/10.1080/1359432x.2021.1948400 .

Bourbonnais R, Brisson C, Vézina M. Long-term effects of an intervention on psychosocial work factors among healthcare professionals in a hospital setting. Occup Environ Med. 2010;68(7):479–86. https://doi.org/10.1136/oem.2010.055202 .

Shanafelt TD, Boone S, Tan L, Dyrbye LN, Sotile W, Satele D, et al. Burnout and satisfaction with work-life balance among us physicians relative to the general US population. Arch Intern Med. 2012;172(18):1377. https://doi.org/10.1001/archinternmed.2012.3199 .

Download references

Acknowledgements

The authors wish to thank the Specialists Team Care Working Group for their leadership and Alison Foulds and Elayne McIvor for their contribution to the data collection and analysis for the Specialists Team Care initiative.

The Specialists Team Care initiative was funded by the SSC, one of four Joint Collaborative Committees representing a partnership between the Doctors of British Columbia and the Government of British Columbia. The SSC receives funding via the BC Ministry of Health. The funders had no role in the implementation or evaluation of the Specialists Team Care initiative.

Author information

Authors and affiliations.

Doctors of BC, 1665 West Broadway, Vancouver, BC, V6J 5A4, Canada

Eric R. Young, Garth Vatkin & Jason Kur

Department of Healthcare Administration, Sawyer Business School, Suffolk University, Sargent Hall Room 5623, 110 Tremont Street, Boston, MA, 02108, USA

Erin E. Sullivan

You can also search for this author in PubMed   Google Scholar

Contributions

E.Y. was involved in the study design, data acquisition, and data analysis. G.V. was involved in the study conception and design. E.S. was involved in the study design. All the authors were involved in drafting and revisioning the article, and approved the final manuscript.

Corresponding author

Correspondence to Eric R. Young .

Ethics declarations

Ethics approval and consent to participate.

This quality improvement project focused on the implementation and evaluation of team-based care models in outpatient specialist clinics was conducted as part of an initiative to enhance the physician experience and patient care in British Columbia. Formal ethics approval and informed consent were not required as this was a quality improvement project and not research involving human subjects as defined by Article 2.5 of the Tri-Council Policy Statement: Ethical Conduct for Research Involving Humans (TCPS 2).

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1: stc mini z survey., additional file 2: stc patient survey and follow-up call., rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ .

Reprints and permissions

About this article

Cite this article.

Young, E.R., Vatkin, G., Kur, J. et al. Team-based care in specialist practice: a path to improved physician experience in British Columbia. BMC Health Serv Res 24 , 1000 (2024). https://doi.org/10.1186/s12913-024-11482-2

Download citation

Received : 06 June 2024

Accepted : 22 August 2024

Published : 28 August 2024

DOI : https://doi.org/10.1186/s12913-024-11482-2

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Team-based care
• Specialist physician
• Quadruple aim

BMC Health Services Research

ISSN: 1472-6963

• General enquiries: [email protected]

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here .

Loading metrics

Open Access

Peer-reviewed

Research Article

Assessment of deep learning image reconstruction (DLIR) on image quality in pediatric cardiac CT datasets type of manuscript: Original research

Roles Conceptualization, Data curation, Investigation, Methodology, Software, Writing – original draft

* E-mail: [email protected]

Affiliation Department of Radiology and Medical Research Institute, College of Medicine, Ewha Womans University Seoul Hospital, Seoul, Republic of Korea

Roles Formal analysis, Investigation, Methodology, Validation, Visualization

Affiliation Department of Radiology, School of Medicine, Kyungpook National University, Kyungpook National University Hospital, Daegu, South Korea

Roles Conceptualization, Methodology, Software, Supervision

Affiliation Department of Radiology, Chungnam National University Hospital, Daejeon, Republic of Korea

• Hyun-Hae Cho, 
• So Mi Lee, 
• Sun Kyoung You

• Published: August 26, 2024
• https://doi.org/10.1371/journal.pone.0300090
• Peer Review
• Reader Comments

To evaluate the quantitative and qualitative image quality using deep learning image reconstruction (DLIR) of pediatric cardiac computed tomography (CT) compared with conventional image reconstruction methods.

Between January 2020 and December 2022, 109 pediatric cardiac CT scans were included in this study. The CT scans were reconstructed using an adaptive statistical iterative reconstruction-V (ASiR-V) with a blending factor of 80% and three levels of DLIR with TrueFidelity (low-, medium-, and high-strength settings). Quantitative image quality was measured using signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). The edge rise distance (ERD) and angle between 25% and 75% of the line density profile were drawn to evaluate sharpness. Qualitative image quality was assessed using visual grading analysis scores.

A gradual improvement in the SNR and CNR was noted among the strength levels of the DLIR in sequence from low to high. Compared to ASiR-V, high-level DLIR showed significantly improved SNR and CNR ( P <0.05). ERD decreased with increasing angle as the level of DLIR increased.

High-level DLIR showed improved SNR and CNR compared to ASiR-V, with better sharpness on pediatric cardiac CT scans.

Citation: Cho H-H, Lee SM, You SK (2024) Assessment of deep learning image reconstruction (DLIR) on image quality in pediatric cardiac CT datasets type of manuscript: Original research. PLoS ONE 19(8): e0300090. https://doi.org/10.1371/journal.pone.0300090

Editor: Ashraful Hoque, Sheikh Hasina National Institute of Burn & Plastic Surgery, BANGLADESH

Received: March 11, 2024; Accepted: May 28, 2024; Published: August 26, 2024

Copyright: © 2024 Cho et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting information files.

Funding: This work was supported by the Ewha Womans University Research Grant of 2021. The funders had no role in study design or data collection. But the financial support for research, including statistical advice, translation advice, and research, is based on the funder.

Competing interests: There exist no other competing interests.

Abbreviations: ASiR-V, adaptive statistical iterative reconstruction-V; CNR, contrast-to-noise ratio; CT, computed tomography; DLIR, deep learning image reconstruction; ERD, edge rise distance; HU, Hounsfield unit; LV, left ventricle; PACS, picture archiving and communication systems; ROI, region of interest; RV, right ventricle; SNR, signal-to-noise ratio; VGA, visual grading analysis

Congenital heart diseases (CHD) are rare diseases and their prognosis is difficult to predict, but an understanding of the exact anatomical structure and hemodynamic function is necessary to determine subsequent treatment and provide proper medical support [ 1 – 3 ]. For evaluation, echocardiography is yet to be used as primary tool in neonates with CHD [ 1 , 3 ], but echocardiography has limitations in the great vessels and coronary arteries [ 4 ]. In terms of the postoperative status, acoustic shadowing can occur due to surgical materials, and echocardiography may not be able to obtain an image suitable for exact evaluation [ 4 ].

Recently, the usage of computed tomography (CT) is widely increased in evaluating CHD. It was proved CT showed similar accuracy compared with echocardiography [ 2 – 6 ].And it shows advantages in limited situations to evaluating the complex heart anatomy, postoperative changes and ventricle functions compared with using only echocardiography [ 1 , 2 , 4 – 8 ]. Recently, the incidence of adulthood-diagnosed congenital heart disease has increased [ 1 , 2 , 4 , 6 ], which is hard to evaluated by using echocardiography. For these reasons, CT is now used as important tool for evaluating CHD.

Actually, it is hard to obtain qualified pediatric CT due to motion artifacts caused by the limitations of respiratory hold and higher heart rate. For more accurate delineation of the small complex anatomy is required for pediatric cardiac CT scans, improving the image quality of pediatric cardiac CT is important for reducing unnecessary radiation exposure for relatively radiation sensitive pediatric age group [ 9 – 11 ].

For increasing image quality of pediatric cardiac CT scan, many post processing reconstruction tools were adapted [ 8 , 12 – 15 ]. Recently, the latest developed image reconstruction tool is released by using adaptation of deep-learning techniques highlighted in the medical imaging field [ 13 , 16 – 20 ]. It has been proven to be effective for improving the image quality of many body parts [ 16 – 20 ]. Therefore, the purpose of this study was to evaluate the image quality using deep learning image reconstruction (DLIR) of pediatric cardiac CT compared to conventional IR methods.

Approval from the institutional review board was obtained (IRB number 2022-02-026-010), and the requirement for patient/parent informed consent was waived.

Existing cardiac CT scans were examined with same CT machine (512-slice CT scan; Revolution; GE Healthcare, Milwaukee, USA). We used retrospectively electrocardiography-gated spiral scan for achieve both systolic and diastolic phase images for volume measurement and calculation of EF. Lowest possible tube voltage was decided between 80Kv or 100Kv according to the patient’s age and weight. Contrast material was injected with triphasic or quadriphasic pattern for obtaining homogeneously filled chamber images with decreased artifacts. We used dual head power injector at a dose of 1.5–2mL/ kg and a flow rate of 0.3–3.0 mL/s. Scan delay was determined using a bolus tracking system with ROI within the LV.

Study design

Existing cardiac CT scans were reconstructed using adaptive statistical iterative reconstruction-V (ASiR-v) and performed using a level of 80% blending with edge contrast reconstruction according to in-hospital agreement, using a 512-slice CT scan (Revolution; GE Healthcare, Milwaukee, USA). After the DLIR of this vendor was approved by the Food and Drug Administration as a commercialized deep learning reconstruction method, adaptation of DLIR (TrueFidelity; GE Healthcare, Milwaukee, USA) was performed at our center in December 2019. Subsequently, all pediatric cardiac protocol CT scans were reconstructed using ASiR-V and all three levels of DLIR: low, medium, and high.

For an exact comparison, we included pediatric patients with underlying congenital heart disease who underwent pediatric cardiac CT with same vender after adaptation of DLIR. We included cases which canbe reconstructed by using both ASiR-V and DLIR. We excluded studies with severe artifacts including motion or metallic artifacts that can make measurements difficult or insufficient for reconstruction.

Clinical data and radiation dose

Clinical data, including age at the examination date, sex, body weight, height, and body mass index, were recorded for each patient. Patients’ underlying cardiac diseases and recent surgical records were also collected. The duration between the most recent operation and the CT examination was calculated. The reasons for the examinations were also recorded.

We collected the estimated CT dose index volume and dose-length product data for all scans. These data were automatically calculated using a CT machine during the examination and displayed on a picture archiving and communication systems (PACS).

Image analysis

All CT image datasets were displayed on the PACS workstation (G3 PACS; Infinitt Inc.) with a mediastinal setting of 800 Hounsfield unit (HU) window width and 150 HU window level.

Quantitative image quality

Objective analysis of quantitative image quality was performed by calculating the signal-to-noise ratio (SNR) and contrast-to-noise ratios (CNR). The average signal attenuation was measured using the mean HU within the region of interest (ROI). The standard deviation within the ROI was calculated and considered a noise parameter.

To reduce bias, two pediatric radiologists (H.-H.C. and S.K.Y, each with 10 years of experience with CT) were blinded to all clinical data, medical information, and radiation dose data.

Each observer drew regions of interest in each area and level for each image set, and the mean value was used for the final analysis. We measured the noise as the average standard deviation calculated using the mean value of each drawn ROI. At the T7–8 level, four ROIs were measured in the right ventricle (RV), left ventricle (LV) chamber, interventricular septum, and paravertebral muscles. At the T2–4 level, two ROIs were measured in the ascending and descending aortas.

For single-ventricle patients, the ROI for the chamber was measured at the right and left sides of a single ventricle. The ROI was selected for muscle in apical area of the cardiac muscle or small portion of residual interventricular septum.

To reduce bias and variability, averaging data of the standard deviation of each area were calculated after drawing three different 10-mm 2 circular ROIs in the area where we wanted to measure.

The CNR was calculated in four areas: RV, LV chamber, and ascending and descending aortas.

Analysis for image sharpness

Edge rise distance (ERD) has been used in previous studies to evaluate CT image quality [ 21 – 24 ]. Axial CT images were obtained at T8. For measurement of ERD, we used a commercialized program to measure the line density profile (Image J program) with a 1 cm reference line drawn from the center of the descending aorta to the adjacent lung parenchyma, according to a previous report [ 17 , 21 ].

Subsequently, we drew a graph based on the measured data and calculated the ERD and angle between 25% and 75% of the line density profile using the statistical program R. The ERD and angle values were compared among the reconstruction algorithms. All measurements were performed by one of the authors (H-H.C. with 11 years of experience in pediatric radiology), and the measurements were repeated three times for each image to reduce measurement variability.

Qualitative image quality

Qualitative image quality was rated with reference to the visualization of structures for the diagnostic quality of pediatric cardiac CT [ 15 ] with visual grading analysis (VGA) scores to grade visibility ( Table 1 ). Qualitative image quality analysis was performed by two attending pediatric radiologists (H.-H.C and S.M.L., each with 11 years of clinical experience). To reduce bias, all identifying data were removed from each image set, and the image sets were reordered randomly before analysis. Interobserver agreement between the two radiologists was also calculated.

• PPT PowerPoint slide
• PNG larger image
• TIFF original image

A) Set of structures established as diagnostic requirements. B) Scores for visibility of the structures in relation to reference structure.

https://doi.org/10.1371/journal.pone.0300090.t001

Statistical analysis

To compare the quantitative parameters of the DLIR at high, medium, and low levels with those of the ASiR-V group, a paired Student’s t-test was used. To compare the results of the qualitative analysis of image quality, the results of DLIR high, medium, and low levels were compared with those of the ASiR-V group using the Kruskal-Wallis test and analysis of variation (ANOVA). Inter-observer agreement was assessed using weighted Cohen’s kappa statistics. All statistical analyses were conducted using commercially available software (SPSS Statistics, Version 19.0; IBMm, Armonk, NY, USA).

Between January 2020 and December 2022, 123 pediatric cardiac CT scans were included in this study. Among these, a total of 109 patients (48 females, 61 males; mean age, 102.1 months) underwent pediatric protocol cardiac CT. Underlying cardiac diseases included pulmonary stenosis (n = 15), pulmonary atresia with VSD (n = 5), Tetrology of Fallot (n = 23), functional single ventricle (n = 30), interrupted aortic arch (n = 9), coarctation of the aorta (n = 3), and other diseases (n = 38).

CT scans were performed before the operation in 63 patients, during routine follow-up in 40 patients, and during evaluation of postoperative complications in six patients.

Quantitative analysis

The results of the quantitative image analysis are presented in Tables 2 and 3 . A gradual improvement in the SNR was noted among the strength levels of the DLIR in sequence from low to high. When compared with ASiR-V in all measured areas except muscles, high-level DLIR showed better SNR without significant difference. The SNR of low, medium-level DLIR showed a significantly lower P value than that of ASiR-V in all measured areas except RV (P <0.05).

https://doi.org/10.1371/journal.pone.0300090.t002

https://doi.org/10.1371/journal.pone.0300090.t003

The CNR of the RV, LV, and ascending and descending aortas showed an improvement in strength levels of the DLIR in sequence from low to high. Compared with ASiR-V, high-level DLIR showed better CNR without significant difference. The CNR of low, medium-level DLIR showed a significantly lower P value than that of ASiR-V ( P <0.05) at all levels.

The distance between the 25% and 75% levels decreased as the DLIR level increased ( Table 4 ). The level of high-level DLIR was significantly higher than that of ASiR-V ( P = 0.048). The angle between 25% and 75% increased as the DLIR level increased. The value of high-level DLIR was significantly higher than that of ASiR-V ( P = 0.002). The results of quantitative analysis are also shown in graphs ( Fig 1 ).

Graphs for SNR (A), CNR (B), ERD (C) and ERA (D) among reconstruction methods. Asir-V adaptive statistical iterative reconstruction, DLIR Deep Learning Image Reconstruction adapted image sets with strength level (high, medium, low), Asc Ascending aorta, Des Descending aorta, LV left ventricle, RV right ventricle, Inter Interventricular septum, Para Paravertebral septum.

https://doi.org/10.1371/journal.pone.0300090.g001

https://doi.org/10.1371/journal.pone.0300090.t004

Qualitative analysis of image quality

The results of the qualitative image analysis are presented in Table 5 . Qualitative analysis of image quality scored using VGA revealed sequentially increased scores among the strength levels of DLIR from low to high, indicating improvement in the detection of anatomical structures (Figs 2 – 4 ). All scores were significantly better in the higher-level DLIR ( P = 0.001).

Representative case of cardiac CT of 7 years old boy, which was reconstructed by ASiR -V (A), DLIR- High (B), Med (C) and Low (D). There noted increment of differentiation of intraventricular muscles (black arrow) with decreased noise in contrast filled cavity.

https://doi.org/10.1371/journal.pone.0300090.g002

Representative case of cardiac CT of 4 months old boy, which was reconstructed by ASiR -V (A), DLIR- High (B), Med (C) and Low (D). There noted increment of differentiation of coronary arteries (dot white arrows). Distal branch of left pulmonary artery is more well visualized in high level DLIR (white arrows).

https://doi.org/10.1371/journal.pone.0300090.g003

Representative case of cardiac CT of 7 months old boy, which was reconstructed by ASiR -V (A), DLIR- High (B), Med (C) and Low (D). There noted increment of differentiation of aortic valve leaflets (black arrows).

https://doi.org/10.1371/journal.pone.0300090.g004

https://doi.org/10.1371/journal.pone.0300090.t005

Recent studies on the image quality of cardiac CT scans have focused on the adaptation of post-processing reconstruction methods, including many iterative reconstructions such as SAFIRE and ASiR [ 12 – 14 , 25 ] in both pediatric and adult patients [ 15 , 26 , 27 ]. Because the DLR algorithm is a recently developed reconstruction method that lacks many clinical results [ 18 , 19 , 28 ], the results of this study might be helpful in substituting DLIR, especially for pediatric cardiac CT. Compared to recent studies using the DLIR in pediatric CT [ 9 , 29 ], this study has the advantage of conducting a more objective assessment with more objective parameters in a larger population.

The results of this study indicate that high-level DLIR can achieve significantly higher as highe SNR and CNR like as those of ASiR-V. As ASiR-V is an established reconstruction method for pediatric CT studies according to previous studies [ 12 , 13 , 15 , 25 ], high-level DLIR can be used as an alternative method for pediatric cardiac CT. Although CNR and SNR showed high values in this study, but they didn’t show significantly increased value compared to ASiR-V, as noted in previous study [ 9 ] which used different blending factor for ASiR-V. So, there needed further studies using different blending factors for ASiR-V. H

However, the measured ERD was significantly shorter with a larger ERA in high-level DLIR than in ASiR-V, indicating that a sharper graph was drawn with a high-level DLIR. The measurement of ERD and ERA in this study is significant because these methods can be more objective in proving that the sharpness of the DLIR CT scan is better than that of ASiR-V using methods that have not been used as comparison methods in a previous study [ 9 ]. ERD and ERA can be used as the most accurate indicators of image sharpness by measuring distances of 25–75% and angles of 25–75%, which would be shorter and larger on the sharper border of the changing density of each reconstructed image [ 17 , 21 – 24 ]. In particular, considering that the part to be checked in pediatric cardiac CT is the structure formed by the vessels, atria, and ventricles, which are filled with contrast media, this difference in sharpness plays an important role in evaluation of anatomical structures in pediatric cardiac CT [ 21 ].

Qualitative analysis measuring the VGA also showed significantly better scores for the higher-level DLIR. VGA scoring is important because a better score indicates better delineation of the distal small part of the thoracic structures, which is important for evaluating small anatomical structures in pediatric patients [ 15 ]. Especially in patients with complex congenital heart disease, clear visualization of small vessels and muscle fibers is very important for the pre- or post-operative evaluation of pediatric cardiac CT scans.

We also evaluated the distortion artifact, which is a characteristic artifact of DLIR, as suggested in previous studies [ 16 , 17 ]. This distortion artifact, noted as a ‘checkered pattern’ artifact [ 16 , 17 ], is noted in our cases, but it was also noted in the ASiR-V ( Fig 5 ); therefore, this artifact is not a typical reconstruction for the DLIR. This finding supports the hypothesis of a previous report that the artifact may be due to the CT scanner hardware or reconstruction algorithm [ 16 , 17 ].

Representative case of cardiac CT of 4 years old boy, which was reconstructed by ASiR -V (A), DLIR- High (B), Med (C) and Low (D). There noted distortion artifacts in all 4 reconstruction methods and artifact due to contrast showed more severe on DLIR methods than ASiR-V (white arrows).

https://doi.org/10.1371/journal.pone.0300090.g005

Our previous study about pediatric brain CT scan revealed that there noted no improvement of artifact or even worsened after adaptation of DLIR in some age groups [ 30 ]. In this study some cases also showed worsening of artifacts by contrast in high level DLIR than in ASiR-V ( Fig 5 ), unless, which do not effect on the evaluating anatomy. And it can be thought to be by shortness of artifacts for training this reconstruction method [ 30 ].

This study had several limitations. The sample included patients of various age groups. Hence, we evaluated the image quality of the two reconstruction methods. The results showed similar trends among different age groups. Due to the retrospective nature of this study, we could not compare FBP and DLIR. In terms of lesion detection, lesions on pediatric cardiac CT are not significantly affected by reconstruction methods; therefore, the comparison of lesion detection was not meaningful in these patients.

According to this study, high-level DLIR showed better qualitative and quantitative image quality than ASiR-V and other levels of DLIR. Although ASiR-V is an established reconstruction method for pediatric CT studies, high-level DLIR can be used as an alternative to pediatric cardiac CT.

Supporting information

S1 file. included data file..

https://doi.org/10.1371/journal.pone.0300090.s001

• View Article
• PubMed/NCBI
• Google Scholar

Information

• Author Services

Initiatives

You are accessing a machine-readable page. In order to be human-readable, please install an RSS reader.

All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess .

Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications.

Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the reviewers.

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

Original Submission Date Received: .

• Active Journals
• Find a Journal
• Proceedings Series
• For Authors
• For Reviewers
• For Editors
• For Librarians
• For Publishers
• For Societies
• For Conference Organizers
• Open Access Policy
• Institutional Open Access Program
• Special Issues Guidelines
• Editorial Process
• Research and Publication Ethics
• Article Processing Charges
• Testimonials
• Preprints.org
• SciProfiles
• Encyclopedia

Article Menu

• Subscribe SciFeed
• Recommended Articles
• Google Scholar
• on Google Scholar
• Table of Contents

Find support for a specific problem in the support section of our website.

Please let us know what you think of our products and services.

Visit our dedicated information section to learn more about MDPI.

JSmol Viewer

Contribution to school design through assessment of corridor conditions in foundation schools in erbil, iraq, 1. introduction, 2.1. spatial zoning in school buildings, 2.1.1. corridors as spatial areas, 2.1.2. circulation ratio guidelines, 2.2. considered factors, 2.2.1. usability, 2.2.2. flexibility and adaptability, 2.2.3. cost-effective school design, 2.2.4. sustainable and green building practices, 3. research methodologies, 3.1. study design and methods, 3.2. sample schools, 3.3. data collection tool and procedure, 3.4. statistical methods, 4. results and discussion, 4.1. analysis of revit software, 4.2. questionnaire survey result analysis, 4.2.1. descriptive analysis of the respondents’ opinions, 4.2.2. paired-sample t -test results, gender variable, school corridor design, 4.2.3. one-way anova results, 4.3. development of innovative design concepts, 4.3.1. elimination of traditional corridors, 4.3.2. separate units for different educational stages, 4.3.3. private and common outdoor areas, 4.3.4. flexible partitions, 4.3.5. phased construction, 4.3.6. redesign proposals for l-shaped and o-shaped schools, 5. conclusions, future study, data availability statement, conflicts of interest.

• Mahmoodi, A.S. The Design Process in Architecture: A Pedagogic Approach Using Interactive Thinking. Ph.D. Thesis, University of Leeds, Leeds, UK, 2001. [ Google Scholar ]
• Schiefelbein, E.; Hallak, J.; Caillods, F. Redefining Basic Education for Latin America: Lessons to Be Learned from the Colombian Escuela Nueva (No. 42) ; UNESCO, International Institute for Educational Planning: Paris, France, 1992. [ Google Scholar ]
• Nair, P.; Fielding, R.; Lackney, J.A. The Language of School Design: Design Patterns for 21st Century Schools ; DesignShare: New York, NY, USA, 2013. [ Google Scholar ]
• Perkins, B. Building Type Basics for Elementary and Secondary Schools ; John Wiley & Sons: Hoboken, NJ, USA, 2002. [ Google Scholar ]
• Baper, S.Y.; Rashid, S.J. The impact of sustainability factors on the usability of residential spaces. Int. Trans. J. Eng. Manag. Appl. Sci. Technol. 2018 , 9 , 271–281. [ Google Scholar ]
• Claessens, D.P.; Boonstra, S.; Hofmeyer, H. Spatial zoning for better structural topology design and performance. Adv. Eng. Inform. 2020 , 46 , 101162. [ Google Scholar ] [ CrossRef ]
• Frederick, M. 101 Things I Learned in Architecture School ; MIT Press: Cambridge, MA, USA, 2007. [ Google Scholar ]
• Department of Education, Queensland Government. Education Facilities Design Principles and Generic Functional Brief. In Design Standards Part A ; Department for Education: London, UK, 2024; pp. 1–195. Available online: https://alt-qed.qed.qld.gov.au/our-publications/standards/Documents/design/education-facilities-design-principles-general-functional-brief.pdf (accessed on 25 August 2024).
• Pasalar, C. The Effects of Spatial Layouts on Students’ Interactions in Middle Schools: Multiple Case Analysis ; North Carolina State University: Raleigh, NC, USA, 2004. [ Google Scholar ]
• Sailer, K. Corridors, classrooms, classification: The impact of school layout on pedagogy and social behaviors. In Designing Buildings for the Future of Schooling ; Routledge: London, UK, 2018; pp. 87–111. [ Google Scholar ]
• Zhang, A.; Sun, Y.; Huang, Q.; Bokel, R.; van den Dobbelsteen, A.; Zhang, A.; Sun, Y.; Huang, Q.; Bokel, R.; van den Dobbelsteen, A. Effect of Corridor Design on Energy Consumption for School Buildings in the Cold Climate. In Proceedings of the World Sustainable Built Environment Conference (WSBE17), Hongkong, China, 5–7 June 2017; Construction Industry Council: Hongkong, China, 2017. [ Google Scholar ]
• Area Guidelines for Mainstream Schools, Building Bulletin 103 ; Department for Education: London, UK, 2014.
• Neufert, E.; Neufert, P. Neufert Architects’ Data , 4th ed.; Wiley-Blackwell: Hoboken, NJ, USA, 2012; Volume 53, Issue 9. [ Google Scholar ]
• Subissati, S. School without Corridor, more than a classroom. In The Plan ; Maggioli SpA: Bologna, Italy, 2022; Available online: https://www.theplan.it/eng/award-2022-Education/school-without-corridor-more-than-a-classroom-simone-subissati-architects (accessed on 25 August 2024).
• Bittencourt, M.C.; do Valle Pereira, V.L.D.; Júnior, W.P. The usability of architectural spaces: Objective and subjective qualities of built environment as multidisciplinary construction. Procedia Manuf. 2015 , 3 , 6429–6436. [ Google Scholar ] [ CrossRef ]
• ISO 9241-11:1998 ; Ergonomic Requirements for Office Work with Visual Display Terminals (VDTs). The International Organization for Standardization: Geneva, Switzerland, 1998.
• Duca, G. Usability requirements for buildings: A case study on primary schools. Work 2012 , 41 (Suppl. S1), 1441–1448. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Kim, Y.J. Organism of Options: A Design Strategy for Flexible Space. Ph.D. Thesis, Massachusetts Institute of Technology, Cambridge, MA, USA, 2008. [ Google Scholar ]
• Shabha, G.S. Flexibility and the design for change in school buildings. Archit. Sci. Rev. 1993 , 36 , 87–96. [ Google Scholar ] [ CrossRef ]
• Dobson, D.W.; Sourani, A.; Sertyesilisik, B.; Tunstall, A. Sustainable construction: Analysis of its costs and benefits. Am. J. Civ. Eng. Archit. 2013 , 1 , 32–38. [ Google Scholar ]
• Bredenoord, J. Sustainable building materials for low-cost housing and the challenges facing their technological developments: Examples and lessons regarding bamboo, earth-block technologies, building blocks of recycled materials, and improved concrete panels. J. Archit. Eng. Technol. 2017 , 6 , 1000187. [ Google Scholar ] [ CrossRef ]
• Tam, V.W. Cost effectiveness of using low cost housing technologies in construction. Procedia Eng. 2011 , 14 , 156–160. [ Google Scholar ] [ CrossRef ]
• Hassan, A.; Yahya, M. Cost optimization for public school building projects during design stage using value engineering. In MATEC Web of Conferences ; EDP Sciences: Les Ulis, France, 2018; Volume 162, p. 02033. [ Google Scholar ]
• Srivastava, R.D. A Method of Reducing Classroom Requirements in Primary Schools in Asia ; Occasional Paper-School Building No. 13; ERIC Clearinghouse: Washington, DC, USA, 1967. [ Google Scholar ]
• Abdullah, S.; Ali, H.M.; Sipan, I.; Awang, M.; Rahman, M.S.A.; Shika, S.A.; Jibril, J.D. Classroom management: Measuring space usage. Procedia-Soc. Behav. Sci. 2012 , 65 , 931–936. [ Google Scholar ] [ CrossRef ]
• Najib, S.H.; Ekici, B.B. Investigation of the effect of courtyard direction and width on energy loads of buildings. Karesi J. Archit. 2022 , 1 , 89–103. [ Google Scholar ]
• Pereira, P.R.P.; Kowaltowski, D.C.C.K.; Deliberador, M.S. Analysis support for the design process of school buildings. Ambiente Construído 2018 , 18 , 375–390. [ Google Scholar ] [ CrossRef ]
• Sureshkumar, M.; Uthra, V.; Puneeth, S. Low-cost school building. Development 2018 , 5 , 87–89. [ Google Scholar ]
• Ahmed, W.; Lim, C.W. Production of sustainable and structural fiber reinforced recycled aggregate concrete with improved fracture properties: A review. J. Clean. Prod. 2021 , 279 , 123832. [ Google Scholar ] [ CrossRef ]
• Liu, T.; Chen, L.; Yang, M.; Sandanayake, M.; Miao, P.; Shi, Y.; Yap, P.S. Sustainability considerations of green buildings: A detailed overview on current advancements and future considerations. Sustainability 2022 , 14 , 14393. [ Google Scholar ] [ CrossRef ]
• Doan, D.T.; Van Tran, H.; Aigwi, I.E.; Naismith, N.; Ghaffarianhoseini, A.; Ghaffarianhoseini, A. Green building rating systems: A critical comparison between LOTUS, LEED, and Green Mark. Environ. Res. Commun. 2023 , 5 , 075008. [ Google Scholar ] [ CrossRef ]
• Hahn Fox, B.; Jennings, W.G. How to write a methodology and results section for empirical research. J. Crim. Justice Educ. 2014 , 25 , 137–156. [ Google Scholar ] [ CrossRef ]
• Creswell, J.W. Educational Research: Planning, Conducting, and Evaluating Quantitative and Qualitative Research ; Pearson: London, UK, 2012; p. 4. [ Google Scholar ]
• Shield, B.M.; Dockrell, J.E. The effects of noise on children at school: A review. Build. Acoust. 2003 , 10 , 97–116. [ Google Scholar ] [ CrossRef ]
• Khansari, S.; Movahed Mohammadi, Z.S. Life around Circles. Passive Design Strategies for a Secondary School in Benga, Malawi. 2021. Available online: https://www.politesi.polimi.it/handle/10589/189147 (accessed on 25 August 2024).
• Keniger, L.E.; Gaston, K.J.; Irvine, K.N.; Fuller, R.A. What are the benefits of interacting with nature? Int. J. Environ. Res. Public Health 2013 , 10 , 913–935. [ Google Scholar ] [ CrossRef ] [ PubMed ]

Click here to enlarge figure

Share and Cite

Zewar, S.S. Contribution to School Design through Assessment of Corridor Conditions in Foundation Schools in Erbil, Iraq. Buildings 2024 , 14 , 2678. https://doi.org/10.3390/buildings14092678

Zewar SS. Contribution to School Design through Assessment of Corridor Conditions in Foundation Schools in Erbil, Iraq. Buildings . 2024; 14(9):2678. https://doi.org/10.3390/buildings14092678

Zewar, Sardar Suwar. 2024. "Contribution to School Design through Assessment of Corridor Conditions in Foundation Schools in Erbil, Iraq" Buildings 14, no. 9: 2678. https://doi.org/10.3390/buildings14092678

Article Metrics

Article access statistics, further information, mdpi initiatives, follow mdpi.

Subscribe to receive issue release notifications and newsletters from MDPI journals