is qualitative research harder than quantitative

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.

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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.

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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

A Common Student Question: Which Is Easier, Quantitative or Qualitative Research?

Leading a peer mentoring scheme means that I get a constant stream of messages and emails from first- and second-year psychology students. It is that time of the year where second-year students are choosing their final-year units and planning what they want to do for their final year research project (their dissertation).

The most common question I receive is: ‘Which is easier, quantitative or qualitative research?’ Of course, some researchers will have some biased views on this – probably based on what they are involved with themselves. But any good researcher will know that there is no straightforward answer to this question.

I remind students that they need to consider their research question. I conceptualise it between two questions: ‘w hat’ and ‘w hy’

The ‘what’ questions typically relate to a research question that requires a quantitative analysis to get a view of what variables influence other variables, or even how and to what extent one variable influences other variables (note that I use influence here, but such a question may also seek to establish causality).

The ‘why’ question, in my mind, would typically require a qualitative analysis . Why are students not receptive to feedback? Why is there a spike in teenage STD contraction? These questions will require asking samples from the population you’re interested in.

Of course, as with most things, there are some exceptions to this rule. For example, a ‘what’ question may require a qualitative analysis. Such as: ‘How does stress at work relate to quality of life in people working night shifts?’ This inevitably means seeking out a sample of people working nights shift.

Alternatively, a ‘why’ question may require a quantitative analysis. But researchers tend to form these ‘why’ questions in the way of a hypothesis. They may have an initial ‘why’ question, but then reflect this in an experimental hypothesis. For example: Why a consumer behaves in a certain why or how they’d feel if a certain situation were to take place.

A lot of students are also concerned about the time consumption of research for a final-year dissertation project. It is important to recognise that one approach (quantitative versus qualitative) is not necessarily faster than the other.

I conceptualise the time consumption of the methods as following, and find this helps students (for quantitative, then qualitative respectively):

data collection > data analysis data collection < data analysis

I have also noticed something peculiar, and I believe I may have experienced this myself before getting more involved in research: statistics anxiety .

Many students are coming to me asking how hard statistics is and whether they will get lots of support from their supervisors on their ‘independent’ projects.

I know many current final-year students, and second-year students, who are opting for a qualitative research project just to avoid running statistical analyses. It is apparent that this reasoning for choosing a qualitative project is a wrong one, especially the aforementioned discussion on choosing a method based on your research question.

A ‘why’ question may require a quantitative analysis.

This raises an important question: Are universities failing to engage students in Research Methods and Statistics? Unfortunately, in my own opinion (as a student), the answer to this question is yes, yes they are.

However, there is a way to fix this. Universities need to realise that the current way of teaching Research Methods and Statistics is failing. I have had countless lectures on different statistical tests, which are important, but I have had to retain knowledge on different pieces of logic and philosophy, which is impractical. At the end of the day, the real world of research does not require this knowledge. It requires you to:

• Formulate a research question;
• Read the literature;
• Design an experiment (or qualitative alternative);
• Collect data;
• Analyse that data;
• Interpret your results.

In my second-year I had a multiple choice question section of my examination, which I strongly believe was pointless on many levels. I failed this section of the examination. The second half of the examination required me to read some SPSS outputs, interpret them, and write up design, results, and discussion (first paragraph) sections of a laboratory report.

I excelled this section of the examination. This, of course, is far more representative of real-life research. I also wonder why students are not being assessed on their quantitative knowledge via using software such as SPSS – this is one of the most common statistics software that researchers use in the real world .

Universities have a duty to teach students to decide for themselves which is most important.

The concern that I have here is that Research Methods and Statistics is not being taught, nor examined, in a practical or realistic way. Another concern I have is that universities are giving the limelight to quantitative methodology, and not giving enough to qualitative methodology. In my first- and second-year, I had six lab classes that were quantitative-based and only two that were qualitative-based – both of which based on thematic analysis and nothing else.

This will lead students to believing that qualitative methodology is secondary to quantitative methodology. I cannot help but find the irony in this. Psychologists, with a wealth of knowledge on behaviour and attitudes, are still yet to develop curricula that will make the researchers of tomorrow. Universities have a duty to teach students to decide for themselves which is most important. In the case of those lab classes I mentioned above, surely this should be a 50/50 split. 

I think academia needs to reflect about the current way in which Research Methods and Statistics is taught. The discipline really must pay attention to the apparent trade-off between quantitative and qualitative methodology and the impression that it makes on students.

Callum Mogridge is an undergraduate psychology student at the University of Manchester. He leads the peer support on the degree programme. 

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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.

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Princeton Correspondents on Undergraduate Research

Quantitative vs. Qualitative Research: What’s the Difference and How Do I Choose?

It’s almost November now, and if you’re a junior, you’re used to everyone asking you the same question: How’s your junior paper going? If your experience has been anything like mine, your initial reaction may be, “It’s great!” I’ve finally come up with a JP topic that interests me, I’ve already talked to (and received incredible advice from) my professors, and I’m in the process of mastering my  Magic Research Statement . Getting started on my JP feels like a walk in the park!

But as November creeps nearer, my reaction to the JP question is a little less confident and a little more like, “Ummmm……” For me, this pause and sense of apprehension grow from two measly words that have plagued the minds of researchers for years: quantitative and qualitative. Sure, I may know what I want to research, but that still leaves me with the challenge of choosing my research method. How does one go about choosing between quantitative versus qualitative research anyways?

Before we make any hasty decisions, let’s take a quick step back and look at how the two methodologies are different from one another. When thinking about quantitative research, the first word that typically comes to mind is “numbers,” and that’s essentially right! As I learned in my junior seminar, quantitative research is a means of collecting data that can easily be quantified and/or coded in a computer program because it uses concrete variables. Qualitative research, on the other hand, analyzes data that cannot  be easily quantified. Without the concrete variables in quantitative research, qualitative research relies on the interpretative analysis of the themes, patterns, and categories found in texts or interviews.

Now that the definitions are out of the way, you’re still probably asking yourself, “Which one do I choose?” For the most part, this answer depends on the specifics of your research. In my case, I’m doing a content analysis of gender progressive advertisements and my goal is to analyze how the type of product being marketed influences the script of the commercial. From this standpoint, I could go either way. If I chose the quantitative route, not only would I need over fifty ads to analyze, but I would also conduct my study in a way that focuses on concrete variables. For example, I might do an analysis of how many times the word “beautiful” is used in a commercial depending on what kind of product is being sold.

For the qualitative path, I would focus on fewer commercials and perform an in-depth analysis on the full scripts. Instead of just looking at one keyword, I might look for changes in themes between commercials for sports apparel and ads for body products. Another aspect to consider is your preference. Ask yourself, Which methodology do I feel more comfortable using? Which would I enjoy more? Not only is qualitative research more appealing to me because I prefer to closely analyze texts, but it’s also a better-suited methodology for getting more answers to my research question.

In the end, choosing between quantitative and qualitative research doesn’t have to be the end of the world. There are various arguments for why one format may be better than the other, like how a qualitative method would allow me to do more in-depth research for my specific topic. But know that choosing your methodology is about choosing which one you believe is a better fit for your research. Sometimes the answer may even be a combination of the two! Also, know that you’re not alone; your professors and adviser can help you sort out which methodology can best suit your needs. So the next time someone asks how you’re junior paper is going, you can answer confidently, knowing that you have a plan for how to conduct your research when you get back from break!

–Taylor Griffith, Social Sciences Correspondent

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The Ultimate Guide to Qualitative Research - Part 1: The Basics

• Introduction and overview
• What is qualitative research?
• What is qualitative data?
• Examples of qualitative data
• Introduction

Quantitative data

Qualitative data analysis, forms of qualitative data, limitations of qualitative data, how to balance qualitative and quantitative research.

• Mixed methods
• Qualitative research preparation
• Theoretical perspective
• Theoretical framework
• Literature reviews
• Research question
• Conceptual framework
• Conceptual vs. theoretical framework
• Data collection
• Qualitative research methods
• Focus groups
• Observational research
• Case studies
• Ethnographical research
• Ethical considerations
• Confidentiality and privacy
• Power dynamics
• Reflexivity

Qualitative vs. quantitative research: Methods & data analysis

It might be easy to get bogged down in a "qualitative vs. quantitative data" debate, particularly when quantitative and qualitative research seem like very different things. However, both qualitative and quantitative data have their uses in research. Hence, researchers need to know what each approach has to offer before deciding which research approach and methods are best for them.

Over time, your research might rely on both qualitative and quantitative data. It's important not to treat one as more important or better than the other. Instead, it will benefit your research if you know when and how to use both forms of data to address your research inquiries.

Quantitative data refers to any numerical data that can be used in statistical analysis or experimental research.

Researchers in quantitative research often collect data and conduct analysis to make generalizable conclusions about a particular phenomenon or subject. Survey researchers can sample a portion of a population and assert whether the survey results are indicative of the perspectives of the whole population.

Collecting quantitative data

Generally, quantitative data collection methods are more straightforward than their qualitative data counterparts. Suppose your research question involves measuring foot traffic around a city. In such a project, a researcher could place volunteers at selected places and have them count how many times people cross a street in their view.

The volunteers' counts make the quantitative data needed to answer the research questions. Making assertions about the foot traffic in different places is a relatively simple task, given that the numbers are easily collected and readily available for comparison.

Forms of quantitative data

Quantitative data collection relies on structure and a clear understanding of what the numerical values mean to the research. Quantitative researchers can readily take a spreadsheet of test scores, for example, to generate descriptive statistics and inferential statistics. The shape of that spreadsheet (e.g., rows and columns) and its content (e.g., numerical data) ultimately make analyzing quantitative data feasible.

Limitations of quantitative data

Some phenomena cannot be reduced to mere numbers. For example, quantitative data may tell you the value of a particular product, but it faces significant challenges in helping explain a product's inherent beauty or effectiveness.

Such concepts can be difficult for quantitative data to define. After all, what is beautiful to someone will be less so to someone else, and vice versa.

Quantitative research may also face limitations in measuring people's perspectives. Survey research often relies on Likert scales or rating scales asking respondents to rate something on a numerical scale (e.g., from one to five or one to ten).

However, is one respondent's idea of a "4" on a five-point scale the same as another’s idea of a "4" on this same scale? Moreover, subjective concepts are especially difficult to capture with numerical data.

Qualitative research tends to look at the detail of a phenomenon rather than its numerical value. Qualitative research methods allow for theoretical development or exploration of a relatively unfamiliar phenomenon.

Think about a beautiful song. It might be beautiful because of the melody, singer, lyrics, or perhaps some combination of these and other factors. Collecting quantitative data on each aspect (e.g., "Give the melody of the song a score between one and five") might allow for some statistical analysis of a song.

However, what exactly does someone mean when they give a high rating for a song's melody or lyrics? Do they mean the melody is relaxing, inspiring, or something else? Quantitative approaches alone are insufficient in allowing researchers to determine what people think is a "beautiful melody."

Coding qualitative data

Qualitative research relies on methods like interviews to explore social phenomena beyond the use of numbers. ATLAS.ti lets researchers code qualitative data , summarizing large sets of information more succinctly so that gathering insights becomes easier.

When someone speaks at length about a song's melody being "relaxing," a researcher can apply the code "relaxing melody" to an entire segment of text in ATLAS.ti. That way, analyzing the data means looking at brief codes instead of lengthy paragraphs or pages where the meaning might be unclear.

Developing theoretical insights

Qualitative analysis can also prompt us to look at a phenomenon from new and different angles. A researcher may conduct in-depth interviews at places where individuals think a song is beautiful, like at a live concert.

The findings may not fit our prior understanding of a beautiful song, meaning quantitative research wouldn't likely capture it. Statistical analysis might have difficulty reaching a reliable conclusion since different people might have different definitions of what makes a beautiful song.

As a result, the potential for qualitative research to further develop theory cannot be understated, particularly when it allows researchers to document new insights that quantitative methods might miss. While the qualitative research process can be daunting, it has the potential to provide more detail than a simple statistical analysis can.

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Qualitative studies often draw from the following data collection methods:

• surveys or questionnaires
• in-depth interviews
• focus groups
• observations
• document collection

This is not an exhaustive list, as any unstructured data that can be organized might be considered qualitative data.

What is especially important is that qualitative data is not confined to text. Most forms of information can be analyzed for more insightful discussion. ATLAS.ti allows researchers to code major forms of qualitative data , including images, audio, and video . With the structure provided by coding, researchers can identify recurring themes and patterns in all forms of qualitative data.

Unlike quantitative data, which is often readily available in spreadsheets, qualitative data tend to lack an easily defined structure that facilitates data analysis . In addition, interpreting non-numerical data can be challenging, while clear formulas exist that researchers can follow to compare quantitative values.

Moreover, in semi-structured interviews or focus groups , researchers may ask follow-up questions that can't easily be predicted. An interesting answer may lead to deeper questions to search for more in-depth insights.

The need for the interviewer to pursue deeper answers can impede the organization of data into neat rows and columns. However, it is important to organize the data so that different meanings that emerged across participants or data sources can be assessed. Researchers often need to take time to reorganize their data to facilitate interpretation .

Moreover, interpreting non-numerical data is a significant challenge for qualitative researchers. The relative quantitative value of different things can be relatively easy to interpret.

If someone takes the temperature of New York and the temperature of Chicago on the same day and gets two different values, asserting that one city is warmer than the other would be uncontroversial. After all, one need only get a numerical value representing the temperature in each city to come to a fairly straightforward conclusion.

However, people may disagree about what makes a city interesting or exciting. To take from our example about music, people may even disagree about whether the visual or performative elements of music should be considered. Thus, the researcher needs to clarify the potential differences in understanding between people.

Analyzing qualitative data to answer such research questions requires transparency in analysis. Researchers analyzing socially constructed, subjective concepts should clearly define their concepts so their audiences understand the data analysis.

People can make the mistake of choosing qualitative or quantitative data exclusively. Both approaches are useful in determining cause-and-effect relationships and drawing conclusions based on rigorous analyses.

Choosing research questions

Your inquiry will determine whether quantitative data or qualitative data are more appropriate for your research. In any study, think about how your research question guides what data to collect and how to analyze it.

A quantitative research question seeks to confirm something based on theory that researchers have already developed. On the other hand, a qualitative research question looks at something unfamiliar for which theory does not yet exist to explain it.

In the end, the research question you ask is more important than deciding whether one approach is generally better than the other. By clearly defining what you want to know, you will have a better understanding of what methods will work best for your research project.

Filling research gaps

Quantitative data collection methods can miss nuances that cannot be measured statistically. In contrast, qualitative data collection methods may lack the necessary precision in research contexts where numerical assessment is required. Ultimately, a multitude of data collection and analysis methods may address your research inquiry better than any singular approach.

In situations where a more comprehensive understanding is required, you may want to consider a mixed methods study that collects and analyzes quantitative and qualitative data. A mixed methods approach that employs both quantitative and qualitative methods can be more time-consuming and cumbersome, but the multiple approaches work hand in hand so that each approach covers the shortcomings of the other.

Advancing the overall research agenda

When choosing whether to collect quantitative data, qualitative data, or both, the bigger question is what you want to know, which determines the data collection methods and data analysis that are most effective for your research project. Researchers can benefit from understanding the strengths and weaknesses of quantitative and qualitative data and deciding how both can benefit their research.

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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.  

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Qualitative Research Vs Quantitative Research: Key Comparisons

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Qualitative Research Vs Quantitative Research: Key Differences

Qualitative and quantitative research are two distinct approaches to research, each with its own set of characteristics and methods. Here are detailed points outlining key differences between qualitative and quantitative research:

Research Philosophy:

• Epistemology: Qualitative research recognizes subjective realities and seeks to understand the depth and context of human experiences by exploring the meanings individuals assign to their experiences.
• Ontology: Qualitative research embraces constructivism, acknowledging that reality is socially constructed, and individuals’ interpretations of the world may vary based on their unique perspectives and cultural contexts.
• Epistemology: Quantitative research aims for objective knowledge through systematic measurement, statistical analysis, and the application of predetermined variables to quantify phenomena.
• Ontology: Quantitative research adheres to a realist perspective, assuming that there is an objective reality that can be measured and observed using standardized methods.
• Research Design:
• Flexibility: Qualitative research designs are often flexible and emergent, allowing researchers to adapt their approach as new insights emerge during the study.
• Exploratory Nature: Qualitative research is primarily exploratory, seeking to generate theories and hypotheses rather than testing predefined ones.
• Predefined Structure: Quantitative research follows a predefined structure with a detailed plan, including hypotheses and a systematic procedure for data collection and analysis.
• Confirmatory Nature: Quantitative research is typically confirmatory, aiming to test predetermined hypotheses and establish relationships between variables through statistical analysis.
• Nature of Data: Qualitative research involves non-numerical data such as words, images, or observations, emphasizing rich and context-dependent information.
• Detail and Depth: Qualitative data allows for detailed exploration of individual experiences and behaviors, capturing the nuances and complexities of the studied phenomena.
• Nature of Data: Quantitative research involves numerical data, facilitating statistical analysis and the quantification of patterns and relationships.
• Standardization: Quantitative data is typically standardized, allowing for objective comparisons and statistical generalizability.
• Sampling Approach: Qualitative research often employs purposeful or snowball sampling, selecting participants based on their ability to provide rich information about the research topic.
• Sample Size: Sample sizes in qualitative studies are generally smaller due to the emphasis on in-depth exploration and the complexity of data analysis.
• Sampling Approach: Quantitative research frequently uses random or stratified sampling to ensure representativeness and reduce bias.
• Sample Size: Larger sample sizes are common in quantitative studies to enhance statistical power and improve the generalizability of findings to the larger population.
• Data Collection:
• Methods: Qualitative data collection methods include open-ended techniques such as interviews, focus groups, participant observation, and document analysis.
• Researcher’s Role: The researcher often serves as the main instrument, engaging directly with participants to gather in-depth information.
• Methods: Quantitative data collection involves structured techniques such as surveys, experiments, and standardized assessments.
• Instrumentation: Various instruments, including surveys and laboratory equipment, are utilized to ensure consistency and reliability in data collection.
• Data Analysis:
• Approach: Qualitative data analysis is interpretive, involving methods such as thematic analysis , content analysis, or grounded theory.
• Depth of Analysis: Analysis focuses on identifying patterns, themes, and relationships, often requiring subjective interpretation.
• Approach: Quantitative data analysis is deductive and statistical, involving techniques like regression analysis, t-tests, or ANOVA.
• Quantification: Analysis aims to quantify relationships, assess statistical significance, and draw objective conclusions based on numerical data.
• Reporting Findings:
• Narrative Style: Qualitative research findings are often presented in a narrative style, using quotes and examples to illustrate themes and patterns.
• Contextual Understanding: Findings provide a nuanced, contextually rich understanding of the studied phenomenon.
• Statistical Reporting: Quantitative research findings are typically reported using statistical measures such as means, percentages, and p-values.
• Generalizability: Emphasis is on generalizability to a larger population based on statistical inference.

These additional points provide a more detailed exploration of the distinctions between qualitative and quantitative research , covering aspects such as sampling, data collection, analysis, and reporting findings.

Qualitative Research and Quantitative Research: Key Similarities

While qualitative and quantitative research differ in their methodologies and philosophical foundations, there are some key similarities in terms of the broader research process and goals. Here are detailed points highlighting the commonalities between qualitative and quantitative research:

1. Research Process:

• Systematic Approach: Both qualitative and quantitative research follow a systematic and organized approach to inquiry. They involve clear planning, data collection, analysis, and interpretation of results.
• Iterative Nature: Both approaches may involve an iterative process where researchers refine their methods and research questions based on emerging insights during the study.
• Ethical Considerations: Ethical considerations, such as obtaining informed consent, ensuring participant confidentiality, and addressing potential harm, are integral to both types of research.

2. Research Rigor:

• Validity: Both qualitative and quantitative researchers strive for validity in their studies. Validity in qualitative research is often assessed through criteria such as credibility, transferability, dependability, and confirmability.
• Reliability: Quantitative research emphasizes reliability, ensuring that measurements are consistent and reproducible. Qualitative research also seeks reliability by ensuring that findings are consistent and replicable.

3. Literature Review:

• Literature Integration: Both types of research require a comprehensive review of existing literature to situate the study within the broader academic context. A thorough literature review informs the research design, methodology, and interpretation of findings.

4. Research Question Development:

• Clarity of Purpose: Regardless of the research approach, formulating clear and focused research questions or hypotheses is essential. Both qualitative and quantitative studies require a well-defined purpose and research objectives.

5. Data Collection:

• Systematic Data Collection: Whether through surveys, interviews, observations, or experiments, both qualitative and quantitative research involve systematic data collection. Researchers plan and execute data collection methods to address their research questions.
• Instrumentation: Both types of research use instruments to collect data. In qualitative research, this might include interview guides or observation protocols, while quantitative research may involve structured surveys or experiments.

6. Data Analysis:

• Thematic Analysis: While the specific techniques differ, both qualitative and quantitative research involve analysis and interpretation of data. Thematic analysis, for instance, is a qualitative approach to identifying and interpreting patterns, while quantitative research involves statistical analysis to identify patterns and relationships.

7. Research Reporting:

• Documentation: Both types of research require thorough documentation of methods, findings, and interpretations. Researchers must transparently report their processes, ensuring that others can assess the study’s credibility and reliability.

8. Contributions to Knowledge:

• Advancement of Knowledge: Both qualitative and quantitative research contribute to the advancement of knowledge within their respective fields. Findings from each type of research can inform theory, practice, and policy.

These similarities highlight that, despite their methodological differences, both qualitative and quantitative research share common principles, including systematic planning, ethical considerations, rigorous data collection and analysis, and the goal of contributing valuable insights to the broader body of knowledge.

25 Essential Qualitative Research Questions with Context

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Quantitative vs. Qualitative Research in Psychology

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

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

• Key Differences

Quantitative Research Methods

Qualitative research methods.

• How They Relate

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

At a Glance

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

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

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

Qualitative Research vs. Quantitative Research

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

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

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

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

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

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

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

Used to develop theories

Takes a broad, complex approach

Answers "why" and "how" questions

Explores patterns and themes

Used to test theories

Takes a narrow, specific approach

Answers "what" questions

Explores statistical relationships

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

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

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

Quantitative methods are about measuring phenomena, not explaining them.

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

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

Basic Assumptions

Quantitative methods assume:

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

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

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

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

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

Correlation and Causation

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

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

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

Pitfalls of Quantitative Research

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

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

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

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

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

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

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

Qualitative Approaches

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

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

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

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

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

Interpretation

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

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

Relationship Between Qualitative and Quantitative Research

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

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

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

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

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

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

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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.