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the main difference between qualitative and quantitative research

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.

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What is the difference between quantitative and qualitative?

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

Quantitative research collects numerical data and analyzes it using statistical methods. The aim is to produce objective, empirical data that can be measured and expressed in numerical terms. Quantitative research is often used to test hypotheses, identify patterns, and make predictions.

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

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

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

What Is Qualitative Research?

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

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

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

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

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

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

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

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

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

Qualitative Methods

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

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

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

Here are some examples of qualitative data:

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

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

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

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

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

Qualitative Data Analysis

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

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

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

RESEARCH THEMATICANALYSISMETHOD

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

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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

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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.

Educational resources and simple solutions for your research journey

qualitative vs quantitative research

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.    

     
  Understanding meanings, exploring ideas, behaviors, and contexts, and formulating theories  Generating and analyzing numerical data, quantifying variables by using logical, statistical, and mathematical techniques to test or prove hypothesis  
  Limited sample size, typically not representative  Large sample size to draw conclusions about the population  
  Expressed using words. Non-numeric, textual, and visual narrative  Expressed using numerical data in the form of graphs or values. Statistical, measurable, and numerical 
  Interviews, focus groups, observations, ethnography, literature review, and surveys  Surveys, experiments, and structured observations 
  Inductive, thematic, and narrative in nature  Deductive, statistical, and numerical in nature 
  Subjective  Objective 
  Open-ended questions  Close-ended (Yes or No) or multiple-choice questions 
  Descriptive and contextual   Quantifiable and generalizable 
  Limited, only context-dependent findings  High, results applicable to a larger population 
  Exploratory research method  Conclusive research method 
  To delve deeper into the topic to understand the underlying theme, patterns, and concepts  To analyze the cause-and-effect relation between the variables to understand a complex phenomenon 
  Case studies, ethnography, and content analysis  Surveys, experiments, and correlation studies 

the main difference between qualitative and quantitative research

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.  

the main difference between qualitative and quantitative research

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.   

the main difference between qualitative and quantitative research

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

A woman sits at a library table with stacks of books and a laptop.

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

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

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

Qualitative vs. Quantitative Research in Education: Definitions

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

Generate Hypotheses with Qualitative Research

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

Form Strong Conclusions with Quantitative Research

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

Differences in Data Collection Methods

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

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

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

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

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

Qualitative Research Methods

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

Quantitative Research Methods

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

Choosing a Research Strategy

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

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

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

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

Learn How to Put Education Research into Action

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

What’s the Difference Between Educational Equity and Equality?

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

Top Education Technology Jobs for Doctorate in Education Graduates

American University, EdD in Education Policy and Leadership

Edutopia, “2019 Education Research Highlights”

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

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

Scribbr, “Qualitative vs. Quantitative Research”

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

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

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

Qualitative vs. quantitative research - what’s the difference

What is quantitative research?

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

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

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

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

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

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

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

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

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

Some methods to collect this data include:

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

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

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

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

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

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

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

Some of these data collection methods include:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Focuses on testing hypotheses. Can also be used to determine general facts about a topic.

Focuses on developing an idea or hypotheses. Can also be used to gain a deeper understanding into specific topics.

Analysis is mainly done through mathematical or statistical analytics.

Analysis is more interpretive and involves summarizing and categorizing topics or themes and interpreting data.

Data is typically expressed in numbers, graphs, tables, or other numerical formats.

Data is generally expressed in words or text.

Requires a reasonably large sample size to be reliable.

Requires smaller sample sizes with only a few respondents.

Data collection is focused on closed-ended questions.

Data collection is focused on open-ended questions to extract the opinions and views on a particular subject.

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

3 examples of qualitative research would be:

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

3 examples of quantitative research include:

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

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

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

the main difference between qualitative and quantitative research

The differences between qualitative and quantitative research methods

Last updated

15 January 2023

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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.

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  • 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. 

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the main difference between qualitative and quantitative research

Quantitative and Qualitative Research

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

Mixed Methods Research

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

What is Empirical Research?

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

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

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

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

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

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

What is Quantitative Research?

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

Indicators of quantitative research include:

contains statistical analysis 

large sample size 

objective - little room to argue with the numbers 

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

What is Qualitative Research?

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

Indicators of qualitative research include:

interviews or focus groups 

small sample size 

subjective - researchers are often interpreting meaning 

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

Video: Empirical Studies: Qualitative vs. Quantitative

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

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  • 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.

Qualitative vs quantitative research

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

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

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

Quantitative data collection methods

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

Qualitative data collection methods

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

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

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

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

Quantitative research approach

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

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

Qualitative research approach

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

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

Mixed methods approach

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

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

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

Analysing quantitative data

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

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

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

Analysing qualitative data

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

Some common approaches to analysing qualitative data include:

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

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

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

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

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

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

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

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

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

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

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Raimo Streefkerk

Raimo Streefkerk

Qualitative vs. Quantitative

While quantitative research is based on numbers and mathematical calculations (aka quantitative data ), qualitative research is based on written or spoken narratives (or qualitative data ). Qualitative and quantitative research techniques are used in marketing , sociology , psychology , public health and various other disciplines.

Comparison chart

Qualitative versus Quantitative comparison chart
QualitativeQuantitative
Purpose The purpose is to explain and gain insight and understanding of phenomena through intensive collection of narrative data Generate hypothesis to be test , inductive. The purpose is to explain, predict, and/or control phenomena through focused collection of numerical data. Test hypotheses, deductive.
Approach to Inquiry subjective, holistic, process- oriented Objective, focused, outcome- oriented
Hypotheses Tentative, evolving, based on particular study Specific, testable, stated prior to particular study
Research Setting Controlled setting not as important Controlled to the degree possible
Sampling Purposive: Intent to select “small, ” not necessarily representative, sample in order to get in-depth understanding Random: Intent to select “large, ” representative sample in order to generalize results to a population
Measurement Non-standardized, narrative (written word), ongoing Standardized, numerical (measurements, numbers), at the end
Design and Method Flexible, specified only in general terms in advance of study Nonintervention, minimal disturbance All Descriptive— History, Biography, Ethnography, Phenomenology, Grounded Theory, Case Study, (hybrids of these) Consider many variable, small group Structured, inflexible, specified in detail in advance of study Intervention, manipulation, and control Descriptive Correlation Causal-Comparative Experimental Consider few variables, large group
Data Collection Strategies Document and artifact (something observed) that is collection (participant, non-participant). Interviews/Focus Groups (un-/structured, in-/formal). Administration of questionnaires (open ended). Taking of extensive, detailed field notes. Observations (non-participant). Interviews and Focus Groups (semi-structured, formal). Administration of tests and questionnaires (close ended).
Data Analysis Raw data are in words. Essentially ongoing, involves using the observations/comments to come to a conclusion. Raw data are numbers Performed at end of study, involves statistics (using numbers to come to conclusions).
Data Interpretation Conclusions are tentative (conclusions can change), reviewed on an ongoing basis, conclusions are generalizations. The validity of the inferences/generalizations are the reader’s responsibility. Conclusions and generalizations formulated at end of study, stated with predetermined degree of certainty. Inferences/generalizations are the researcher’s responsibility. Never 100% certain of our findings.

Type of data

Qualitative research gathers data that is free-form and non-numerical, such as diaries, open-ended questionnaires, interviews and observations that are not coded using a numerical system.

On the other hand, quantitative research gathers data that can be coded in a numerical form. Examples of quantitative research include experiments or interviews/questionnaires that used closed questions or rating scales to collect information .

Applications of Quantitative and Qualitative Data

Qualitative data and research is used to study individual cases and to find out how people think or feel in detail. It is a major feature of case studies.

Quantitative data and research is used to study trends across large groups in a precise way. Examples include clinical trials or censuses.

When to use qualitative vs. quantitative research?

Quantitative and qualitative research techniques are each suitable in specific scenarios. For example, quantitative research has the advantage of scale. It allows for vast amounts of data to be collected -- and analyzed -- from a large number of people or sources. Qualitative research, on the other hand, usually does not scale as well. It is hard, for example, to conduct in-depth interviews with thousands of people or to analyze their responses to open-ended questions. But it is relatively easier to analyze survey responses from thousands of people if the questions are closed-ended and responses can be mathematically encoded in, say, rating scales or preference ranks.

Conversely, qualitative research shines when it is not possible to come up with closed-ended questions. For example, marketers often use focus groups of potential customers to try and gauge what influences brand perception, product purchase decisions, feelings and emotions . In such cases, researchers are usually at very early stages of forming their hypotheses and do not want to limit themselves to their initial understanding. Qualitative research often opens up new options and ideas that quantitative research cannot due to its closed-ended nature.

Analysis of data

Qualitative data can be difficult to analyze, especially at scale, as it cannot be reduced to numbers or used in calculations. Responses may be sorted into themes, and require an expert to analyze. Different researchers may draw different conclusions from the same qualitative material.

Quantitative data can be ranked or put into graphs and tables to make it easier to analyze.

Data Explosion

Data is being generated at an increasing rate because of the expansion in the number of computing devices and the growth of the Internet . Most of this data is quantitative and special tools and techniques are evolving to analyze this " big data ".

Effects of Feedback

The following diagram illustrates the effects of positive and negative feedback on Qualitative vs Quantitative research:

the main difference between qualitative and quantitative research

  • Qualitative Quantitative - Simply Psychology
  • Qualitative and Quantitative Research - University of Oxford

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Qualitative vs. Quantitative Research: What’s the Difference?

researchers discussing qualitative vs. quantitative study

There are two distinct types of data collection and study: qualitative and quantitative. Although both provide an analysis of data, they differ in their approach and the type of data they collect. Awareness of these approaches can help researchers construct their study and data collection methods.

In This Article:

What Is the Difference Between Qualitative vs. Quantitative Research?

Qualitative vs. quantitative outcomes, benefits and limitations of qualitative vs. quantitative research, how to analyze qualitative vs. quantitative data, become a qualitative or quantitative researcher.

Because qualitative and quantitative studies collect different types of data, their data collection methods differ considerably. Quantitative studies rely on numerical or measurable data. In contrast, qualitative studies rely on personal accounts or documents that illustrate in detail how people think or respond within society.

Qualitative Research: Data Collection for Your Doctorate Degree

Qualitative research methods include gathering and interpreting non-numerical data. The following are some sources of qualitative data 1 :

  • Focus groups
  • Personal accounts or papers
  • Cultural records
  • Observation

In the course of a qualitative study, the researcher may conduct interviews or focus groups to collect data that is not available in existing documents or records. To allow freedom for varied or unexpected answers, interviews and focus groups may be unstructured or semi-structured.

An unstructured or semi-structured format allows the researcher to pose open-ended questions and follow wherever the responses lead. The responses provide a comprehensive perspective on each individual’s experiences, which are then compared with those of other participants in the study.

Quantitative Research: Data Collection for Your Doctorate Degree

Quantitative studies, in contrast, require different data collection methods. These methods include compiling numerical data to test causal relationships among variables. Some forms of data collection for this type of study include 1 :

  • Experiments
  • Questionnaires
  • Database reports

The above collection methods yield data that lends itself to numerical analysis. Questionnaires in this case have a multiple-choice format to generate countable answers, such as “yes” or “no,” which can be turned into quantifiable data.

One of the factors distinguishing qualitative from quantitative studies is the nature of the intended outcome. Qualitative researchers seek to learn from details of the testimonies of those they are studying. Over the course of a study, conclusions are drawn by compiling, comparing and evaluating the participants’ feedback and input. Qualitative research is often focused on answering the “why” behind a phenomenon, correlation or behavior.

In contrast, quantitative data are analyzed numerically to develop a statistical picture of a trend or connection. Such statistical results may shed light on cause-and-effect relationships, and they may either confirm or disprove the study’s original hypothesis. Whether positive or negative, the outcome can enrich understanding of a subject and spark action. Quantitative research is often focused on answering the questions of “what” or “how” in regards to a phenomenon, correlation or behavior.

Another difference between qualitative and quantitative research lies in their advantages and limitations. Each form of research has benefits and shortcomings. Researchers must consider their hypotheses and what forms of data collection and analysis are likely to produce the most relevant findings.

Benefits of Qualitative Research

There are some significant benefits of qualitative research that should be considered when evaluating the difference between qualitative and quantitative research. The qualitative method allows for creativity, varied interpretations and flexibility. The scope of the research project can change as more information is gathered.

Limitations of Qualitative Research

Qualitative studies are more subjective in their results and interpretation than are quantitative studies. The expertise and perspective of the researcher may strongly influence the interpretation of results and the conclusions reached, because personal bias can be hard to manage. In addition, qualitative studies often test a smaller sample size due to the costs and efforts associated with qualitative data collection methods. 1

Benefits of Quantitative Research

The similarities of qualitative and quantitative research do not encompass their respective benefits, because each approach has unique advantages. For example, unlike qualitative studies, quantitative studies produce objective data, and their results can be clearly communicated through statistics and numbers. Quantitative studies can be quickly analyzed with the benefit of data computing software.

Limitations of Quantitative Research

Yet, although objectivity is a benefit of the quantitative method, this approach can be viewed as a more restrictive form of study. Participants cannot tailor their responses or add context. Furthermore, statistical analysis requires a large data sample, which calls for a large pool of participants. 1

Another of the similarities of qualitative and quantitative research is that both look for patterns in the data they collect that point to a relationship between elements. Both qualitative and quantitative data are instrumental in supporting existing theories and developing new ones. Ultimately, the researcher must determine which kind of research best serves the goals of their study.

Analyzing Qualitative Data

Because qualitative data doesn’t allow for numerical data analysis, any analytical approach must be developed with care and caution. Here are a few different methods of qualitative data analysis, as follows:

  • Content analysis: Groups together similar concepts, themes and words that emerge from the data in order to understand interrelationships
  • Discourse analysis: Evaluates the way in which people often express themselves in various contexts through the lens of cultural and power dynamics
  • Thematic analysis: Seeks to understand the true meaning behind subjects’ words by uncovering recurrent themes in the data

Analyzing Quantitative Data

The question of how to analyze quantitative data is slightly more straightforward compared to the various approaches for qualitative data. When working with quantitative data, doctoral researchers will generally review the collected data and organize it into visual elements, such as charts and graphs.

The data can be evaluated using either descriptive or inferential statistics. Descriptive statistics provide an avenue for describing the population or data set. Inferential statistics can be used to generalize results, as well as to project future trends or predictions about a larger dataset or population.

Some researchers choose to adhere to and hone a single methodological approach throughout their time as doctoral learners — or in their profession. Research skills are critical in a variety of  careers.

If you have a desire to conduct research, a qualitative or quantitative doctoral degree can support your initiative. Throughout your program, you will learn methods for constructing a qualitative or quantitative study and producing written research findings. Interested in starting your doctoral journey? Grand Canyon University has a wide variety of qualitative and quantitative programs and resources to help you. Fill out the form on this page to get started. 

1 Mcleod, S. (2023, May 10). Qualitative vs quantitative research: methods & data analysis. Simply Psychology. Retrieved in May 2023. 

Approved by the dean of the College of Doctoral Studies on Oct. 2, 2023. 

The views and opinions expressed in this article are those of the author’s and do not necessarily reflect the official policy or position of Grand Canyon University. Any sources cited were accurate as of the publish date.

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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 .

Quantitative research questionsQuantitative research hypotheses
Descriptive research questionsSimple hypothesis
Comparative research questionsComplex hypothesis
Relationship research questionsDirectional hypothesis
Non-directional hypothesis
Associative hypothesis
Causal hypothesis
Null hypothesis
Alternative hypothesis
Working hypothesis
Statistical hypothesis
Logical hypothesis
Hypothesis-testing
Qualitative research questionsQualitative research hypotheses
Contextual research questionsHypothesis-generating
Descriptive research questions
Evaluation research questions
Explanatory research questions
Exploratory research questions
Generative research questions
Ideological research questions
Ethnographic research questions
Phenomenological research questions
Grounded theory questions
Qualitative case study questions

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 .

Quantitative research questions
Descriptive research question
- Measures responses of subjects to variables
- Presents variables to measure, analyze, or assess
What is the proportion of resident doctors in the hospital who have mastered ultrasonography (response of subjects to a variable) as a diagnostic technique in their clinical training?
Comparative research question
- Clarifies difference between one group with outcome variable and another group without outcome variable
Is there a difference in the reduction of lung metastasis in osteosarcoma patients who received the vitamin D adjunctive therapy (group with outcome variable) compared with osteosarcoma patients who did not receive the vitamin D adjunctive therapy (group without outcome variable)?
- Compares the effects of variables
How does the vitamin D analogue 22-Oxacalcitriol (variable 1) mimic the antiproliferative activity of 1,25-Dihydroxyvitamin D (variable 2) in osteosarcoma cells?
Relationship research question
- Defines trends, association, relationships, or interactions between dependent variable and independent variable
Is there a relationship between the number of medical student suicide (dependent variable) and the level of medical student stress (independent variable) in Japan during the first wave of the COVID-19 pandemic?

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 .

Quantitative research hypotheses
Simple hypothesis
- Predicts relationship between single dependent variable and single independent variable
If the dose of the new medication (single independent variable) is high, blood pressure (single dependent variable) is lowered.
Complex hypothesis
- Foretells relationship between two or more independent and dependent variables
The higher the use of anticancer drugs, radiation therapy, and adjunctive agents (3 independent variables), the higher would be the survival rate (1 dependent variable).
Directional hypothesis
- Identifies study direction based on theory towards particular outcome to clarify relationship between variables
Privately funded research projects will have a larger international scope (study direction) than publicly funded research projects.
Non-directional hypothesis
- Nature of relationship between two variables or exact study direction is not identified
- Does not involve a theory
Women and men are different in terms of helpfulness. (Exact study direction is not identified)
Associative hypothesis
- Describes variable interdependency
- Change in one variable causes change in another variable
A larger number of people vaccinated against COVID-19 in the region (change in independent variable) will reduce the region’s incidence of COVID-19 infection (change in dependent variable).
Causal hypothesis
- An effect on dependent variable is predicted from manipulation of independent variable
A change into a high-fiber diet (independent variable) will reduce the blood sugar level (dependent variable) of the patient.
Null hypothesis
- A negative statement indicating no relationship or difference between 2 variables
There is no significant difference in the severity of pulmonary metastases between the new drug (variable 1) and the current drug (variable 2).
Alternative hypothesis
- Following a null hypothesis, an alternative hypothesis predicts a relationship between 2 study variables
The new drug (variable 1) is better on average in reducing the level of pain from pulmonary metastasis than the current drug (variable 2).
Working hypothesis
- A hypothesis that is initially accepted for further research to produce a feasible theory
Dairy cows fed with concentrates of different formulations will produce different amounts of milk.
Statistical hypothesis
- Assumption about the value of population parameter or relationship among several population characteristics
- Validity tested by a statistical experiment or analysis
The mean recovery rate from COVID-19 infection (value of population parameter) is not significantly different between population 1 and population 2.
There is a positive correlation between the level of stress at the workplace and the number of suicides (population characteristics) among working people in Japan.
Logical hypothesis
- Offers or proposes an explanation with limited or no extensive evidence
If healthcare workers provide more educational programs about contraception methods, the number of adolescent pregnancies will be less.
Hypothesis-testing (Quantitative hypothesis-testing research)
- Quantitative research uses deductive reasoning.
- This involves the formation of a hypothesis, collection of data in the investigation of the problem, analysis and use of the data from the investigation, and drawing of conclusions to validate or nullify the hypotheses.

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 research questions
Contextual research question
- Ask the nature of what already exists
- Individuals or groups function to further clarify and understand the natural context of real-world problems
What are the experiences of nurses working night shifts in healthcare during the COVID-19 pandemic? (natural context of real-world problems)
Descriptive research question
- Aims to describe a phenomenon
What are the different forms of disrespect and abuse (phenomenon) experienced by Tanzanian women when giving birth in healthcare facilities?
Evaluation research question
- Examines the effectiveness of existing practice or accepted frameworks
How effective are decision aids (effectiveness of existing practice) in helping decide whether to give birth at home or in a healthcare facility?
Explanatory research question
- Clarifies a previously studied phenomenon and explains why it occurs
Why is there an increase in teenage pregnancy (phenomenon) in Tanzania?
Exploratory research question
- Explores areas that have not been fully investigated to have a deeper understanding of the research problem
What factors affect the mental health of medical students (areas that have not yet been fully investigated) during the COVID-19 pandemic?
Generative research question
- Develops an in-depth understanding of people’s behavior by asking ‘how would’ or ‘what if’ to identify problems and find solutions
How would the extensive research experience of the behavior of new staff impact the success of the novel drug initiative?
Ideological research question
- Aims to advance specific ideas or ideologies of a position
Are Japanese nurses who volunteer in remote African hospitals able to promote humanized care of patients (specific ideas or ideologies) in the areas of safe patient environment, respect of patient privacy, and provision of accurate information related to health and care?
Ethnographic research question
- Clarifies peoples’ nature, activities, their interactions, and the outcomes of their actions in specific settings
What are the demographic characteristics, rehabilitative treatments, community interactions, and disease outcomes (nature, activities, their interactions, and the outcomes) of people in China who are suffering from pneumoconiosis?
Phenomenological research question
- Knows more about the phenomena that have impacted an individual
What are the lived experiences of parents who have been living with and caring for children with a diagnosis of autism? (phenomena that have impacted an individual)
Grounded theory question
- Focuses on social processes asking about what happens and how people interact, or uncovering social relationships and behaviors of groups
What are the problems that pregnant adolescents face in terms of social and cultural norms (social processes), and how can these be addressed?
Qualitative case study question
- Assesses a phenomenon using different sources of data to answer “why” and “how” questions
- Considers how the phenomenon is influenced by its contextual situation.
How does quitting work and assuming the role of a full-time mother (phenomenon assessed) change the lives of women in Japan?
Qualitative research hypotheses
Hypothesis-generating (Qualitative hypothesis-generating research)
- Qualitative research uses inductive reasoning.
- This involves data collection from study participants or the literature regarding a phenomenon of interest, using the collected data to develop a formal hypothesis, and using the formal hypothesis as a framework for testing the hypothesis.
- Qualitative exploratory studies explore areas deeper, clarifying subjective experience and allowing formulation of a formal hypothesis potentially testable in a future quantitative approach.

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.

VariablesUnclear and weak statement (Statement 1) Clear and good statement (Statement 2) Points to avoid
Research questionWhich is more effective between smoke moxibustion and smokeless moxibustion?“Moreover, regarding smoke moxibustion versus smokeless moxibustion, it remains unclear which is more effective, safe, and acceptable to pregnant women, and whether there is any difference in the amount of heat generated.” 1) Vague and unfocused questions
2) Closed questions simply answerable by yes or no
3) Questions requiring a simple choice
HypothesisThe smoke moxibustion group will have higher cephalic presentation.“Hypothesis 1. The smoke moxibustion stick group (SM group) and smokeless moxibustion stick group (-SLM group) will have higher rates of cephalic presentation after treatment than the control group.1) Unverifiable hypotheses
Hypothesis 2. The SM group and SLM group will have higher rates of cephalic presentation at birth than the control group.2) Incompletely stated groups of comparison
Hypothesis 3. There will be no significant differences in the well-being of the mother and child among the three groups in terms of the following outcomes: premature birth, premature rupture of membranes (PROM) at < 37 weeks, Apgar score < 7 at 5 min, umbilical cord blood pH < 7.1, admission to neonatal intensive care unit (NICU), and intrauterine fetal death.” 3) Insufficiently described variables or outcomes
Research objectiveTo determine which is more effective between smoke moxibustion and smokeless moxibustion.“The specific aims of this pilot study were (a) to compare the effects of smoke moxibustion and smokeless moxibustion treatments with the control group as a possible supplement to ECV for converting breech presentation to cephalic presentation and increasing adherence to the newly obtained cephalic position, and (b) to assess the effects of these treatments on the well-being of the mother and child.” 1) Poor understanding of the research question and hypotheses
2) Insufficient description of population, variables, or study outcomes

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

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

VariablesUnclear and weak statement (Statement 1)Clear and good statement (Statement 2)Points to avoid
Research questionDoes disrespect and abuse (D&A) occur in childbirth in Tanzania?How does disrespect and abuse (D&A) occur and what are the types of physical and psychological abuses observed in midwives’ actual care during facility-based childbirth in urban Tanzania?1) Ambiguous or oversimplistic questions
2) Questions unverifiable by data collection and analysis
HypothesisDisrespect and abuse (D&A) occur in childbirth in Tanzania.Hypothesis 1: Several types of physical and psychological abuse by midwives in actual care occur during facility-based childbirth in urban Tanzania.1) Statements simply expressing facts
Hypothesis 2: Weak nursing and midwifery management contribute to the D&A of women during facility-based childbirth in urban Tanzania.2) Insufficiently described concepts or variables
Research objectiveTo describe disrespect and abuse (D&A) in childbirth in Tanzania.“This study aimed to describe from actual observations the respectful and disrespectful care received by women from midwives during their labor period in two hospitals in urban Tanzania.” 1) Statements unrelated to the research question and hypotheses
2) Unattainable or unexplorable objectives

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 .

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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.

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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.

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Qualitative or Quantitative Research?

Qualitative research is an umbrella phrase that describes many research methodologies (e.g., ethnography, grounded theory, phenomenology, interpretive description), which draw on data collection techniques such as interviews and observations. A common way of differentiating Qualitative from Quantitative research is by looking at the goals and processes of each. The following table divides qualitative from quantitative research for heuristic purposes; such a rigid dichotomy is not always appropriate. On the contrary, mixed methods studies use both approaches to answer research questions, generating qualitative and quantitative data that are then brought together in order to answer the research question.

 (i.e. human behaviour, cultural or social organization) how do people make sense of their lives, experiences, and their understanding of the world?)

 

 Examples:
Examples:

 

 

 

Department and University Information

Faculty of dentistry.

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the main difference between qualitative and quantitative research

Qualitative and Quantitative Research

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

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

 

Complexity, contextual, inductive logic, discovery, exploration

Experiment, random assignment, independent/dependent variable, causal/correlational, validity, deductive logic

Understand a phenomenon

Discover causal relationships or describe a phenomenon

Purposive sample, small

Random sample, large

Focus groups, interviews, field observation

Tests, surveys, questionnaires

Phenomenological, grounded theory, ethnographic, case study, historical/narrative research, participatory research, clinical research

Experimental, quasi-experimental, descriptive, methodological, exploratory, comparative, correlational, developmental (cross-sectional, longitudinal/prospective/cohort, retrospective/ex post facto/case control)

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

References:

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

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

Quick Overview

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

It's Not Always One or the Other!

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

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  • Key Differences

Know the Differences & Comparisons

Difference Between Qualitative and Quantitative Research

qualitative vs quantitative research

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

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

Content: Qualitative Research Vs Quantitative Research

Comparison chart.

Basis for ComparisonQualitative ResearchQuantitative Research
MeaningQualitative research is a method of inquiry that develops understanding on human and social sciences, to find the way people think and feel.Quantitative research is a research method that is used to generate numerical data and hard facts, by employing statistical, logical and mathematical technique.
NatureHolisticParticularistic
ApproachSubjectiveObjective
Research typeExploratoryConclusive
ReasoningInductiveDeductive
SamplingPurposiveRandom
DataVerbalMeasurable
InquiryProcess-orientedResult-oriented
HypothesisGeneratedTested
Elements of analysisWords, pictures and objectsNumerical data
ObjectiveTo explore and discover ideas used in the ongoing processes.To examine cause and effect relationship between variables.
MethodsNon-structured techniques like In-depth interviews, group discussions etc.Structured techniques such as surveys, questionnaires and observations.
ResultDevelops initial understandingRecommends final course of action

Definition of Qualitative Research

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

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

Definition of Quantitative Research

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

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

Key Differences Between Qualitative And Quantitative Research

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

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

Video: Qualitative Vs Quantitative Research

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

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qualitative vs quantitative data

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

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Though the terms sound similar, qualitative vs. quantitative research are two significantly different data collection methods. Understanding that difference can make a large impact on how you analyze the success of a product, service update, or overall company performance. 

Let’s take a look at the difference between qualitative and quantitative research, when to use each (or both), and how to gather the data sets effectively.

What’s the difference between qualitative and quantitative research?

Qualitative and quantitative research are two different approaches to collecting data used to test hypotheses. Quantitative research is a numeric method of collecting data, whereas qualitative research is a non-numerical approach to data collection. 

Quantitative research 

Any data that can fall neatly into a numerical system or rating – number of customers, dates of purchases, revenue, Net Promoter Score (NPS) , and so on – falls under the quantitative research bracket. 

Quantitative data forms the what : the tangible aspects of an audience’s interest, such as sales data or customer engagement metrics. 

The key benefit of quantitative data is that it is easy to analyze, as it is highly structured. Once collected, you can generate and categorize information easily with graphs, percentages, and tables – making it ideal for organizing on a dashboard. 

This type of data helps you to more easily spot trends, make predictions, and see correlations. It’s easy to replicate your research, compare results, and analyze large quantities of data.

The downside of this type of data is that it’s hard to understand the motivation or reasoning – in essence, the context – behind the information you collect, making it difficult for you to confirm any theories you have based on what drives the data. Or, there might be structural bias, as you might be looking for the wrong type of data for your problem, measuring data incorrectly, or using an incorrect sampling method. 

This is why qualitative research is equally as important to consider.

Qualitative research

Qualitative research usually involves studying language – words, their meaning, concepts, and opinions. It analyzes the why – what an audience thinks and why they hold a certain opinion. This data can be gathered from text, images, audio or video clips, and more. 

The key benefit of qualitative data is that it helps you understand the motivations for your audience’s actions. It can explain the “what” as outlined in quantitative data, helping you to troubleshoot issues and create new ideas for research. 

Qualitative data is also flexible and represents your audience’s views authentically. It’s descriptive, which helps you understand context more fully. 

The downside of qualitative data – as most qualitative researchers will agree – is that it is by its very nature difficult to quantify, as it’s likely to be unstructured or semi-structured data. Qualitative data is also subjective, and relies on your audience to be truthful throughout the data collection process.

When to use qualitative vs. quantitative research and why

Both approaches can help you gain insight into your target audience group, but when is it more appropriate to use quantitative or qualitative data collection? 

Use qualitative research to understand a problem, opinion, or experience

Qualitative data gives you the ability to understand the more nebulous facets of your audience’s experience and their opinion about these aspects. 

Examples of when you might use qualitative research include:

  • Understanding why a product isn’t performing as well as you’d hoped
  • Finding out how to improve your retail experience
  • Getting insight into why a customer values you over a competitor

Collect quantitative research to test a hypothesis

Quantitative data gives you concrete results, meaning you can use it to test or confirm theories you might have about your audience’s experiences. 

Examples of when you might use quantitative research include:

  • Understanding customer effort , customer satisfaction , and more
  • Testing how a change in customer experience affects your audience
  • Confirming a theory, such as why customers like a certain product or service

Using a combined or mixed method to get the whole picture

Using both qualitative and quantitative data will give you a more comprehensive understanding of your audience’s drives and the tangible outcomes of their attitudes and opinions. 

You could use both types of data to:

  • Get insights from your audience (qualitative data) to create a theory and use quantitative research to test it
  • Identify patterns in your quantitative data, and understand why they’re occurring with qualitative information

How to gather qualitative and quantitative data

There are many ways to gather qualitative and quantitative data, no matter what sample size you’re working with in your study. The methods below include both qualitative and quantitative research methods.

Likely the easiest way to gather qualitative or quantitative data, surveys allow you to deliver your research questions to your audience quickly and easily gather data for analysis. They can be served to participants in multiple ways – via email, in-app, on your website, and more.

Focus groups

Interviewing a select group of individuals to get their opinions on certain products or topics can give you honest insights from consumers. 

Observational research

Observing how people use your business’s products and services can help you spot problems and troubleshoot them first-hand. 

In-depth interviews

One-on-one discussions with individuals who could have keen insight into your business can help unveil more human insights into your company.

Case studies

Collecting stories from those who’ve used your products and services can help illuminate problems and successes. 

Third-party research

Using third-party data can help you understand your business’ position when compared to others. This research is more likely to yield quantitative data.

Examples of quantitative survey questions

If you’re aiming to take action to improve your customer experience, you’ll need to ask the right questions. Depending on whether you’re looking for quantitative, qualitative data, or a combination of both, you’ll want to use different question styles. 

Let’s review some of the most common quantitative survey question types .

Likert scale questions

Likert scale questions evaluate how much the survey respondent agrees with a particular statement by asking them to select a score on a numerical scale as it aligns with their sentiment. 

You can then calculate the quantitative data for a chosen group of responses to produce an overall score to determine if you’re meeting or not meeting expectations (depending on what you’re measuring).

Sample quantitative Likert scale questions include: 

  • Net Promoter Score (NPS): How likely are you to recommend [brand/product/service] to a friend? Answer scale: 0 to 10
  • Customer Satisfaction Score (CSAT): How satisfied are you with [product/service]? Answer scale: 1 to 5, Very dissatisfied to Very satisfied
  • Customer Effort Score (CES): [Product feature] made it easy for me to accomplish [feature goal]. Answer scale: 1 to 5, Strongly disagree to Strongly agree
  • How satisfied are you with the quality of the product? Answer scale: 1 to 5, Very dissatisfied to Very satisfied
  • How likely are you to [repurchase/renew the contract]? Answer scale: 1 to 5, Very unlikely to Very likely
  • This [product/service] helps me accomplish my goals. Answer scale: 1 to 5, Strongly disagree to Strongly agree

Star rating, smileys and thumbs up/down rating

Similar to the Likert scale, a 5-point rating scale can be used with Smileys or Stars surveys. Universally recognizable and visually intuitive, it’s a simple way to get quantitative responses and sentiment data that can be tracked over time.

Qualitative vs quantitative research can be gathered using a variety of survey types

Thumbs up/down surveys are also an easy way to gauge your audience’s views. Innately straightforward, a two-option survey can lead to faster survey completion from your respondents and instant quantitative data collection.

Multiple choice and multiple answer questions

Giving your audience multiple options can help narrow down details on preferences, usage, quantity, frequency, and more. Though these questions contain words in the selection options, you are actually gathering objective, quantitative data that can work to support your statistical analysis.

Gather quantitative research with multiple choice and multiple answer survey questions

Some question examples include: 

  • How often do you visit our online store? [Option 1] [Option 2] [Option 3] [Option 4]
  • Which of our services have you used? Check all that apply: [Option 1] [Option 2] [Option 3]

Examples of qualitative survey questions

Numerical, quantitative data makes tracking, reporting, and sharing data across your organization possible. However, gathering qualitative feedback from your audience can unveil specific details about your quantitative data – why a customer gave a negative score or verbatim suggestions for how to improve – that can make a more strategic impact.

“Other” option after multiple select or multiple answer

If the respondent does not find that your provided options match their opinion, you can provide an “other” box to have them write their answer. This reduces the chance of survey bias and provides qualitative feedback on why they don’t align with the given options.

Open-ended questions and free response questions

An open-ended or free response survey question gives the respondent freedom to describe their experience or score decision in their own words. The verbatim comments provided from open-ended questions can shed light on why your quantitative data improves or decreases over time. 

Gather qualitative research with open-ended questions

TIP: When using open-ended questions, it’s important to think about which research type (qualitative or quantitative) will really give you the data you’re looking for. 

For example, if you simply want to learn about customer preferences, it may be best to get specific about the selection options in a multiple select question instead of an open-ended question to avoid obscure or a wide range of answers that will be difficult to quantify. That’s why instead of choosing between qualitative and quantitative research, combining quantitative (multiple choice) and qualitative (“other” option) can be beneficial.

Get started now with Delighted

As you can see, it’s not really a question of qualitative vs. quantitative data – it’s a blend of both that give you real audience insights. 

Fortunately, your data collection method doesn’t have to be complicated. Delighted’s self-serve  free online survey maker  is equipped with quantitative and qualitative survey options to help you make the most out of your data analysis research.

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Frequently asked questions

What’s the difference between quantitative and qualitative methods.

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.

Frequently asked questions: Methodology

Attrition refers to participants leaving a study. It always happens to some extent—for example, in randomized controlled trials for medical research.

Differential attrition occurs when attrition or dropout rates differ systematically between the intervention and the control group . As a result, the characteristics of the participants who drop out differ from the characteristics of those who stay in the study. Because of this, study results may be biased .

Action research is conducted in order to solve a particular issue immediately, while case studies are often conducted over a longer period of time and focus more on observing and analyzing a particular ongoing phenomenon.

Action research is focused on solving a problem or informing individual and community-based knowledge in a way that impacts teaching, learning, and other related processes. It is less focused on contributing theoretical input, instead producing actionable input.

Action research is particularly popular with educators as a form of systematic inquiry because it prioritizes reflection and bridges the gap between theory and practice. Educators are able to simultaneously investigate an issue as they solve it, and the method is very iterative and flexible.

A cycle of inquiry is another name for action research . It is usually visualized in a spiral shape following a series of steps, such as “planning → acting → observing → reflecting.”

To make quantitative observations , you need to use instruments that are capable of measuring the quantity you want to observe. For example, you might use a ruler to measure the length of an object or a thermometer to measure its temperature.

Criterion validity and construct validity are both types of measurement validity . In other words, they both show you how accurately a method measures something.

While construct validity is the degree to which a test or other measurement method measures what it claims to measure, criterion validity is the degree to which a test can predictively (in the future) or concurrently (in the present) measure something.

Construct validity is often considered the overarching type of measurement validity . You need to have face validity , content validity , and criterion validity in order to achieve construct validity.

Convergent validity and discriminant validity are both subtypes of construct validity . Together, they help you evaluate whether a test measures the concept it was designed to measure.

  • Convergent validity indicates whether a test that is designed to measure a particular construct correlates with other tests that assess the same or similar construct.
  • Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related. This type of validity is also called divergent validity .

You need to assess both in order to demonstrate construct validity. Neither one alone is sufficient for establishing construct validity.

  • Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related

Content validity shows you how accurately a test or other measurement method taps  into the various aspects of the specific construct you are researching.

In other words, it helps you answer the question: “does the test measure all aspects of the construct I want to measure?” If it does, then the test has high content validity.

The higher the content validity, the more accurate the measurement of the construct.

If the test fails to include parts of the construct, or irrelevant parts are included, the validity of the instrument is threatened, which brings your results into question.

Face validity and content validity are similar in that they both evaluate how suitable the content of a test is. The difference is that face validity is subjective, and assesses content at surface level.

When a test has strong face validity, anyone would agree that the test’s questions appear to measure what they are intended to measure.

For example, looking at a 4th grade math test consisting of problems in which students have to add and multiply, most people would agree that it has strong face validity (i.e., it looks like a math test).

On the other hand, content validity evaluates how well a test represents all the aspects of a topic. Assessing content validity is more systematic and relies on expert evaluation. of each question, analyzing whether each one covers the aspects that the test was designed to cover.

A 4th grade math test would have high content validity if it covered all the skills taught in that grade. Experts(in this case, math teachers), would have to evaluate the content validity by comparing the test to the learning objectives.

Snowball sampling is a non-probability sampling method . Unlike probability sampling (which involves some form of random selection ), the initial individuals selected to be studied are the ones who recruit new participants.

Because not every member of the target population has an equal chance of being recruited into the sample, selection in snowball sampling is non-random.

Snowball sampling is a non-probability sampling method , where there is not an equal chance for every member of the population to be included in the sample .

This means that you cannot use inferential statistics and make generalizations —often the goal of quantitative research . As such, a snowball sample is not representative of the target population and is usually a better fit for qualitative research .

Snowball sampling relies on the use of referrals. Here, the researcher recruits one or more initial participants, who then recruit the next ones.

Participants share similar characteristics and/or know each other. Because of this, not every member of the population has an equal chance of being included in the sample, giving rise to sampling bias .

Snowball sampling is best used in the following cases:

  • If there is no sampling frame available (e.g., people with a rare disease)
  • If the population of interest is hard to access or locate (e.g., people experiencing homelessness)
  • If the research focuses on a sensitive topic (e.g., extramarital affairs)

The reproducibility and replicability of a study can be ensured by writing a transparent, detailed method section and using clear, unambiguous language.

Reproducibility and replicability are related terms.

  • Reproducing research entails reanalyzing the existing data in the same manner.
  • Replicating (or repeating ) the research entails reconducting the entire analysis, including the collection of new data . 
  • A successful reproduction shows that the data analyses were conducted in a fair and honest manner.
  • A successful replication shows that the reliability of the results is high.

Stratified sampling and quota sampling both involve dividing the population into subgroups and selecting units from each subgroup. The purpose in both cases is to select a representative sample and/or to allow comparisons between subgroups.

The main difference is that in stratified sampling, you draw a random sample from each subgroup ( probability sampling ). In quota sampling you select a predetermined number or proportion of units, in a non-random manner ( non-probability sampling ).

Purposive and convenience sampling are both sampling methods that are typically used in qualitative data collection.

A convenience sample is drawn from a source that is conveniently accessible to the researcher. Convenience sampling does not distinguish characteristics among the participants. On the other hand, purposive sampling focuses on selecting participants possessing characteristics associated with the research study.

The findings of studies based on either convenience or purposive sampling can only be generalized to the (sub)population from which the sample is drawn, and not to the entire population.

Random sampling or probability sampling is based on random selection. This means that each unit has an equal chance (i.e., equal probability) of being included in the sample.

On the other hand, convenience sampling involves stopping people at random, which means that not everyone has an equal chance of being selected depending on the place, time, or day you are collecting your data.

Convenience sampling and quota sampling are both non-probability sampling methods. They both use non-random criteria like availability, geographical proximity, or expert knowledge to recruit study participants.

However, in convenience sampling, you continue to sample units or cases until you reach the required sample size.

In quota sampling, you first need to divide your population of interest into subgroups (strata) and estimate their proportions (quota) in the population. Then you can start your data collection, using convenience sampling to recruit participants, until the proportions in each subgroup coincide with the estimated proportions in the population.

A sampling frame is a list of every member in the entire population . It is important that the sampling frame is as complete as possible, so that your sample accurately reflects your population.

Stratified and cluster sampling may look similar, but bear in mind that groups created in cluster sampling are heterogeneous , so the individual characteristics in the cluster vary. In contrast, groups created in stratified sampling are homogeneous , as units share characteristics.

Relatedly, in cluster sampling you randomly select entire groups and include all units of each group in your sample. However, in stratified sampling, you select some units of all groups and include them in your sample. In this way, both methods can ensure that your sample is representative of the target population .

A systematic review is secondary research because it uses existing research. You don’t collect new data yourself.

The key difference between observational studies and experimental designs is that a well-done observational study does not influence the responses of participants, while experiments do have some sort of treatment condition applied to at least some participants by random assignment .

An observational study is a great choice for you if your research question is based purely on observations. If there are ethical, logistical, or practical concerns that prevent you from conducting a traditional experiment , an observational study may be a good choice. In an observational study, there is no interference or manipulation of the research subjects, as well as no control or treatment groups .

It’s often best to ask a variety of people to review your measurements. You can ask experts, such as other researchers, or laypeople, such as potential participants, to judge the face validity of tests.

While experts have a deep understanding of research methods , the people you’re studying can provide you with valuable insights you may have missed otherwise.

Face validity is important because it’s a simple first step to measuring the overall validity of a test or technique. It’s a relatively intuitive, quick, and easy way to start checking whether a new measure seems useful at first glance.

Good face validity means that anyone who reviews your measure says that it seems to be measuring what it’s supposed to. With poor face validity, someone reviewing your measure may be left confused about what you’re measuring and why you’re using this method.

Face validity is about whether a test appears to measure what it’s supposed to measure. This type of validity is concerned with whether a measure seems relevant and appropriate for what it’s assessing only on the surface.

Statistical analyses are often applied to test validity with data from your measures. You test convergent validity and discriminant validity with correlations to see if results from your test are positively or negatively related to those of other established tests.

You can also use regression analyses to assess whether your measure is actually predictive of outcomes that you expect it to predict theoretically. A regression analysis that supports your expectations strengthens your claim of construct validity .

When designing or evaluating a measure, construct validity helps you ensure you’re actually measuring the construct you’re interested in. If you don’t have construct validity, you may inadvertently measure unrelated or distinct constructs and lose precision in your research.

Construct validity is often considered the overarching type of measurement validity ,  because it covers all of the other types. You need to have face validity , content validity , and criterion validity to achieve construct validity.

Construct validity is about how well a test measures the concept it was designed to evaluate. It’s one of four types of measurement validity , which includes construct validity, face validity , and criterion validity.

There are two subtypes of construct validity.

  • Convergent validity : The extent to which your measure corresponds to measures of related constructs
  • Discriminant validity : The extent to which your measure is unrelated or negatively related to measures of distinct constructs

Naturalistic observation is a valuable tool because of its flexibility, external validity , and suitability for topics that can’t be studied in a lab setting.

The downsides of naturalistic observation include its lack of scientific control , ethical considerations , and potential for bias from observers and subjects.

Naturalistic observation is a qualitative research method where you record the behaviors of your research subjects in real world settings. You avoid interfering or influencing anything in a naturalistic observation.

You can think of naturalistic observation as “people watching” with a purpose.

A dependent variable is what changes as a result of the independent variable manipulation in experiments . It’s what you’re interested in measuring, and it “depends” on your independent variable.

In statistics, dependent variables are also called:

  • Response variables (they respond to a change in another variable)
  • Outcome variables (they represent the outcome you want to measure)
  • Left-hand-side variables (they appear on the left-hand side of a regression equation)

An independent variable is the variable you manipulate, control, or vary in an experimental study to explore its effects. It’s called “independent” because it’s not influenced by any other variables in the study.

Independent variables are also called:

  • Explanatory variables (they explain an event or outcome)
  • Predictor variables (they can be used to predict the value of a dependent variable)
  • Right-hand-side variables (they appear on the right-hand side of a regression equation).

As a rule of thumb, questions related to thoughts, beliefs, and feelings work well in focus groups. Take your time formulating strong questions, paying special attention to phrasing. Be careful to avoid leading questions , which can bias your responses.

Overall, your focus group questions should be:

  • Open-ended and flexible
  • Impossible to answer with “yes” or “no” (questions that start with “why” or “how” are often best)
  • Unambiguous, getting straight to the point while still stimulating discussion
  • Unbiased and neutral

A structured interview is a data collection method that relies on asking questions in a set order to collect data on a topic. They are often quantitative in nature. Structured interviews are best used when: 

  • You already have a very clear understanding of your topic. Perhaps significant research has already been conducted, or you have done some prior research yourself, but you already possess a baseline for designing strong structured questions.
  • You are constrained in terms of time or resources and need to analyze your data quickly and efficiently.
  • Your research question depends on strong parity between participants, with environmental conditions held constant.

More flexible interview options include semi-structured interviews , unstructured interviews , and focus groups .

Social desirability bias is the tendency for interview participants to give responses that will be viewed favorably by the interviewer or other participants. It occurs in all types of interviews and surveys , but is most common in semi-structured interviews , unstructured interviews , and focus groups .

Social desirability bias can be mitigated by ensuring participants feel at ease and comfortable sharing their views. Make sure to pay attention to your own body language and any physical or verbal cues, such as nodding or widening your eyes.

This type of bias can also occur in observations if the participants know they’re being observed. They might alter their behavior accordingly.

The interviewer effect is a type of bias that emerges when a characteristic of an interviewer (race, age, gender identity, etc.) influences the responses given by the interviewee.

There is a risk of an interviewer effect in all types of interviews , but it can be mitigated by writing really high-quality interview questions.

A semi-structured interview is a blend of structured and unstructured types of interviews. Semi-structured interviews are best used when:

  • You have prior interview experience. Spontaneous questions are deceptively challenging, and it’s easy to accidentally ask a leading question or make a participant uncomfortable.
  • Your research question is exploratory in nature. Participant answers can guide future research questions and help you develop a more robust knowledge base for future research.

An unstructured interview is the most flexible type of interview, but it is not always the best fit for your research topic.

Unstructured interviews are best used when:

  • You are an experienced interviewer and have a very strong background in your research topic, since it is challenging to ask spontaneous, colloquial questions.
  • Your research question is exploratory in nature. While you may have developed hypotheses, you are open to discovering new or shifting viewpoints through the interview process.
  • You are seeking descriptive data, and are ready to ask questions that will deepen and contextualize your initial thoughts and hypotheses.
  • Your research depends on forming connections with your participants and making them feel comfortable revealing deeper emotions, lived experiences, or thoughts.

The four most common types of interviews are:

  • Structured interviews : The questions are predetermined in both topic and order. 
  • Semi-structured interviews : A few questions are predetermined, but other questions aren’t planned.
  • Unstructured interviews : None of the questions are predetermined.
  • Focus group interviews : The questions are presented to a group instead of one individual.

Deductive reasoning is commonly used in scientific research, and it’s especially associated with quantitative research .

In research, you might have come across something called the hypothetico-deductive method . It’s the scientific method of testing hypotheses to check whether your predictions are substantiated by real-world data.

Deductive reasoning is a logical approach where you progress from general ideas to specific conclusions. It’s often contrasted with inductive reasoning , where you start with specific observations and form general conclusions.

Deductive reasoning is also called deductive logic.

There are many different types of inductive reasoning that people use formally or informally.

Here are a few common types:

  • Inductive generalization : You use observations about a sample to come to a conclusion about the population it came from.
  • Statistical generalization: You use specific numbers about samples to make statements about populations.
  • Causal reasoning: You make cause-and-effect links between different things.
  • Sign reasoning: You make a conclusion about a correlational relationship between different things.
  • Analogical reasoning: You make a conclusion about something based on its similarities to something else.

Inductive reasoning is a bottom-up approach, while deductive reasoning is top-down.

Inductive reasoning takes you from the specific to the general, while in deductive reasoning, you make inferences by going from general premises to specific conclusions.

In inductive research , you start by making observations or gathering data. Then, you take a broad scan of your data and search for patterns. Finally, you make general conclusions that you might incorporate into theories.

Inductive reasoning is a method of drawing conclusions by going from the specific to the general. It’s usually contrasted with deductive reasoning, where you proceed from general information to specific conclusions.

Inductive reasoning is also called inductive logic or bottom-up reasoning.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess — it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Triangulation can help:

  • Reduce research bias that comes from using a single method, theory, or investigator
  • Enhance validity by approaching the same topic with different tools
  • Establish credibility by giving you a complete picture of the research problem

But triangulation can also pose problems:

  • It’s time-consuming and labor-intensive, often involving an interdisciplinary team.
  • Your results may be inconsistent or even contradictory.

There are four main types of triangulation :

  • Data triangulation : Using data from different times, spaces, and people
  • Investigator triangulation : Involving multiple researchers in collecting or analyzing data
  • Theory triangulation : Using varying theoretical perspectives in your research
  • Methodological triangulation : Using different methodologies to approach the same topic

Many academic fields use peer review , largely to determine whether a manuscript is suitable for publication. Peer review enhances the credibility of the published manuscript.

However, peer review is also common in non-academic settings. The United Nations, the European Union, and many individual nations use peer review to evaluate grant applications. It is also widely used in medical and health-related fields as a teaching or quality-of-care measure. 

Peer assessment is often used in the classroom as a pedagogical tool. Both receiving feedback and providing it are thought to enhance the learning process, helping students think critically and collaboratively.

Peer review can stop obviously problematic, falsified, or otherwise untrustworthy research from being published. It also represents an excellent opportunity to get feedback from renowned experts in your field. It acts as a first defense, helping you ensure your argument is clear and that there are no gaps, vague terms, or unanswered questions for readers who weren’t involved in the research process.

Peer-reviewed articles are considered a highly credible source due to this stringent process they go through before publication.

In general, the peer review process follows the following steps: 

  • First, the author submits the manuscript to the editor.
  • Reject the manuscript and send it back to author, or 
  • Send it onward to the selected peer reviewer(s) 
  • Next, the peer review process occurs. The reviewer provides feedback, addressing any major or minor issues with the manuscript, and gives their advice regarding what edits should be made. 
  • Lastly, the edited manuscript is sent back to the author. They input the edits, and resubmit it to the editor for publication.

Exploratory research is often used when the issue you’re studying is new or when the data collection process is challenging for some reason.

You can use exploratory research if you have a general idea or a specific question that you want to study but there is no preexisting knowledge or paradigm with which to study it.

Exploratory research is a methodology approach that explores research questions that have not previously been studied in depth. It is often used when the issue you’re studying is new, or the data collection process is challenging in some way.

Explanatory research is used to investigate how or why a phenomenon occurs. Therefore, this type of research is often one of the first stages in the research process , serving as a jumping-off point for future research.

Exploratory research aims to explore the main aspects of an under-researched problem, while explanatory research aims to explain the causes and consequences of a well-defined problem.

Explanatory research is a research method used to investigate how or why something occurs when only a small amount of information is available pertaining to that topic. It can help you increase your understanding of a given topic.

Clean data are valid, accurate, complete, consistent, unique, and uniform. Dirty data include inconsistencies and errors.

Dirty data can come from any part of the research process, including poor research design , inappropriate measurement materials, or flawed data entry.

Data cleaning takes place between data collection and data analyses. But you can use some methods even before collecting data.

For clean data, you should start by designing measures that collect valid data. Data validation at the time of data entry or collection helps you minimize the amount of data cleaning you’ll need to do.

After data collection, you can use data standardization and data transformation to clean your data. You’ll also deal with any missing values, outliers, and duplicate values.

Every dataset requires different techniques to clean dirty data , but you need to address these issues in a systematic way. You focus on finding and resolving data points that don’t agree or fit with the rest of your dataset.

These data might be missing values, outliers, duplicate values, incorrectly formatted, or irrelevant. You’ll start with screening and diagnosing your data. Then, you’ll often standardize and accept or remove data to make your dataset consistent and valid.

Data cleaning is necessary for valid and appropriate analyses. Dirty data contain inconsistencies or errors , but cleaning your data helps you minimize or resolve these.

Without data cleaning, you could end up with a Type I or II error in your conclusion. These types of erroneous conclusions can be practically significant with important consequences, because they lead to misplaced investments or missed opportunities.

Data cleaning involves spotting and resolving potential data inconsistencies or errors to improve your data quality. An error is any value (e.g., recorded weight) that doesn’t reflect the true value (e.g., actual weight) of something that’s being measured.

In this process, you review, analyze, detect, modify, or remove “dirty” data to make your dataset “clean.” Data cleaning is also called data cleansing or data scrubbing.

Research misconduct means making up or falsifying data, manipulating data analyses, or misrepresenting results in research reports. It’s a form of academic fraud.

These actions are committed intentionally and can have serious consequences; research misconduct is not a simple mistake or a point of disagreement but a serious ethical failure.

Anonymity means you don’t know who the participants are, while confidentiality means you know who they are but remove identifying information from your research report. Both are important ethical considerations .

You can only guarantee anonymity by not collecting any personally identifying information—for example, names, phone numbers, email addresses, IP addresses, physical characteristics, photos, or videos.

You can keep data confidential by using aggregate information in your research report, so that you only refer to groups of participants rather than individuals.

Research ethics matter for scientific integrity, human rights and dignity, and collaboration between science and society. These principles make sure that participation in studies is voluntary, informed, and safe.

Ethical considerations in research are a set of principles that guide your research designs and practices. These principles include voluntary participation, informed consent, anonymity, confidentiality, potential for harm, and results communication.

Scientists and researchers must always adhere to a certain code of conduct when collecting data from others .

These considerations protect the rights of research participants, enhance research validity , and maintain scientific integrity.

In multistage sampling , you can use probability or non-probability sampling methods .

For a probability sample, you have to conduct probability sampling at every stage.

You can mix it up by using simple random sampling , systematic sampling , or stratified sampling to select units at different stages, depending on what is applicable and relevant to your study.

Multistage sampling can simplify data collection when you have large, geographically spread samples, and you can obtain a probability sample without a complete sampling frame.

But multistage sampling may not lead to a representative sample, and larger samples are needed for multistage samples to achieve the statistical properties of simple random samples .

These are four of the most common mixed methods designs :

  • Convergent parallel: Quantitative and qualitative data are collected at the same time and analyzed separately. After both analyses are complete, compare your results to draw overall conclusions. 
  • Embedded: Quantitative and qualitative data are collected at the same time, but within a larger quantitative or qualitative design. One type of data is secondary to the other.
  • Explanatory sequential: Quantitative data is collected and analyzed first, followed by qualitative data. You can use this design if you think your qualitative data will explain and contextualize your quantitative findings.
  • Exploratory sequential: Qualitative data is collected and analyzed first, followed by quantitative data. You can use this design if you think the quantitative data will confirm or validate your qualitative findings.

Triangulation in research means using multiple datasets, methods, theories and/or investigators to address a research question. It’s a research strategy that can help you enhance the validity and credibility of your findings.

Triangulation is mainly used in qualitative research , but it’s also commonly applied in quantitative research . Mixed methods research always uses triangulation.

In multistage sampling , or multistage cluster sampling, you draw a sample from a population using smaller and smaller groups at each stage.

This method is often used to collect data from a large, geographically spread group of people in national surveys, for example. You take advantage of hierarchical groupings (e.g., from state to city to neighborhood) to create a sample that’s less expensive and time-consuming to collect data from.

No, the steepness or slope of the line isn’t related to the correlation coefficient value. The correlation coefficient only tells you how closely your data fit on a line, so two datasets with the same correlation coefficient can have very different slopes.

To find the slope of the line, you’ll need to perform a regression analysis .

Correlation coefficients always range between -1 and 1.

The sign of the coefficient tells you the direction of the relationship: a positive value means the variables change together in the same direction, while a negative value means they change together in opposite directions.

The absolute value of a number is equal to the number without its sign. The absolute value of a correlation coefficient tells you the magnitude of the correlation: the greater the absolute value, the stronger the correlation.

These are the assumptions your data must meet if you want to use Pearson’s r :

  • Both variables are on an interval or ratio level of measurement
  • Data from both variables follow normal distributions
  • Your data have no outliers
  • Your data is from a random or representative sample
  • You expect a linear relationship between the two variables

Quantitative research designs can be divided into two main categories:

  • Correlational and descriptive designs are used to investigate characteristics, averages, trends, and associations between variables.
  • Experimental and quasi-experimental designs are used to test causal relationships .

Qualitative research designs tend to be more flexible. Common types of qualitative design include case study , ethnography , and grounded theory designs.

A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources . This allows you to draw valid , trustworthy conclusions.

The priorities of a research design can vary depending on the field, but you usually have to specify:

  • Your research questions and/or hypotheses
  • Your overall approach (e.g., qualitative or quantitative )
  • The type of design you’re using (e.g., a survey , experiment , or case study )
  • Your sampling methods or criteria for selecting subjects
  • Your data collection methods (e.g., questionnaires , observations)
  • Your data collection procedures (e.g., operationalization , timing and data management)
  • Your data analysis methods (e.g., statistical tests  or thematic analysis )

A research design is a strategy for answering your   research question . It defines your overall approach and determines how you will collect and analyze data.

Questionnaires can be self-administered or researcher-administered.

Self-administered questionnaires can be delivered online or in paper-and-pen formats, in person or through mail. All questions are standardized so that all respondents receive the same questions with identical wording.

Researcher-administered questionnaires are interviews that take place by phone, in-person, or online between researchers and respondents. You can gain deeper insights by clarifying questions for respondents or asking follow-up questions.

You can organize the questions logically, with a clear progression from simple to complex, or randomly between respondents. A logical flow helps respondents process the questionnaire easier and quicker, but it may lead to bias. Randomization can minimize the bias from order effects.

Closed-ended, or restricted-choice, questions offer respondents a fixed set of choices to select from. These questions are easier to answer quickly.

Open-ended or long-form questions allow respondents to answer in their own words. Because there are no restrictions on their choices, respondents can answer in ways that researchers may not have otherwise considered.

A questionnaire is a data collection tool or instrument, while a survey is an overarching research method that involves collecting and analyzing data from people using questionnaires.

The third variable and directionality problems are two main reasons why correlation isn’t causation .

The third variable problem means that a confounding variable affects both variables to make them seem causally related when they are not.

The directionality problem is when two variables correlate and might actually have a causal relationship, but it’s impossible to conclude which variable causes changes in the other.

Correlation describes an association between variables : when one variable changes, so does the other. A correlation is a statistical indicator of the relationship between variables.

Causation means that changes in one variable brings about changes in the other (i.e., there is a cause-and-effect relationship between variables). The two variables are correlated with each other, and there’s also a causal link between them.

While causation and correlation can exist simultaneously, correlation does not imply causation. In other words, correlation is simply a relationship where A relates to B—but A doesn’t necessarily cause B to happen (or vice versa). Mistaking correlation for causation is a common error and can lead to false cause fallacy .

Controlled experiments establish causality, whereas correlational studies only show associations between variables.

  • In an experimental design , you manipulate an independent variable and measure its effect on a dependent variable. Other variables are controlled so they can’t impact the results.
  • In a correlational design , you measure variables without manipulating any of them. You can test whether your variables change together, but you can’t be sure that one variable caused a change in another.

In general, correlational research is high in external validity while experimental research is high in internal validity .

A correlation is usually tested for two variables at a time, but you can test correlations between three or more variables.

A correlation coefficient is a single number that describes the strength and direction of the relationship between your variables.

Different types of correlation coefficients might be appropriate for your data based on their levels of measurement and distributions . The Pearson product-moment correlation coefficient (Pearson’s r ) is commonly used to assess a linear relationship between two quantitative variables.

A correlational research design investigates relationships between two variables (or more) without the researcher controlling or manipulating any of them. It’s a non-experimental type of quantitative research .

A correlation reflects the strength and/or direction of the association between two or more variables.

  • A positive correlation means that both variables change in the same direction.
  • A negative correlation means that the variables change in opposite directions.
  • A zero correlation means there’s no relationship between the variables.

Random error  is almost always present in scientific studies, even in highly controlled settings. While you can’t eradicate it completely, you can reduce random error by taking repeated measurements, using a large sample, and controlling extraneous variables .

You can avoid systematic error through careful design of your sampling , data collection , and analysis procedures. For example, use triangulation to measure your variables using multiple methods; regularly calibrate instruments or procedures; use random sampling and random assignment ; and apply masking (blinding) where possible.

Systematic error is generally a bigger problem in research.

With random error, multiple measurements will tend to cluster around the true value. When you’re collecting data from a large sample , the errors in different directions will cancel each other out.

Systematic errors are much more problematic because they can skew your data away from the true value. This can lead you to false conclusions ( Type I and II errors ) about the relationship between the variables you’re studying.

Random and systematic error are two types of measurement error.

Random error is a chance difference between the observed and true values of something (e.g., a researcher misreading a weighing scale records an incorrect measurement).

Systematic error is a consistent or proportional difference between the observed and true values of something (e.g., a miscalibrated scale consistently records weights as higher than they actually are).

On graphs, the explanatory variable is conventionally placed on the x-axis, while the response variable is placed on the y-axis.

  • If you have quantitative variables , use a scatterplot or a line graph.
  • If your response variable is categorical, use a scatterplot or a line graph.
  • If your explanatory variable is categorical, use a bar graph.

The term “ explanatory variable ” is sometimes preferred over “ independent variable ” because, in real world contexts, independent variables are often influenced by other variables. This means they aren’t totally independent.

Multiple independent variables may also be correlated with each other, so “explanatory variables” is a more appropriate term.

The difference between explanatory and response variables is simple:

  • An explanatory variable is the expected cause, and it explains the results.
  • A response variable is the expected effect, and it responds to other variables.

In a controlled experiment , all extraneous variables are held constant so that they can’t influence the results. Controlled experiments require:

  • A control group that receives a standard treatment, a fake treatment, or no treatment.
  • Random assignment of participants to ensure the groups are equivalent.

Depending on your study topic, there are various other methods of controlling variables .

There are 4 main types of extraneous variables :

  • Demand characteristics : environmental cues that encourage participants to conform to researchers’ expectations.
  • Experimenter effects : unintentional actions by researchers that influence study outcomes.
  • Situational variables : environmental variables that alter participants’ behaviors.
  • Participant variables : any characteristic or aspect of a participant’s background that could affect study results.

An extraneous variable is any variable that you’re not investigating that can potentially affect the dependent variable of your research study.

A confounding variable is a type of extraneous variable that not only affects the dependent variable, but is also related to the independent variable.

In a factorial design, multiple independent variables are tested.

If you test two variables, each level of one independent variable is combined with each level of the other independent variable to create different conditions.

Within-subjects designs have many potential threats to internal validity , but they are also very statistically powerful .

Advantages:

  • Only requires small samples
  • Statistically powerful
  • Removes the effects of individual differences on the outcomes

Disadvantages:

  • Internal validity threats reduce the likelihood of establishing a direct relationship between variables
  • Time-related effects, such as growth, can influence the outcomes
  • Carryover effects mean that the specific order of different treatments affect the outcomes

While a between-subjects design has fewer threats to internal validity , it also requires more participants for high statistical power than a within-subjects design .

  • Prevents carryover effects of learning and fatigue.
  • Shorter study duration.
  • Needs larger samples for high power.
  • Uses more resources to recruit participants, administer sessions, cover costs, etc.
  • Individual differences may be an alternative explanation for results.

Yes. Between-subjects and within-subjects designs can be combined in a single study when you have two or more independent variables (a factorial design). In a mixed factorial design, one variable is altered between subjects and another is altered within subjects.

In a between-subjects design , every participant experiences only one condition, and researchers assess group differences between participants in various conditions.

In a within-subjects design , each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions.

The word “between” means that you’re comparing different conditions between groups, while the word “within” means you’re comparing different conditions within the same group.

Random assignment is used in experiments with a between-groups or independent measures design. In this research design, there’s usually a control group and one or more experimental groups. Random assignment helps ensure that the groups are comparable.

In general, you should always use random assignment in this type of experimental design when it is ethically possible and makes sense for your study topic.

To implement random assignment , assign a unique number to every member of your study’s sample .

Then, you can use a random number generator or a lottery method to randomly assign each number to a control or experimental group. You can also do so manually, by flipping a coin or rolling a dice to randomly assign participants to groups.

Random selection, or random sampling , is a way of selecting members of a population for your study’s sample.

In contrast, random assignment is a way of sorting the sample into control and experimental groups.

Random sampling enhances the external validity or generalizability of your results, while random assignment improves the internal validity of your study.

In experimental research, random assignment is a way of placing participants from your sample into different groups using randomization. With this method, every member of the sample has a known or equal chance of being placed in a control group or an experimental group.

“Controlling for a variable” means measuring extraneous variables and accounting for them statistically to remove their effects on other variables.

Researchers often model control variable data along with independent and dependent variable data in regression analyses and ANCOVAs . That way, you can isolate the control variable’s effects from the relationship between the variables of interest.

Control variables help you establish a correlational or causal relationship between variables by enhancing internal validity .

If you don’t control relevant extraneous variables , they may influence the outcomes of your study, and you may not be able to demonstrate that your results are really an effect of your independent variable .

A control variable is any variable that’s held constant in a research study. It’s not a variable of interest in the study, but it’s controlled because it could influence the outcomes.

Including mediators and moderators in your research helps you go beyond studying a simple relationship between two variables for a fuller picture of the real world. They are important to consider when studying complex correlational or causal relationships.

Mediators are part of the causal pathway of an effect, and they tell you how or why an effect takes place. Moderators usually help you judge the external validity of your study by identifying the limitations of when the relationship between variables holds.

If something is a mediating variable :

  • It’s caused by the independent variable .
  • It influences the dependent variable
  • When it’s taken into account, the statistical correlation between the independent and dependent variables is higher than when it isn’t considered.

A confounder is a third variable that affects variables of interest and makes them seem related when they are not. In contrast, a mediator is the mechanism of a relationship between two variables: it explains the process by which they are related.

A mediator variable explains the process through which two variables are related, while a moderator variable affects the strength and direction of that relationship.

There are three key steps in systematic sampling :

  • Define and list your population , ensuring that it is not ordered in a cyclical or periodic order.
  • Decide on your sample size and calculate your interval, k , by dividing your population by your target sample size.
  • Choose every k th member of the population as your sample.

Systematic sampling is a probability sampling method where researchers select members of the population at a regular interval – for example, by selecting every 15th person on a list of the population. If the population is in a random order, this can imitate the benefits of simple random sampling .

Yes, you can create a stratified sample using multiple characteristics, but you must ensure that every participant in your study belongs to one and only one subgroup. In this case, you multiply the numbers of subgroups for each characteristic to get the total number of groups.

For example, if you were stratifying by location with three subgroups (urban, rural, or suburban) and marital status with five subgroups (single, divorced, widowed, married, or partnered), you would have 3 x 5 = 15 subgroups.

You should use stratified sampling when your sample can be divided into mutually exclusive and exhaustive subgroups that you believe will take on different mean values for the variable that you’re studying.

Using stratified sampling will allow you to obtain more precise (with lower variance ) statistical estimates of whatever you are trying to measure.

For example, say you want to investigate how income differs based on educational attainment, but you know that this relationship can vary based on race. Using stratified sampling, you can ensure you obtain a large enough sample from each racial group, allowing you to draw more precise conclusions.

In stratified sampling , researchers divide subjects into subgroups called strata based on characteristics that they share (e.g., race, gender, educational attainment).

Once divided, each subgroup is randomly sampled using another probability sampling method.

Cluster sampling is more time- and cost-efficient than other probability sampling methods , particularly when it comes to large samples spread across a wide geographical area.

However, it provides less statistical certainty than other methods, such as simple random sampling , because it is difficult to ensure that your clusters properly represent the population as a whole.

There are three types of cluster sampling : single-stage, double-stage and multi-stage clustering. In all three types, you first divide the population into clusters, then randomly select clusters for use in your sample.

  • In single-stage sampling , you collect data from every unit within the selected clusters.
  • In double-stage sampling , you select a random sample of units from within the clusters.
  • In multi-stage sampling , you repeat the procedure of randomly sampling elements from within the clusters until you have reached a manageable sample.

Cluster sampling is a probability sampling method in which you divide a population into clusters, such as districts or schools, and then randomly select some of these clusters as your sample.

The clusters should ideally each be mini-representations of the population as a whole.

If properly implemented, simple random sampling is usually the best sampling method for ensuring both internal and external validity . However, it can sometimes be impractical and expensive to implement, depending on the size of the population to be studied,

If you have a list of every member of the population and the ability to reach whichever members are selected, you can use simple random sampling.

The American Community Survey  is an example of simple random sampling . In order to collect detailed data on the population of the US, the Census Bureau officials randomly select 3.5 million households per year and use a variety of methods to convince them to fill out the survey.

Simple random sampling is a type of probability sampling in which the researcher randomly selects a subset of participants from a population . Each member of the population has an equal chance of being selected. Data is then collected from as large a percentage as possible of this random subset.

Quasi-experimental design is most useful in situations where it would be unethical or impractical to run a true experiment .

Quasi-experiments have lower internal validity than true experiments, but they often have higher external validity  as they can use real-world interventions instead of artificial laboratory settings.

A quasi-experiment is a type of research design that attempts to establish a cause-and-effect relationship. The main difference with a true experiment is that the groups are not randomly assigned.

Blinding is important to reduce research bias (e.g., observer bias , demand characteristics ) and ensure a study’s internal validity .

If participants know whether they are in a control or treatment group , they may adjust their behavior in ways that affect the outcome that researchers are trying to measure. If the people administering the treatment are aware of group assignment, they may treat participants differently and thus directly or indirectly influence the final results.

  • In a single-blind study , only the participants are blinded.
  • In a double-blind study , both participants and experimenters are blinded.
  • In a triple-blind study , the assignment is hidden not only from participants and experimenters, but also from the researchers analyzing the data.

Blinding means hiding who is assigned to the treatment group and who is assigned to the control group in an experiment .

A true experiment (a.k.a. a controlled experiment) always includes at least one control group that doesn’t receive the experimental treatment.

However, some experiments use a within-subjects design to test treatments without a control group. In these designs, you usually compare one group’s outcomes before and after a treatment (instead of comparing outcomes between different groups).

For strong internal validity , it’s usually best to include a control group if possible. Without a control group, it’s harder to be certain that the outcome was caused by the experimental treatment and not by other variables.

An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways.

Individual Likert-type questions are generally considered ordinal data , because the items have clear rank order, but don’t have an even distribution.

Overall Likert scale scores are sometimes treated as interval data. These scores are considered to have directionality and even spacing between them.

The type of data determines what statistical tests you should use to analyze your data.

A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviors. It is made up of 4 or more questions that measure a single attitude or trait when response scores are combined.

To use a Likert scale in a survey , you present participants with Likert-type questions or statements, and a continuum of items, usually with 5 or 7 possible responses, to capture their degree of agreement.

In scientific research, concepts are the abstract ideas or phenomena that are being studied (e.g., educational achievement). Variables are properties or characteristics of the concept (e.g., performance at school), while indicators are ways of measuring or quantifying variables (e.g., yearly grade reports).

The process of turning abstract concepts into measurable variables and indicators is called operationalization .

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 .

There are five common approaches to qualitative research :

  • Grounded theory involves collecting data in order to develop new theories.
  • Ethnography involves immersing yourself in a group or organization to understand its culture.
  • Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
  • Phenomenological research involves investigating phenomena through people’s lived experiences.
  • Action research links theory and practice in several cycles to drive innovative changes.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

When conducting research, collecting original data has significant advantages:

  • You can tailor data collection to your specific research aims (e.g. understanding the needs of your consumers or user testing your website)
  • You can control and standardize the process for high reliability and validity (e.g. choosing appropriate measurements and sampling methods )

However, there are also some drawbacks: data collection can be time-consuming, labor-intensive and expensive. In some cases, it’s more efficient to use secondary data that has already been collected by someone else, but the data might be less reliable.

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 several methods you can use to decrease the impact of confounding variables on your research: restriction, matching, statistical control and randomization.

In restriction , you restrict your sample by only including certain subjects that have the same values of potential confounding variables.

In matching , you match each of the subjects in your treatment group with a counterpart in the comparison group. The matched subjects have the same values on any potential confounding variables, and only differ in the independent variable .

In statistical control , you include potential confounders as variables in your regression .

In randomization , you randomly assign the treatment (or independent variable) in your study to a sufficiently large number of subjects, which allows you to control for all potential confounding variables.

A confounding variable is closely related to both the independent and dependent variables in a study. An independent variable represents the supposed cause , while the dependent variable is the supposed effect . A confounding variable is a third variable that influences both the independent and dependent variables.

Failing to account for confounding variables can cause you to wrongly estimate the relationship between your independent and dependent variables.

To ensure the internal validity of your research, you must consider the impact of confounding variables. If you fail to account for them, you might over- or underestimate the causal relationship between your independent and dependent variables , or even find a causal relationship where none exists.

Yes, but including more than one of either type requires multiple research questions .

For example, if you are interested in the effect of a diet on health, you can use multiple measures of health: blood sugar, blood pressure, weight, pulse, and many more. Each of these is its own dependent variable with its own research question.

You could also choose to look at the effect of exercise levels as well as diet, or even the additional effect of the two combined. Each of these is a separate independent variable .

To ensure the internal validity of an experiment , you should only change one independent variable at a time.

No. The value of a dependent variable depends on an independent variable, so a variable cannot be both independent and dependent at the same time. It must be either the cause or the effect, not both!

You want to find out how blood sugar levels are affected by drinking diet soda and regular soda, so you conduct an experiment .

  • The type of soda – diet or regular – is the independent variable .
  • The level of blood sugar that you measure is the dependent variable – it changes depending on the type of soda.

Determining cause and effect is one of the most important parts of scientific research. It’s essential to know which is the cause – the independent variable – and which is the effect – the dependent variable.

In non-probability sampling , the sample is selected based on non-random criteria, and not every member of the population has a chance of being included.

Common non-probability sampling methods include convenience sampling , voluntary response sampling, purposive sampling , snowball sampling, and quota sampling .

Probability sampling means that every member of the target population has a known chance of being included in the sample.

Probability sampling methods include simple random sampling , systematic sampling , stratified sampling , and cluster sampling .

Using careful research design and sampling procedures can help you avoid sampling bias . Oversampling can be used to correct undercoverage bias .

Some common types of sampling bias include self-selection bias , nonresponse bias , undercoverage bias , survivorship bias , pre-screening or advertising bias, and healthy user bias.

Sampling bias is a threat to external validity – it limits the generalizability of your findings to a broader group of people.

A sampling error is the difference between a population parameter and a sample statistic .

A statistic refers to measures about the sample , while a parameter refers to measures about the population .

Populations are used when a research question requires data from every member of the population. This is usually only feasible when the population is small and easily accessible.

Samples are used to make inferences about populations . Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.

There are seven threats to external validity : selection bias , history, experimenter effect, Hawthorne effect , testing effect, aptitude-treatment and situation effect.

The two types of external validity are population validity (whether you can generalize to other groups of people) and ecological validity (whether you can generalize to other situations and settings).

The external validity of a study is the extent to which you can generalize your findings to different groups of people, situations, and measures.

Cross-sectional studies cannot establish a cause-and-effect relationship or analyze behavior over a period of time. To investigate cause and effect, you need to do a longitudinal study or an experimental study .

Cross-sectional studies are less expensive and time-consuming than many other types of study. They can provide useful insights into a population’s characteristics and identify correlations for further research.

Sometimes only cross-sectional data is available for analysis; other times your research question may only require a cross-sectional study to answer it.

Longitudinal studies can last anywhere from weeks to decades, although they tend to be at least a year long.

The 1970 British Cohort Study , which has collected data on the lives of 17,000 Brits since their births in 1970, is one well-known example of a longitudinal study .

Longitudinal studies are better to establish the correct sequence of events, identify changes over time, and provide insight into cause-and-effect relationships, but they also tend to be more expensive and time-consuming than other types of studies.

Longitudinal studies and cross-sectional studies are two different types of research design . In a cross-sectional study you collect data from a population at a specific point in time; in a longitudinal study you repeatedly collect data from the same sample over an extended period of time.

Longitudinal study Cross-sectional study
observations Observations at a in time
Observes the multiple times Observes (a “cross-section”) in the population
Follows in participants over time Provides of society at a given point

There are eight threats to internal validity : history, maturation, instrumentation, testing, selection bias , regression to the mean, social interaction and attrition .

Internal validity is the extent to which you can be confident that a cause-and-effect relationship established in a study cannot be explained by other factors.

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.

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

Discrete and continuous variables are two types of quantitative variables :

  • Discrete variables represent counts (e.g. the number of objects in a collection).
  • Continuous variables represent measurable amounts (e.g. water volume or weight).

Quantitative variables are any variables where the data represent amounts (e.g. height, weight, or age).

Categorical variables are any variables where the data represent groups. This includes rankings (e.g. finishing places in a race), classifications (e.g. brands of cereal), and binary outcomes (e.g. coin flips).

You need to know what type of variables you are working with to choose the right statistical test for your data and interpret your results .

You can think of independent and dependent variables in terms of cause and effect: an independent variable is the variable you think is the cause , while a dependent variable is the effect .

In an experiment, you manipulate the independent variable and measure the outcome in the dependent variable. For example, in an experiment about the effect of nutrients on crop growth:

  • The  independent variable  is the amount of nutrients added to the crop field.
  • The  dependent variable is the biomass of the crops at harvest time.

Defining your variables, and deciding how you will manipulate and measure them, is an important part of experimental design .

Experimental design means planning a set of procedures to investigate a relationship between variables . To design a controlled experiment, you need:

  • A testable hypothesis
  • At least one independent variable that can be precisely manipulated
  • At least one dependent variable that can be precisely measured

When designing the experiment, you decide:

  • How you will manipulate the variable(s)
  • How you will control for any potential confounding variables
  • How many subjects or samples will be included in the study
  • How subjects will be assigned to treatment levels

Experimental design is essential to the internal and external validity of your experiment.

I nternal validity is the degree of confidence that the causal relationship you are testing is not influenced by other factors or variables .

External validity is the extent to which your results can be generalized to other contexts.

The validity of your experiment depends on your experimental design .

Reliability and validity are both about how well a method measures something:

  • Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
  • Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research, you also have to consider the internal and external validity of your experiment.

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

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

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

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

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

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

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  • Qualitative vs Quantitative Research: When to Use Each

qualitative vs quantitative user research

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.

A researcher conducting 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 interview

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 documenting in their 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

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 survey example

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

Google Analytics Dashboard

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:

ApproachExploratory, open-endedStructured, statistical
FocusUncovering the “why” and “how” behind user behaviours and motivationsQuantifying and measuring “what” users do
MethodsEthnography, interviews, focus groups, usability studiesSurveys, analytics, controlled experiments, metrics
Sample SizeSmaller (individuals to dozens)Larger (hundreds to thousands)
Data AnalysisInterpretation of non-numerical data like text, audio, videoStatistical analysis of numerical data
OutcomesRich behavioral and contextual insightsGeneralizable benchmarks, metrics, models
AppropriatenessExcellent early in product development to explore needsValidates concepts and compares solutions quantitatively

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.

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Qualitative vs. Quantitative: Key Differences in Research Types

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Let's say you want to learn how a group will vote in an election. You face a classic decision of gathering qualitative vs. quantitative data.

With one method, you can ask voters open-ended questions that encourage them to share how they feel, what issues matter to them and the reasons they will vote in a specific way. With the other, you can ask closed-ended questions, giving respondents a list of options. You will then turn that information into statistics.

Neither method is more right than the other, but they serve different purposes. Learn more about the key differences between qualitative and quantitative research and how you can use them.

What Is Qualitative Research?

What is quantitative research, qualitative vs. quantitative research: 3 key differences, benefits of combining qualitative and quantitative research.

Qualitative research aims to explore and understand the depth, context and nuances of human experiences, behaviors and phenomena. This methodological approach emphasizes gathering rich, nonnumerical information through methods such as interviews, focus groups , observations and content analysis.

In qualitative research, the emphasis is on uncovering patterns and meanings within a specific social or cultural context. Researchers delve into the subjective aspects of human behavior , opinions and emotions.

This approach is particularly valuable for exploring complex and multifaceted issues, providing a deeper understanding of the intricacies involved.

Common qualitative research methods include open-ended interviews, where participants can express their thoughts freely, and thematic analysis, which involves identifying recurring themes in the data.

Examples of How to Use Qualitative Research

The flexibility of qualitative research allows researchers to adapt their methods based on emerging insights, fostering a more organic and holistic exploration of the research topic. This is a widely used method in social sciences, psychology and market research.

Here are just a few ways you can use qualitative research.

  • To understand the people who make up a community : If you want to learn more about a community, you can talk to them or observe them to learn more about their customs, norms and values.
  • To examine people's experiences within the healthcare system : While you can certainly look at statistics to gauge if someone feels positively or negatively about their healthcare experiences, you may not gain a deep understanding of why they feel that way. For example, if a nurse went above and beyond for a patient, they might say they are content with the care they received. But if medical professional after medical professional dismissed a person over several years, they will have more negative comments.
  • To explore the effectiveness of your marketing campaign : Marketing is a field that typically collects statistical data, but it can also benefit from qualitative research. For example, if you have a successful campaign, you can interview people to learn what resonated with them and why. If you learn they liked the humor because it shows you don't take yourself too seriously, you can try to replicate that feeling in future campaigns.

Types of Qualitative Data Collection

Qualitative data captures the qualities, characteristics or attributes of a subject. It can take various forms, including:

  • Audio data : Recordings of interviews, discussions or any other auditory information. This can be useful when dealing with events from the past. Setting up a recording device also allows a researcher to stay in the moment without having to jot down notes.
  • Observational data : With this type of qualitative data analysis, you can record behavior, events or interactions.
  • Textual data : Use verbal or written information gathered through interviews, open-ended surveys or focus groups to learn more about a topic.
  • Visual data : You can learn new information through images, photographs, videos or other visual materials.

Quantitative research is a systematic empirical investigation that involves the collection and analysis of numerical data. This approach seeks to understand, explain or predict phenomena by gathering quantifiable information and applying statistical methods for analysis.

Unlike qualitative research, which focuses on nonnumerical, descriptive data, quantitative research data involves measurements, counts and statistical techniques to draw objective conclusions.

Examples of How to Use Quantitative Research

Quantitative research focuses on statistical analysis. Here are a few ways you can employ quantitative research methods.

  • Studying the employment rates of a city : Through this research you can gauge whether any patterns exist over a given time period.
  • Seeing how air pollution has affected a neighborhood : If the creation of a highway led to more air pollution in a neighborhood, you can collect data to learn about the health impacts on the area's residents. For example, you can see what percentage of people developed respiratory issues after moving to the neighborhood.

Types of Quantitative Data

Quantitative data refers to numerical information you can measure and count. Here are a few statistics you can use.

  • Heights, yards, volume and more : You can use different measurements to gain insight on different types of research, such as learning the average distance workers are willing to travel for work or figuring out the average height of a ballerina.
  • Temperature : Measure in either degrees Celsius or Fahrenheit. Or, if you're looking for the coldest place in the universe , you may measure in Kelvins.
  • Sales figures : With this information, you can look at a store's performance over time, compare one company to another or learn what the average amount of sales is in a specific industry.

Quantitative and qualitative research methods are both valid and useful ways to collect data. Here are a few ways that they differ.

  • Data collection method : Quantitative research uses standardized instruments, such as surveys, experiments or structured observations, to gather numerical data. Qualitative research uses open-ended methods like interviews, focus groups or content analysis.
  • Nature of data : Quantitative research involves numerical data that you can measure and analyze statistically, whereas qualitative research involves exploring the depth and richness of experiences through nonnumerical, descriptive data.
  • Sampling : Quantitative research involves larger sample sizes to ensure statistical validity and generalizability of findings to a population. With qualitative research, it's better to work with a smaller sample size to gain in-depth insights into specific contexts or experiences.

You can simultaneously study qualitative and quantitative data. This method , known as mixed methods research, offers several benefits, including:

  • A comprehensive understanding : Integration of qualitative and quantitative data provides a more comprehensive understanding of the research problem. Qualitative data helps explain the context and nuances, while quantitative data offers statistical generalizability.
  • Contextualization : Qualitative data helps contextualize quantitative findings by providing explanations into the why and how behind statistical patterns. This deeper understanding contributes to more informed interpretations of quantitative results.
  • Triangulation : Triangulation involves using multiple methods to validate or corroborate findings. Combining qualitative and quantitative data allows researchers to cross-verify results, enhancing the overall validity and reliability of the study.

This article was created in conjunction with AI technology, then fact-checked and edited by a HowStuffWorks editor.

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Qualitative vs. quantitative research: A simple guide

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

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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.

  Qualitative Research Quantitative Research
Goal Understand reasons or trends Quantify or measure data
Sample size Smaller, often nonrepresentative Larger
Analysis Nonstatistical Statistical
Question type Open-ended Close-ended
Response type Personalized Predetermined

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. 

"How would you describe our brand to a friend or colleague?" is a qualitative question.

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.

"On a scale of 1-10, how likely are you to recommend our brands to a friend or colleague?" is a quantitative question.

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.

The author Lydia Kentowski

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.

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What is descriptive research? Definition, examples, and use cases

Descriptive research is a research methodology that focuses on understanding the particular characteristics of a group, phenomenon, or experience.

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Difference Between Qualitative and Quantitative Research

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In the fields of business, science and technology, economics, etc., they use two standard ways of conducting research. One is qualitative research and the other is quantitative research. Quantitative research uses statistical and logical observations to get a conclusion whereas the qualitative search relies on verbal and written data. In short, quantitative research is generally expressed in numbers or represented using graphs, whereas qualitative research is expressed using the words for the given data sets . Now, in this article, we are going to discuss the difference between qualitative and quantitative research of different data sets.

Why Do We Need Quantitative and Qualitative Research?

Quantitative research is useful in order to gain an understanding of the underlying opinions, motivations, and reasons. It gives insights into the problems. Also, quantitative research helps to develop ideas and hypotheses, whereas qualitative research is useful in uncovering trends, ideas and opinions, and gives deeper insights into the problem.

Definition of Qualitative and Quantitative Research

Qualitative Research: Qualitative research is used to gain an understanding of human behaviour, intentions, attitudes, experience, etc., based on the observation and interpretation of people. It is an unstructured and exploratory technique that deals with highly complex phenomena. This kind of research is usually done to understand the topic in-depth. It is carried out by taking interviews with open-ended questions, observations that are described in words, and so on.

Quantitative Research: Quantitative research method relies on the methods of natural sciences, which develops hard facts and numerical data. It establishes the cause-and-effect relationship between two variables using different statistical, computational, and statistical methods. As the results are accurately and precisely measured, this research method is also termed as “Empirical Research”. This type of research is generally used to establish generalised facts about a particular topic. This type of research is usually done using surveys, experiments, and so on.

What are the Differences Between Qualitative and Quantitative Research?

Quantitative research is a more methodical approach to solving problems by generating and using data. This form of research is used in quantifying data and variables into concrete data. The surveys used in Quantitative Research includes online surveys, paper surveys and other forms of survey used to complete the research.

A method for developing a better understanding of human and social sciences, in understanding human behaviour and personalities better It is the method used to generate numerical data by using a lot of techniques such as logical, statistical and mathematical techniques
It employs a subjective approach It employs an objective approach
It is generally expressed using words It is expressed using graphs and numbers
It has open-ended questions It has multiple choice questions
Qualitative research needs only a few respondents Quantitative research requires many respondents
The data collection methods involved are interviews, focus groups, literature review, ethnography The data collection methods involved are experiments, surveys, and observations expressed in  numbers
Qualitative research is holistic in nature Quantitative Research is particularistic in nature
The reasoning used to synthesise data in this research is inductive The reasoning used to synthesise data in this research is deductive
This method involves a process-oriented inquiry This method does not involve a process-oriented inquiry
It develops the initial understanding of data It recommends a final course of action
The data taken in the Qualitative research method is pretty verbal The data taken in this method is pretty measurable
The objective of this research method is to engage and discover various ideas The main objective of Quantitative research is to examine the cause and effect between the variables
It is one of the exploratory research methods It is a conclusive research method

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Frequently Asked Questions on the Difference Between Qualitative and Quantitative Research

Mention the types of quantitative research..

The four different types of quantitative research are descriptive research, experimental research, quasi-experimental research, and correlational research.

Mention the types of qualitative research

The different types of qualitative research are case study, ethnographic method, phenomenological method, narrative model, historical model, grounded theory method

Mention the major difference between qualitative and quantitative data.

The major difference between the qualitative and quantitative data is that quantitative data is about the numbers and the qualitative data is descriptive.

Give the examples for quantitative and qualitative data

The examples of quantitative data are age, salary, height, shoe size, etc. The examples of qualitative data are taste, smell, colour, etc

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the main difference between qualitative and quantitative research

Dana Stanley

Greenbook’s Chief Revenue Officer

Research Methodologies

August 14, 2024

Qualitative vs. Quantitative Market Research: Why Not Both?

Discover the benefits of qualitative and quantitative methods. Learn how to leverage both approaches for insights into consumer behavior and industry trends.

Qualitative vs. Quantitative Market Research: Why Not Both?

by Ashley Shedlock

Content Coordinator at Greenbook

Market research is vital for understanding target markets, consumer behavior, and industry trends. Qualitative and quantitative methods offer distinct advantages. Qualitative research explores concepts, opinions, and motivations, uncovering rich insights into consumer preferences through techniques like interviews and focus groups.

Quantitative market research involves gathering numerical data through surveys and questionnaires for statistical analysis to identify trends in a target market. It offers measurable results from a large sample size for generalization and prediction. On the other hand, qualitative research explores subjective experiences and motivations, aiming to provide deeper insights into consumer behavior through non-statistical analysis.

Selecting the appropriate research approach is vital for successful studies. Qualitative dives into intricate matters, while quantitative gauges broader market trends. The choice between qualitative and quantitative methods depends on research objectives and results. Both offer valuable insights for strategic decisions in competitive markets. Knowing the distinctions between the methods aids in picking the optimal approach for meaningful data and a competitive advantage.

What is Qualitative Market Research?

Qualitative research methodologies, such as in-depth interviews and focus groups, play a crucial role in providing a deeper understanding of consumer behavior. These methodologies delve into the intricacies of individual attitudes and motivations, allowing researchers to uncover rich insights that quantitative data alone cannot reveal.

One of the key advantages of qualitative research lies in its ability to explore the underlying reasons behind consumer behaviors. By allowing researchers to engage directly with participants, qualitative methods offer a more holistic view of consumer attitudes, preferences, and decision-making processes. This approach can uncover nuanced insights that quantitative surveys may overlook.

However, it is important to acknowledge the limitations of qualitative research, particularly in its subjective nature. The reliance on human interpretation in qualitative analysis introduces the potential for biases to influence outcomes. Researchers must be vigilant in managing subjectivity and potential biases throughout the research process to ensure the credibility and reliability of their findings.

In real-world applications, qualitative research proves invaluable in various contexts, from product development to marketing strategies. By gaining a deep understanding of consumer perspectives and behaviors, businesses can tailor their offerings to meet customer needs more effectively. Qualitative research also plays a crucial role in shaping communication strategies and enhancing customer engagement by tapping into the emotional and psychological drivers behind consumer decisions.

While qualitative research may come with its challenges in terms of subjectivity and potential biases, its ability to provide rich, nuanced insights into consumer behavior makes it a valuable tool for any market researcher looking to gain a deeper understanding of their target audience.

Types of Qualitative Research 

When it comes to qualitative research, there are various types that provide unique insights into consumer behavior and preferences. 

Ethnographic Research: Involves immersing researchers in the environment or context of the subjects to observe their behaviors in real-life settings. This approach goes beyond simply asking questions, allowing researchers to uncover deeper insights into how consumers interact with products or services on a day-to-day basis.

In-depth Interviews: Enable researchers to explore participants' thoughts and motivations thoroughly by asking open-ended questions. Focus groups, another qualitative method, involve a small group discussing a topic, allowing researchers to observe group dynamics and shared perspectives.

Observational Research: the direct observation and recording of participant behavior in natural settings. This approach is particularly valuable for capturing nonverbal cues and subconscious behaviors that might not surface during standard interview formats. By focusing on body language, gestures, and interactions, researchers can gain profound insights into consumer preferences and the intricacies of decision-making processes.

Advantages of Qualitative Research

Qualitative research offers valuable insights into the emotional and psychological aspects of consumer decision-making, uncovering the reasons behind consumer behaviors.

Qualitative research offers flexibility and adaptability, allowing researchers to adjust approaches based on emerging insights. By directly engaging with participants, it uncovers hidden motivations and attitudes that may not be easily quantified, fostering a human connection for authentic data collection.

Qualitative data delves into intricate consumer emotions, brand perceptions, and societal influences on purchasing choices. It forms hypotheses tested later via quantitative research , enhancing insights into consumer behavior. Utilizing the qualitative aspect provides a profound grasp of intricacies determining consumer preferences and market trends.

What is Quantitative Market Research?

Quantitative methods play a vital role in market research by using surveys and questionnaires to gather structured data systematically. This approach provides measurable insights into consumer behavior, trends, and preferences, offering a comprehensive view of the market. A key benefit is the statistical analysis capability, allowing researchers to identify correlations and predictive patterns within large datasets. This analytical depth enables data-driven decision-making and strategic planning based on empirical evidence.

However, like any methodological approach, quantitative research does have its limitations. One notable constraint is the potential difficulty in capturing nuanced or complex phenomena that may be better understood through qualitative means. While quantitative research excels in quantifying data and providing statistical significance, it may sometimes overlook the underlying reasons or motivations driving consumer behavior. This limitation underscores the complementary role that qualitative research plays in offering deeper insights into the 'whys' behind the 'whats' uncovered through quantitative analysis.

Quantitative research is widely used in different industries for tasks like market segmentation , customer profiling, and product testing. It helps businesses evaluate market share, consumer preferences, and marketing campaign effectiveness with credibility and objectivity. Stakeholders can rely on the empirical evidence from quantitative research to make informed decisions for business growth and innovation.

Types of Quantitative Research

Quantitative research involves the collection and analysis of numerical data to understand patterns, correlations, and trends in a target audience or market. This method utilizes structured questionnaires, surveys, and experiments to gather information that can be statistically analyzed for meaningful insights. 

One common type of quantitative research is surveys, which provide a snapshot of the opinions, behaviors, and preferences of a large group of people. Another approach is experiments, where variables are manipulated to study cause-and-effect relationships within a controlled environment. These quantitative methods offer the advantage of producing numerical data that can be easily quantified and compared across different groups or time periods.

Advantages of Quantitative Research

Quantitative research offers a plethora of advantages that make it an indispensable tool in the realm of market analysis. 

Quantitative research excels in offering numerical data for statistical analysis, ensuring objective insights into market trends and consumer behavior through structured surveys and experiments.

Quantitative data is ideal for large sample sizes, providing generalizable insights applicable to broader populations. The scalability enhances reliability, revealing trends not obvious in smaller studies. In fast-paced markets, the quick data collection, analysis, and interpretation of quantitative research are advantageous for swift decision-making.

Quantitative research is crucial for marketers to measure variables and determine causal relationships, aiding in understanding consumer behavior and strategic decision-making. Techniques like regression analysis or correlation studies unveil hidden patterns, offering valuable market insights and ensuring businesses remain competitive.

Quantitative research not only provides analytical rigor and scalability but also allows for easy comparability and benchmarking. It quantifies data in numerical terms, facilitating comparisons of variables, tracking changes over time, and benchmarking against industry standards or competitors. This comparative nature empowers businesses to identify best practices, evaluate market positioning, and optimize strategies effectively.

Comparing Qualitative and Quantitative Approaches

Combining qualitative and quantitative research methods provides a comprehensive understanding of market dynamics. Triangulating data from both sources offers a well-rounded perspective. Qualitative methods like interviews reveal consumer motivations, while surveys give broader trends. Selecting between qualitative and quantitative research hinges on research objectives and the nature of questions. Qualitative research delves into complex phenomena, while quantitative uncovers patterns at scale. The choice depends on study goals and required information for decision-making.

Common misconceptions about qualitative research often revolve around its perceived subjectivity and lack of generalizability. However, when conducted rigorously, qualitative research can provide valuable insights into the underlying reasons behind consumer behaviors and preferences. On the other hand, quantitative research is sometimes criticized for being too rigid and detached from the human experience. In reality, quantitative methods can yield actionable data that reveal trends and correlations with a high degree of reliability.

Market research methodologies are evolving to prioritize a balance between qualitative depth and quantitative breadth. Combining qualitative richness with quantitative rigor provides a holistic view of consumer behavior, uncovering patterns and underlying motivations effectively. Emerging trends in market research incorporate advanced analytics such as predictive modeling and machine learning for faster, more precise decision-making. The future of market research lies in integrating qualitative and quantitative approaches, emphasizing innovation to navigate complexities and derive valuable strategic insights.

How to Choose the Right Method? Quantitative vs Qualitative Research 

When choosing between quantitative and qualitative market research, align the method with your research goals. Quantitative research gathers numerical data for statistical analysis, while qualitative research explores underlying reasons and opinions through interviews or observation.

The decision depends on research objectives. Quantitative research quantifies data for testing hypotheses and generalizing results, ideal for customer satisfaction or A/B testing . Conversely, qualitative research offers insights into behavior and preferences through open-ended questions and exploratory techniques like content analysis or ethnographic studies.

Consider timing and resources: quantitative research needs larger samples for statistical significance, while qualitative research is flexible with smaller samples. For quick feedback, opt for quantitative surveys or experimental studies.

Balancing detailed insights with ample data is crucial in research. A mixed-method approach often provides the most complete understanding. The key is to align your research method with your goals, resources, and desired insights to make informed decisions that enhance your marketing strategies and business outcomes.

Ashley Shedlock

10 articles

The views, opinions, data, and methodologies expressed above are those of the contributor(s) and do not necessarily reflect or represent the official policies, positions, or beliefs of Greenbook.

Comments are moderated to ensure respect towards the author and to prevent spam or self-promotion. Your comment may be edited, rejected, or approved based on these criteria. By commenting, you accept these terms and take responsibility for your contributions.

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  2. Quantitative vs Qualitative Research

    the main difference between qualitative and quantitative research

  3. The Difference Between Quantitative and Qualitative Research

    the main difference between qualitative and quantitative research

  4. Qualitative vs Quantitative Research: Differences and Examples

    the main difference between qualitative and quantitative research

  5. Qualitative vs Quantitative

    the main difference between qualitative and quantitative research

  6. Qualitative vs Quantitative Research: What's the Difference?

    the main difference between qualitative and quantitative research

COMMENTS

  1. Qualitative vs Quantitative Research: What's the Difference?

    The main difference between quantitative and qualitative research is the type of data they collect and analyze. Quantitative research collects numerical data and analyzes it using statistical methods. The aim is to produce objective, empirical data that can be measured and expressed in numerical terms.

  2. Qualitative vs. Quantitative Research

    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. Quantitative research. Quantitative research is expressed in numbers and graphs. It is used to test or confirm theories and assumptions.

  3. Difference Between Qualitative and Qualitative Research

    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.

  4. Qualitative vs Quantitative Research

    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 ...

  5. Qualitative vs. Quantitative Research: Comparing the Methods and

    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.

  6. Qualitative vs. quantitative research

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

  7. Qualitative and Quantitive Research: What's the Difference?

    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.

  8. Qualitative vs. Quantitative Research

    Qualitative research offers the advantage of generating detailed and nuanced data. It allows researchers to explore complex issues and gain a deeper understanding of participants' thoughts, emotions, and behaviors. However, qualitative research can be time-consuming, and data analysis may be subjective. In contrast, quantitative research ...

  9. Quantitative vs. Qualitative Research

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

  10. Qualitative vs Quantitative Research

    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. Quantitative research. Quantitative research is expressed in numbers and graphs. It is used to test or confirm theories and assumptions.

  11. Qualitative vs Quantitative

    Qualitative vs. Quantitative. While quantitative research is based on numbers and mathematical calculations (aka quantitative data ), qualitative research is based on written or spoken narratives (or qualitative data ). Qualitative and quantitative research techniques are used in marketing, sociology, psychology, public health and various other ...

  12. Qualitative vs. Quantitative Research: What's the Difference?

    Because qualitative and quantitative studies collect different types of data, their data collection methods differ considerably. Quantitative studies rely on numerical or measurable data. In contrast, qualitative studies rely on personal accounts or documents that illustrate in detail how people think or respond within society.

  13. A Practical Guide to Writing Quantitative and Qualitative Research

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

  14. Qualitative or Quantitative Research?

    Qualitative research is an umbrella phrase that describes many research methodologies (e.g., ethnography, grounded theory, phenomenology, interpretive description), which draw on data collection techniques such as interviews and observations. A common way of differentiating Qualitative from Quantitative research is by looking at the goals and processes of each. The following table divides ...

  15. SU Library: Qualitative vs. Quantitative Research: Overview

    In general, quantitative research seeks to understand the causal or correlational relationship between variables through testing hypotheses, whereas qualitative research seeks to understand a phenomenon within a real-world context through the use of interviews and observation. Both types of research are valid, and certain research topics are better suited to one approach or the other.

  16. Difference Between Qualitative and Quantitative Research

    The qualitative research follows a subjective approach as the researcher is intimately involved, whereas the approach of quantitative research is objective, as the researcher is uninvolved and attempts to precise the observations and analysis on the topic to answer the inquiry. Qualitative research is exploratory.

  17. Guide to qualitative vs. quantitative research

    It can explain the "what" as outlined in quantitative data, helping you to troubleshoot issues and create new ideas for research. Qualitative data is also flexible and represents your audience's views authentically. It's descriptive, which helps you understand context more fully. The downside of qualitative data - as most qualitative ...

  18. What's the difference between quantitative and qualitative ...

    Convergent parallel: Quantitative and qualitative data are collected at the same time and analyzed separately. After both analyses are complete, compare your results to draw overall conclusions. Embedded: Quantitative and qualitative data are collected at the same time, but within a larger quantitative or qualitative design. One type of data is ...

  19. Qualitative vs Quantitative Research: When to Use Each

    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 ...

  20. Qualitative vs. Quantitative: Key Differences in Research Types

    This method, known as mixed methods research, offers several benefits, including: A comprehensive understanding: Integration of qualitative and quantitative data provides a more comprehensive understanding of the research problem. Qualitative data helps explain the context and nuances, while quantitative data offers statistical generalizability.

  21. Qualitative vs. quantitative research: A simple guide

    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.

  22. Qualitative Vs Quantitative

    Quantitative and Qualitative Research Guide Quantitative and Qualitative Research Click here for more information on the differences between Qualitative and Quantitative Research.

  23. Difference Between Qualitative and Quantitative Research

    Quantitative research is useful in order to gain an understanding of the underlying opinions, motivations, and reasons. It gives insights into the problems. Also, quantitative research helps to develop ideas and hypotheses, whereas qualitative research is useful in uncovering trends, ideas and opinions, and gives deeper insights into the problem.

  24. Qualitative vs. Quantitative Market Research: Why Not Both?

    Combining qualitative and quantitative research methods provides a comprehensive understanding of market dynamics. Triangulating data from both sources offers a well-rounded perspective. Qualitative methods like interviews reveal consumer motivations, while surveys give broader trends. Selecting between qualitative and quantitative research ...