qualitative and quantitative research benefits

Qualitative vs Quantitative Research Methods & Data Analysis

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

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

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

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What Is Qualitative Research?

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

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

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

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

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

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

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

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

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

Qualitative Methods

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

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

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

Here are some examples of qualitative data:

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

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

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

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

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

Qualitative Data Analysis

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

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

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

Key Features

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

Limitations of Qualitative Research

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

Advantages of Qualitative Research

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

What Is Quantitative Research?

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

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

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

Quantitative Methods

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

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

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

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

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

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

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

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

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

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

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

Quantitative Data Analysis

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

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

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

Limitations of Quantitative Research

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

Advantages of Quantitative Research

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

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

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

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

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

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

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

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

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

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

Further Information

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

• Norsk bokmål

5 Reasons To Combine Qualitative And Quantitative Research

Qualitative  and  quantitative  market research approaches are designed to give you very different perspectives, even if you are using them with the same audience. Qualitative research gives you rich, detailed, and often emotionally driven insights based on the personal views of those you interview – for example, what do people feel about your product? In contrast, quantitative surveys give you a broader, full view, based on hard statistics – i.e. what % of people like or dislike your product?

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Advantages of using both qualitative and quantitative research methods

Given that both qualitative and quantitative market research provides vital ingredients of the understanding you are looking for (the Why and the What), combining them should deliver significant benefits, enabling you to compare and contrast results and gain much deeper insights.

However, traditionally this hasn’t been achievable due to deeply rooted industry perceptions of the different purposes of each methodology. Firstly, qualitative and quantitative methods are often seen as providing opposing viewpoints, with the former a more open style, based on the power of human interaction, and the latter more closed and metrics-driven. This meant they were seen as requiring different skill sets and to meet different needs, leading to specialism in one or the other. This, in turn, meant gaining a combined and universal view proved to be complex and costly.

Thanks to recent advances in market research technology, in many cases these challenges can now be overcome. Here are five ways that using qualitative and quantitative research together delivers real benefits:

1. The power of online research

Previously all qualitative research had to be carried out face-to-face through focus groups. The growth of digital channels provides new and more accessible ways of gauging qualitative insights, such as through online communities or online focus groups where you can share and show information, stimulus, and materials with your audience. Well-designed online communities allow you to collect quantitative data through quick polls and surveys , from the same audience, in a unified way.

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2. How to gain a holistic picture

Bringing qualitative and quantitative market research together through one, unified online platform enables you to gain a holistic picture. It means you can have a multi-stage discussion where you can validate a hypothesis, gain an understanding of it (through qualitative research) then widen your scope to get statistical data (quantitative), before testing a solution through further qualitative exploration. This creates a virtuous circle – all in the same system and with the same audience.

3. The benefits of combining the Why and the What

You don’t need to run a multi-stage process to gain value from combining qualitative and quantitative market research. You can simply use the two methodologies together to gain deeper insight into particular questions. For example, recent research found that  83% of e-commerce shoppers add products to their online carts , but don’t then finalize the transaction, costing businesses millions in lost revenue. Rather than merely quantifying the challenge, retailers could add a qualitative dimension to find out the precise reasons why a shopper didn’t buy, giving a much richer and more directly actionable insight into consumer behavior.

4. How automation drives agility

Previously a large part of the complexity of bringing qualitative and quantitative research together was that they used different systems to collect, analyze and report results. Put very simply, budgets didn’t stretch to both so you had to choose. Technology can now provide a single platform that handles both, lowering time to results through automation and reducing cost, complexity, and resources. Qualitative research was previously very time-consuming and labor-intensive – online qualitative projects are considerably faster.

5. Connect more deeply with your audience

At a time of growing competition, brands realize that they need to build strong connections to their customers if they are to retain their loyalty. This means creating a deeper understanding based on empathy, and combining qualitative and quantitative market research enables you to build a more human, emotional connection to your audience, but also take direct action to address their needs.

When is qualitative or quantitative research used?

In the past combining qualitative and quantitative market research approaches was both difficult and costly. And while there are still some areas where face-to-face qualitative research is the only answer, more and more business challenges can benefit from bringing the two methodologies together through online platforms that deliver the What and the Why together in one place. Through this, you can uncover the full story, provide deeper insights, build a narrative around customer needs, ultimately driving better, more informed decision-making.

Want to learn how you can benefit from combining qualitative and quantitative market research? Find out more on our market research page .

If you’d like to  sign up for a free trial with our market research tool or know more about how to create effective surveys  click here.  We’d love to hear from you!

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Qualitative vs Quantitative Research: Differences, Examples, and Methods

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

Table of Contents

Qualitative v s Quantitative Research  

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

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

What Are the Differences Between Qualitative and Quantitative Research  

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

Data Collection Methods  

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

Qualitative Research Data Collection  

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

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

Quantitative Research Data Collection  

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

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

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

Qualitative vs Quantitative Research Outcomes  

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

When to Use Qualitative vs Quantitative Research Approach  

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

Qualitative research approach  

Qualitative research approach is used under following scenarios:   

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

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

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

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

Quantitative research approach  

Quantitative research approach is used under following scenarios:   

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

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

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

Mixed methods approach  

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

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

How to Analyze Qualitative and Quantitative Data  

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

Analyzing qualitative data  

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

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

Analyzing quantitative data  

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

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

Benefits and limitations of qualitative vs quantitative research  

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

Benefits of qualitative research  

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

Limitations of qualitative research  

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

Benefits of quantitative research  

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

Limitations of quantitative research  

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

Frequently asked questions  

• What is the difference between qualitative and quantitative research? 

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

• What are the types of qualitative research? 

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

• What are the types of quantitative research? 

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

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

Qualitative Research Example: 

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

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

Quantitative Research Example: 

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

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

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Posted January 26, 2022 | Reviewed by Davia Sills

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

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

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

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

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

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

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

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

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

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

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

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

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

What's the point of this argument?

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

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

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

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

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• Qualitative vs Quantitative Research: When to Use Each

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

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

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

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

Let’s get started.

Table of Contents

What is user research.

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

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

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

Qualitative User Research

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

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

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

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

Key characteristics of qualitative research include:

Asking open-ended questions – 

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

Small but focused sample sizes – 

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

Naturalistic observations – 

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

Immersive techniques – 

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

Common Qualitative Research Methods

1. one-on-one interviews.

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

One-on-one interviews shine when:

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

Effective one-on-one interview tips include:

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

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

2. Focus Groups

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

Focus groups are advantageous when:

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

Tips for productive focus groups include:

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

3. User Diaries

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

Diary studies are advantageous when:

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

Tips for productive diary studies include:

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

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

4. Ethnographic Studies

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

Ethnographies excel when:

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

Tips for effective ethnographies:

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

5. User Testing

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

User testing shines when:

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

Tips for effective user testing:

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

6. Think-Aloud-Protocol

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

Think-aloud testing is ideal when:

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

Effective think-aloud tips include:

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

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

Applications Of Qualitative Research

Early product development stages:.

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

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

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

Understanding user needs:

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

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

Problem identification:

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

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

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

Understanding context of use:

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

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

Benefits Of Qualitative Research

Gaining deep insights:.

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

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

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

Understanding user emotions:

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

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

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

Exploring new ideas:

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

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

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

Uncovering underlying reasons:

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

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

Facilitating empathy:

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

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

Quantitative User Research

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

Key characteristics of quantitative research include:

Structured methodology: 

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

Numerical and statistical analysis: 

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

Large representative samples: 

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

Rating scales: 

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

Objectivity : 

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

Common Quantitative Research Methods

1. online surveys.

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

Online surveys are ideal when:

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

Effective online survey tips:

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

2. Usability Benchmarking

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

Usability benchmarking is ideal when:

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

Effective usability benchmarking tips:

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

3. Analytics

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

Analytics excel when:

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

Effective analytics tips:

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

Applications of Quantitative Research

Validating hypotheses:.

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

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

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

Generalizing findings:

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

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

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

Tracking granular changes:

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

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

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

Quantifying problem severity:

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

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

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

Benefits of Quantitative Research

Quantifying user behaviours:.

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

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

Validating hypotheses rigorously:

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

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

Precisely tracking granular trends:

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

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

Informed decision-making:

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

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

Mitigating biases:

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

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

Comparing Qualitative and Quantitative User Research

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

When to Use Each Method

When to use qualitative research:.

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

When to use quantitative research:

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

Frequently Asked Questions (FAQs)

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

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

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

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

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

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

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

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

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

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

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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

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

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

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

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• Knowledge Base

Methodology

• Mixed Methods Research | Definition, Guide & Examples

Mixed Methods Research | Definition, Guide & Examples

Published on August 13, 2021 by Tegan George . Revised on June 22, 2023.

Mixed methods research combines elements of quantitative research and qualitative research in order to answer your research question . Mixed methods can help you gain a more complete picture than a standalone quantitative or qualitative study, as it integrates benefits of both methods.

Mixed methods research is often used in the behavioral, health, and social sciences, especially in multidisciplinary settings and complex situational or societal research.

• To what extent does the frequency of traffic accidents ( quantitative ) reflect cyclist perceptions of road safety ( qualitative ) in Amsterdam?
• How do student perceptions of their school environment ( qualitative ) relate to differences in test scores ( quantitative ) ?
• How do interviews about job satisfaction at Company X ( qualitative ) help explain year-over-year sales performance and other KPIs ( quantitative ) ?
• How can voter and non-voter beliefs about democracy ( qualitative ) help explain election turnout patterns ( quantitative ) in Town X?
• How do average hospital salary measurements over time (quantitative) help to explain nurse testimonials about job satisfaction (qualitative) ?

Table of contents

When to use mixed methods research, mixed methods research designs, advantages of mixed methods research, disadvantages of mixed methods research, other interesting articles, frequently asked questions.

Mixed methods research may be the right choice if your research process suggests that quantitative or qualitative data alone will not sufficiently answer your research question. There are several common reasons for using mixed methods research:

• Generalizability : Qualitative research usually has a smaller sample size , and thus is not generalizable. In mixed methods research, this comparative weakness is mitigated by the comparative strength of “large N,” externally valid quantitative research.
• Contextualization: Mixing methods allows you to put findings in context and add richer detail to your conclusions. Using qualitative data to illustrate quantitative findings can help “put meat on the bones” of your analysis.
• Credibility: Using different methods to collect data on the same subject can make your results more credible. If the qualitative and quantitative data converge, this strengthens the validity of your conclusions. This process is called triangulation .

As you formulate your research question , try to directly address how qualitative and quantitative methods will be combined in your study. If your research question can be sufficiently answered via standalone quantitative or qualitative analysis, a mixed methods approach may not be the right fit.

But mixed methods might be a good choice if you want to meaningfully integrate both of these questions in one research study.

Keep in mind that mixed methods research doesn’t just mean collecting both types of data; you need to carefully consider the relationship between the two and how you’ll integrate them into coherent conclusions.

Mixed methods can be very challenging to put into practice, and comes with the same risk of research biases as standalone studies, so it’s a less common choice than standalone qualitative or qualitative research.

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There are different types of mixed methods research designs . The differences between them relate to the aim of the research, the timing of the data collection , and the importance given to each data type.

As you design your mixed methods study, also keep in mind:

• Your research approach ( inductive vs deductive )
• Your research questions
• What kind of data is already available for you to use
• What kind of data you’re able to collect yourself.

Here are a few of the most common mixed methods designs.

Convergent parallel

In a convergent parallel design, you collect quantitative and qualitative data at the same time and analyze them separately. After both analyses are complete, compare your results to draw overall conclusions.

• On the qualitative side, you analyze cyclist complaints via the city’s database and on social media to find out which areas are perceived as dangerous and why.
• On the quantitative side, you analyze accident reports in the city’s database to find out how frequently accidents occur in different areas of the city.

In an embedded design, you collect and analyze both types of data at the same time, but within a larger quantitative or qualitative design. One type of data is secondary to the other.

This is a good approach to take if you have limited time or resources. You can use an embedded design to strengthen or supplement your conclusions from the primary type of research design.

Explanatory sequential

In an explanatory sequential design, your quantitative data collection and analysis occurs first, followed by qualitative data collection and analysis.

You should use this design if you think your qualitative data will explain and contextualize your quantitative findings.

Exploratory sequential

In an exploratory sequential design, qualitative data collection and analysis occurs first, followed by quantitative data collection and analysis.

You can use this design to first explore initial questions and develop hypotheses . Then you can use the quantitative data to test or confirm your qualitative findings.

“Best of both worlds” analysis

Combining the two types of data means you benefit from both the detailed, contextualized insights of qualitative data and the generalizable , externally valid insights of quantitative data. The strengths of one type of data often mitigate the weaknesses of the other.

For example, solely quantitative studies often struggle to incorporate the lived experiences of your participants, so adding qualitative data deepens and enriches your quantitative results.

Solely qualitative studies are often not very generalizable, only reflecting the experiences of your participants, so adding quantitative data can validate your qualitative findings.

Method flexibility

Mixed methods are less tied to disciplines and established research paradigms. They offer more flexibility in designing your research, allowing you to combine aspects of different types of studies to distill the most informative results.

Mixed methods research can also combine theory generation and hypothesis testing within a single study, which is unusual for standalone qualitative or quantitative studies.

Mixed methods research is very labor-intensive. Collecting, analyzing, and synthesizing two types of data into one research product takes a lot of time and effort, and often involves interdisciplinary teams of researchers rather than individuals. For this reason, mixed methods research has the potential to cost much more than standalone studies.

Differing or conflicting results

If your analysis yields conflicting results, it can be very challenging to know how to interpret them in a mixed methods study. If the quantitative and qualitative results do not agree or you are concerned you may have confounding variables , it can be unclear how to proceed.

Due to the fact that quantitative and qualitative data take two vastly different forms, it can also be difficult to find ways to systematically compare the results, putting your data at risk for bias in the interpretation stage.

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

• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Non-probability sampling
• Quantitative research
• Inclusion and exclusion criteria

Research bias

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

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

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

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

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.

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.

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.

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Qualitative Research: Goals, Methods & Benefits

By Jim Frost 5 Comments

Qualitative research aims to understand ideas, experiences, and opinions using non-numeric data, such as text, audio, and visual recordings. The focus is on language, behaviors, and social structures. Qualitative researchers want to present personal experiences and produce narrative stories that use natural language to provide meaningful answers to their research questions.

Qualitative research focuses on descriptions, opinions, and experiences rather than numbers. Standard data collection techniques include interviews, diaries, focus groups, documents, artifacts, and direct observations.

Qualitative research provides a sharp contrast to quantitative research, which uses numeric data and statistical analyses to understand a concrete reality. The vast majority of content on my website is about quantitative research and statistical analyses. However, there are areas where qualitative research is more effective at understanding dynamic social structures and subjective perceptions in a real-world that can be convoluted.

Psychologists created qualitative research because the traditional methods failed to understand the human experience. Consequently, they developed a naturalistic approach that focuses on human behavior, what gives people meaning, how they perceive things, and why they act in a particular manner. This process involves understanding the people in their natural settings and social interactions.

Psychology, sociology, anthropology, education, and history frequently use qualitative research. Marketing groups also use it to understand how real people use their products, what factors increase usage, and obstacles that reduce usage. Ultimately, they want to market their products better, which requires understanding consumer mindsets.

Examples of Qualitative Research Questions

Qualitative research can answer a wide range of questions. Below are six example research questions.

• What factors shape body image?
• How do single-parent homes affect children?
• What challenges do consumers face in adopting a company’s new product?
• How does social media affect anxiety?
• What effect does previous domestic violence have on current relationships?
• What are the unique problems that night shift workers face?

Learn how to create research questions for scientific studies .

Qualitative Research Methods

Ethnography

The researchers embed themselves in the daily lives of their subjects and their social groups. Their goal is to understand their habits, routines, beliefs, and challenges.

For an excellent guide to observing participants in the field, read Qualitative Research Methods: A Data Collector’s Field Guide [external PDF].

Narrative Research

An alternative qualitative approach is to interview several subjects in-depth, gather documents, and collect artifacts. The researchers then piece these multiple lines of evidence together to create a narrative that answers the research question.

Phenomenology

Qualitative researchers can study an event as it happens from different vantage points. For instance, they can conduct interviews, record videos, and directly observe the proceedings to understand the participants’ subjective experiences.

Grounded Theory

This form of qualitative research differs from most other methods. The researchers start with a qualitative dataset and then sort through these data, tagging concepts and ideas. As the study continues, they organize and group the conceptual tags. During this process, the researchers watch for hypotheses to emerge. This method seeks to let the scientists organically react to the dataset but yet ground the results in as much empirical data as possible.

Case Studies

A case study usually examines one subject in great detail. The subject can be a person, business, or other organization. The goal is to understand the subject as much as possible and use that information to understand the larger population to some extent. This qualitative research method can foster understanding of the motivations, influences, and factors that lead to success or failure. Learn more about What is a Case Study? Definition & Examples .

Qualitative Research Data Collection Methods

Below are the standard data collection methods for qualitative research. Studies can combine multiple methods.

• Secondary research : Use existing documents, photographs, audio, and video.
• Interviews : One-on-one guided conversations.
• Direct observations : Researchers observe the subjects in the field and take notes.
• Questionnaires : Qualitative research frequently uses surveys with open-ended questions.
• Focus groups : A guided small group conversation where the discussion provides the data.

Analyzing Qualitative Data

After collecting their data, qualitative researchers have multiple ways to analyze the content. A common approach is to add codes that represent meaningful ideas to communications, documents, videos, etc. The researchers evaluate frequencies and patterns of these conceptual codes. They can also find the most common words, thematic patterns, communications structure, and the method by which communications obtain specific goals. Analysts refer to these approaches with names such as content analysis, thematic analysis, textual analysis, etc.

Advantages and Disadvantages of Qualitative Research

Qualitative research has many advantages because it seeks to record the subjects’ lived experiences and understand them in ways that quantitative data cannot. Going beyond just the numbers, they can gain insights into opinions, emotions, and perceptions. These studies frequently occur in natural environments and real-world social contexts rather than labs and other artificial environments that might affect the participants, particularly when talking about personal matters.

Unlike quantitative research, qualitative methods are flexible. Researchers can change their methodology and theories as they gather information. The open-ended nature of qualitative research allows the researchers to uncover new ideas they hadn’t anticipated and adjust accordingly.

However, qualitative research has some disadvantages.

Its primary disadvantage is that it is more subjective than quantitative research. It’s harder to separate the researchers’ opinions and predilections from the more personal nature of qualitative data. Determining what concepts to code and when to apply those codes can be highly subjective. Flexibly adapting the research on the fly can be great, but it also increases the prominence of the researcher’s personal determination of relevance.

Furthermore, consider how ordinary people can observe the same reality in all its real-world messiness and draw different conclusions. Similarly, qualitative researchers can evaluate the same real-world data and produce dissimilar findings.

Qualitative research typically uses small samples that are less likely to be representative , which limits generalizability . Finally, as with other types of observational studies , the real-world settings in qualitative research can be an advantage, but they potentially introduce a host of confounding variables that can bias the results.

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Reader Interactions

August 1, 2023 at 10:42 am

If qualitative data is counted in categorical, ordinal, or binary forms does it become quantitative data?

January 2, 2023 at 11:27 am

Who are the actual people at the foundations of qualitative research as we know it? We know they are generally psychologists, like creswell who seems to have updated a but for the modern era, but who stands out the most in research throughout the age of qualitative research?

November 22, 2022 at 11:04 am

Have you publish on qualitative methods and surveys?

November 22, 2022 at 4:19 pm

I haven’t as of yet. Probably down the road, particularly for surveys.

April 23, 2022 at 2:16 pm

Can regression results from another study be used for my data collection, as a form of secondary data? I believe that the regression results are important to my study, but I don’t know if “results” from another study, specifically taken from their appendix table can be pasted into my “data collection section” of my research paper. I wish to employ a grounded theory research methodology that is mixed methods in approach, because I can apply regression analysis to the regression results, but I question the possibility of doing this for my data collection section.

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How to use qualitative and quantitative research to your advantage

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Qualitative and quantitative research are market research methods that are widely used by market research companies and consumer research platforms . They act as powerful sources of insight for marketers, storytellers, journalists, psychologists, economists, brand managers, social scientists – the list goes on.

What is quantitative research?

Quantitative research is a numbers thing. It gives you an idea of how many people think, feel, or behave in a certain way. You tend to be dealing with a large sample here – one that more accurately represents a wider group.

Quantitative research calls on surveys or analytics to quantify consumer behaviors, perceptions, attitudes, and interests, giving you the hard numbers you need to back your ideas .

Here’s an example: Logging into TikTok every day has increased by 54% outside China since the end of 2020.

What is qualitative research?

Generally speaking, qualitative research explores what people think, feel, and do. It’s non-numerical, which means your insights will consist of words and stories, like people talking about their experiences and sharing their opinions. 

You tend to be dealing with a small sample here. Qualitative research is usually gathered from sources such as one-on-one interviews, focus groups, and discussion forums.

This is great for generating first-hand insight, like uncovering a customer’s perception of your value proposition, or how their attitudes are changing.

Here’s an example: Consumers feel that treating themselves and indulging has become more important.

If quantitative research is the outline of a picture, qualitative research colors it in.

Qualitative research vs. quantitative research: how do they fare?

Both research methods have pros and cons, and depending on what type of data you’re after, one will be better suited.

The benefits of qualitative research

• Get depth and detail: A qualitative research method helps you analyze thoughts, feelings, and behaviors. In doing so, it lets you explore the ‘why’ behind things. This is immensely valuable when it comes to understanding what motivates consumers – and in turn, what drives their behavior.
• You can encourage discussion: The joy of qualitative data is that it allows people to expand on the ins and outs of how they’re feeling. Often, these discussions can introduce new topic areas you didn’t originally think of, providing deeper insight.
• You can stay flexible: On the back of the above point, using a qualitative method lets you adapt your questions in real-time, depending on the information you’re gathering. If you’ve scratched the surface of something interesting, you can dig a little deeper. And if it’s not hitting the mark, you can shift the focus of the question.

The drawbacks of qualitative research

• You’re dealing with small sample sizes: Qualitative analysis tends to be more in-depth, which is great, but it’s more time-consuming as a result. And because it’s resource-intensive, the number of people you can actually speak to is limited. Chances are, you won’t survey as many people as you’d like to.
• It’s harder to generalize the results: With any qualitative study, because you’re dealing with a small pool of opinions, you can’t accurately say the views you gathered represent the views of a wider population.
• You need a skilled qualitative researcher: There are so many ways to accidentally influence the responses you get from a qualitative survey – your tone of voice, your rapport with the people you’re speaking to, and even the order in which you ask the questions. Unfortunately, the quality of the responses you get is largely based on how well the researchers conduct the interviews or focus groups.
• There’s no anonymity: Let’s face it, not everybody is comfortable talking about everything to everyone all the time. There are some topics that people will shy away from – especially in a one-on-one session or a discussion group full of strangers. If so, they’re likely to conceal their full answers if they’re feeling shy or judged, which will skew the results of your study. Some people might only be willing to do an anonymous quantitative study.

The benefits of quantitative research

• You get your hands on a larger sample: With a quantitative survey, a much broader study can be done – one which involves more people. Naturally, you’ll be able to more accurately generalize your results across an even wider group of people.
• You get objectivity and accuracy: There are far fewer variables involved with quantitative research. The data you’re collecting is often ‘close-ended’, which means people are choosing clear-cut multiple choice answers, such as yes/no, or Instagram/Facebook/TikTok. And when it comes to diving into the results, there’s no room for debate. A certain number of people do one thing, and a certain number of people do another.
• It’s faster and easier: With quantitative data collection, you can step into the world of automation. You don’t need a physical researcher to help – you simply opt for digital or mobile surveys. These can conduct thousands of interviews at the same time across multiple countries.
• You can save money: Because they’re quicker to run, quantitative methods are famously cost-effective. That’s why the cost of someone participating in a quantitative survey is typically far less than the price of a focus group. And you just need skilled researchers to write the survey, rather than conduct it.

The drawbacks of quantitative research:

• You get a less detailed picture: With this research method, results are based on numerical responses and, as a result, you get slightly less insight into the thoughts, motivations, and drivers of your group. You’re lacking a key component: context. To get around this, you can include ‘open-end’ answers, which allow a participant to write down more detailed responses rather than just ticking a box. But doing so relies on respondents having the time and truly understanding the question.
• It’s somewhat artificial: Quantitative research needs to be carried out in an unnatural environment so that it can be controlled. And while this is important, it means the results you gather might differ from ‘real world’ findings.
• You’re faced with limitations: A quantitative method needs to have pre-set answers, and sometimes, how a participant thinks, feels, or behaves might not be featured in the list. Their true answer is masked behind your lack of options, and it might push them to pick one that doesn’t really reflect how they feel.

Get the best of both worlds

Both approaches have strengths and weaknesses. By combining the two together (which is often referred to as mixed method research), you can seriously boost the quality and accuracy of your findings, adding both breadth and depth.

The advantages of mixed method research

• Enrich your story: You can use qualitative data to color the insights that were revealed in your quantitative survey.
• Examine your narrative: You can generate hypotheses from the opinions uncovered in qualitative research, then cross-reference these against a wider sample with a quantitative approach.
• Explain the surprises: You can use qualitative data to better understand any unexpected results from quantitative data.

How a combined approach can generate a results-driven campaign

Combining both data methods in a way that yields awesome results requires planning.

Like any successful data analysis, finding the right answers relies on asking the right questions.

And in order to ask the right questions, you need to identify your key goals – mapping out exactly what you want to achieve.

For example, companies looking to drive campaigns focused on ROI can use quantitative tracking tools like Google Analytics, Data Studio, or Power BI. If set up correctly, you can quickly uncover key performance indicators like website visits, time on page, traffic from social media, number of leads, and even revenue.

Pairing this with some qualitative information on how your customers feel about your brand – through questionnaires, reviews, case studies, or customer interviews will give you a detailed picture of what you need to know.

This kind of intelligence enables brands to gain a deep understanding of how well their campaigns are working and, critically, why.

Using qualitative analysis to streamline the user journey

Research can answer strategic business questions – but to do that well, you need to interrogate the information and gather the most actionable insight.

Qualitative analytics can give your brand answers around why a customer bought a certain product or service and what their end-to-end experience was like.

These findings provide clear data that can be actioned, enabling brands to do more of what’s working and address any kinks in the user journey.

Plus, qualitative proof like custome r reviews can help you drive more conversions. In fact:

99.9% of consumers read reviews, and 98% consider them an essential step on the consumer path to purchase.

So not only can qualitative research help you configure things behind the scenes, but it can also help you make more money. (As long as people are saying nice things about your brand.)

Using quantitative analysis to fix what’s broken

Quantitative analytics, on the other hand, can provide specific answers relating to how the purchase journey looks, enabling brands to spot any areas that are causing issues on touchpoints that matter.

For example, if a high percentage of buyers are dropping off on a certain page, or abandoning their basket at the same spot, marketers can address this pretty quickly, either by redesigning the page or making the transaction process faster.

Combining qualitative and quantitative research to make the magic happen

While quantitative data might flag issues around basket abandonment, ecommerce brands may still be unsure as to why consumers are dropping off.

Is the page a bit sluggish? Are the payment options confusing? Or is poor page design making the CTA hard to find?

Combining the hard numbers with the ‘why’ gives brands a clear idea of where the problems lie and how best to fix them.

Deeper insight gives a competitive edge

Combined research can be used in numerous ways depending on a brand’s business objectives.

For example, data might reveal that over-70s with disposable income and an interest in technology would buy more devices if the product designs accounted for failing eyesight and inhibited manual dexterity.

These sorts of insights could open up a whole new audience – and product category – giving brands more of a competitive edge.

Meeting the personalized future

Qualitative and quantitative research methods have different roles to play. Using the two together can be a powerful move, especially as consumer demand for personalization continues to rise.

To meet this demand, more and more brands and marketers are turning to audience profiling data , analyzing audience behaviors and perceptions on a massive scale, to tailor their activity to their consumers.

By combining qualitative personas with quantitative data, you can identify and define your audiences in as much detail as possible, understanding how, where, and when to reach them for maximum impact.

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Advantages & Disadvantages of Qualitative & Quantitative Research

Qualitative and Quantitative Research Methods

Selecting the best research method allows you to successfully answer a research question or test a hypothesis. Missteps at the onset of the research process may derail an otherwise promising study. Knowing the advantages and disadvantages of quantitative and qualitative methods will help you make a better decision. Both methods are quite useful depending on the type of study. Some dissertations and research studies take a mixed method approach, which incorporates qualitative and quantitative methods in different phases to obtain a broader perspective.

Quantitative Advantages

You may be very familiar with quantitative research from your science classes where you learned and practiced using the scientific method. A problem or question is examined by deductively forming a hypothesis derived from theory. Controlled, objective testing and experimentation ultimately supports or rejects your hypotheses. Each step is standardized to reduce bias when collecting and analyzing data. A big advantage of this approach is that the results are valid, reliable and generalizable to a larger population. Quantitative research is advantageous for studies that involve numbers, such as measuring achievement gaps between different groups of students or assessing the effectiveness of a new blood pressure medication.

Quantitative Disadvantages

While quantitative research methods work well in the laboratory under tightly controlled conditions, measuring phenomena like human behavior in natural settings is trickier. Survey instruments are vulnerable to errors such as mistakes in measurement and flawed sampling techniques. Another disadvantage is that quantitative research involves numbers, but some topics are too difficult to quantify in numbers. For example, constructing an effective survey with closed-ended questions about how people fall in love would be difficult.

Qualitative Advantages

Qualitative research is often used to conduct social and behavioral studies because human interactions are more complex than molecular reactions in a beaker. Subjectivity, nonrandom sampling and small sample size distinguishes qualitative research from quantitative research. A big advantage of qualitative research is the ability to deeply probe and obtain rich descriptive data about social phenomena through structured interviews, cultural immersion, case studies and observation, for instance. Examples include ethnography, narratives and grounded theory.

Qualitative Disadvantages

Qualitative studies often take more time to complete due to the pain staking nature of gathering and analyzing field notes, transcribing interviews, identifying themes and studying photographs, for instance. Studies are not easily replicable or generalizable to the general population. Conscious or unconscious bias can influence the researcher’s conclusions. Lacking rigorous scientific controls and numerical data, qualitative findings may be dismissed by some researchers as anecdotal information.

Mixed Methods

A mixed method approach capitalizes on the advantages of the quantitative and qualitative methods while offsetting the drawbacks of each. For instance, a principal interested in building rapport with parents of school children might undertake a mixed method study. First, the principal would send out a school climate survey to parents asking them to rate their satisfaction with the school and quality of instruction. After analyzing the data, the principal would identify areas needing further exploration such as parent complaints about the school’s response to bullying incidents. Focus groups could then be organized to gather qualitative information from parents to better understand their concerns.

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• Temple University: Qualitative Research: Grounded Theory: Advantages and Disadvantages

Dr. Mary Dowd is a dean of students whose job includes student conduct, leading the behavioral consultation team, crisis response, retention and the working with the veterans resource center. She enjoys helping parents and students solve problems through advising, teaching and writing online articles that appear on many sites. Dr. Dowd also contributes to scholarly books and journal articles.

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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 .

Research questions in quantitative research

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

Hypotheses in quantitative research

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

Research questions in qualitative research

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

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

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

Hypotheses in qualitative research

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

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

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

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

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

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

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

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

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

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

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

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

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

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

EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

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

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

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

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

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

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

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

Author Contributions:

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

23 Advantages and Disadvantages of Qualitative Research

Investigating methodologies. Taking a closer look at ethnographic, anthropological, or naturalistic techniques. Data mining through observer recordings. This is what the world of qualitative research is all about. It is the comprehensive and complete data that is collected by having the courage to ask an open-ended question.

Print media has used the principles of qualitative research for generations. Now more industries are seeing the advantages that come from the extra data that is received by asking more than a “yes” or “no” question.

The advantages and disadvantages of qualitative research are quite unique. On one hand, you have the perspective of the data that is being collected. On the other hand, you have the techniques of the data collector and their own unique observations that can alter the information in subtle ways.

That’s why these key points are so important to consider.

What Are the Advantages of Qualitative Research?

1. Subject materials can be evaluated with greater detail. There are many time restrictions that are placed on research methods. The goal of a time restriction is to create a measurable outcome so that metrics can be in place. Qualitative research focuses less on the metrics of the data that is being collected and more on the subtleties of what can be found in that information. This allows for the data to have an enhanced level of detail to it, which can provide more opportunities to glean insights from it during examination.

2. Research frameworks can be fluid and based on incoming or available data. Many research opportunities must follow a specific pattern of questioning, data collection, and information reporting. Qualitative research offers a different approach. It can adapt to the quality of information that is being gathered. If the available data does not seem to be providing any results, the research can immediately shift gears and seek to gather data in a new direction. This offers more opportunities to gather important clues about any subject instead of being confined to a limited and often self-fulfilling perspective.

3. Qualitative research data is based on human experiences and observations. Humans have two very different operating systems. One is a subconscious method of operation, which is the fast and instinctual observations that are made when data is present. The other operating system is slower and more methodical, wanting to evaluate all sources of data before deciding. Many forms of research rely on the second operating system while ignoring the instinctual nature of the human mind. Qualitative research doesn’t ignore the gut instinct. It embraces it and the data that can be collected is often better for it.

4. Gathered data has a predictive quality to it. One of the common mistakes that occurs with qualitative research is an assumption that a personal perspective can be extrapolated into a group perspective. This is only possible when individuals grow up in similar circumstances, have similar perspectives about the world, and operate with similar goals. When these groups can be identified, however, the gathered individualistic data can have a predictive quality for those who are in a like-minded group. At the very least, the data has a predictive quality for the individual from whom it was gathered.

5. Qualitative research operates within structures that are fluid. Because the data being gathered through this type of research is based on observations and experiences, an experienced researcher can follow-up interesting answers with additional questions. Unlike other forms of research that require a specific framework with zero deviation, researchers can follow any data tangent which makes itself known and enhance the overall database of information that is being collected.

6. Data complexities can be incorporated into generated conclusions. Although our modern world tends to prefer statistics and verifiable facts, we cannot simply remove the human experience from the equation. Different people will have remarkably different perceptions about any statistic, fact, or event. This is because our unique experiences generate a different perspective of the data that we see. These complexities, when gathered into a singular database, can generate conclusions with more depth and accuracy, which benefits everyone.

7. Qualitative research is an open-ended process. When a researcher is properly prepared, the open-ended structures of qualitative research make it possible to get underneath superficial responses and rational thoughts to gather information from an individual’s emotional response. This is critically important to this form of researcher because it is an emotional response which often drives a person’s decisions or influences their behavior.

8. Creativity becomes a desirable quality within qualitative research. It can be difficult to analyze data that is obtained from individual sources because many people subconsciously answer in a way that they think someone wants. This desire to “please” another reduces the accuracy of the data and suppresses individual creativity. By embracing the qualitative research method, it becomes possible to encourage respondent creativity, allowing people to express themselves with authenticity. In return, the data collected becomes more accurate and can lead to predictable outcomes.

9. Qualitative research can create industry-specific insights. Brands and businesses today need to build relationships with their core demographics to survive. The terminology, vocabulary, and jargon that consumers use when looking at products or services is just as important as the reputation of the brand that is offering them. If consumers are receiving one context, but the intention of the brand is a different context, then the miscommunication can artificially restrict sales opportunities. Qualitative research gives brands access to these insights so they can accurately communicate their value propositions.

10. Smaller sample sizes are used in qualitative research, which can save on costs. Many qualitative research projects can be completed quickly and on a limited budget because they typically use smaller sample sizes that other research methods. This allows for faster results to be obtained so that projects can move forward with confidence that only good data is able to provide.

11. Qualitative research provides more content for creatives and marketing teams. When your job involves marketing, or creating new campaigns that target a specific demographic, then knowing what makes those people can be quite challenging. By going through the qualitative research approach, it becomes possible to congregate authentic ideas that can be used for marketing and other creative purposes. This makes communication between the two parties to be handled with more accuracy, leading to greater level of happiness for all parties involved.

12. Attitude explanations become possible with qualitative research. Consumer patterns can change on a dime sometimes, leaving a brand out in the cold as to what just happened. Qualitative research allows for a greater understanding of consumer attitudes, providing an explanation for events that occur outside of the predictive matrix that was developed through previous research. This allows the optimal brand/consumer relationship to be maintained.

What Are the Disadvantages of Qualitative Research?

1. The quality of the data gathered in qualitative research is highly subjective. This is where the personal nature of data gathering in qualitative research can also be a negative component of the process. What one researcher might feel is important and necessary to gather can be data that another researcher feels is pointless and won’t spend time pursuing it. Having individual perspectives and including instinctual decisions can lead to incredibly detailed data. It can also lead to data that is generalized or even inaccurate because of its reliance on researcher subjectivisms.

2. Data rigidity is more difficult to assess and demonstrate. Because individual perspectives are often the foundation of the data that is gathered in qualitative research, it is more difficult to prove that there is rigidity in the information that is collective. The human mind tends to remember things in the way it wants to remember them. That is why memories are often looked at fondly, even if the actual events that occurred may have been somewhat disturbing at the time. This innate desire to look at the good in things makes it difficult for researchers to demonstrate data validity.

3. Mining data gathered by qualitative research can be time consuming. The number of details that are often collected while performing qualitative research are often overwhelming. Sorting through that data to pull out the key points can be a time-consuming effort. It is also a subjective effort because what one researcher feels is important may not be pulled out by another researcher. Unless there are some standards in place that cannot be overridden, data mining through a massive number of details can almost be more trouble than it is worth in some instances.

4. Qualitative research creates findings that are valuable, but difficult to present. Presenting the findings which come out of qualitative research is a bit like listening to an interview on CNN. The interviewer will ask a question to the interviewee, but the goal is to receive an answer that will help present a database which presents a specific outcome to the viewer. The goal might be to have a viewer watch an interview and think, “That’s terrible. We need to pass a law to change that.” The subjective nature of the information, however, can cause the viewer to think, “That’s wonderful. Let’s keep things the way they are right now.” That is why findings from qualitative research are difficult to present. What a research gleans from the data can be very different from what an outside observer gleans from the data.

5. Data created through qualitative research is not always accepted. Because of the subjective nature of the data that is collected in qualitative research, findings are not always accepted by the scientific community. A second independent qualitative research effort which can produce similar findings is often necessary to begin the process of community acceptance.

6. Researcher influence can have a negative effect on the collected data. The quality of the data that is collected through qualitative research is highly dependent on the skills and observation of the researcher. If a researcher has a biased point of view, then their perspective will be included with the data collected and influence the outcome. There must be controls in place to help remove the potential for bias so the data collected can be reviewed with integrity. Otherwise, it would be possible for a researcher to make any claim and then use their bias through qualitative research to prove their point.

7. Replicating results can be very difficult with qualitative research. The scientific community wants to see results that can be verified and duplicated to accept research as factual. In the world of qualitative research, this can be very difficult to accomplish. Not only do you have the variability of researcher bias for which to account within the data, but there is also the informational bias that is built into the data itself from the provider. This means the scope of data gathering can be extremely limited, even if the structure of gathering information is fluid, because of each unique perspective.

8. Difficult decisions may require repetitive qualitative research periods. The smaller sample sizes of qualitative research may be an advantage, but they can also be a disadvantage for brands and businesses which are facing a difficult or potentially controversial decision. A small sample is not always representative of a larger population demographic, even if there are deep similarities with the individuals involve. This means a follow-up with a larger quantitative sample may be necessary so that data points can be tracked with more accuracy, allowing for a better overall decision to be made.

9. Unseen data can disappear during the qualitative research process. The amount of trust that is placed on the researcher to gather, and then draw together, the unseen data that is offered by a provider is enormous. The research is dependent upon the skill of the researcher being able to connect all the dots. If the researcher can do this, then the data can be meaningful and help brands and progress forward with their mission. If not, there is no way to alter course until after the first results are received. Then a new qualitative process must begin.

10. Researchers must have industry-related expertise. You can have an excellent researcher on-board for a project, but if they are not familiar with the subject matter, they will have a difficult time gathering accurate data. For qualitative research to be accurate, the interviewer involved must have specific skills, experiences, and expertise in the subject matter being studied. They must also be familiar with the material being evaluated and have the knowledge to interpret responses that are received. If any piece of this skill set is missing, the quality of the data being gathered can be open to interpretation.

11. Qualitative research is not statistically representative. The one disadvantage of qualitative research which is always present is its lack of statistical representation. It is a perspective-based method of research only, which means the responses given are not measured. Comparisons can be made and this can lead toward the duplication which may be required, but for the most part, quantitative data is required for circumstances which need statistical representation and that is not part of the qualitative research process.

The advantages and disadvantages of qualitative research make it possible to gather and analyze individualistic data on deeper levels. This makes it possible to gain new insights into consumer thoughts, demographic behavioral patterns, and emotional reasoning processes. When a research can connect the dots of each information point that is gathered, the information can lead to personalized experiences, better value in products and services, and ongoing brand development.

Learn More About:

• Customer Acquisition
• Optimization
• Customer Experience
• Data & Analytics

Quantitative vs. Qualitative Research: Learning The Benefits

Your comprehensive guide to discerning between quantitative vs. qualitative research practices and selecting the best approach for your business needs.

If you aren’t sure about when to use quantitative vs qualitative research, you aren’t alone. It’s tough to discern between the two and select the best approach for your business needs.

But in the same way your team performs best when each individual’s strengths are considered, quantitative and qualitative research methods perform best when leveraged according to their benefits.

Quantitative research examines hard data and is measured in numbers. You would likely use it for testing a hypothesis or measuring the effectiveness of a strategy. Qualitative research examines behavioral data and is measured non-numerically. You might implement it for the creation of a hypothesis or to inform the direction of an optimization strategy. 

In this article, we’ll answer the following questions:

• What is the difference between quantitative and qualitative research in CRO?
• When do I use qualitative research? When do I use quantitative research?
• What are examples of quantitative and qualitative data?
• What are the best quantitative and qualitative research methods?

Let’s dive in.

What’s the difference between quantitative and qualitative research?

Quantitative and qualitative user research describe two different types of data that can be used to improve website usability and conversion rates.

Here are practical definitions of the two:

1. Quantitative research is primarily concerned with the numbers.

It uses metrics like completion rates, bounce rates, and conversion rate to measure the effectiveness of your sales funnel.

Quantitative research answers this question: “How much of what is happening … and where?” This data is typically used to form and test a theory that it will ultimately support or reject. In conversion optimization, that’s usually best accomplished with A/B testing.

2. Qualitative research is primarily concerned with your website users.

It leverages special tools like heatmaps, clickmaps, and surveys to find out “Who is doing what … and why are they doing that?”

This data tells you what design features are difficult or easy to complete. 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.

The screenshot above is a heatmap. It shows you where visitor attention is most centered. You can use that information to optimize page layouts and guide visitors along your sales path .

Whereas quantitative data tells us that a specific number of visitors behaved in a particular way at a certain point along your pathway to sales, qualitative data provides clues about why they did so.

Both types of research are essential to a data-driven design cycle. Much of the time, though, companies collect quantitative data only.

That’s a mistake.

Qualitative data allows deeper insight and can help you quickly determine where your user experience is sagging. The more user-friendly you can make your path to sales, the more customers you’ll have walking it to the end over and over again.

Here’s a summary of some of the key differences:

How to apply quantitative and qualitative research: a step-by-step process

Digital marketing managers and conversion rate optimization specialists sometimes disagree over which type of research should be conducted first: quantitative or qualitative.

While the best strategy is always the one that fits your particular needs, the conversion rate optimization team at The Good recommend the following sequence in most cases:

• Check to ensure appropriate data collection tools are in place and set up correctly

Do an audit of your tool stack to make sure their respective data streams are flowing properly. In terms of website behavior, this means making sure Google Analytics is set up correctly and feeding real-time visitor data.

Sign in to Google Analytics, and then go into incognito mode on another browser to visit your website. If everything is fully operational, you should see yourself pop up on the real-time map.

• Begin by observing quantitative data to identify stuck points in your sales funnel

Look at the metrics within Google Analytics that reflect the different stages of your sales funnel. Analyze things like bounce rate and cart abandonment rate to search for evidence of bottlenecks in your funnel.

• Use qualitative tools and observations to find out why the bottlenecks exist

Tools like heatmaps and click maps allow you to gauge the effectiveness of your page content. Strategic user testing campaigns allow you to see sentiment trends regarding which content is driving action, and which content may actually be preventing action.

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• Propose solutions to the problems, activate those solutions (through A/B and multivariate testing), then observe results

Combine the insights from your quantitative and qualitative research to create a solution for any problem you have identified. Put those solutions in action and then compare their effectiveness against each other through A/B testing.

• Continue observing, constructing theories (hypotheses), and iterating on the testing to monitor and improve conversion rates across the board

Keep the iterate, test, analyze loop going until you reach your target conversion rate. It is rare that your first round of A/B testing will yield the ultimate increase in conversions. Once this process is implemented the first time, it should be perpetually running as a part of your overall growth strategy.

Tool selection can be tricky. Even if you’re the digital marketing manager of an enterprise-level business with in-house conversion optimization capabilities, you’ll probably save money by contracting with a conversion optimization partner to get started.

At The Good, we help businesses work through the development or fine-tuning of their conversion optimization strategy. This includes identifying and recommending necessary tools and helping secure the best price for those tools. 

Since conversion rate optimization is our business, we stay up to date on the latest developments and can help keep teams from getting mired down with the wrong tools and counterproductive methods.

Quantitative vs Qualitative Research Methods in CRO

It is nearly impossible for a CRO strategy to succeed if it is built using purely quantitative or qualitative research methods. Success hinges upon a mixed-method approach that maximizes the value of both quantitative and qualitative data to synthesize an effective CRO strategy.

We work through four key areas of CRO research using this mixed research methodology:

• Acquisition and behavior
• Content effectiveness
• Conversion effectiveness
• Iterative improvement

Acquisition and Behavior

Acquisition and behavior research examines where visitors come from and how they behave. It looks at quantitative data that shows which actions are being taken and quantifies the occurrences of that action over a given period of time.

Google Analytics dominates this stage of the game. The user data they give allows you to get a snapshot of how many users actually become customers, and what path of pages led the most of them over the line.

Content Effectiveness

Content effectiveness research looks at the value users assign to the words on a page, focusing on which words keep them engaged and which lead to an attention drop-off. Heatmaps rule this domain, and there are three types: click, scroll, and movement maps.

Click maps track where on the content the user is clicking during their session, and scroll maps measure how far along the page they scroll. Finally, movement maps track where the user’s cursor goes on a page. This is correlated with user eye movement.

Heatmaps help you give a comparative value to user behavior, which informs the design-based initiatives within a CRO strategy.

Conversion Effectiveness

Conversion effectiveness research analyzes the gaps between user desire and the fulfillment of that desire. This is the domain of usability testing , which also makes use of both qualitative and quantitative data.

There are several user testing platforms used to gather both types of data. This matrix below, created by Hotjar , displays these platforms based on two orientating factors. 

Along the x-axis is the level of moderation, which is reflective of the level of involvement from the researcher. An interview is on the extreme side of moderation, for example, because it cannot be done without human involvement.

Along the y-axis looks at whether the user testing platform requires in-person conditions to be valid or if it can be done remotely. Observation-based platforms have to be in-person, for example, because such qualitative data requires the behavior to be both natural and directly observable.

Iterative Improvements

Finally, iterative improvements focus on how the learnings from the other three areas can be tested on and improved. A/B testing rules this area of research.

This is the area where design-based and copy-based CRO techniques merge into one cohesive CRO strategy that is designed to perpetually measure and analyze user behavior.

The key to executing such a mixed-method strategy is to know when to use quantitative research and when to use qualitative research.

When should you use quantitative vs. qualitative research?

I’ve touched on this in the process description above. Let’s go a little deeper, though, to make sure you and your team are clear on when to use quantitative research and when to use qualitative research.

Quantitative Research

Conversion rate optimization isn’t a one-stop fix for sales. It’s a series of events and actions occurring between the time a prospect first becomes aware of your company and the point where that prospect makes a purchase.

That means there isn’t just one conversion rate to consider, there are many. Here are some of the most common data collection points in conversion rate optimization that can help you identify trouble spots in your sales path:

• Bounce rate: Once a visitor arrives on your website, how long does that visitor stay?
• Opt-in rate: Of the visitors given an opportunity to subscribe to your email list, how many respond to the offer?
• Search rate: How many visitors take advantage of your on-site search engine?
• Click-through to a product detail page: How many of those who visit a category page go on to view a product detail page?
• Cart abandonment rate : How many visitors enter the checkout process but fail to place an order?

That list could be extended to point out dozens of potential ‘micro conversions’ your visitors can take on the way to becoming paying customers.

Website designers often focus on making your site pleasing to the eye. Your visitors care more about usability. (see graph above)

You can compare quantitative to driving on a highway. You’re clipping along at good speed when you come to a spot where traffic stopped completely. Is there an accident ahead? Construction? What’s the problem? Cars begin pulling U-turns and looking for a different route.

When visitors to your website are confronted by confusing or irrelevant sections of your path to the sale, they’ll bail out too. If you can uncover and fix those trouble spots, you’ll keep visitors happier and see sales revenue grow.

Qualitative Research

Once you’ve identified the areas of your website that are of special concern, you need to understand what’s going on and why your website visitors are leaving at that junction. That’s where qualitative research comes into play and user experience is closely examined.

Perhaps you set up live sessions with users who fit your preferred customer personas but have never visited your website. You give them a task to complete, then observe as they attempt to carry out the assignment. User experience studies can provide invaluable feedback.

Do visitors tend to get confused on a certain page? Is your on-site search engine returning irrelevant or misleading data? Is the information you provide clear and helpful? Those qualitative discoveries can help you determine what needs to change to keep visitors engaged and help them find the product that best suits their needs.

Other qualitative tools allow you to observe real-time actions of any visitor you choose, map out exactly where clicks occur (and don’t occur) on your site, see how far visitors are scrolling down each page, get feedback from customers, and more.

Which is Better: Quantitative or Qualitative Research?

Quantitative research tells you at a high level what activity is occurring on your website. Qualitative research helps you view your ecommerce website through your customers’ eyes to tell you why those things are happening.

By combining the two correctly, you get a wider view and deeper understanding of customer actions. Armed with that knowledge, you are better prepared to develop effective A/B testing hypotheses that will get quicker results and increase conversions. That is the essence of conversion optimization.

Now that you have a better understanding of quantitative vs. qualitative research, you can pass this article on to your team, discuss how it might inform your conversion rate optimization strategy, and take action to put these principles to work.

When you hit a snag or have questions, call The Good . Helping you streamline your path to sales and boost bottom line revenue is what we do best.

About the Author

Jon macdonald.

Jon MacDonald is founder and President of The Good, a digital experience optimization firm that has achieved results for some of the largest companies including Adobe, Nike, Xerox, Verizon, Intel and more. Jon regularly contributes to publications like Entrepreneur and Inc.

Understanding Qualitative and Quantitative Data

• 7 minute read
• August 22, 2024

Written by:

Smith Alex is a committed data enthusiast and an aspiring leader in the domain of data analytics. With a foundation in engineering and practical experience in the field of data science

Summary: This article delves into qualitative and quantitative data, defining each type and highlighting their key differences. It discusses when to use each data type, the benefits of integrating both, and the challenges researchers face. Understanding these concepts is crucial for effective research design and achieving comprehensive insights.

Introduction

In the realm of research and Data Analysis , two fundamental types of data play pivotal roles: qualitative and quantitative data. Understanding the distinctions between these two categories is essential for researchers, analysts, and decision-makers alike, as each type serves different purposes and is suited to various contexts.

This article will explore the definitions, characteristics, uses, and challenges associated with both qualitative and quantitative data, providing a comprehensive overview for anyone looking to enhance their understanding of data collection and analysis.

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Defining Qualitative Data

Qualitative data is non-numerical in nature and is primarily concerned with understanding the qualities, characteristics, and attributes of a subject.

This type of data is descriptive and often involves collecting information through methods such as interviews, focus groups, observations, and open-ended survey questions. The goal of qualitative data is to gain insights into the underlying motivations, opinions, and experiences of individuals or groups.

Characteristics of Qualitative Data

• Descriptive : Qualitative data provides rich, detailed descriptions of phenomena, allowing researchers to capture the complexity of human experiences.
• Subjective : The interpretation of qualitative data can vary based on the researcher’s perspective, making it inherently subjective.
• Contextual : This type of data is often context-dependent, meaning that the insights gained can be influenced by the environment or situation in which the data was collected.
• Exploratory : Qualitative data is typically used in exploratory research to generate hypotheses or to understand phenomena that are not well understood.

Examples of Qualitative Data

• Interview transcripts that capture participants’ thoughts and feelings.
• Observational notes from field studies.
• Responses to open-ended questions in surveys.
• Personal narratives or case studies that illustrate individual experiences.

Defining Quantitative Data

Quantitative data, in contrast, is numerical and can be measured or counted. This type of data is often used to quantify variables and analyse relationships between them. Quantitative research typically employs statistical methods to test hypotheses, identify patterns, and make predictions based on numerical data.

Characteristics of Quantitative Data

• Objective : Quantitative data is generally considered more objective than qualitative data, as it relies on measurable values that can be statistically analysed.
• Structured : This type of data is often collected using structured methods such as surveys with closed-ended questions, experiments, or observational checklists.
• Generalizable : Because quantitative data is based on numerical values, findings can often be generalised to larger populations if the sample is representative.
• Statistical Analysis : Quantitative data lends itself to various statistical analyses , allowing researchers to draw conclusions based on numerical evidence.

Examples of Quantitative Data

• Age, height, and weight measurements.
• Survey results with numerical ratings (e.g., satisfaction scores).
• Test scores or academic performance metrics.
• Financial data such as income, expenses, and profit margins.

Key Differences Between Qualitative and Quantitative Data

Understanding the differences between qualitative and quantitative data is crucial for selecting the appropriate research methods and analysis techniques. Here are some key distinctions:

When to Use Qualitative Data

Qualitative data is particularly useful in situations where the research aims to explore complex phenomena, understand human behaviour, or generate new theories. Here are some scenarios where qualitative data is the preferred choice:

Exploratory Research

When investigating a new area of study where little is known, qualitative methods can help uncover insights and generate hypotheses.

Understanding Context

Qualitative data is valuable for capturing the context surrounding a particular phenomenon, providing depth to the analysis.

Gaining Insights into Attitudes and Behaviours

When the goal is to understand why individuals think or behave in a certain way, qualitative methods such as interviews can provide rich, nuanced insights.

Developing Theories

Qualitative research can help in the development of theories by exploring relationships and patterns that quantitative methods may overlook.

When to Use Quantitative Data

Quantitative data is best suited for research that requires measurement, comparison, and statistical analysis. Here are some situations where quantitative data is the preferred choice:

Testing Hypotheses

When researchers have specific hypotheses to test , quantitative methods allow for rigorous statistical analysis to confirm or reject these hypotheses.

Measuring Variables

Quantitative data is ideal for measuring variables and establishing relationships between them, making it useful for experiments and surveys.

Generalising Findings

When the goal is to generalise findings to a larger population, quantitative research provides the necessary data to support such conclusions.

Identifying Patterns and Trends

Quantitative analysis can reveal patterns and trends in data that can inform decision-making and policy development.

Integrating Qualitative and Quantitative Data

While qualitative and quantitative data are distinct, they can be effectively integrated to provide a more comprehensive understanding of a research question. This mixed-methods approach combines the strengths of both types of data, allowing researchers to triangulate findings and gain deeper insights.

Benefits of Integration

Integrating qualitative and quantitative data enhances research by combining numerical analysis with rich, descriptive insights. This mixed-methods approach allows for a comprehensive understanding of complex phenomena, validating findings and providing a more nuanced perspective on research questions.

• Enhanced Validity: By using both qualitative and quantitative data, researchers can validate their findings through multiple sources of evidence.
• Rich Insights : Qualitative data can provide context and depth to quantitative findings, helping to explain the “why” behind numerical trends.
• Comprehensive Understanding: Integrating both types of data allows for a more holistic understanding of complex phenomena, leading to more informed conclusions and recommendations.

Examples of Integration

• Surveys with Open-Ended Questions: Combining closed-ended questions (quantitative) with open-ended questions (qualitative) in surveys can provide both measurable data and rich descriptive insights.
• Case Studies with Statistical Analysis: Researchers can conduct case studies (qualitative) while also collecting quantitative data to support their findings, offering a more robust analysis.
• Focus Groups with Follow-Up Surveys: After conducting focus groups (qualitative), researchers can administer surveys (quantitative) to a larger population to validate the insights gained.

Challenges and Considerations

While qualitative and quantitative data offer distinct advantages, researchers must also be aware of the challenges and considerations associated with each type:

Challenges of Qualitative Data

The challenges of qualitative data are multifaceted and can significantly impact the research process. Here are some of the primary challenges faced by researchers when working with qualitative data:

Subjectivity and Bias

One of the most significant challenges in qualitative research is the inherent subjectivity involved in data collection and analysis. Researchers’ personal beliefs, assumptions, and experiences can influence their interpretation of data.

Data Overload

Qualitative research often generates large volumes of data, which can be overwhelming. This data overload can make it challenging to identify key themes and insights. Researchers may struggle to manage and analyse vast amounts of qualitative data, leading to potential insights being overlooked.

Lack of Structure

Qualitative data is often unstructured, making it difficult to analyse systematically. The absence of a predefined format can lead to challenges in drawing meaningful conclusions from the data.

Time-Consuming Nature

Qualitative analysis can be extremely time-consuming, especially when dealing with extensive data sets. The process of collecting, transcribing, and analysing qualitative data often requires significant time and resources, which can be a barrier for researchers.

Challenges of Quantitative Data

Quantitative data provides objective, measurable evidence, it also faces challenges in capturing the full complexity of human experiences, maintaining data accuracy, and avoiding misinterpretation of statistical results. Integrating qualitative data can help overcome some of these limitations.

Limits in Capturing Complexity

Quantitative data, by its nature, can oversimplify complex phenomena and miss important nuances that qualitative data can capture. The focus on numerical measurements may not fully reflect the depth and richness of human experiences and behaviours.

Chances for Misinterpretation

Numbers can be twisted or misinterpreted if not analysed properly. Researchers must be cautious in interpreting statistical results, as correlation does not imply causation. Poor knowledge of statistical analysis can negatively impact the analysis and interpretation of quantitative data.

Influence of Measurement Errors

Due to the numerical nature of quantitative data, even small measurement errors can skew the entire dataset. Inaccuracies in data collection methods can lead to drawing incorrect conclusions from the analysis.

Lack of Context

Quantitative experiments often do not take place in natural settings. The data may lack the context and nuance that qualitative data can provide to fully explain the phenomena being studied.

Sample Size Limitations

Small sample sizes in quantitative studies can reduce the reliability of the data. Large sample sizes are needed for more accurate statistical analysis. This also affects the ability to generalise findings to wider populations.

Confirmation Bias

Researchers may miss observing important phenomena due to their focus on testing pre-determined hypotheses rather than generating new theories. The confirmation bias inherent in hypothesis testing can limit the discovery of unexpected insights.

In conclusion, understanding the distinctions between qualitative and quantitative data is essential for effective research and Data Analysis . Each type of data serves unique purposes and is suited to different contexts, making it crucial for researchers to select the appropriate methods based on their research objectives.

By integrating both qualitative and quantitative data, researchers can gain a more comprehensive understanding of complex phenomena, leading to richer insights and more informed decision-making.

As the landscape of research continues to evolve, the ability to effectively utilise and integrate both types of data will remain a valuable skill for researchers and analysts alike.

Frequently Asked Questions

What is the primary difference between qualitative and quantitative data.

The primary difference is that qualitative data is descriptive and non-numerical, focusing on understanding qualities and experiences, while quantitative data is numerical and measurable, focusing on quantifying variables and testing hypotheses.

When Should I Use Qualitative Data in My Research?

Qualitative data is best used when exploring new topics, understanding complex behaviours, or generating hypotheses, particularly when context and depth are important.

Can Qualitative and Quantitative Data Be Used Together?

Yes, integrating qualitative and quantitative data can provide a more comprehensive understanding of a research question, allowing researchers to validate findings and gain richer insights.

Reviewed by:

Post written by: Smith Alex

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In this essential science conversation, expert panelists explore how qualitative and quantitative research approaches can be used as complementary tools, each with specific advantages and limitations, that have evolved to meet emerging research needs.

This program does not offer CE credit.

Elizabeth G. Creamer, EdD

Professor emerita, Virginia Tech.

Joseph P. Gone, PhD

Harvard University.

Eric A. Youngstrom, PhD

Professor of psychology and neuroscience, and psychiatry at University of North Carolina at Chapel Hill.

Mitch Prinstein, PhD

Chief Science Officer, APA.

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Advantages And Disadvantages of Quantitative and Qualitative Research

• Post author: Edeh Samuel Chukwuemeka ACMC
• Post published: April 19, 2023
• Post category: Scholarly Articles

Advantages and Disadvantages of Quantitative and Qualitative Research : The purpose of research is to enhance society by advancing knowledge through the development of scientific theories, concepts and ideas. The key aim of research is to have a detailed understanding of a subject matter which can be achieved by exploration, description and explanation.

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Table of Contents

Meaning of Quantitative Research Method

Quantitative research involves the gathering of information and collection of data in quantities and numbers. It involves the observative strategy of research and uses statistics, computational methods and mathematics in developing theories.

It is purely a scientific/experimental method and does not rely on opinions. Rather this form of research is heavily based on formulating theories about events or phenomena through quantification before reaching a conclusion.

An example of Quantitative research is conducting surveys to determine the approval ratings of students in a Public University regarding the increase of tuition fees. In this scenario, one can distribute paper questionnaires, online surveys and polls to collate the figure representing the number of students who are either in agreement or in disagreement of the increase of tuition fees.

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Advantages of Quantitative Research

I. It allows you to reach an accurate conclusion no matter how large the subject matter is. Take for example the scenario above, if the number of students were 2000 in number and you want to do a research on the approval ratings annually. The approach makes it simplistic for the researcher to easily deduce the accurate conclusion no matter how fast the number of students grow.

ii. It is less time consuming since it is based on statistical analysis. Thus, researchers are not burdened by drawing out explanatory strategies to generate an outcome.

iii. Quantitative research does not focus on opinions but only on accurate data which is more reliable and concrete.

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iv. The research approach keeps the personal information anonymous. It protects the identity of the information provider. It only focuses on collection of data and people with this knowledge of identity preservation give honest opinions.

v. The research does not require a study group to be observed on a frequent basis. The problem of monitoring the subject matter to provide adequate information is eliminated by adopting this research. There is no need for  face to face conversations or time consuming cross examinations to get the data the researcher needs.

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vi. Objectivity: The objectivity of quantitative research is one of its key benefits. The foundation of quantitative research is the utilization of numerical data, which is frequently considered to be more unbiased and trustworthy than qualitative data. Statistical methods make it simple to assess numerical data, and the results can be impartially understood and extrapolated to larger populations. This makes it possible for researchers to make accurate and trustworthy findings based on actual data.

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Disadvantages of Quantitative research

I. As society grows, the opinions of people become so diversified and they are susceptible to the changes in the society when giving their opinions.

ii. There is no accurate generalisation of data the researcher received. In simpler words if for example, a researcher wants to know how many people are in support of secession in Nigeria. Qualitative research may show a large percentage in support of it but because there is no available option to revisit the data, the opinions could change in some time.

So it is an initial success but an eventual fail. Present circumstances may influence the opinions and ultimately the conclusion. It is the dynamic of society; As society evolves, so do the people’s perspectives and quantitative research does nit make provision for this dynamic.

iii. The cost of Quantitative research is relatively high. If you have ever conducted a physical or online survey which involves the distribution of questionnaires among targeted study groups, you will attest to the expensive nature of this research. Sometimes high profile firms and companies are involved which makes the research work more expensive.

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iv. Experienced researchers are usually uncertain about the eventual data: The purpose of research is to explore a subject matter and generate an accurate conclusion. What happens when the data collected do not represent the entire study group?

It becomes extremely difficult to reach a valid conclusion when the data gathered is not an accurate representation of everyone involved especially when it involves a large study group. This is one of the worries that concern expert researchers.

v. Reductionist: One of the main criticisms of quantitative research is that it can be reductionist in nature. Quantitative research often focuses on specific variables and measures, which may not capture the complexity and richness of human experiences.

It may overlook important nuances, context, and qualitative aspects of a phenomenon, leading to a limited understanding of the research topic.

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Meaning of Qualitative Research Method

This type of research involves investigating methodologies by collecting data where the researcher engages in open ended questions. This means that the researcher is more engaging in his questions and attempts to elicit the most positively accurate data from his targeted subject group.

Unlike Quantitative research, it does not quantify hypothesis by numbers or statistical measurements. Rather it has a more exploratory approach with the “ how ” and “ why ” which is more detailed than a “ yes ” or a “ no “. While Quantitative research deals with numerical figures, qualitative research deals more with words and meanings.

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Advantages of Qualitative Research

I . Due to the depth of qualitative research, subject matters can be examined on a larger scale in  greater detail.

ii . Qualitative Research has a more real feel as it deals with human experiences and observations. The researcher has a  more concrete foundation to gather accurate data. Take for instance, if there is a survey on the evaluation of academic performance in secondary schools.

A Qualitative researcher has an advantageous position in knowing the reason behind the increase or decline of academic performance by having long and stretched out conversations with the students to get a comprehensive data and accurate conclusion.

iii . The researcher can flow with the initial data by asking further questions in respect of the answers. This is not the case in other forms of research.

iv . Qualitative Research allows the researcher to provide a more generalised data notwithstanding the multiplicity of perspectives and opinions. For example if majority of the students are split concerning the reason for academic decline with half of them saying it is due to bad teaching while the other half attributes the decline to inadequate facilities, all these are different opinions which only a Qualitative researcher can accommodate to arrive at a definite conclusion.

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v . The respondents to the researcher are authentic, unfiltered and creative with their answers which promises a more accurate data.

vi. Rich and Detailed Data: One of the main advantages of qualitative research is its ability to provide rich and detailed data that captures the complexity and nuances of human experiences. Qualitative data can provide in-depth insights into the thoughts, feelings, and behaviors of individuals, and can offer a holistic understanding of the research topic.

This can provide a deeper and more nuanced understanding of social phenomena and human behavior.

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Disadvantages of Qualitative Research

I . One of the challenges in this type of research is that the collected data is purely based on open ended discussions. This makes the researcher the controlling figure as the interviewer which results to gathering of data which he may find useful or not, necessary or unnecessary because of its highly subjective nature.

ii . The researcher may become too opinionated in the subject matter which may influence his recollection of data. Hence there is likely to be error in gathering the right information.

iii . Qualitative Research takes a lot of time and effort in execution. The means of eliciting information from a subject group and analysing the data received, filtering the relevant ones from the irrelevant ones are tedious processes. This is more complex when large companies are involved in the research.

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iv . There is the possibility of lost data in the process of gathering. Qualitative Research is more demanding and requires a more meticulous approach than quantitative research. It is an enormous responsibility which non experienced researchers may have difficulty to bear.

v. Researchers must be experienced and have detailed knowledge in the subject matter in order to attain the most accurate data. This requires a special skill set and the process of searching for those researchers that fit the right caliber is not only costly but equally difficult, depending on the subject matter.

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vi. Subjectivity and Bias: The subjectivity and potential for bias of qualitative research are two of its key complaints. The interpretation and analysis of data used in qualitative research are subject to the researcher’s own biases, viewpoints, and preconceived beliefs. Given that various researchers may interpret the same data in different ways, the subjective aspect of qualitative research can have an impact on the validity and trustworthiness of the conclusions.

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In conclusion, it is worthy to note that both Quantitative and Qualitative researches are equally beneficial in the field of gathering data or information. Whether it is mathematically based or more of open ended discussions, it is imperative for a researcher to evaluate the essence of the research, the size of the target group or subject matter and the expenses involved. All these factors will guide a diligent researcher in determining the most trustworthy approach in research.

Edeh Samuel Chukwuemeka, ACMC, is a lawyer and a certified mediator/conciliator in Nigeria. He is also a developer with knowledge in various programming languages. Samuel is determined to leverage his skills in technology, SEO, and legal practice to revolutionize the legal profession worldwide by creating web and mobile applications that simplify legal research. Sam is also passionate about educating and providing valuable information to people.

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Qualitative vs. quantitative data analysis: How do they differ?

Learning analytics have become the cornerstone for personalizing student experiences and enhancing learning outcomes. In this data-informed approach to education there are two distinct methodologies: qualitative and quantitative analytics. These methods, which are typical to data analytics in general, are crucial to the interpretation of learning behaviors and outcomes. This blog will explore the nuances that distinguish qualitative and quantitative research, while uncovering their shared roles in learning analytics, program design and instruction.

What is qualitative data?

Qualitative data is descriptive and includes information that is non numerical. Qualitative research is used to gather in-depth insights that can't be easily measured on a scale like opinions, anecdotes and emotions. In learning analytics qualitative data could include in depth interviews, text responses to a prompt, or a video of a class period. 1

What is quantitative data?

Quantitative data is information that has a numerical value. Quantitative research is conducted to gather measurable data used in statistical analysis. Researchers can use quantitative studies to identify patterns and trends. In learning analytics quantitative data could include test scores, student demographics, or amount of time spent in a lesson. 2

Key difference between qualitative and quantitative data

It's important to understand the differences between qualitative and quantitative data to both determine the appropriate research methods for studies and to gain insights that you can be confident in sharing.

Data Types and Nature

Examples of qualitative data types in learning analytics:

• Observational data of human behavior from classroom settings such as student engagement, teacher-student interactions, and classroom dynamics
• Textual data from open-ended survey responses, reflective journals, and written assignments
• Feedback and discussions from focus groups or interviews
• Content analysis from various media

Examples of quantitative data types:

• Standardized test, assessment, and quiz scores
• Grades and grade point averages
• Attendance records
• Time spent on learning tasks
• Data gathered from learning management systems (LMS), including login frequency, online participation, and completion rates of assignments

Methods of Collection

Qualitative and quantitative research methods for data collection can occasionally seem similar so it's important to note the differences to make sure you're creating a consistent data set and will be able to reliably draw conclusions from your data.

Qualitative research methods

Because of the nature of qualitative data (complex, detailed information), the research methods used to collect it are more involved. Qualitative researchers might do the following to collect data:

• Conduct interviews to learn about subjective experiences
• Host focus groups to gather feedback and personal accounts
• Observe in-person or use audio or video recordings to record nuances of human behavior in a natural setting
• Distribute surveys with open-ended questions

Quantitative research methods

Quantitative data collection methods are more diverse and more likely to be automated because of the objective nature of the data. A quantitative researcher could employ methods such as:

• Surveys with close-ended questions that gather numerical data like birthdates or preferences
• Observational research and record measurable information like the number of students in a classroom
• Automated numerical data collection like information collected on the backend of a computer system like button clicks and page views

Analysis techniques

Qualitative and quantitative data can both be very informative. However, research studies require critical thinking for productive analysis.

Qualitative data analysis methods

Analyzing qualitative data takes a number of steps. When you first get all your data in one place you can do a review and take notes of trends you think you're seeing or your initial reactions. Next, you'll want to organize all the qualitative data you've collected by assigning it categories. Your central research question will guide your data categorization whether it's by date, location, type of collection method (interview vs focus group, etc), the specific question asked or something else. Next, you'll code your data. Whereas categorizing data is focused on the method of collection, coding is the process of identifying and labeling themes within the data collected to get closer to answering your research questions. Finally comes data interpretation. To interpret the data you'll take a look at the information gathered including your coding labels and see what results are occurring frequently or what other conclusions you can make. 3

Quantitative analysis techniques

The process to analyze quantitative data can be time-consuming due to the large volume of data possible to collect. When approaching a quantitative data set, start by focusing in on the purpose of your evaluation. Without making a conclusion, determine how you will use the information gained from analysis; for example: The answers of this survey about study habits will help determine what type of exam review session will be most useful to a class. 4

Next, you need to decide who is analyzing the data and set parameters for analysis. For example, if two different researchers are evaluating survey responses that rank preferences on a scale from 1 to 5, they need to be operating with the same understanding of the rankings. You wouldn't want one researcher to classify the value of 3 to be a positive preference while the other considers it a negative preference. It's also ideal to have some type of data management system to store and organize your data, such as a spreadsheet or database. Within the database, or via an export to data analysis software, the collected data needs to be cleaned of things like responses left blank, duplicate answers from respondents, and questions that are no longer considered relevant. Finally, you can use statistical software to analyze data (or complete a manual analysis) to find patterns and summarize your findings. 4

Qualitative and quantitative research tools

From the nuanced, thematic exploration enabled by tools like NVivo and ATLAS.ti, to the statistical precision of SPSS and R for quantitative analysis, each suite of data analysis tools offers tailored functionalities that cater to the distinct natures of different data types.

Qualitative research software:

NVivo: NVivo is qualitative data analysis software that can do everything from transcribe recordings to create word clouds and evaluate uploads for different sentiments and themes. NVivo is just one tool from the company Lumivero, which offers whole suites of data processing software. 5

ATLAS.ti: Similar to NVivo, ATLAS.ti allows researchers to upload and import data from a variety of sources to be tagged and refined using machine learning and presented with visualizations and ready for insert into reports. 6

SPSS: SPSS is a statistical analysis tool for quantitative research, appreciated for its user-friendly interface and comprehensive statistical tests, which makes it ideal for educators and researchers. With SPSS researchers can manage and analyze large quantitative data sets, use advanced statistical procedures and modeling techniques, predict customer behaviors, forecast market trends and more. 7

R: R is a versatile and dynamic open-source tool for quantitative analysis. With a vast repository of packages tailored to specific statistical methods, researchers can perform anything from basic descriptive statistics to complex predictive modeling. R is especially useful for its ability to handle large datasets, making it ideal for educational institutions that generate substantial amounts of data. The programming language offers flexibility in customizing analysis and creating publication-quality visualizations to effectively communicate results. 8

Applications in Educational Research

Both quantitative and qualitative data can be employed in learning analytics to drive informed decision-making and pedagogical enhancements. In the classroom, quantitative data like standardized test scores and online course analytics create a foundation for assessing and benchmarking student performance and engagement. Qualitative insights gathered from surveys, focus group discussions, and reflective student journals offer a more nuanced understanding of learners' experiences and contextual factors influencing their education. Additionally feedback and practical engagement metrics blend these data types, providing a holistic view that informs curriculum development, instructional strategies, and personalized learning pathways. Through these varied data sets and uses, educators can piece together a more complete narrative of student success and the impacts of educational interventions.

Master Data Analysis with an M.S. in Learning Sciences From SMU

Whether it is the detailed narratives unearthed through qualitative data or the informative patterns derived from quantitative analysis, both qualitative and quantitative data can provide crucial information for educators and researchers to better understand and improve learning. Dive deeper into the art and science of learning analytics with SMU's online Master of Science in the Learning Sciences program . At SMU, innovation and inquiry converge to empower the next generation of educators and researchers. Choose the Learning Analytics Specialization to learn how to harness the power of data science to illuminate learning trends, devise impactful strategies, and drive educational innovation. You could also find out how advanced technologies like augmented reality (AR), virtual reality (VR), and artificial intelligence (AI) can revolutionize education, and develop the insight to apply embodied cognition principles to enhance learning experiences in the Learning and Technology Design Specialization , or choose your own electives to build a specialization unique to your interests and career goals.

For more information on our curriculum and to become part of a community where data drives discovery, visit SMU's MSLS program website or schedule a call with our admissions outreach advisors for any queries or further discussion. Take the first step towards transforming education with data today.

• Retrieved on August 8, 2024, from nnlm.gov/guides/data-glossary/qualitative-data
• Retrieved on August 8, 2024, from nnlm.gov/guides/data-glossary/quantitative-data
• Retrieved on August 8, 2024, from cdc.gov/healthyyouth/evaluation/pdf/brief19.pdf
• Retrieved on August 8, 2024, from cdc.gov/healthyyouth/evaluation/pdf/brief20.pdf
• Retrieved on August 8, 2024, from lumivero.com/solutions/
• Retrieved on August 8, 2024, from atlasti.com/
• Retrieved on August 8, 2024, from ibm.com/products/spss-statistics
• Retrieved on August 8, 2024, from cran.r-project.org/doc/manuals/r-release/R-intro.html#Introduction-and-preliminaries

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Measuring developer productivity via humans

Somewhere, right now, a technology executive tells their directors: “Wwe need a way to measure the productivity of our engineering teams.” A working group assembles to explore potential solutions, and weeks later, proposes implementing the metrics: lead time, deployment frequency, and number of pull requests created per engineer.

Soon after, senior engineering leaders meet to review their newly created dashboards. Immediately, questions and doubts are raised. One leader says: “Our lead time is two days which is ‘low performing’ according to those benchmarks – but is there actually a problem?”. Another leader says: “I“it’s unsurprising to see that some of our teams are deploying less often than others. But I’m not sure why or what to do.”

If this story arc is familiar to you, don’t worry – it’s familiar to most, including some of the biggest tech companies in the world. It is not uncommon for measurement programs to fall short when metrics like DORA fail to provide the depth for insights leaders need.

The solution to this relies on capturing insights from developers themselves, rather than solely relying on basic measures of speed and output. What we are referring to here is qualitative measurement . We’ve helped many organizations adopt this approach, and we’ve seen firsthand the dramatically improved understanding of developer productivity that it provides.

In this article, we provide a primer on this approach derived from our experience helping many organizations on this journey. We begin with a definition of qualitative metrics and how to advocate for them. We follow with practical guidance on how to capture, track, and utilize this data.

Today, developer productivity is a critical concern for businesses amid the backdrop of fiscal tightening and transformational technologies such as AI. In addition, developer experience and platform engineering are garnering increased attention as enterprises look beyond Agile and DevOps transformation. What all these concerns share is a reliance on measurement to help guide decisions and track progress. And for this, qualitative measurement is key.

What is a qualitative metric?

We define a qualitative metric as a measurement derived from self-reported data. This is a practical definition – we haven’t found a singular definition within the social sciences, and the alternative definitions we’ve seen have flaws that we discuss later in this section.

The definition of the word “metric” is unambiguous. The term “qualitative,” however, has no authoritative definition as noted in the 2019 journal paper What is Qualitative in Qualitative Research : “There are many definitions of qualitative research, but if we look for a definition that addresses its distinctive feature of being ‘“qualitative,’” the literature across the broad field of social science is meager. The main reason behind this article lies in the paradox, which, to put it bluntly, is that researchers act as if they know what it is, but they cannot formulate a coherent definition.”

An alternate definition we’ve heard is that qualitative metrics measure quality, while quantitative metrics measure quantity. We’ve found this definition problematic for two reasons: first, the term “qualitative metric” includes the term metric , which implies that the output is a quantity (i.e., a measurement). Second, quality is typically measured through ordinal scales that are translated into numerical values and scores—which again, contradicts the definition.

Another argument we have heard is that the output of sentiment analysis is quantitative because the analysis results in numbers. While we agree that the data resulting from sentiment analysis is quantitative, based on our original definition this is still a qualitative metric (i.e., a quantity produced qualitatively) unless one were to take the position that “qualitative metric” is altogether an oxymoron.

Aside from the problem of defining what a qualitative metric is, we’ve also encountered problematic colloquialisms. One example is the term “soft metric.”. We caution against this phrase because it harmfully and incorrectly implies that data collected from humans is weaker than “hard metrics” collected from systems. We also discourage the term “subjective metrics” because it misconstrues the fact that data collected from humans can be either objective or subjective—as we discuss in the next section.

Advocating for qualitative metrics

Executives are often skeptical about the reliability and usefulness of qualitative metrics. Even highly scientific organizations like Google have had to overcome these biases. Engineering leaders are inclined toward system metrics since they are accustomed to working with telemetry data for inspecting systems. However, we cannot solely rely on system data for measuring people.

We’ve seen some organizations get into an internal “battle of the metrics” which is not a good use of time or energy. Our advice for champions is to avoid pitting qualitative and quantitative metrics against each other as an either/or. It’s better to make the argument that they are complementary tools – as we cover at the end of this article.

We’ve found that the underlying cause of opposition to qualitative data are misconceptions which we address below. Later in this article, we outline the distinct benefits of self-reported data such as its ability to measure intangibles and surface critical context.

Misconception: Qualitative data is only subjective

Traditional workplace surveys typically focus on the subjective opinions and feelings of their employees. Thus many engineering leaders intuitively believe that surveys can only collect subjective data from developers.

As we describe in the following section, surveys can also capture objective information about facts or events. Google’s DevOps Research and Assessment (DORA) program is an excellent concrete example.

Some examples of objective survey questions:

• How long does it take to go from code committed to code successfully running in production?
• How often does your organization deploy code to production or release it to end users?

Misconception: Qualitative data is unreliable

One challenge of surveys is that people with all manner of backgrounds write survey questions with no special training. As a result, many workplace surveys do not meet the minimum standards needed to produce reliable or valid measures. Well designed surveys, however, produce accurate and reliable data (we provide guidance on how to do this later in the article).

Some organizations have concerns that people may lie in surveys, w. Which can happen in situations where there is fear around how the data will be used. In our experience, when surveys are deployed as a tool to help understand and improve bottlenecks affecting developers, there is no incentive for respondents to lie or game the system.

While it’s true that survey data isn’t always 100% accurate, we often remind leaders that system metrics are often imperfect too. For example, many organizations attempt to measure CI build times using data aggregated from their pipelines, only to find that it requires significant effort to clean the data to produce an accurate result.

The two types of qualitative metrics

There are two key types of qualitative metrics:

• Attitudinal metrics capture subjective feelings, opinions, or attitudes toward a specific subject. An example of an attitudinal measure would be the numeric value captured in response to the question: “How satisfied are you with your IDE, on a scale of 1-10?”.
• Behavioral metrics capture objective facts or events pertaining to an individuals’ work experiences. An example of a behavioral measure would be the quantity captured in response to the question: “How long does it take for you to deploy a change to production?”

We’ve found that most tech practitioners overlook behavioral measures when thinking about qualitative metrics. This occurs despite the prevalence of qualitative behavioral measures in software research, such as the Google’s DORA program mentioned earlier.

DORA publishes annual benchmarks for metrics such as lead time for changes, deployment frequency, and change failure rate. Unbeknownst to many, DORA’s benchmarks are captured using qualitative methods with the survey items shown below.

We’ve found that the ability to collect attitudinal and behavioral data at the same time is a powerful benefit of qualitative measurement.

For example, behavioral data might show you that your release process is fast and efficient. But only attitudinal data could tell you whether it is smooth and painless, which has important implications for developer burnout and retention.

To use a non-tech analogy: imagine you are feeling sick and visit a doctor. The doctor takes your blood pressure, your temperature, your heart rate, and they say “Well, it looks like you’re all good. There’s nothing wrong with you.” You would be taken aback! You’d say, "Wait, I’m telling you that something feels wrong.”

The benefits of qualitative metrics

One argument for qualitative metrics is that they avoid subjecting developers to the feeling of “being measured” by management. While we’ve found this to be true—especially when compared to metrics derived from developers’ Git or Jira data—it doesn’t address the main objective benefits that qualitative approaches can provide.

There are three main benefits of qualitative metrics when it comes to measuring developer productivity:

Qualitative metrics allow you to measure things that are otherwise unmeasurable

System metrics like lead time and deployment volume capture what’s happening in our pipelines or ticketing systems. But there are many more aspects of developers’ work that need to be understood in order to improve productivity: for example, whether developers are able to stay in the flow or work or easily navigate their codebases. Qualitative metrics let you measure these intangibles that are otherwise difficult or impossible to measure.

An interesting example of this is technical debt. At Google, a study to identify metrics for technical debt included an analysis of 117 metrics that were proposed as potential indicators. To the disappointment of Google researchers, no single metric or combination of metrics were found to be valid indicators.

While there may exist an undiscovered objective metric for technical debt, one can suppose that this may be impossible due to the fact that assessment of technical debt relies on the comparison between the current state of a system or codebase versus its imagined ideal state. In other words, human judgment is essential.

Qualitative metrics provide missing visibility across teams and systems

Metrics from ticketing systems and pipelines give us visibility into some of the work that developers do. But this data alone cannot give us the full story. Developers do a lot of work that’s not captured in tickets or builds: for example, designing key features, shaping the direction of a project, or helping a teammate get onboarded.

It’s impossible to gain visibility into all these activities through data from our systems alone. And even if we could theoretically collect all the data through systems, there are additional challenges to capturing metrics through instrumentation.

One example is the difficulty of normalizing metrics across different team workflows. For example, if you’re trying to measure how long it takes for tasks to go from start to completion, you might try to get this data from your ticketing tool. But individual teams often have different workflows that make it difficult to produce an accurate metric. In contrast, simply asking developers how long tasks typically take can be much simpler.

Another common challenge is cross-system visibility. For example, a small startup can measure TTR (time to restore) using just an issue tracker such as Jira. A large organization, however, will likely need to consolidate and cross-attribute data across planning systems and deployment pipelines in order to gain end-to-end system visibility. This can be a yearlong effort, whereas capturing this data from developers can provide a baseline quickly.

Qualitative metrics provide context for quantitative data

As technologists, it is easy to focus heavily on quantitative measures. They seem clean and clear, after all. There is a risk, however, that the full story isn’t being told without richer data and that this may lead us into focusing on the wrong thing.

One example of this is code review: a typical optimization is to try to speed up the code review. This seems logical as waiting for a code review can cause wasted time or unwanted context switching. We could measure the time it takes for reviews to be completed and incentivize teams to improve it. But this approach may encourage negative behavior: reviewers rushing through reviews or developers not finding the right experts to perform reviews.

Code reviews exist for an important purpose: to ensure high quality software is delivered. If we do a more holistic analysis – focusing on the outcomes of the process rather than just speed – we find that optimization of code review must ensure good code quality, mitigation of security risks, building shared knowledge across team members, as well as ensuring that our coworkers aren’t stuck waiting. Qualitative measures can help us assess whether these outcomes are being met.

Another example is developer onboarding processes. Software development is a team activity. Thus if we only measure individual output metrics such as the rate new developers are committing or time to first commit, we miss important outcomes, for example e.g. whether we are fully utilizing the ideas the developers are bringing, whether they feel safe to ask questions and if they are collaborating with cross-functional peers.

How to capture qualitative metrics

Many tech practitioners don’t realize how difficult it is to write good survey questions and design good survey instruments. In fact, there are whole fields of study related to this, such as psychometrics and industrial psychology. It is important to bring or build expertise here when possible.

Below are few good rules for writing surveys to avoid the most common mistakes we see organizations make:

• Survey items need to be carefully worded and every question should only ask one thing.
• If you want to compare results between surveys, be careful about changing the wording of questions such that you’re measuring something different.
• If you change any wording, you must do rigorous statistical tests.

In survey parlance, ”good surveys” means “valid and reliable” or “demonstrating good psychometric properties.” Validity is the degree to which a survey item actually measures the construct you desire to measure. Reliability is the degree to which a survey item produces consistent results from your population and over time.

One way of thinking about survey design that we’ve found helpful to tech practitioners: think of the survey response process as an algorithm that takes place in the human mind.

When an individual is presented a survey question, a series of mental steps take place in order to arrive at a response. The model below is from the seminal 2012 book, The Psychology of Survey Response :

Decomposing the survey response process and inspecting each step can help us refine our inputs to produce more accurate survey results. Developing good survey items requires rigorous design, testing, and analysis – just like the process of designing software!

But good survey design is just one aspect of running successful surveys. Additional challenges include participation rates, data analysis, and knowing how to act on data. Below are some of the best practices we’ve learned.

Segment results by team and persona

A common mistake made by organizational leaders is to focus on companywide results instead of data broken down by team and persona (e.g., role, tenure, seniority). As previously described, developer experience is highly contextual and can differ radically across teams or roles. Focusing only on aggregate results can lead to overlooking problems that affect small but important populations within the company, such as mobile developers.

Free text comments are often most valuable

We’ve been talking about qualitative metrics but free text comments are an extremely valuable form of qualitative data. Beyond describing the friction or workflow, developers will have many great ideas to improve their developer experience, the free text allows us to capture those, and identify who to follow up with. Free text comments can also surface areas that your survey did not cover, which could be added in the future.

Compare results against benchmarks

Comparative analysis can help contextualize data and help drive action. For example, developer sentiment toward code quality commonly skews negative, making it difficult to identify true problems or gauge their magnitude. The more actionable data point is: “Aare our developers more frustrated about code quality than other teams or organizations?” Teams with lower sentiment scores than their peers and organizations with lower scores than their industry peers can surface notable opportunities for improvement.

Use transactional surveys where appropriate

Transactional surveys capture feedback during specific touchpoints or interactions in the developer workflow. For example, platform teams can use transactional surveys to prompt developers for feedback while they are in the midst of creating a new service in an internal developer portal. Transactional surveys can also augment data from periodic surveys by producing higher-frequency feedback and more granular insights.

Avoid survey fatigue

Many organizations struggle to sustain high participation rates in surveys over time. Lack of follow-up can cause developers to feel that repeatedly responding to surveys is not worthwhile. It is therefore critical that leaders and teams follow up and take meaningful action after surveys. While a quarterly or semi-annual survey cadence is optimal for most organizations, we’ve seen some organizations be successful with more frequent surveys that are integrated into regular team rituals such as retrospectives.

Using qualitative and quantitative metrics together

Qualitative metrics and quantitative metrics are complementary approaches to measuring developer productivity. Qualitative metrics, derived from surveys, provide a holistic view of productivity that includes both subjective and objective measurements. Quantitative metrics, on the other hand, provide distinct advantages as well:

• Precision. Humans can tell you whether their CI/CD builds are generally fast or slow (i.e., whether durations are closer to a minute or an hour), but they cannot report on build times down to millisecond precision. Quantitative metrics are needed when a high degree of precision is needed in our measurements.
• Continuity. Typically, the frequency at which an organization can survey their developers is at most once or twice per quarter. In order to collect more frequent or continuous metrics, organizations must gather data systematically.

Ultimately, it is through the combination of qualitative and quantitative metrics – a mixed-methods approach – that organizations can gain maximum visibility into the productivity and experience of developers. So how do you use qualitative and quantitative metrics together?

We’ve seen organizations find success when they start with qualitative metrics to establish baselines and determine where to focus. Then, follow with quantitative metrics to help drill in deeper into specific areas.

Engineering leaders find this approach to be effective because qualitative metrics provide a holistic view and context, providing wide understanding of potential opportunities. Quantitative metrics, on the other hand, are typically only available for a narrower set of the software delivery process.

Google similarly advises its engineering leaders to go to survey data first before looking at logs data for this reason. Google engineering researcher Ciera Jaspan explains: “We encourage leaders to go to the survey data first, because if you only look at logs data it doesn’t really tell you whether something is good or bad. For example, we have a metric that tracks the time to make a change, but that number is useless by itself. You don’t know, is this a good thing? Is it a bad thing? Do we have a problem?”.

A mixed-methods approach allows us to take advantage of the benefits of both qualitative and quantitative metrics while getting a full understand of developer productivity:

• Start with qualitative data to identify your top opportunities
• Once you know what you want to improve, use quantitative metrics to drill-in further
• Track your progress using both qualitative and quantitative metrics

It is only by combining as much data as possible—both qualitative and quantitative—that organizations can begin to build a full understanding of developer productivity.

In the end, however, it’s important to remember: organizations spend a lot on highly qualified humans that can observe and detect problems that log-based metrics can’t. By tapping into the minds and voices of developers, organizations can unlock insights previously seen as impossible.

About the authors

Abi Noda is co-founder and CEO of DX where he leads the company’s strategic direction and R&D efforts. Tim Cochran is a Principal in Amazon’s Software Builder Experience (ASBX) group.

The authors wish to thank Martin Fowler, Laura Tacho, Max Kanat-Alexander, Laurent Ploix, Bethany Otto, Andrew Cornwall, Carol Costello, and Vanessa Towers for their feedback on this article.

This article was originally published for martinfowler.com .

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