limitations of qualitative and quantitative research

Qualitative vs Quantitative Research Methods & Data Analysis

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

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

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

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What is the difference between quantitative and qualitative?

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

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

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

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

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

What Is Qualitative Research?

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

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

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

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

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

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

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

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

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

Qualitative Methods

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

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

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

Here are some examples of qualitative data:

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

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

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

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

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

Qualitative Data Analysis

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

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

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

Key Features

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

Limitations of Qualitative Research

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

Advantages of Qualitative Research

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

What Is Quantitative Research?

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

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

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

Quantitative Methods

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

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

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

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

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

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

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

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

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

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

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

Quantitative Data Analysis

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

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

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

Limitations of Quantitative Research

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

Advantages of Quantitative Research

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

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

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

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

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

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

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

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

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

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

Further Information

• 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

Educational resources and simple solutions for your research journey

Qualitative vs Quantitative Research: Differences, Examples, and Methods

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

Table of Contents

Qualitative v s Quantitative Research  

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

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

What Are the Differences Between Qualitative and Quantitative Research  

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

Data Collection Methods  

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

Qualitative Research Data Collection  

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

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

Quantitative Research Data Collection  

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

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

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

Qualitative vs Quantitative Research Outcomes  

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

When to Use Qualitative vs Quantitative Research Approach  

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

Qualitative research approach  

Qualitative research approach is used under following scenarios:   

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

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

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

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

Quantitative research approach  

Quantitative research approach is used under following scenarios:   

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

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

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

Mixed methods approach  

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

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

How to Analyze Qualitative and Quantitative Data  

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

Analyzing qualitative data  

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

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

Analyzing quantitative data  

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

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

Benefits and limitations of qualitative vs quantitative research  

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

Benefits of qualitative research  

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

Limitations of qualitative research  

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

Benefits of quantitative research  

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

Limitations of quantitative research  

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

Frequently asked questions  

• What is the difference between qualitative and quantitative research? 

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

• What are the types of qualitative research? 

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

• What are the types of quantitative research? 

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

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

Qualitative Research Example: 

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

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

Quantitative Research Example: 

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

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

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Quantitative vs. Qualitative Research in Psychology

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

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

• Key Differences

Quantitative Research Methods

Qualitative research methods.

• How They Relate

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

At a Glance

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

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

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

Qualitative Research vs. Quantitative Research

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

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

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

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

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

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

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

Used to develop theories

Takes a broad, complex approach

Answers "why" and "how" questions

Explores patterns and themes

Used to test theories

Takes a narrow, specific approach

Answers "what" questions

Explores statistical relationships

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

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

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

Quantitative methods are about measuring phenomena, not explaining them.

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

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

Basic Assumptions

Quantitative methods assume:

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

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

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

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

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

Correlation and Causation

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

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

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

Pitfalls of Quantitative Research

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

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

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

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

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

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

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

Qualitative Approaches

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

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

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

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

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

Interpretation

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

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

Relationship Between Qualitative and Quantitative Research

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

This website does not fully support Internet Explorer. For a better experience, please consider using a modern browser such as Chrome , Firefox , or Edge .

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

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

In This Article:

What Is the Difference Between Qualitative vs. Quantitative Research?

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

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

Qualitative Research: Data Collection for Your Doctorate Degree

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

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

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

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

Quantitative Research: Data Collection for Your Doctorate Degree

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

• Experiments
• Questionnaires
• Database reports

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

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

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

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

Benefits of Qualitative Research

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

Limitations of Qualitative Research

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

Benefits of Quantitative Research

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

Limitations of Quantitative Research

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

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

Analyzing Qualitative Data

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

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

Analyzing Quantitative Data

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

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

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

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

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

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

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

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The differences between qualitative and quantitative research methods

Last updated

15 January 2023

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

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• The differences between quantitative and qualitative research

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

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

• Qualitative vs. quantitative data collection

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

Quantitative data collection methods

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

Qualitative data collection methods

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

• Qualitative vs. quantitative outcomes

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

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

• How to analyze qualitative and quantitative data

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

Analyzing quantitative data

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

Analyzing qualitative data

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

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

• When to use qualitative vs. quantitative research

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

Qualitative research approach

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

Quantitative research approach

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

Mixed methods approach

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

• Benefits and limitations

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

Qualitative studies: Pros and cons

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

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

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

Quantitative studies: Pros and cons

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

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

• How to balance qualitative and quantitative research

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

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

Open-ended vs. close-ended questions

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

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

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

A. Purchase fewer items

B. Opt for less expensive choices

C. Take money from other parts of the budget

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

E. Make more food from scratch

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

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

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Strengths and Weaknesses of Quantitative and Qualitative Research

There are few things more useful in developing and implementing strategies than reliable data. The only drawback is that this information can be difficult to understand, which results in many business owners knowing nothing about their own research.

When starting a company or building a product, most people ask themselves the question: qualitative or quantitative research? Given the importance of coming up with a good strategy, this is not an easy question to answer.

Here is a quick look at the strengths and weaknesses of quantitative research.

What Is Quantitative Research?

Quantitative research is a study of numerical data whose purpose is to measure the strength and direction of relationships between variables. Quantitative research uses statistics to make sense of numerical data.

Quantitative research is based on numerical data gathered from different types of research methods, such as questionnaires, structured interviews, and statistical analysis.

Quantitative research involves questions that can be answered by counting or measuring, such as, how many people purchased a product? How many people are satisfied with the customer service ? What are the demographics of customers in different age groups?

For your study to be quantitative, you need to use numerical data to either prove or disprove your hypothesis.

For example, a quantitative research about a new product launch could use data like the average consumption of products in the category among the target population, the number of competitors and their individual market share, pricing points, and the marketing budget required to launch a brand awareness campaign, to mention a few.

This type of research helps you to understand your market and target audience, so you can make informed decisions about your product or service.

The biggest advantage of quantitative research is the ability to analyze large volumes of data and make conclusions based on that data.

Difference Between Qualitative And Quantitative Research

The main difference is this – Qualitative research methods include the collection of data through the use of open-ended questions, unstructured interviews, or observations, whereas, Quantitative research focuses on gathering numerical data and making generalizations about groups of people, situations, or phenomena.

Understanding human behavior and its governing reasons are the ultimate goals of Qualitative research. The discipline explores the “why” and “how” of decision-making.

Quantitative data collection methods are more structured than qualitative data collection ones.

When you need to gather a large amount of information from a group of people, there are many ways to do so. In quantitative research, data can be collected using a variety of methods, including surveys, interviews, observation, and online polls.

A good researcher knows when to use qualitative research (to understand opinions) vs quantitative research (to test objectively). 

For example, if you want to know what people think about a particular topic, then qualitative research would be best; but if you want to determine how many people are aware of a particular issue, then quantitative research would be better.

When you use both qualitative and quantitative research methods in your surveys, you will gain results that reach a lot of people as well as deeper insights from those people. With the right question types and analysis, you can use quantitative research to gain statistically significant insights into your target audience’s attitudes and behaviors.

Qualitative questions are useful for gathering detailed feedback on open-ended topics like:

Customer satisfaction. Qualitative questions let customers explain how they feel about your company’s products or services, and why they feel that way.

Employee engagement. Use open-ended questions to solicit employee feedback on company culture, management practices, benefits, and more.

Service performance. Learn why customers choose your brand over competitors’ by asking for the specific reasons for their decision.

Market research. Open-ended questions help you identify the most important factors that influence customers’ purchasing decisions in your market.

Quantitative research is ideal for:

• Collecting data at scale (e.g., using survey software)
• Reaching a large number of respondents in a short period of time
• Analyzing trends that apply to large groups of people (e.g., gender differences)
• Highlighting broad patterns or relationships between variables
• Predicting likelihoods based on certain factors (e.g., age, income)
• Driving the direction of future quantitative studies (i.e., hypothesis testing)

Importance Of Quantitative Research

The importance of quantitative research is that it provides an objective way to measure things, as well as a means of testing theories. Additionally, the results of quantitative research may be more easily replicated by other researchers.

Quantitative research is conducted in an effort to find numbers and statistical analysis to determine relationships between two or more variables. The process involves taking data from various sources and then organizing it into a format that can be used for statistical analysis.

One advantage of quantitative research is its ability to measure hard numbers and facts. This makes it much simpler to analyze data. 

For example, if you wanted to know the average income of people living in a certain area, all you would have to do is calculate the number of participants in your study who earn above and below a certain amount. You could also compare this data with other areas to see which has the highest average income levels.

Another advantage is that quantitative research allows researchers to replicate their findings using different samples or methods. The ability to replicate results ensures accuracy and consistency in results obtained from different studies conducted on similar topics over time. 

Furthermore, this type of research may reveal new insights into how something works because it focuses on measurable relationships rather than just observations about what happens in nature or human behavior itself.

Characteristics Of Quantitative Research

Quantitative research is the type of research that most people think about when they hear the word “research”. It involves creating statistical models, analyzing data, and using mathematical theories to understand how things work.

Quantitative research is used to identify factors that affect relationships between variables. Quantitative research is widely used in psychology, economics, demography, and marketing. It is often used in natural sciences, such as biology and chemistry, and in social sciences, such as sociology and psychology. Quantitative research involves the use of computational, mathematical, or statistical techniques.

For example, if a researcher believes that watching television makes people more violent, he or she may use quantitative methods to test this theory by counting the number of violent acts depicted in a week’s worth of programming and comparing it with the number of violent crimes committed for the same time period.

These are some essential characteristics of Quantitative research:

• The focus is on measurement, analysis, and prediction of phenomena through the use of mathematical models and theories.
• Quantitative research’s objective is to obtain information about the current status of a given phenomenon.
• The focus is on variables and the relationships between them.
• The researcher can manipulate variables, which is why experiments are often used in quantitative research.
• Quantitative research includes formal data collection methods.
• The results are based on large sample sizes, so the results have high statistical power and are more likely to be statistically significant (i.e., not due to chance).
• Data is analyzed using statistical techniques.
• Quantitative research typically uses deductive reasoning.
• Variables must be identified and measured using reliable instruments and procedures; using multiple methods of measurement increases the reliability and validity of results (triangulation).

The design of a quantitative research question must be structured or ‘closed’ so that it can be answered using a predetermined response format (usually dichotomous or multiple choice) or scaled responses. 

The design of the quantitative research question should not allow respondents to answer in their own words. This will make it impossible to use the data in any meaningful way. 

The quantitative design will measure whether a change has occurred from a specific point in time, but will not determine why a change has occurred.

Quantitative research questions are best for giving an overview or analysis of a particular business, industry, or topic. Therefore, they need to be researched in detail so that the researcher can be confident that enough information exists to answer the questions. If there is no literature available on the topic, then it is unlikely that you will have sufficient knowledge to investigate the topic effectively.

Conducting thorough industry research is crucial in ensuring that the quantitative research questions are well-informed and grounded in existing knowledge.

Strengths Of Quantitative Research

Quantitative research is often used to ask questions that can be answered with numerical data. It has a number of strengths:

• Standardized data collection

This means that the same instruments are used with all the participants in a study, and the data is collected in a uniform way. This makes it easier to compare results across groups of participants or to test hypotheses on a larger scale.

• Objectivity

The standardization of both data collection and analysis can make results from studies more objective than those with qualitative research methods. The use of statistics and hard numbers can also give your findings authority when you publish them online or in a print journal. This objectivity makes it easier for researchers to explain why their findings are reliable and true.

• Difficult Data Collection

Quantitative studies can also provide researchers with data about phenomena that are difficult or impossible to measure directly, such as attitudes, beliefs, and values.

Quantitative research allows for larger sample sizes, which increases the reliability of your results. It also moves quickly and can produce results that are easy to share with others, because they’re often presented as percentages.

• Generalizability

You might find that what you learn applies not only to your research participants but also to people who weren’t included in your study. For example, if you ask 1,000 people what’s important to them about their job, you might find out some things about how work affects happiness that could be true for other people as well.

• Evidence Collection

The design of a quantitative study allows the researcher to collect numerical data that can be analyzed using statistical tests. This provides an opportunity for the researcher to support or refute theories by collecting evidence that is statistically significant.

Weaknesses Of Quantitative Research

Quantitative research is a useful tool for measuring and describing the world as it exists, but it has its weaknesses as well.

Quantitative data is often criticized for being too detached from real-life situations; this criticism typically stems from the fact that the data collected tends to be structured and limited in nature. 

Some have argued that quantitative analysis does not provide people with a full picture of complex issues or human behavior since it is concerned with measuring and counting specific variables.

Quantitative researchers are concerned with how much and how many, but their methods don’t allow them to understand why something happens. They can find correlations between factors, but not necessarily causes. 

For example, they might discover that people who drink more coffee have higher rates of cardiovascular disease than people who drink less coffee, but they can’t conclude that drinking coffee causes heart problems.

Quantitative research doesn’t always take into account a human element. People make decisions based on more than just mathematical calculations, and that’s an important part of the human experience. It’s also difficult to account for the subjective nature of human experience in quantitative methods such as surveys and questionnaires.

Quantitative research tends to minimize the role of the researcher in the research process, thereby reducing the amount of information that can be obtained on contextual factors.

Quantitative research tends not to generate new ideas or shed light on unexplored areas because they focus on testing hypotheses derived from existing theories and concepts.

Types Of Quantitative Research

There are five main types of Quantitative research:

• Descriptive Research

Descriptive research produces a description of what already exists in a group or population. It usually involves taking a sample from the population in order to describe a certain characteristic of the entire group. 

It does not seek to explain why things are a certain way or how they came about but rather describes what is and what is not.

• Correlational Research

Correlational research investigates relationships between variables as well as how these variables interact with one another. 

Unlike descriptive research, correlational research goes beyond description by seeking to identify the strength, direction, and nature of relationships between two or more variables. 

While it cannot be used to determine causality due to its correlational nature, it can be used to predict outcomes based on the relationship that exists between variables.

• Experimental Research

Experimental research involves testing a hypothesis by conducting experiments using various methods such as controlled laboratory-based scenarios, field experiments, and randomized trials. 

Experimental design involves the manipulation and measurement of variables to observe their effect on each other. This enables researchers to determine cause-and-effect relationships between variables.

• Survey Research

Survey research is a quantitative method that involves the usage of different research instruments such as questionnaires or schedules to gather data. 

Surveys are usually done in cases where it is difficult to conduct an experiment such as in the case of social sciences. 

The most common forms of survey research include mail surveys, telephone interviews, and face-to-face interviews.

• Causal-Comparative Research

Causal-comparative research is a type of research that is used when the researcher has limited control over variables, such as in a field experiment. This type of research does not involve randomization of participants or experimental manipulation, as in true experimental studies.

The name causal-comparative research comes from two terms, causal and comparative. Causal implies that the study attempts to determine whether one variable causes another. Comparative indicates that groups are compared but not randomly assigned to groups by the researcher.

When To Use Quantitative Research

Quantitative research is a great way to collect data on a large scale when you have many respondents. 

This can be useful when you need a lot of data points and/or want to record responses for future analysis. It’s also good for surveys that are complex and/or have any questions. 

If your audience is large (across multiple locations, or across countries) or if you have a smaller audience but want them to complete your survey in their own language, quantitative research is the way to go.

If your business is just getting started with market research, quantitative methods will give you an excellent baseline of information upon which to build later qualitative research projects.

Qualitative research gets to the heart of your problem, giving you much more detailed data than quantitative methods would. 

Qualitative research is more appropriate for projects that:

• require more in-depth answers than “yes” or “no”
• have small sample sizes
• require detailed interviews or observations
• are exploratory in nature

Is Qualitative Or Quantitative Research Better?

A good thing to keep in mind is that there isn’t really a “right” answer – it all depends on what you are trying to find out!

Qualitative and Quantitative research is often seen as opposing approaches to research, but they both have their advantages and disadvantages. While there is a lot of debate between these two types of studies, they are not mutually exclusive and can work together to generate meaningful results.

Qualitative research gathers information that seeks to describe a topic more than measure it. Qualitative research is often used to conduct market analysis and identify consumer trends, motivations and behaviors.

Quantitative research is the best way to reveal and prove a cause-and-effect relationship. If you want to make an argument about why something is happening, quantitative research can help you do that. 

For example, if you wanted to say that more guns in the hands of private citizens lowered crime rates, you could run a study with data on crime rates and gun ownership across states and find statistical correlations between them.

Qualitative research describes and interprets what people say and do. Instead of using numbers to describe some phenomenon, it uses words and pictures instead. It’s best for exploring questions that don’t have clear answers yet, like how people feel about a new product or how they respond to a new marketing campaign.

For example, if you wanted to know how people reacted when they saw your new TV commercial, the best way would probably be to show it to people in a focus group and tape their reactions. The group moderator might ask some follow-up questions and people might comment on each other’s reactions, but the goal is less about making an argument than understanding what’s happening.

Is Survey Qualitative Or Quantitative Research?

A survey can be considered qualitative or quantitative depending on the type of questions asked. 

Quantitative surveys ask closed-ended questions – those requiring a “yes” or “no”, a number rating, or a selection from a predetermined list of answers (e.g., choose from “Excellent”, “Good”, etc.). These kinds of questions allow for analysis that can be statistically inferred across the entire population being surveyed.

Qualitative surveys (also known as unstructured interviews) ask open-ended questions that require respondents to provide free-form answers, which cannot be statistically inferred across the entire population being surveyed and therefore may not scale well if the sample size is very large.

Is Questionnaire A Quantitative Research?

A questionnaire is a series of questions or other prompts for gathering information about a subject. Although many researchers use questionnaires for statistical analysis, this is not always the case. So, yes, a questionnaire can be both, qualitative as well as quantitative, depending on the type of questions it contains.

The questionnaire is an integral part of survey research. It is a written or verbal series of questions pertaining to a specific topic, to which the respondent provides answers. 

Questionnaires are usually designed to obtain information from a large number of respondents on one or more occasions. 

The structured interview is normally used where it is necessary to keep close control over the questioning and to ensure that all respondents are asked exactly the same questions in precisely the same way.

The design process can be complex and time-consuming and many aspects need to be decided by the researcher before starting to write up the questionnaire:

• How will you distribute it? By hand? By mail? Online?
• What type of language will you use? Formal? Informal? Will it be general, or will specific jargon be included?
• How long will your questionnaire be?

Is Statistics Quantitative Research?

Quantitative research involves statistical analysis, such as calculating averages or percentages in surveys. In its most basic form, you count things, and then you make conclusions based on the numbers — usually about how common something is.

Statistics is a quantitative research method. It is used to quantify opinions, attitudes, and behaviors. This method involves the statistical analysis of data collected through polls, questionnaires, or surveys. The survey could be administered through personal interviews, telephone conversations, or the use of online survey forms.

This method is the most widely used method in business research. Most businesses make decisions based on quantitative methods. It is easy to administer with a large population size by using computers for ease of calculation and preparation of reports. It is also easy to understand and implement because it uses statistical terms that are easy to understand and interpret. This method is also used in both small and large businesses to make decisions based on quantified data.

Is Quasi-Experimental Quantitative Research?

Quasi-Experimental research is another type of experimental research design. Therefore, it is quantitative research. The difference between them is that the quasi-experimental design does not include a random sample. With this type of design, a researcher will create an experimental group and a control group, but not through random selection. Instead, the researcher will identify participants in each group based on criteria such as specific characteristics or behavior.

One advantage of Quasi-Experimental research is that it is easier to carry out than randomized experiments. It can also be less expensive because it does not require random assignment to groups. 

However, the researcher may have trouble determining whether the results from these groups are credible because there could be mitigating factors impacting the results that were not controlled for in the study’s design.

Does Quantitative Research Have Hypothesis?

Yes, quantitative research methods do have hypotheses. In fact, the whole idea of quantitative research is to test a hypothesis.

The hypothesis of quantitative research must always be stated in a clear manner. This is because the hypothesis helps to explain the relationship that exists between the different variables that have been used for the study.

However, quantitative research does not have a single hypothesis; it always has more than one hypothesis. The number and nature of these hypotheses will depend on the scope and coverage of the study or even research. The researcher will use these hypotheses to conclude whether there was any correlation between the variables that were used, or rather whether one variable had an effect on another variable.

Does Quantitative Research Use Interviews?

Interviews in quantitative research are often structured. This means that the interviewer asks the same questions, in the same order, of every respondent.

This is so that researchers are able to make comparisons between groups of people and draw conclusions about them.

For example, if a survey was looking at how many hours a week people spend on homework, it would be useful to know the subject they are studying and their level of education. These questions would be asked before asking about study time specifically so that any differences between groups can be explored further.

Respondents are also given a limited number of response options to choose from, for example, 1-5 hours 6-10 hours 11-15 hours 16-20 hours 20+ hours. 

Structured interviews also make it easier for data to be analyzed by computer programs or entered into databases.

Does Quantitative Research Focus On Human Experiences?

Quantitative research focuses on human experiences and looks into why people do certain things while others do not carry out the same actions at all. 

Quantitative research is also known as positivist research. 

It is a systematic process of collecting, organizing, analyzing, and interpreting numerical data. 

Quantitative researchers are involved in the entire research process from defining the problem to shaping the findings for presentation. 

They use probability sampling techniques, which refer to selecting samples from a population in such a way that each individual has an equal chance of being selected.

How To Determine Sample Size For Quantitative Research?

There are several methods you can use to determine the sample size. Some methods include using statistic tables and online calculators. Other methods involve using formulas to estimate sample size.

1. Using Statistic Tables

The first method you can use to calculate sample size involves using statistic tables. You need two parameters to do this; they include a confidence level and margin of error.

2. Online Calculators

The second method is by using online calculators like Survey Monkey or Raosoft Sample Size Calculator. To use these calculators, you need to fill out information such as the population, confidence interval, and margin of error among others, and click on calculate button.

3. Using Formulas

A sample size formula can be used to calculate the appropriate sample size based on factors such as population size, the margin of error, and confidence level. There are various formulas you can choose from.

Cochran’s Sample Size Formula is a common one: 

This formula can be used when one needs to determine the appropriate sample size for estimating a proportion or a percentage. 

The formula is: n = (Z 2 *p*q)/e 2 ; 

where n = sample size; p = estimated proportion; q = 1-p; e = margin of error; Z = z-score for confidence level selected. For example, 0.05 for 95% confidence interval.

Is Quantitative Research Objective?

Quantitative research focuses on measurable concepts and uses precise measurements and analysis to answer a specific question. It is thoroughly objective in nature. 

This type of research aims at testing theories by examining the relationship among variables with the help of different research tools. The relationship between variables can be causal or correlational.

In other words, quantitative researchers are more interested in determining whether the data gathered shows a true representation of the population under study.

Is Quantitative Research Scientific And Measurable?

The scientific and measurable characteristic of quantitative research is one of its greatest strengths. In fact, it’s the reason why so many scientists prefer quantitative research over qualitative research. Quantitative research can be reproduced and validated by other researchers, which makes the results generalizable and very reliable.

Because quantitative research is so reliable, it can be used to create a theory or model that accurately describes a phenomenon. 

For example, because Newton’s laws of motion have been verified by countless experiments, we can use them to develop complex models for predicting how objects will behave in different situations.

The data can be obtained using various instruments such as questionnaires and surveys. Quantitative research gathers information that is measurable, such as age, number of hours worked, and so on.

The main objective of quantitative research is to measure phenomena. It allows for the collection of numerical data that can be analyzed in order to explain what is being measured. This type of research aims at verifying theories and hypotheses by means of observation and measurements of variables.

Quantitative research does not deal with subjective ideas or opinions, but with measurable facts. It uses a deductive approach to gather information from a large sample, which then can be used to infer conclusions about the population from which it was drawn.

It can be quite useful to understand what quantitative research is, particularly when you are doing some research of your own. By understanding more about the process, you will be better prepared to make quantitative research and turn it into useful information.

Quantitative research is one of the more scientific/technical forms of market research. It’s a good way to get specific and detailed data (hence quantitative). Not only will you get statistics, numbers, etc., but you’ll actually truly learn something. It’s a great way to find out exactly what your audience wants.

Ultimately, both types of research complement one another. If you don’t have enough data yet, qualitative research can help you identify potential problems in your quantitative study. Even if you have an abundance of data from a previous research project, conducting a qualitative study prior to analyzing your quantitative data and drawing conclusions can lead to better results.

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• 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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STRENGTHS AND LIMITATIONS OF QUALITATIVE AND QUANTITATIVE RESEARCH METHODS

• André Queirós , Daniel Faria , Fernando Almeida
• Published 7 September 2017
• Sociology, Business
• European Journal of Education Studies

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STRENGTHS AND LIMITATIONS OF QUALITATIVE AND QUANTITATIVE RESEARCH METHODS

Scientific research adopts qualitative and quantitative methodologies in the modeling and analysis of numerous phenomena. The qualitative methodology intends to understand a complex reality and the meaning of actions in a given context. On the other hand, the quantitative methodology seeks to obtain accurate and reliable measurements that allow a statistical analysis. Both methodologies offer a set of methods, potentialities and limitations that must be explored and known by researchers. This paper concisely maps a total of seven qualitative methods and five quantitative methods. A comparative analysis of the most relevant and adopted methods is done to understand the main strengths and limitations of them. Additionally, the work developed intends to be a fundamental reference for the accomplishment of a research study, in which the researcher intends to adopt a qualitative or quantitative methodology. Through the analysis of the advantages and disadvantages of each method, it becomes possible to formulate a more accurate, informed and complete choice.

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There's no hard and fast rule for qualitative versus quantitative research, and it's often taken for granted. It is claimed here that the divide between qualitative and quantitative research is ambiguous, incoherent, and hence of little value, and that its widespread use could have negative implications. This conclusion is supported by a variety of arguments. Qualitative researchers, for example, have varying perspectives on fundamental problems (such as the use of quantification and causal analysis), which makes the difference as such shaky. In addition, many elements of qualitative and quantitative research overlap significantly, making it difficult to distinguish between the two. Practically in the case of field research, the Qualitative and quantitative approach can't be distinguished clearly as the study pointed. The distinction may limit innovation in the development of new research methodologies, as well as cause complication and wasteful activity. As a general rule, it may be desirable not to conceptualise research approaches at such abstract levels as are done in the context of qualitative or quantitative methodologies. Discussions of the benefits and drawbacks of various research methods, rather than general research questions, are recommended.

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Research design methods, such as qualitative, quantitative as well as mixed methods were introduced and subsequently each method was discussed in detail with the help of literature review as well as some personal and live examples to substantiate the findings of various literature. From various literature as well as from the own experiences, it is concluded that both qualitative research design method and quantitative research design method are equally important. It is not fair to criticize one method as the researcher is inclined towards the other method. It is practically evidenced that usage of both methods in the research, the researcher can substantiate the case better. However, duration part while using mixed methods to be kept in mind as it will take more time compared to the qualitative and quantitative methods. Hurrying and aborting in the middle due to time constraint ultimately result in poor research. It would be better if the world view towards these methods changes from criticizing mode to effective utilization mode, which will help research community in focusing and bring up better research outcomes rather than wasting time in arguing which method is scientifically acceptable and which method is biased. While I agree that the ontological, epistemological, axiological, and methodological assumptions for qualitative research method and quantitative research method, researchers should know fully about these methods and keep them as effective tools to utilize them in mixed mode, wherever it is appropriate and required to arrive at adequate research findings.

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In this paper, as a practitioner, I describe quantitative and qualitative inquiries. I explain some of their differences and similarities. I emphasize most important differences that researchers should consider when selecting one of the methods or when running a mixed study. I list differences in worldviews, research design, research processes, reliability assurance, and validity assurance. I propose a process flowchart for each type of inquiry. The purpose of the essay is to give researchers and primarily doctorate students a review of differences and similarities between the two methods of inquiry.

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Much of the literature on research in applied linguistics views quantitative and qualitative research as distinct entities embodying contrasting philosophies. In this paper, however, we present an example of how preliminary quantitative analyses of data can inform a qualitative discourse analysis study. Data from an online discussion forum concerning the Thai political crisis of 2010 were initially analysed quantitatively to identify keywords, word clusters, length of postings and user ratings of postings for the contributions from the opposing political factions. Each posting was also rated for level of antagonism and credibility of argumentation. These quantitative data provide an overview of the discussion forum and the patterns of discussion within it which was then used as a framework to guide the main qualitative analysis ensuring that the key revealed meanings and functions were covered in the analysis and reducing potential bias in data analysis and presentation. Introduction Mixed methods research has a long history in disciplines that attempt to explain behavior and social phenomena (Dörnyei, 2007). The practice includes a mix or qualitative and quantitative methods, a mix of quantitative methods or a mix of qualitative methods. The type of mixed methods approach that is most popular and is increasingly employed is the first, the mix of the two, which are often based on different research paradigms. Single method research is normally criticised by their opposition as inferior and insufficient. In a pure quantitative study, with the focus on theory or hypothesis testing, the researcher may not be sensitive on contextual details. Moreover, it requires a large amount of data to be able to give an effective ground. A qualitative method, on the other hand, is prone to high subjectivity of the researcher and is unlikely to be generalisable. It can only deal with a small size of data, which makes decision making of the overview and conclusive deduction an ordeal. Four models of mixed methods design are proposed including concurrent design, explanatory sequential design, exploratory sequential design and embedded sequential/concurrent designs (Creswell & Zhang, 2009). The first model, the concurrent design compares and contrasts the results between the two methods to present evidence. Second, the explanatory sequential design utilises the explanation of one set of results to support the other's. In an exploratory sequential design, one method's results are followed by the other's to strengthen the claims made, in the name of generalisability, for instance. In the last design, embedded sequential/concurrent design, a small database is made part of the big database and is used to experiment or enhance the major findings. Typically, a mixed method research deals with different types of data but it may also mean applying different methods to investigate the same data. Two dimensions of advancements that mixed methods data analyses (MMDA) have to offer can be considered as 1) the design virtue and b) research expertise. The research virtue obtained from mixed methods may refer to the strategies that are used to display trustworthiness of the research such as triangulation, complementarity, development, initiation and expansion (Green et al, 1989). In greater details, four major advantages of MMDA have been put forward (Dörnyei, 2007). First, oversimplification, decontexualisation and reduction of the quantitative analysis can be disputed by in depth meaningful qualitative analysis, while content-specificity and unrepresentativeness can be overcome by the generalisable quantitative analysis. Second, for multi-level analyses, MMDA can add more meaning by converging numbers into words and vice versa. Third, triangulation through multi-methods analyses means increasing validity of the study of the results. Fourth, a study with mixed methods analyses tends to attract more attention from the

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André Queirós Higher Polytechnic Institute of Gaya, V. N. Gaia, Portugal

Daniel Faria Higher Polytechnic Institute of Gaya, V. N. Gaia, Portugal

Fernando Almeida Faculty of Engineering of Oporto University, INESC TEC, Porto, Portugal

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Qualitative Research Design

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Qualitative Research Design is a method focused on understanding and interpreting the experiences of individuals or groups. Unlike quantitative research , which quantifies data and identifies patterns through statistical analysis, Qualitative Research Design explores phenomena in depth using interviews, focus groups, and observations. This approach gathers rich narratives that provide insights into thoughts, feelings, and behaviors, uncovering underlying reasons and motivations. Essential in fields like social sciences, education, and health, a strong Qualitative Research Proposal or Qualitative Research Plan must carefully consider the Research Design and relevant Research Terms for a comprehensive approach.

What is Qualitative Research Design?

Qualitative Research Design is a method that aims to understand and interpret the meaning and experiences of individuals or groups. It employs in-depth techniques like interviews, focus groups, and observations to gather detailed, rich narratives. Unlike quantitative research, which uses statistical analysis to identify patterns, qualitative research seeks to uncover the underlying reasons and motivations behind thoughts, feelings, and behaviors.

Types of Qualitative Research Design

1. Ethnography

Ethnography involves the detailed study of cultures or social groups through direct observation and participation. Researchers immerse themselves in the group’s daily life to understand their customs, behaviors, and social interactions. This method is often used to study communities, workplaces, or organizations. Example : Observing and interviewing members of a remote community to understand their social practices and traditions.

2. Grounded Theory

Grounded theory aims to generate a theory grounded in the data collected from participants. Researchers gather data through interviews, observations, and other methods, then use coding techniques to develop a theory. This approach is useful for studying processes, actions, and interactions, such as developing a theory on how people cope with job loss. Example : Analyzing interviews with employees to develop a theory about workplace motivation.

3. Focus Groups

Focus groups involve guided discussions with a small group of participants to explore their perceptions, opinions, and attitudes towards a particular topic. This method allows researchers to gather a wide range of insights and observe group dynamics. Focus groups are commonly used in market research, social science studies, and product development. Example : Conducting focus groups with parents to understand their views on remote learning during the COVID-19 pandemic.

4. Interviews

Interviews are one-on-one conversations between the researcher and the participant, designed to gather in-depth information on the participant’s experiences, thoughts, and feelings. Interviews can be structured, semi-structured, or unstructured, allowing flexibility in exploring the research topic. This method is widely used across various qualitative research studies. Example : Conducting semi-structured interviews with veterans to explore their reintegration experiences into civilian life.

5. Narrative Research

Narrative research focuses on the stories and personal accounts of individuals. Researchers collect narratives through interviews, journals, letters, or autobiographies and analyze them to understand how people make sense of their experiences. This type of research might explore life stories, personal journeys, or historical accounts. Example : Collecting and analyzing life stories of refugees to understand their migration experiences.

6. Action Research

Action research is a participatory approach that involves researchers and participants working together to address a problem or improve a situation. This method focuses on practical solutions and often includes cycles of planning, action, observation, and reflection. It is commonly used in educational settings to improve teaching practices, school policies, or community development projects. Example : Teachers working together to implement and assess a new curriculum in their school.

Qualitative Research Design Methods

Interviews are one-on-one conversations designed to gather in-depth information about a participant’s experiences, thoughts, and feelings. They can be structured, semi-structured, or unstructured, allowing flexibility in exploring topics. Example : Semi-structured interviews with veterans to explore their reintegration experiences into civilian life.

Focus Groups

Focus groups involve guided discussions with small groups to explore their perceptions, opinions, and attitudes on a topic. This method gathers diverse insights and observes group dynamics. Example : Focus groups with parents to understand their views on remote learning during the COVID-19 pandemic.

Observational Studies

Observational studies involve systematically watching and recording behaviors and interactions in natural settings without interference. Example : Observing children in a playground to study social development and peer relationships.

Discussion Boards

Discussion boards are online forums where participants post responses and engage in discussions. This method collects data from participants in different locations over time. Example : Analyzing posts on a discussion board for chronic illness patients to understand their coping strategies and support systems.

Difference between Qualitative Research vs. Quantitative Research

Characteristics of Qualitative Research Design

• Naturalistic Inquiry: Conducted in natural settings where participants experience the issue or phenomenon under study.
• Contextual Understanding: Emphasizes understanding the cultural, social, and historical contexts of participants.
• Participant Perspectives: Prioritizes the views, feelings, and interpretations of participants.
• Flexibility and Adaptiveness: Designs are flexible and can be adjusted as new insights emerge.
• Rich, Descriptive Data: Collects detailed data in words, images, and objects for comprehensive understanding.
• Inductive Approach: Develops theories and patterns from the data collected rather than testing predefined theories.
• Emergent Design: Research design evolves during the study based on emerging themes and insights.
• Multiple Data Sources: Uses various data sources like interviews, focus groups, observations, and document analysis.
• Subjectivity and Reflexivity: Researchers acknowledge their influence on the research process and examine their biases and assumptions.
• Holistic Perspective: Considers the entire phenomenon and its complexity, looking at interrelated components.
• Iterative Process: Data collection and analysis occur simultaneously in an iterative manner.
• Ethical Considerations: Ensures informed consent, confidentiality, and sensitivity to participants’ needs and well-being.
• Detailed Reporting: Results are reported in a detailed narrative style, often using direct quotes from participants.

How to Find Qualitative Research Design

1. identify the research problem.

Define the specific problem or phenomenon you want to study. For example, you might explore the experiences of first-generation college students.

2. Conduct a Literature Review

Review existing research to understand what has been studied and identify gaps. This helps to build a foundation for your research.

3. Formulate Research Questions

Create open-ended questions to guide your study. Example: “What challenges do first-generation college students face?”

4. Choose a Qualitative Research Approach

Select a methodology that fits your research question, such as phenomenology, grounded theory, ethnography, case study, or narrative research.

5. Select the Research Setting

Decide where you will conduct your study, such as a university campus or online forums relevant to your topic.

6. Identify and Recruit Participants

Determine criteria for participant selection and recruit individuals who meet these criteria, such as first-generation college students.

7. Choose Data Collection Methods

Select methods like interviews, focus groups, observations, or document analysis to gather rich data.

8. Collect and Analyze Data

Gather your data and analyze it by identifying patterns and themes. Use coding and software tools if necessary.

9. Validate Findings

Ensure the credibility of your research through techniques like triangulation, member checking, and peer debriefing.

FAQ’s

How does qualitative research differ from quantitative research.

Qualitative research focuses on understanding meaning and experiences, while quantitative research measures variables and uses statistical analysis to test hypotheses.

What is the purpose of qualitative research?

The purpose is to gain in-depth insights into people’s behaviors, motivations, and social interactions to understand complex phenomena.

What methods are commonly used in qualitative research?

Common methods include interviews, focus groups, participant observation, and content analysis of texts and media.

What is a case study in qualitative research?

A case study is an in-depth exploration of a single case or multiple cases within a real-life context to uncover detailed insights.

What is narrative research in qualitative research?

Narrative research explores the stories and personal accounts of individuals to understand how they make sense of their experiences.

How is data analyzed in qualitative research?

Data analysis involves coding and categorizing data to identify patterns, themes, and meanings, often using software like NVivo or manual methods.

What is the role of the researcher in qualitative research?

The researcher acts as a primary instrument for data collection and analysis, often engaging closely with participants and their contexts.

What are the strengths of qualitative research?

Strengths include rich, detailed data, the ability to explore complex issues, and flexibility in data collection and analysis.

What are the limitations of qualitative research?

Limitations include potential researcher bias, time-consuming data collection, and challenges in generalizing findings to larger populations.

How is validity ensured in qualitative research?

Validity is ensured through strategies like triangulation, member checking, prolonged engagement, and reflexivity to enhance credibility and trustworthiness.

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Table of Contents

Business research methodologies enable organizations to gather meaningful data and derive actionable insights. From qualitative interviews to quantitative analytics, selecting the appropriate research approach is foundational. This article will explore “ DBA research methodologies ” utilizing both qualitative and quantitative techniques to demonstrate how “doctoral studies” rigorously examine topics for optimal “business research methods”.

The Building Blocks: DBA Research Methodologies

“ DBA research methodologies ” incorporate diverse academic disciplines to study complex business problems. Common approaches include:

“Qualitative research” methods like ethnography and case study analysis are used to gather in-depth, descriptive data on behaviors, processes, and “why” questions through interviews, focus groups, observations, etc.

“ Quantitative analysis ” uses statistical modeling and large datasets to identify correlations and patterns that answer “what” and “how many” questions.

Tailoring the Methodology to the Research Question

Not all techniques work for every research scenario. Choosing the proper methodology requires clearly defining the question and desired outcomes upfront.

“DBA research methodologies” selection criteria include:

• Purpose: Is it an exploratory, descriptive, or explanatory study? What data is needed to address the research problem?
• Resources: Are enough participants, funding, tools, and researcher expertise available?
• “Business research methods” suitability: Do qualitative, quantitative or mixed methods fit best?

Qualitative Business Research in Practice

“ Qualitative research “, like case studies, offers an intimate understanding of management challenges through first-hand experiences and perspectives.

For example, interviewing executives on leadership development initiatives may reveal:

• Onboarding program weaknesses increasing early leader turnover
• Communication breakdowns around development goal-setting
• Lack of coaching support to reinforce training concepts

Such vivid insights direct specific improvements. They also inform quantitative follow-up studies predicting retention boosts from particular changes.

Advantages of Quantitative Analytics

While qualitative designs provide depth, “quantitative analysis” delivers breadth by statistically testing hypotheses on large samples. Benefits include:

• Generalizability: Findings represent broader populations within confidence levels
• Objectivity: Statistical tests quantify reliability more definitively than subjective assessments
• Predictiveness: Data patterns forecast future outcomes to guide decisions

For example, leadership surveys across 500 managers could model links between specific coaching interactions, engagement gains and productivity metrics.

Achieving Research Objectives Through a Mixed Methods Approach

Combining qualitative and quantitative business research techniques as part of robust “DBA research methodologies” boosts the credibility and practical value of findings. The strengths of each approach offset the other’s limitations.

Qualitative stage:

• Gathers detailed observational and interview data on coaching interactions from a small leader sample
• Provides behaviors and sentiment themes to explore further using surveys

Quantitative stage:

• Tests earlier findings across wider groups using correlational statistics and regression analysis
• Connects coaching frequency and techniques to engagement, retention and performance measures
• Together, these phases yield nuanced discoveries that are impossible using one methodology.

Conclusion 

Sophisticated “DBA research methodologies” necessitate understanding the full toolkit of “business research methods” from ethnographies to experiments. While qualitative designs reveal key psychological and social dynamics, quantitative analytics assess their business impacts more conclusively. Combining these techniques produces superior insights to empower impactful organizational decisions and leadership strategies.

1. What are some examples of qualitative business research methods?

Common qualitative methods include in-depth interviews, focus groups, participant observations, case study analysis, and ethnographic research. These techniques gather non-numerical data on behaviors, emotions, organizational processes, and experiential perspectives.

2. When should quantitative methods be used instead?

Quantitative analytics, such as surveys and experiments, that collect numerical data for statistical analysis are preferred for testing hypotheses, predicting outcomes, generalizing results to wider populations, and establishing causal, correlational, or probabilistic relationships between variables.

3. What are the main benefits of mixed methods research?

Combining qualitative and quantitative techniques mitigates the limitations of each, providing richer insights through an initial exploratory phase to uncover themes, behaviors and language for follow-up hypothesis testing using broader samples and correlational statistics.

4. Does the research question determine the best methodology?

Yes, clearly defining the research purpose and goals upfront provides criteria to select the most appropriate primary and supporting techniques, whether qualitative, quantitative or both.

5. How can business researchers ensure high-quality studies?

Rigorous quality standards include mitigating bias, establishing validity and reliability measures, choosing representative samples, aligning analysis with data collected, accurately reporting limitations, and ethically obtaining informed consent.

6. What role do literature reviews play in research design?

Literature reviews critically examine prior theories and findings to position new questions, avoid duplication, select proven measurements, build foundational knowledge, and identify promising methodological directions.

7. Which analytics methods are trending in business research?

Data mining, machine learning predictive modeling, social network analysis, multivariate statistics, and text mining are increasingly supplementing traditional analytics to uncover insights from today’s complex business datasets.

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Conducting and Writing Quantitative and Qualitative Research

Edward barroga.

1 Department of Medical Education, Showa University School of Medicine, Tokyo, Japan.

Glafera Janet Matanguihan

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

Atsuko Furuta

Makiko arima, shizuma tsuchiya, chikako kawahara, yusuke takamiya.

Comprehensive knowledge of quantitative and qualitative research systematizes scholarly research and enhances the quality of research output. Scientific researchers must be familiar with them and skilled to conduct their investigation within the frames of their chosen research type. When conducting quantitative research, scientific researchers should describe an existing theory, generate a hypothesis from the theory, test their hypothesis in novel research, and re-evaluate the theory. Thereafter, they should take a deductive approach in writing the testing of the established theory based on experiments. When conducting qualitative research, scientific researchers raise a question, answer the question by performing a novel study, and propose a new theory to clarify and interpret the obtained results. After which, they should take an inductive approach to writing the formulation of concepts based on collected data. When scientific researchers combine the whole spectrum of inductive and deductive research approaches using both quantitative and qualitative research methodologies, they apply mixed-method research. Familiarity and proficiency with these research aspects facilitate the construction of novel hypotheses, development of theories, or refinement of concepts.

Graphical Abstract

INTRODUCTION

Novel research studies are conceptualized by scientific researchers first by asking excellent research questions and developing hypotheses, then answering these questions by testing their hypotheses in ethical research. 1 , 2 , 3 Before they conduct novel research studies, scientific researchers must possess considerable knowledge of both quantitative and qualitative research. 2

In quantitative research, researchers describe existing theories, generate and test a hypothesis in novel research, and re-evaluate existing theories deductively based on their experimental results. 1 , 4 , 5 In qualitative research, scientific researchers raise and answer research questions by performing a novel study, then propose new theories by clarifying their results inductively. 1 , 6

RATIONALE OF THIS ARTICLE

When researchers have a limited knowledge of both research types and how to conduct them, this can result in substandard investigation. Researchers must be familiar with both types of research and skilled to conduct their investigations within the frames of their chosen type of research. Thus, meticulous care is needed when planning quantitative and qualitative research studies to avoid unethical research and poor outcomes.

Understanding the methodological and writing assumptions 7 , 8 underpinning quantitative and qualitative research, especially by non-Anglophone researchers, is essential for their successful conduct. Scientific researchers, especially in the academe, face pressure to publish in international journals 9 where English is the language of scientific communication. 10 , 11 In particular, non-Anglophone researchers face challenges related to linguistic, stylistic, and discourse differences. 11 , 12 Knowing the assumptions of the different types of research will help clarify research questions and methodologies, easing the challenge and help.

SEARCH FOR RELEVANT ARTICLES

To identify articles relevant to this topic, we adhered to the search strategy recommended by Gasparyan et al. 7 We searched through PubMed, Scopus, Directory of Open Access Journals, and Google Scholar databases using the following keywords: quantitative research, qualitative research, mixed-method research, deductive reasoning, inductive reasoning, study design, descriptive research, correlational research, experimental research, causal-comparative research, quasi-experimental research, historical research, ethnographic research, meta-analysis, narrative research, grounded theory, phenomenology, case study, and field research.

AIMS OF THIS ARTICLE

This article aims to provide a comparative appraisal of qualitative and quantitative research for scientific researchers. At present, there is still a need to define the scope of qualitative research, especially its essential elements. 13 Consensus on the critical appraisal tools to assess the methodological quality of qualitative research remains lacking. 14 Framing and testing research questions can be challenging in qualitative research. 2 In the healthcare system, it is essential that research questions address increasingly complex situations. Therefore, research has to be driven by the kinds of questions asked and the corresponding methodologies to answer these questions. 15 The mixed-method approach also needs to be clarified as this would appear to arise from different philosophical underpinnings. 16

This article also aims to discuss how particular types of research should be conducted and how they should be written in adherence to international standards. In the US, Europe, and other countries, responsible research and innovation was conceptualized and promoted with six key action points: engagement, gender equality, science education, open access, ethics and governance. 17 , 18 International ethics standards in research 19 as well as academic integrity during doctoral trainings are now integral to the research process. 20

POTENTIAL BENEFITS FROM THIS ARTICLE

This article would be beneficial for researchers in further enhancing their understanding of the theoretical, methodological, and writing aspects of qualitative and quantitative research, and their combination.

Moreover, this article reviews the basic features of both research types and overviews the rationale for their conduct. It imparts information on the most common forms of quantitative and qualitative research, and how they are carried out. These aspects would be helpful for selecting the optimal methodology to use for research based on the researcher’s objectives and topic.

This article also provides information on the strengths and weaknesses of quantitative and qualitative research. Such information would help researchers appreciate the roles and applications of both research types and how to gain from each or their combination. As different research questions require different types of research and analyses, this article is anticipated to assist researchers better recognize the questions answered by quantitative and qualitative research.

Finally, this article would help researchers to have a balanced perspective of qualitative and quantitative research without considering one as superior to the other.

TYPES OF RESEARCH

Research can be classified into two general types, quantitative and qualitative. 21 Both types of research entail writing a research question and developing a hypothesis. 22 Quantitative research involves a deductive approach to prove or disprove the hypothesis that was developed, whereas qualitative research involves an inductive approach to create a hypothesis. 23 , 24 , 25 , 26

In quantitative research, the hypothesis is stated before testing. In qualitative research, the hypothesis is developed through inductive reasoning based on the data collected. 27 , 28 For types of data and their analysis, qualitative research usually includes data in the form of words instead of numbers more commonly used in quantitative research. 29

Quantitative research usually includes descriptive, correlational, causal-comparative / quasi-experimental, and experimental research. 21 On the other hand, qualitative research usually encompasses historical, ethnographic, meta-analysis, narrative, grounded theory, phenomenology, case study, and field research. 23 , 25 , 28 , 30 A summary of the features, writing approach, and examples of published articles for each type of qualitative and quantitative research is shown in Table 1 . 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43

QUANTITATIVE RESEARCH

Deductive approach.

The deductive approach is used to prove or disprove the hypothesis in quantitative research. 21 , 25 Using this approach, researchers 1) make observations about an unclear or new phenomenon, 2) investigate the current theory surrounding the phenomenon, and 3) hypothesize an explanation for the observations. Afterwards, researchers will 4) predict outcomes based on the hypotheses, 5) formulate a plan to test the prediction, and 6) collect and process the data (or revise the hypothesis if the original hypothesis was false). Finally, researchers will then 7) verify the results, 8) make the final conclusions, and 9) present and disseminate their findings ( Fig. 1A ).

Types of quantitative research

The common types of quantitative research include (a) descriptive, (b) correlational, c) experimental research, and (d) causal-comparative/quasi-experimental. 21

Descriptive research is conducted and written by describing the status of an identified variable to provide systematic information about a phenomenon. A hypothesis is developed and tested after data collection, analysis, and synthesis. This type of research attempts to factually present comparisons and interpretations of findings based on analyses of the characteristics, progression, or relationships of a certain phenomenon by manipulating the employed variables or controlling the involved conditions. 44 Here, the researcher examines, observes, and describes a situation, sample, or variable as it occurs without investigator interference. 31 , 45 To be meaningful, the systematic collection of information requires careful selection of study units by precise measurement of individual variables 21 often expressed as ranges, means, frequencies, and/or percentages. 31 , 45 Descriptive statistical analysis using ANOVA, Student’s t -test, or the Pearson coefficient method has been used to analyze descriptive research data. 46

Correlational research is performed by determining and interpreting the extent of a relationship between two or more variables using statistical data. This involves recognizing data trends and patterns without necessarily proving their causes. The researcher studies only the data, relationships, and distributions of variables in a natural setting, but does not manipulate them. 21 , 45 Afterwards, the researcher establishes reliability and validity, provides converging evidence, describes relationship, and makes predictions. 47

Experimental research is usually referred to as true experimentation. The researcher establishes the cause-effect relationship among a group of variables making up a study using the scientific method or process. This type of research attempts to identify the causal relationships between variables through experiments by arbitrarily controlling the conditions or manipulating the variables used. 44 The scientific manuscript would include an explanation of how the independent variable was manipulated to determine its effects on the dependent variables. The write-up would also describe the random assignments of subjects to experimental treatments. 21

Causal-comparative/quasi-experimental research closely resembles true experimentation but is conducted by establishing the cause-effect relationships among variables. It may also be conducted to establish the cause or consequences of differences that already exist between, or among groups of individuals. 48 This type of research compares outcomes between the intervention groups in which participants are not randomized to their respective interventions because of ethics- or feasibility-related reasons. 49 As in true experiments, the researcher identifies and measures the effects of the independent variable on the dependent variable. However, unlike true experiments, the researchers do not manipulate the independent variable.

In quasi-experimental research, naturally formed or pre-existing groups that are not randomly assigned are used, particularly when an ethical, randomized controlled trial is not feasible or logical. 50 The researcher identifies control groups as those which have been exposed to the treatment variable, and then compares these with the unexposed groups. The causes are determined and described after data analysis, after which conclusions are made. The known and unknown variables that could still affect the outcome are also included. 7

QUALITATIVE RESEARCH

Inductive approach.

Qualitative research involves an inductive approach to develop a hypothesis. 21 , 25 Using this approach, researchers answer research questions and develop new theories, but they do not test hypotheses or previous theories. The researcher seldom examines the effectiveness of an intervention, but rather explores the perceptions, actions, and feelings of participants using interviews, content analysis, observations, or focus groups. 25 , 45 , 51

Distinctive features of qualitative research

Qualitative research seeks to elucidate about the lives of people, including their lived experiences, behaviors, attitudes, beliefs, personality characteristics, emotions, and feelings. 27 , 30 It also explores societal, organizational, and cultural issues. 30 This type of research provides a good story mimicking an adventure which results in a “thick” description that puts readers in the research setting. 52

The qualitative research questions are open-ended, evolving, and non-directional. 26 The research design is usually flexible and iterative, commonly employing purposive sampling. The sample size depends on theoretical saturation, and data is collected using in-depth interviews, focus groups, and observations. 27

In various instances, excellent qualitative research may offer insights that quantitative research cannot. Moreover, qualitative research approaches can describe the ‘lived experience’ perspectives of patients, practitioners, and the public. 53 Interestingly, recent developments have looked into the use of technology in shaping qualitative research protocol development, data collection, and analysis phases. 54

Qualitative research employs various techniques, including conversational and discourse analysis, biographies, interviews, case-studies, oral history, surveys, documentary and archival research, audiovisual analysis, and participant observations. 26

Conducting qualitative research

To conduct qualitative research, investigators 1) identify a general research question, 2) choose the main methods, sites, and subjects, and 3) determine methods of data documentation access to subjects. Researchers also 4) decide on the various aspects for collecting data (e.g., questions, behaviors to observe, issues to look for in documents, how much (number of questions, interviews, or observations), 5) clarify researchers’ roles, and 6) evaluate the study’s ethical implications in terms of confidentiality and sensitivity. Afterwards, researchers 7) collect data until saturation, 8) interpret data by identifying concepts and theories, and 9) revise the research question if necessary and form hypotheses. In the final stages of the research, investigators 10) collect and verify data to address revisions, 11) complete the conceptual and theoretical framework to finalize their findings, and 12) present and disseminate findings ( Fig. 1B ).

Types of qualitative research

The different types of qualitative research include (a) historical research, (b) ethnographic research, (c) meta-analysis, (d) narrative research, (e) grounded theory, (f) phenomenology, (g) case study, and (h) field research. 23 , 25 , 28 , 30

Historical research is conducted by describing past events, problems, issues, and facts. The researcher gathers data from written or oral descriptions of past events and attempts to recreate the past without interpreting the events and their influence on the present. 6 Data is collected using documents, interviews, and surveys. 55 The researcher analyzes these data by describing the development of events and writes the research based on historical reports. 2

Ethnographic research is performed by observing everyday life details as they naturally unfold. 2 It can also be conducted by developing in-depth analytical descriptions of current systems, processes, and phenomena or by understanding the shared beliefs and practices of a particular group or culture. 21 The researcher collects extensive narrative non-numerical data based on many variables over an extended period, in a natural setting within a specific context. To do this, the researcher uses interviews, observations, and active participation. These data are analyzed by describing and interpreting them and developing themes. A detailed report of the interpreted data is then provided. 2 The researcher immerses himself/herself into the study population and describes the actions, behaviors, and events from the perspective of someone involved in the population. 23 As examples of its application, ethnographic research has helped to understand a cultural model of family and community nursing during the coronavirus disease 2019 outbreak. 56 It has also been used to observe the organization of people’s environment in relation to cardiovascular disease management in order to clarify people’s real expectations during follow-up consultations, possibly contributing to the development of innovative solutions in care practices. 57

Meta-analysis is carried out by accumulating experimental and correlational results across independent studies using a statistical method. 21 The report is written by specifying the topic and meta-analysis type. In the write-up, reporting guidelines are followed, which include description of inclusion criteria and key variables, explanation of the systematic search of databases, and details of data extraction. Meta-analysis offers in-depth data gathering and analysis to achieve deeper inner reflection and phenomenon examination. 58

Narrative research is performed by collecting stories for constructing a narrative about an individual’s experiences and the meanings attributed to them by the individual. 9 It aims to hear the voice of individuals through their account or experiences. 17 The researcher usually conducts interviews and analyzes data by storytelling, content review, and theme development. The report is written as an in-depth narration of events or situations focused on the participants. 2 , 59 Narrative research weaves together sequential events from one or two individuals to create a “thick” description of a cohesive story or narrative. 23 It facilitates understanding of individuals’ lives based on their own actions and interpretations. 60

Grounded theory is conducted by engaging in an inductive ground-up or bottom-up strategy of generating a theory from data. 24 The researcher incorporates deductive reasoning when using constant comparisons. Patterns are detected in observations and then a working hypothesis is created which directs the progression of inquiry. The researcher collects data using interviews and questionnaires. These data are analyzed by coding the data, categorizing themes, and describing implications. The research is written as a theory and theoretical models. 2 In the write-up, the researcher describes the data analysis procedure (i.e., theoretical coding used) for developing hypotheses based on what the participants say. 61 As an example, a qualitative approach has been used to understand the process of skill development of a nurse preceptor in clinical teaching. 62 A researcher can also develop a theory using the grounded theory approach to explain the phenomena of interest by observing a population. 23

Phenomenology is carried out by attempting to understand the subjects’ perspectives. This approach is pertinent in social work research where empathy and perspective are keys to success. 21 Phenomenology studies an individual’s lived experience in the world. 63 The researcher collects data by interviews, observations, and surveys. 16 These data are analyzed by describing experiences, examining meanings, and developing themes. The researcher writes the report by contextualizing and reporting the subjects’ experience. This research approach describes and explains an event or phenomenon from the perspective of those who have experienced it. 23 Phenomenology understands the participants’ experiences as conditioned by their worldviews. 52 It is suitable for a deeper understanding of non-measurable aspects related to the meanings and senses attributed by individuals’ lived experiences. 60

Case study is conducted by collecting data through interviews, observations, document content examination, and physical inspections. The researcher analyzes the data through a detailed identification of themes and the development of narratives. The report is written as an in-depth study of possible lessons learned from the case. 2

Field research is performed using a group of methodologies for undertaking qualitative inquiries. The researcher goes directly to the social phenomenon being studied and observes it extensively. In the write-up, the researcher describes the phenomenon under the natural environment over time with no implantation of controls or experimental conditions. 45

DIFFERENCES BETWEEN QUANTITATIVE AND QUALITATIVE RESEARCH

Scientific researchers must be aware of the differences between quantitative and qualitative research in terms of their working mechanisms to better understand their specific applications. This knowledge will be of significant benefit to researchers, especially during the planning process, to ensure that the appropriate type of research is undertaken to fulfill the research aims.

In terms of quantitative research data evaluation, four well-established criteria are used: internal validity, external validity, reliability, and objectivity. 23 The respective correlating concepts in qualitative research data evaluation are credibility, transferability, dependability, and confirmability. 30 Regarding write-up, quantitative research papers are usually shorter than their qualitative counterparts, which allows the latter to pursue a deeper understanding and thus producing the so-called “thick” description. 29

Interestingly, a major characteristic of qualitative research is that the research process is reversible and the research methods can be modified. This is in contrast to quantitative research in which hypothesis setting and testing take place unidirectionally. This means that in qualitative research, the research topic and question may change during literature analysis, and that the theoretical and analytical methods could be altered during data collection. 44

Quantitative research focuses on natural, quantitative, and objective phenomena, whereas qualitative research focuses on social, qualitative, and subjective phenomena. 26 Quantitative research answers the questions “what?” and “when?,” whereas qualitative research answers the questions “why?,” “how?,” and “how come?.” 64

Perhaps the most important distinction between quantitative and qualitative research lies in the nature of the data being investigated and analyzed. Quantitative research focuses on statistical, numerical, and quantitative aspects of phenomena, and employ the same data collection and analysis, whereas qualitative research focuses on the humanistic, descriptive, and qualitative aspects of phenomena. 26 , 28

Structured versus unstructured processes

The aims and types of inquiries determine the difference between quantitative and qualitative research. In quantitative research, statistical data and a structured process are usually employed by the researcher. Quantitative research usually suggests quantities (i.e., numbers). 65 On the other hand, researchers typically use opinions, reasons, verbal statements, and an unstructured process in qualitative research. 63 Qualitative research is more related to quality or kind. 65

In quantitative research, the researcher employs a structured process for collecting quantifiable data. Often, a close-ended questionnaire is used wherein the response categories for each question are designed in which values can be assigned and analyzed quantitatively using a common scale. 66 Quantitative research data is processed consecutively from data management, then data analysis, and finally to data interpretation. Data should be free from errors and missing values. In data management, variables are defined and coded. In data analysis, statistics (e.g., descriptive, inferential) as well as central tendency (i.e., mean, median, mode), spread (standard deviation), and parameter estimation (confidence intervals) measures are used. 67

In qualitative research, the researcher uses an unstructured process for collecting data. These non-statistical data may be in the form of statements, stories, or long explanations. Various responses according to respondents may not be easily quantified using a common scale. 66

Composing a qualitative research paper resembles writing a quantitative research paper. Both papers consist of a title, an abstract, an introduction, objectives, methods, findings, and discussion. However, a qualitative research paper is less regimented than a quantitative research paper. 27

Quantitative research as a deductive hypothesis-testing design

Quantitative research can be considered as a hypothesis-testing design as it involves quantification, statistics, and explanations. It flows from theory to data (i.e., deductive), focuses on objective data, and applies theories to address problems. 45 , 68 It collects numerical or statistical data; answers questions such as how many, how often, how much; uses questionnaires, structured interview schedules, or surveys 55 as data collection tools; analyzes quantitative data in terms of percentages, frequencies, statistical comparisons, graphs, and tables showing statistical values; and reports the final findings in the form of statistical information. 66 It uses variable-based models from individual cases and findings are stated in quantified sentences derived by deductive reasoning. 24

In quantitative research, a phenomenon is investigated in terms of the relationship between an independent variable and a dependent variable which are numerically measurable. The research objective is to statistically test whether the hypothesized relationship is true. 68 Here, the researcher studies what others have performed, examines current theories of the phenomenon being investigated, and then tests hypotheses that emerge from those theories. 4

Quantitative hypothesis-testing research has certain limitations. These limitations include (a) problems with selection of meaningful independent and dependent variables, (b) the inability to reflect subjective experiences as variables since variables are usually defined numerically, and (c) the need to state a hypothesis before the investigation starts. 61

Qualitative research as an inductive hypothesis-generating design

Qualitative research can be considered as a hypothesis-generating design since it involves understanding and descriptions in terms of context. It flows from data to theory (i.e., inductive), focuses on observation, and examines what happens in specific situations with the aim of developing new theories based on the situation. 45 , 68 This type of research (a) collects qualitative data (e.g., ideas, statements, reasons, characteristics, qualities), (b) answers questions such as what, why, and how, (c) uses interviews, observations, or focused-group discussions as data collection tools, (d) analyzes data by discovering patterns of changes, causal relationships, or themes in the data; and (e) reports the final findings as descriptive information. 61 Qualitative research favors case-based models from individual characteristics, and findings are stated using context-dependent existential sentences that are justifiable by inductive reasoning. 24

In qualitative research, texts and interviews are analyzed and interpreted to discover meaningful patterns characteristic of a particular phenomenon. 61 Here, the researcher starts with a set of observations and then moves from particular experiences to a more general set of propositions about those experiences. 4

Qualitative hypothesis-generating research involves collecting interview data from study participants regarding a phenomenon of interest, and then using what they say to develop hypotheses. It involves the process of questioning more than obtaining measurements; it generates hypotheses using theoretical coding. 61 When using large interview teams, the key to promoting high-level qualitative research and cohesion in large team methods and successful research outcomes is the balance between autonomy and collaboration. 69

Qualitative data may also include observed behavior, participant observation, media accounts, and cultural artifacts. 61 Focus group interviews are usually conducted, audiotaped or videotaped, and transcribed. Afterwards, the transcript is analyzed by several researchers.

Qualitative research also involves scientific narratives and the analysis and interpretation of textual or numerical data (or both), mostly from conversations and discussions. Such approach uncovers meaningful patterns that describe a particular phenomenon. 2 Thus, qualitative research requires skills in grasping and contextualizing data, as well as communicating data analysis and results in a scientific manner. The reflective process of the inquiry underscores the strengths of a qualitative research approach. 2

Combination of quantitative and qualitative research

When both quantitative and qualitative research methods are used in the same research, mixed-method research is applied. 25 This combination provides a complete view of the research problem and achieves triangulation to corroborate findings, complementarity to clarify results, expansion to extend the study’s breadth, and explanation to elucidate unexpected results. 29

Moreover, quantitative and qualitative findings are integrated to address the weakness of both research methods 29 , 66 and to have a more comprehensive understanding of the phenomenon spectrum. 66

For data analysis in mixed-method research, real non-quantitized qualitative data and quantitative data must both be analyzed. 70 The data obtained from quantitative analysis can be further expanded and deepened by qualitative analysis. 23

In terms of assessment criteria, Hammersley 71 opined that qualitative and quantitative findings should be judged using the same standards of validity and value-relevance. Both approaches can be mutually supportive. 52

Quantitative and qualitative research must be carefully studied and conducted by scientific researchers to avoid unethical research and inadequate outcomes. Quantitative research involves a deductive process wherein a research question is answered with a hypothesis that describes the relationship between independent and dependent variables, and the testing of the hypothesis. This investigation can be aptly termed as hypothesis-testing research involving the analysis of hypothesis-driven experimental studies resulting in a test of significance. Qualitative research involves an inductive process wherein a research question is explored to generate a hypothesis, which then leads to the development of a theory. This investigation can be aptly termed as hypothesis-generating research. When the whole spectrum of inductive and deductive research approaches is combined using both quantitative and qualitative research methodologies, mixed-method research is applied, and this can facilitate the construction of novel hypotheses, development of theories, or refinement of concepts.

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

Author Contributions:

• Conceptualization: Barroga E, Matanguihan GJ.
• Data curation: Barroga E, Matanguihan GJ, Furuta A, Arima M, Tsuchiya S, Kawahara C, Takamiya Y, Izumi M.
• Formal analysis: Barroga E, Matanguihan GJ, Furuta A, Arima M, Tsuchiya S, Kawahara C.
• Investigation: Barroga E, Matanguihan GJ, Takamiya Y, Izumi M.
• Methodology: Barroga E, Matanguihan GJ, Furuta A, Arima M, Tsuchiya S, Kawahara C, Takamiya Y, Izumi M.
• Project administration: Barroga E, Matanguihan GJ.
• Resources: Barroga E, Matanguihan GJ, Furuta A, Arima M, Tsuchiya S, Kawahara C, Takamiya Y, Izumi M.
• Supervision: Barroga E.
• Validation: Barroga E, Matanguihan GJ, Furuta A, Arima M, Tsuchiya S, Kawahara C, Takamiya Y, Izumi M.
• Visualization: Barroga E, Matanguihan GJ.
• Writing - original draft: Barroga E, Matanguihan GJ.
• Writing - review & editing: Barroga E, Matanguihan GJ, Furuta A, Arima M, Tsuchiya S, Kawahara C, Takamiya Y, Izumi M.

• Open access
• Published: 22 June 2024

Saliency-driven explainable deep learning in medical imaging: bridging visual explainability and statistical quantitative analysis

• Yusuf Brima 1 &
• Marcellin Atemkeng 2  

BioData Mining volume  17 , Article number:  18 ( 2024 ) Cite this article

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Deep learning shows great promise for medical image analysis but often lacks explainability, hindering its adoption in healthcare. Attribution techniques that explain model reasoning can potentially increase trust in deep learning among clinical stakeholders. In the literature, much of the research on attribution in medical imaging focuses on visual inspection rather than statistical quantitative analysis.

In this paper, we proposed an image-based saliency framework to enhance the explainability of deep learning models in medical image analysis. We use adaptive path-based gradient integration, gradient-free techniques, and class activation mapping along with its derivatives to attribute predictions from brain tumor MRI and COVID-19 chest X-ray datasets made by recent deep convolutional neural network models.

The proposed framework integrates qualitative and statistical quantitative assessments, employing Accuracy Information Curves (AICs) and Softmax Information Curves (SICs) to measure the effectiveness of saliency methods in retaining critical image information and their correlation with model predictions. Visual inspections indicate that methods such as ScoreCAM, XRAI, GradCAM, and GradCAM++ consistently produce focused and clinically interpretable attribution maps. These methods highlighted possible biomarkers, exposed model biases, and offered insights into the links between input features and predictions, demonstrating their ability to elucidate model reasoning on these datasets. Empirical evaluations reveal that ScoreCAM and XRAI are particularly effective in retaining relevant image regions, as reflected in their higher AUC values. However, SICs highlight variability, with instances of random saliency masks outperforming established methods, emphasizing the need for combining visual and empirical metrics for a comprehensive evaluation.

The results underscore the importance of selecting appropriate saliency methods for specific medical imaging tasks and suggest that combining qualitative and quantitative approaches can enhance the transparency, trustworthiness, and clinical adoption of deep learning models in healthcare. This study advances model explainability to increase trust in deep learning among healthcare stakeholders by revealing the rationale behind predictions. Future research should refine empirical metrics for stability and reliability, include more diverse imaging modalities, and focus on improving model explainability to support clinical decision-making.

Peer Review reports

The field of medical image analysis has seen significant advancements in explainability methods for deep learning (DL) models, driven by the imperative for trustworthy artificial intelligence systems in healthcare [ 1 ]. Traditional medical imaging modalities like Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Functional Magnetic Resonance Imaging (fMRI), Positron Emission Tomography (PET), Mammography, Ultrasound, and X-ray play a crucial role in disease detection and diagnosis, often relying on the expertise of radiologists and physicians [ 2 ]. However, the healthcare field faces a growing demand for skilled professionals, leading to potential fatigue and highlighting the need for computer-aided diagnostic (CAD) tools. The rapid advancements in DL architectures and compute have fueled significant progress in automated medical image analysis [ 3 , 4 , 5 , 6 , 7 ]. The maturation of DL offers a promising solution, accelerating the adoption of computer-assisted systems to support experts and reduce reliance on manual analysis. DL holds particular promise for democratizing healthcare globally by alleviating the cost burden associated with scarce expertise [ 8 ]. However, successful clinical adoption hinges on establishing trust in the robustness and explainability of these models [ 9 ]. Despite their inherent complexity, DL models can be illuminated to understand their inference mechanisms, that is, how they process medical images to generate predictions . An adjacent line of work, explainability , focuses on understanding the inner workings of the models, while explainability focuses on explaining the decisions made by these models. Explainable models enable a human-in-the-loop approach, enhancing diagnostic performance through collaboration between domain experts and artificial intelligence.

Various techniques have been proposed, each with distinct advantages and limitations. Concept learning, for example, facilitates multi-stage prediction by leveraging high-level concepts. Studies such as [ 10 , 11 , 12 ] illustrate the potential of concept learning in disease categorization. However, these methods often require extensive annotation to define concepts accurately and risk information leakage if concepts do not align well with the disease pathology. Case-Based Models (CBMs) learn class-specific, disentangled representations and feature mappings, achieving final classification through similarity measurements between input images and stored base templates [ 13 , 14 , 15 ]. While CBMs are robust to noise and compression artifacts, their training is complex, particularly for the large and diverse datasets typical of medical imaging. Counterfactual explanation methods generate pseudo-realistic perturbations of input images to produce opposite predictions, aiming to identify influential features for the model’s original prediction. However, generating realistic perturbations for medical images, which often contain subtle anatomical details, is challenging and can lead to misleading explanations [ 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 ]. Unrealistic perturbations compromise the trustworthiness of these explanations. Another approach involves visualizing internal network representations of learned features in CNN kernels [ 24 ]. Interpreting these feature maps in the context of medical image analysis is difficult due to the abstract nature of the features learned by DL models [ 25 , 26 ]. This abstraction challenges human experts in deriving clinically meaningful insights.

Attribution maps are visual representations that highlight regions of an image most relevant to the predictions made by a DL model. Serving as potent post-hoc explainability tools, these maps provide crucial insights into how models make decisions based on input images. Several studies have demonstrated the application of attribution maps in medical imaging tasks. For instance, Bohle et al. [ 27 ] utilized layer-wise relevance propagation to elucidate deep neural network decisions in MRI-based Alzheimer’s disease classification. Camalan et al. [ 28 ] employed a deep CNN-based Grad-CAM approach for classifying oral lesions in clinical photographs. Similarly, Kermany et al. [ 29 ] applied Grad-CAM for oral dysplasia classification. Shi et al. presented an explainable attention-based model for COVID-19 automatic diagnosis, showcasing the integration of attention mechanisms to improve explainability in radiographic imaging [ 30 ]. Another study by Shi et al. introduced an attention transfer deep neural network for COVID-19 automatic diagnosis, further enhancing the explainability and performance of diagnostic models [ 31 ]. Recently, Nhlapho et al. [ 32 ] presented an overview of select image-based attribution methods for brain tumor detection, though their approach lacked ground-truth segmentation masks and did not quantitatively evaluate the chosen saliency methods.

Building on these efforts, our research leverages both gradient-based and gradient-free image-based saliency methods. However, the deployment of attribution maps alone is insufficient for establishing comprehensive model explainability. A rigorous evaluation framework is essential. We propose a comprehensive evaluation framework that extends beyond qualitative assessment. This framework includes metrics specifically designed to evaluate image-based saliency methods. By incorporating performance information curves (PICs) such as Accuracy Information Curves (AICs) and Softmax Information Curves (SICs), we objectively assess the correlation between saliency map intensity and model predictions. This robust evaluation aims to enhance the transparency and trustworthiness of DL models in clinical settings. Given this context, this paper centers on How effective are state-of-the-art (SoTA) image-based saliency methods in aiding the explainability of DL models for medical image analysis tasks? By investigating this question, we aim to contribute to the broader effort of enhancing the trustworthiness, transparency, and reliability of DL applications in healthcare.

To this end, we leverage the proposed framework to systematically analyze model predictions on brain tumor MRI [ 33 ] and COVID-19 chest X-ray [ 34 ] datasets. Resulting attribution maps highlight the salient features within the input images that most significantly influence the model’s predictions. By evaluating these techniques both qualitatively and quantitatively across different SoTA DL architectures and the aforementioned medical imaging modalities, we aim to assess their effectiveness in promoting explainability. Our assessment is focused on several key aspects:

Clarity of Insights: Do these saliency methods provide clear non-spurious and explainable insights into the relationship between medical image features and model predictions? We achieve this assessment by comparing the highlighted features in the attribution maps with the known anatomical structures and disease signatures relevant to the specific medical imaging task (e.g., brain tumor location in MRI).

Biomarker Identification: Can these techniques aid in identifying potential biomarkers for disease detection or classification? We investigate whether the saliency methods consistently highlight specific image features that correlate with known or emerging disease biomarkers. This analysis can provide valuable insights into potential new avenues for clinical research.

Model Bias Detection: Do saliency methods help uncover potential biases within the DL used for medical image analysis? We explore whether the saliency maps reveal a consistent focus on irrelevant features or artifacts that might not be clinically meaningful. This analysis can help identify potential biases in the training data or model architecture that may require mitigation strategies.

Quantitative Effectiveness: How quantitatively effective are these methods in capturing the relationship between image features and model predictions? We explore this by employing PICs such as AICs and SICs. These metrics assess the correlation between the saliency map intensity and the model’s accuracy or class probabilities.

Contributions

We proposed a comprehensive framework to evaluate SoTA image-based saliency methods applied to Deep Convolutional Neural Networks (CNNs) for medical image classification tasks. Our study included MRI and X-ray modalities, focusing on tasks such as brain tumor classification and COVID-19 detection within these respective imaging techniques. For a novel quantitative evaluation, beyond the visual inspection of saliency maps, we used AICs and SICs to measure the effectiveness of the saliency methods. AICs measure the relationship between the model’s predicted accuracy and the intensity of the saliency map. A strong correlation between high-intensity areas on the saliency map and high model accuracy indicates that the method effectively emphasizes relevant image features. Meanwhile, SICs examine the link between the saliency map and the model’s class probabilities (softmax outputs). An effective saliency method should highlight areas that guide the model toward the correct classification, corresponding to the disease’s localized region in the image.

To our knowledge, this study is the first empirical investigation that uses AICs and SICs to assess saliency methods in medical image analysis using DL. This offers a solid and objective framework for determining the efficacy of saliency methods in elucidating the decision-making mechanisms of DL models for classification and detection tasks in medical imaging.

Paper outline

The paper is organized as follows. Materials and methods  section describes the materials and methods employed in this paper. Results  section presents experimental results on two datasets. Conclusion  section concludes and proposes future directions.

Materials and methods

This section introduces the deep CNN models used for conducting experiments. We also detail the training process for these models and present our proposed framework, which provides an in-depth explanation of image-based saliency methods and their direct applications to DL-based models in medical image analysis.

We use two medical image data modalities to test the attribution framework. The choice of the two modalities depends on the availability of data. Other types of modalities are also applicable to the attribution framework. We leave this for future work.

The brain tumors MRI dataset [ 33 ] is used. MRI data typically comprises a 3D tensor. However, the dataset provided in [ 33 ] is transformed from 3D tensors into 2D slices. Specifically, it includes contrast-enhanced MRI (CE-MRI) T1-weighted images, amounting to 3064 slices obtained from 233 patients. It includes 708 Meningiomas, 1426 Gliomas, and 930 Pituitary tumors. In each slice, the tumor boundary is manually delineated and verified by radiologists. We have plotted 16 random samples from the three classes with tumor borders depicted in red as shown in Fig.  1 . These 2D slices of T1-weighted images train standard deep CNNs for a 3-class classification task into Glioma, Meningioma, and Pituitary tumors. The input to each model is a \(\mathbb {R}^{225\times 225\times 1}\) tensor that is a resized version of the original \(\mathbb {R}^{512\times 512}\) image slices primarily due to computational concerns. Unlike the brain cancer MRI dataset which comes with segmentation masks from experts in the field, the COVID-19 X-ray dataset [ 34 ] used in this work has no ground truth segmentation masks. This was chosen as an edge-case analysis because a vast majority of datasets do not have segmentation masks. This dataset was curated from multiple international COVID-19 X-ray testing facilities during several periods. The dataset is made up of an unbalanced percentage of the four classes in which we have 48.2 \(\%\) normal X-ray images, 28.4 \(\%\) cases with lung opacity, 17.1 \(\%\) of COVID-19 patients and \(6.4\%\) of patients with viral pneumonia of the 19,820 total images in the dataset. This unbalanced nature of the dataset comes with its classification challenges, which has prompted several researchers to implement DL methods to classify the dataset. Out of the four classes, for consistency with the other datasets used in this work, we choose to classify three classes (i.e., Normal, Lung Opacity, and COVID-19). For an in-depth discussion of works that deal with this dataset, we refer to [ 35 ]. Figure  2 shows 16 selected random samples. Table  1 summarizes those three datasets.

MRI Scans of Various Brain Tumors with Annotated Tumor Regions. This figure shows MRI images of different brain tumor types, with the tumor region boundaries highlighted in red. The tumor types include pituitary tumors, gliomas, and meningiomas. Each image presents a different view (axial, sagittal, or coronal) of the brain, illustrating the diversity in tumor appearance and location

Sample chest X-ray images from the dataset used in this study, labeled with their respective conditions. The conditions include Normal, Lung opacity, and Covid. The dataset was curated from multiple international COVID-19 X-ray testing centers during several periods. The diversity in conditions showcases the varying features that the models need to identify for accurate classification

Deep learning architectures

We use 9 standard CNN architectures: Visual Geometric Group (VGG16 and VGG19 [ 7 ]), Deep Residual Network (ResNet50, ResNet50V2) [ 4 ], Densely Connected Convolutional Networks (DenseNet) [ 36 ], DL with Depthwise Separable Convolutions (Xception) [ 5 ], Going deeper with convolutions (Inception) [ 37 ], a hybrid deep Inception and ResNet and EfficientNet: Rethinking model scaling for convolutional neural networks [ 38 ] for classifying COVID-19 X-ray images and brain tumors from the T1-weighted MRI slices. The choice of these deep models is explained by the fact that they are modern techniques that are widely used in solving vision tasks and by extension medical image feature extraction for prediction.

Image-based saliency methods and proposed framework

To facilitate the explainability of model inference mechanisms, which is crucial for building trust in clinical applications of DL-based CAD systems, we have investigated a variety of saliency methods. These saliency methods are integrated into the proposed framework, depicted in Fig.  3 . According to [ 39 ], effective attribution methods must satisfy the fundamental axioms of Sensitivity and Implementation Invariance . All selected saliency methods in this study adhere to these axioms.

An illustration of model development and explainability pipeline for a path-based saliency method. A dataset of m samples say T1-weighted contrast-enhanced image slices, for example, is the input to a standard CNN classification model depicted in the figure as \(h(\cdot )\) that learns the non-linear mapping of the features to the output labels. \(h(\cdot )\) is utilized with an attribution operator \(A_h\) to attribute salient features \(\hat{\textbf{x}}\) of the input image. \(A_h\) is an operator that can be used with varied different architectures. This proposed framework is general and can be applied to any problem instances where explainability is vital

The saliency methods evaluated include both gradient-based and gradient-free techniques. Adaptive path-based integrated gradients (APMs), which are gradient-based, are useful in reducing noise in attribution maps, which is critical for medical imaging diagnostics. Gradient-free techniques do not rely on model gradients, making them suitable for non-differentiable models or scenarios where gradients are noisy. Class Activation Mapping (CAM) and its derivatives are effective in highlighting high-level activations for visual localization, providing clear insights into decision-making processes. Each method’s distinct characteristics justify their inclusion and comparison in this study, aimed at enhancing diagnostic and patient outcomes in medical imaging.

The specific saliency methods employed in this study include several prominent techniques. Vanilla Gradient [ 40 ] computes the gradient of the output with respect to the input image, highlighting the most influential pixels for the target class prediction. Integrated Gradients (IG)[ 39 ], which are gradient-based, attribute the model’s prediction to its input features by integrating the gradients along the path from a baseline to the input image. SmoothGrad IG [ 41 ] enhances IG by averaging the gradients of multiple noisy copies of the input image, thus reducing visual noise in the saliency maps. Guided Integrated Gradient (GIG) [ 42 ] refines IG further by guiding the gradients to produce less noisy and more interpretable saliency maps. eXplanation with Ranked Area Integrals (XRAI) [ 43 ] generates region-based attributions by ranking areas based on their contribution to the prediction, providing a more holistic view of important regions. GradCAM [ 21 ] uses the gradients of the target class flowing into the final convolutional layer to produce a coarse localization map of important regions in the image. GradCAM++ [ 44 ] improves upon GradCAM by providing better localization by considering the importance of each neuron in the last convolutional layer. ScoreCAM [ 45 ], unlike gradient-based methods, uses the model’s confidence scores to weigh the importance of each activation map, potentially leading to more accurate and less noisy explanations.

These methods are integrated into the proposed framework to analyze the attribution of salient features in medical images. As shown in Fig.  3 , a dataset of m samples is input into a standard CNN classification model. The model, represented as \(h(\cdot )\) , learns the non-linear mapping of features to output labels. The trained model is then utilized together with an attribution operator \(A_h\) , which could be any of the saliency methods, to attribute salient features \(\hat{\textbf{x}}\) of the input image. This operator \(A_h\) is versatile and can be applied to any problem where explainability is essential for building trust in the model’s inference mechanism.

Quantitative and empirical assessment of saliency methods

In this work, we adapted and applied empirical methods from Kapishnikov et al. (2021) [ 42 ] for evaluating saliency frameworks in the field of medical image analysis, making slight adjustments to the image entropy calculation. Our adaptation maintained the core approach of using saliency methods to attribute importance to regions within medical images while tailoring them to meet the specific demands of medical imaging.

Our method for estimating image entropy involves computing the Shannon entropy of the image histogram. We begin by deriving the histogram of the original image with 256 bins and density normalization, followed by using the entropy computation as shown in Equation  1 . In contrast, their method estimates image entropy by determining the file size of the image after lossless compression and calculating the buffer length as a proxy for entropy. While both approaches aim to gauge the information content of an image, ours relies on pixel intensity distribution, while theirs assesses file size post-compression.

where, H ( X ) represents the entropy of the image X , \(p_i\) is the probability of occurrence of each intensity level i in the image histogram, and n is the total number of intensity levels (256 in our case).

Our approach provides a direct measure of the information content inherent in the pixel intensity distribution, capturing the relative importance of different intensity levels and offering a comprehensive understanding of the image’s complexity. In contrast, using file size post-compression as a proxy for entropy may not fully capture the nuances of the image’s content. By focusing on pixel intensity distribution, our approach offers a more intrinsic and nuanced measure of image information content, particularly crucial for tasks such as medical image analysis or pattern recognition.

This evaluation framework entails initiating the process with a completely blurred version of the medical image and incrementally reintroducing pixels identified as significant by the saliency method. We then measure the resulting image’s entropy and conduct classification tasks to correlate the model’s performance, such as accuracy, with the calculated entropy or information level for each medical image, resulting in Performance Information Curves (PICs). Thus, two variants of PICs were introduced – Accuracy Information Curve (AIC) and Softmax Information Curve (SIC) – to provide a more nuanced evaluation of the saliency methods’ effectiveness.

Experimental setup

We conducted all experiments on Nvidia Quadro RTX 8000 hardware, leveraging its robust computational capabilities to handle the extensive DL training processes. For the implementation, we used the Keras API with the TensorFlow backend, enabling efficient and flexible development of the CNNs.

In this section, we present a comprehensive analysis of our experimental findings, structured around three key questions: (i) How good are these models on standard classification performance metrics? (ii) How visually explainable are studied image-based saliency-based methods? (iii) How empirically comparable are image-based saliency methods?

How good are these models on standard classification performance metrics?

We evaluated the performance of the 9 DL model architectures on classification tasks using standard metrics such as F1 score and confusion matrices as depicted in Figs. 4 and 5 . Appendix 1 shows the optimal hyperparameters for training the DL models. The results provide insights into the effectiveness of each model in terms of classification accuracy and error distribution.

The F1 scores (top-panel) for each model are compared to assess their accuracy and robustness in classifying brain tumors into three categories: Meningioma, Glioma, and Pituitary tumor. The bottom-panel shows the confusion matrix for the top-performing model, InceptionResNetV2

The performance of various DL models on brain tumor MRI classification is illustrated in Fig.  4 . Figure  4 (top-panel) The bar plot presents the F1 scores of various DL model architectures evaluated on the brain MRI image testset classification task. The F1 scores for these models range from 0.76 to 0.95. The InceptionResNetV2 model achieves the highest F1 score of 0.95, indicating superior performance in accurately classifying brain tumors. EfficientNetB0, on the other hand, scores the lowest with an F1 score of 0.76, showing a relatively lower performance compared to the other models. Figure  4 (bottom-panel) shows the confusion matrix for the top-performing model, InceptionResNetV2, which displays the number of correctly and incorrectly classified cases for different types of brain tumors. The matrix shows that out of the 72 cases of Meningioma, 69 cases are correctly predicted, 1 case is misclassified as Glioma, and 2 cases are misclassified as Pituitary tumor. Out of the 143 cases of Glioma, 133 cases are correctly predicted, 10 cases are misclassified as Meningioma, and no case is misclassified as a Pituitary tumor. Out of the 92 Pituitary tumor cases, 91 cases are correctly predicted, 1 case is misclassified as Glioma, and no cases misclassified as Meningioma. This detailed breakdown demonstrates the model’s effectiveness in correctly identifying the majority of cases while highlighting specific areas where misclassifications occur, particularly in distinguishing between Meningioma and Glioma.

Figure  5 shows the performance comparison of different model architectures for COVID-19 X-ray image classification. The models were evaluated based on their ability to classify images into Normal, Lung Opacity, and COVID-19 categories. Figure  5 (top-panel) shows the F1 scores of various DL model architectures evaluated for COVID-19 classification. The F1 scores range from 0.87 to 0.89. The models perform consistently well, with minimal variation in F1 scores. Figure  5 (bottom-panel) shows the confusion matrix for the Xception model and provides a detailed view of its classification performance for chest X-ray images. The matrix shows that out of the 208 Lung opacity cases, 247 cases are correctly predicted, 1 case is misclassified as COVID-19, and 60 cases are misclassified as Normal. Out of the 19 COVID-19 cases, 7 cases are correctly predicted, 5 cases are misclassified as Lung opacity, and 7 cases are misclassified as Normal. Out of the 651 Normal cases, 621 cases are correctly predicted, no case is misclassified as COVID-19, and 30 cases are misclassified as Lung opacity. This confusion matrix highlights the Xception model’s strengths and weaknesses in COVID-19 classification. While it correctly identifies a large number of cases, there are notable misclassifications, particularly with Lung opacity being misclassified as Normal in 60 instances.

The F1 scores (top panel) for each model are compared to assess their accuracy and robustness in classifying chest X-ray images into three categories: Normal, Lung Opacity, and COVID-19. The bottom panel shows the confusion matrix for the top-performing model, Xception

The results from the F1 scores and confusion matrices demonstrate the effectiveness of various DL architectures in medical image classification tasks. InceptionResNetV2 consistently outperforms other models in brain tumor classification, achieving the highest F1 score and demonstrating excellent accuracy. The detailed confusion matrix for InceptionResNetV2 reveals minimal misclassifications, underscoring its reliability. The performance of models on the COVID-19 X-ray dataset shows high F1 scores across different architectures, with models like Xception also performing exceptionally well. The confusion matrix for Xception indicates strong classification capabilities, although some misclassifications are present, particularly in distinguishing between Lung opacity and Normal. These results underscore the importance of selecting appropriate model architectures for specific medical image classification tasks. The high F1 scores and detailed confusion matrices provide valuable insights into each model’s strengths and areas for improvement. However, the focus of this study is not to beat SoTA performance but to provide a basis for investigating the chosen saliency methods. Therefore, the top-performing models, InceptionResNetV2 for brain tumor classification and Xception for COVID-19 classification will serve as the basis for further analysis Sections in  How visually explainable are image-based saliency methods? and How empirically comparable are image-based saliency methods?  sections.

How visually explainable are image-based saliency methods?

Figure  6 presents the visualization of feature attributions for brain tumor classification using our proposed framework and various explainability methods applied to the Inception-ResNetV2 model. The attribution maps provide insights into the regions of the input images that significantly influence the model’s predictions for three types of brain tumors: Glioma, Meningioma, and Pituitary Tumor. The top row represents the input image with ground-truth tumor boundaries, and the other rows are attribution maps produced by each method.

Visualization of feature attributions for brain tumor classification using various explainability methods for the best-performing model, Inception-ResNetV2. This figure displays the feature attribution maps generated by different explainability techniques for the model on three types of brain tumors: Glioma, Meningioma, and Pituitary Tumor. The columns represent the input image with ground-truth tumor boundaries followed by the attribution maps produced by each method. From visual inspection, Fast XRAI 30% and ScoreCAM outperform other methods. For Glioma, ScoreCAM effectively focuses on the tumor regions. For Meningioma, ScoreCAM highlights some tumor regions, though the heatmap shows three regions instead of the actual two. Most other methods, except GradCAM++ for Glioma, generate coarse and noisy saliency maps, particularly Vanilla Gradient and SmoothGrad. Path-integration methods tend to be more susceptible to image edges compared to GradCAM, GradCAM++, and ScoreCAM methods

From visual inspection, Fast XRAI 30% and ScoreCAM outperform other methods. For Glioma, ScoreCAM effectively focuses on the tumor regions, providing clear and accurate attributions. For Meningioma, ScoreCAM highlights some tumor regions, although the heatmap shows three regions instead of the actual two. Other methods, such as Vanilla Gradient and SmoothGrad, produce coarse and noisy saliency maps. GradCAM and GradCAM++ generate more focused heatmaps but are still less precise than ScoreCAM. Path-integration methods, like Integrated Gradients, are more susceptible to highlighting image edges rather than the tumor regions, reducing their clinical explainability.

Figure  7 illustrates our proposed framework and application of various explainability methods on chest X-ray images for differentiating between Normal, Lung Opacity, and COVID-19 cases using the Xception model. The figure includes input X-ray images in the first row, followed by the attribution maps generated by different explainability methods. GradCAM, GradCAM++, and ScoreCAM tend to produce more focused and clinically explainable heatmaps, accurately highlighting relevant regions such as lung abnormalities. Other methods, like Vanilla Gradient and SmoothGrad, show more dispersed activations, making it challenging to interpret the model’s focus. XRAI and Fast XRAI provide region-based explanations that are intermediate, balancing between detailed local features and broader regions of interest.

Comparison of various explainability methods applied to chest X-ray images for distinguishing between Normal, Lung Opacity, and COVID-19 cases. The figure includes the input X-ray images in the first column, followed by visualization results from different explainability methods across the subsequent columns. For each condition (Normal, Lung Opacity, and COVID-19), the visualization techniques highlight different regions of the X-ray images that contribute to the model’s decision-making process. GradCAM, GradCAM++, and ScoreCAM methods tend to produce more focused and clinically interpretable heatmaps, while other methods show more dispersed activations. XRAI and Fast XRAI provide region-based explanations that are intermediate. Unlike the brain tumor dataset, this dataset does not have ground-truth biomarkers

The comparison of these saliency methods on the two datasets reveals the strengths and limitations of each technique in providing visual explanations. The presence of ground-truth biomarkers in the brain tumor dataset allows for a more nuanced assessment of the methods’ accuracy, whereas the COVID-19 dataset lacks such markers, relying on visual plausibility for evaluation. Overall, the findings suggest that methods like ScoreCAM, XRAI, GradCAM, and GradCAM++ offer more precise and clinically useful explanations, which are crucial for enhancing the transparency and trustworthiness of DL models in medical applications.

How empirically comparable are image-based saliency methods?

While visual explanations provide valuable qualitative insights, it is crucial to quantitatively evaluate the effectiveness of different saliency methods. In this section, we empirically compare these methods using PICs, specifically AICs and SICs. These metrics allow us to objectively assess the correlation between the saliency map intensity and the model’s predictions, providing a comprehensive understanding of each method’s performance.

In Fig.  8 , we present the aggregated AICs for over 1200 data points for various saliency methods applied to brain tumor MRI classification. The AUC values indicate the effectiveness of each method in retaining critical image information necessary for accurate classification. We observe that ScoreCAM achieves the highest AUC of 0.084, followed by XRAI at 0.033. This suggests that these methods are more effective in highlighting relevant regions for the model’s predictions. In contrast, methods like Guided IG, Vanilla IG, SmoothGrad IG, GradCAM, and GradCAM++ show minimal to zero AUC values, indicating limited effectiveness. These empirical results align with our visual inspection findings, where ScoreCAM and XRAI also provided clearer and more accurate attributions.

Aggregated AICs for evaluating the effectiveness of different saliency methods in attributing importance to regions of Brain Tumor MRI images for classification. The plot shows the prediction score as a function of the fraction of the image retained after reintroducing pixels identified as important by each saliency method. The area under the curve (AUC) values are provided for each method, indicating their performance in retaining critical image information necessary for accurate classification. ScoreCAM demonstrates the highest AUC of 0.084, suggesting it retains the most relevant image regions effectively, followed by XRAI with an AUC of 0.033. Other methods, including Guided IG, Vanilla IG, SmoothGrad IG, GradCAM, and GradCAM++, show minimal to zero AUC values, indicating limited effectiveness in this evaluation

Figure  9 illustrates the aggregated SICs for over 1300 samples of a brain tumor MRI dataset. The SIC evaluates how well the saliency methods identify regions that contribute to the model’s class probabilities. Surprisingly, the Random saliency mask shows the highest AUC of 0.705, followed by ScoreCAM (0.579), XRAI (0.574), and Guided IG (0.536). This anomaly indicates that the Random saliency mask may retain some critical regions by chance, emphasizing the need for careful interpretation of this metric. While Guided IG and ScoreCAM perform well, their AUC values suggest that these methods provide moderately effective attributions. These findings partly contrast with our visual evaluations and AICs, where ScoreCAM was a top performer, highlighting the importance of combining visual and empirical assessments for a holistic understanding.

Aggregated SICs for evaluating the effectiveness of different saliency methods in attributing importance to regions of Brain Tumor MRI images. The plot shows the prediction score as a function of the fraction of the image retained after reintroducing pixels identified as significant by each saliency method. The AUC values are provided for each method, indicating their performance in retaining critical image information necessary for accurate classification. Random saliency mask, surprisingly, exhibits the highest AUC of 0.705, followed by ScoreCAM (AUC=0.579), XRAI (AUC=0.574), and Guided IG (AUC=0.536). GradCAM, GradCAM++, Vanilla IG, and SmoothGrad IG show lower AUC values, indicating less effectiveness. This analysis highlights the variability in performance among different saliency methods when applied to medical image analysis, with the Random saliency mask unexpectedly showing the highest effectiveness under this specific evaluation criterion, which indicates the instability of this metric

In Fig.  10 , we evaluate the performance of various saliency methods on chest X-ray classification tasks using the Aggregated AIC. XRAI shows a noticeable deviation from the baseline with an AUC of 0.055, indicating some effectiveness in identifying relevant regions. Other methods, including ScoreCAM, Guided IG, and Vanilla IG, closely follow the random with AUC values of 0.000, suggesting limited effectiveness in this context. This observation is consistent with our visual inspection, where methods like ScoreCAM and XRAI provided intermediate-level explanations compared to others.

Aggregated AICs evaluating the performance of various saliency attribution methods on the chest X-ray image classification problem. The x-axis represents the fraction of the original image retained based on the saliency maps generated by each method. The y-axis shows the corresponding prediction score or accuracy. The curve for XRAI (AUC=0.055) deviates slightly from the baselines, indicating a minimal ability to identify relevant image regions for the classification task. Other methods, including ScoreCAM, Guided IG, GradCAM, and Vanilla IG, show negligible scores with an AUC of 0.000. This plot highlights the limited efficacy of these saliency techniques in attributing importance to salient regions within medical images for model explainability in this specific evaluation

Figure  11 shows the aggregated SICs for chest X-ray classification. Guided IG achieves the highest AUC of 0.735, outperforming the random mask (0.683), Vanilla IG (0.711), and SmoothGrad IG (0.639). This suggests that Guided IG is particularly effective in highlighting regions that influence the model’s class probabilities. The performance of XRAI, GradCAM, GradCAM++, and ScoreCAM is moderate, with lower AUC values (0.610, 0.594, 0.493, and 0.491 respectively), indicating less effective saliency attribution compared to Guided IG. These empirical results, similar to those for the brain tumor dataset, do not align with our visual analysis and AICs, where methods like XRAI, GradCAM, GradCAM++, and ScoreCAM provided more focused and explainable heatmaps. Thus, this metric should be cautiously used for evaluating saliency methods in given datasets.

Aggregated SICs comparing the performance of various saliency methods on the chest X-ray image classification task. The x-axis represents the fraction of the image retained based on the saliency maps, and the y-axis denotes the corresponding prediction score. The guided integrated gradients (Guided IG) method achieves the highest AUC of 0.735, outperforming the random mask (AUC=0.683), vanilla integrated gradients (Vanilla IG, AUC=0.711), SmoothGrad integrated gradients (SmoothGrad IG, AUC=0.639), and other saliency methods like XRAI (AUC=0.610), GradCAM (AUC=0.594), GradCAM++ (AUC=0.493), and ScoreCAM (AUC=0.491)

In summary, the empirical evaluation using AICs closely aligns with the visual results. However, SICs highlight the variability in performance among different saliency methods, with instances of a random mask outperforming established saliency methods. While our visual inspections revealed clear strengths for methods like ScoreCAM and GradCAM++, the empirical metrics provide a nuanced understanding of each method’s effectiveness in retaining and highlighting relevant image regions. By combining visual and empirical analyses, we ensure a robust evaluation of saliency methods, enhancing their applicability in clinical settings.

Further analysis results are included in Appendix  2 . We present a saliency analysis of the second and third-best models for each dataset. Additionally, AICs and SICs based on the entropy method from Kapishnikov et al. (2021) are provided in Appendix  2 “ Buffer-size-based AICs and SICs evaluations ” section. We also explore varied blurred versions of the top-performing saliency methods and their scores in Appendix 2 “ Computed saliency scores for top performing models for each image-based saliency method ” section.

In this study, we proposed a saliency-based attribution framework and assessed various state-of-the-art saliency methods for enhancing the explainability of DL models in medical image analysis, focusing on brain tumor classification using MRI scans and COVID-19 detection using chest X-ray images. Both qualitative and quantitative evaluations provided insights into these methods’ utility in clinical settings.

Qualitative assessments showed that ScoreCAM, XRAI, GradCAM, and GradCAM++ consistently produced focused and clinically interpretable attribution maps. These methods highlighted relevant regions that aligned with known anatomical structures and disease markers, thereby enhancing model transparency and trustworthiness.

This study is the first to use AICs and SICs to quantitatively evaluate these saliency methods for medical image analysis. The AICs confirmed that ScoreCAM and XRAI effectively retained critical image information, while SICs revealed variability, with random saliency masks sometimes outperforming established methods. This underscores the need for combining qualitative and quantitative metrics for a comprehensive evaluation. Our results highlight the importance of selecting appropriate saliency methods for specific tasks. While visual explanations are valuable, empirical metrics offer a nuanced understanding of each method’s effectiveness. Combining these approaches ensures robust assessments, fostering greater trust and adoption of DL models in clinical settings.

Future research should refine empirical metrics for stability and reliability across different models and datasets, include more diverse imaging modalities, and focus on enhancing model explainability to support clinical decision-making.

Availability of data and materials

This research used the brain tumor dataset from the School of Biomedical Engineering Southern Medical University, Guangzhou, contains 3064 T1-weighted contrast-enhanced images with three kinds of brain tumors. The data is publicly available at Brain Tumor Dataset . The Chest X-ray dataset is publicly available at: Chest X-Ray Images (Pneumonia) Dataset .

Code availability

The code is available at XAIBiomedical for reproducibility.

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Acknowledgements

We extend our gratitude to the reviewers for providing constructive feedback and valuable suggestions.

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Models’ configuration

Table 2 shows the optimal hyperparameters for training the DL models discussed in this paper.

Exaplanability results

Visual explainability for top 2nd and 3rd models for each dataset.

Comparative assessment of saliency techniques applied to brain MRI data using the DenseNet121 model, the second-best performing model on this dataset. Among these, ScoreCAM and GradCAM++ appear to provide the more focused highlighting of the tumor regions across all types of tumors, suggesting that they are more effective in localizing and interpreting the model’s important feature areas for accurate prediction

The figure presents a comparison of various saliency techniques applied to brain MRI data using a ResNetV2 model. We noticed that Fast XRAI at 30% feature masking was able to highlight relevant tumor regions across the three disease classes. Other methods produced more coarse-grained saliency masks as depicted in the plot

This figure illustrates a comparative evaluation of various techniques applied to chest X-ray images using an InceptionResNetV2 model, which is identified as the second-best performing model on this chest X-ray dataset. Here, we noticed that most methods other than XRAI, Fast XRAI 30%, and GradCAM did not produce clinically meaningful saliency masks contrary to the models’ prediction performance. It is, however, hard to qualitatively evaluate these methods since the dataset does not have a ground-truth segmentation mask

Visualization of feature importance for different chest X-ray classifications using a VGG16 model. Rows correspond to different diagnostic categories: Lung Opacity, Normal, and COVID-19. Columns represent various explainability methods. We noticed that XRAI Full, Fast XRAI 30%, GradCAM++, and ScoreCAM highlighted more meaningful features compared to other methods. It is also noticed that Fast XRAI has consistent salient features across InceptionResNetV2 and VGG16 models

Computed saliency scores for top performing models for each image-based saliency method

Visualization of GIG SIC scores at varying blurring thresholds for the best-performing model, Inception-ResNetV2, on the Brain Tumor dataset. Each panel displays the GIG Blurred image for a specific threshold, with the corresponding score indicating the model’s confidence level. The thresholds range from 0 to 1.0, showcasing the progression of identified significant regions as the threshold increases. Higher thresholds emphasize more critical features, aligning with the model’s high-confidence predictions, thus offering insights into the explainability and robustness of the Inception-ResNetV2 model in detecting and analyzing brain tumor regions

Visualization of GradCAM SIC scores at varying thresholds for the same Inception-ResNetV2, on the Brain Tumor dataset. Unlike GIG, scores only converge at higher thresholds, row three of this plot

Visualization of GradCAM++ SIC scores at varying thresholds for the best-performing model, Inception-ResNetV2, on the Brain Tumor dataset. Like GradCAM, we noticed a similar trend in score convergence. However, the score converged at a threshold of 0.5 instead of 0.34 as in GradCAM

Visualization of XRAI SIC scores at varying thresholds for the best-performing model, Inception-ResNetV2, on the Brain Tumor dataset. This method also converges in the last three thresholds as depicted in the figure

Visualization of GIG Blurred SIC scores at varying thresholds for the best-performing model, Xception, on the Chest X-ray dataset. Unlike the Brain Tumor case, we noticed a different pattern here. The scores remain constant at the different thresholds which is unexpected and counter-intuitive

Visualization of GradCAM scores at varying thresholds for the best-performing model, Xception, on the Chest X-ray dataset. Like the previous result, we noticed a similar pattern here as the scores remain invariant across varied thresholds of blurring. This is the case for GradCAM++ and XRAI full

Buffer-size-based AICs and SICs evaluations

Aggregated AICs comparing the performance of various saliency methods on the Brain Tumor MRI image classification task. Vanilla IG achieves the highest AUC of 0.871, followed closely by SmoothGrad IG (0.866) and Guided IG (0.835), suggesting these methods are particularly effective in retaining relevant image regions. ScoreCAM shows a respectable AUC of 0.706, indicating good performance as well. GradCAM and GradCAM++ display moderate effectiveness with AUC values of 0.595 and 0.560, respectively. XRAI has an AUC of 0.511, and the Random saliency mask shows an AUC of 0.493, suggesting that some important regions might be retained by chance. This comparison highlights the variability of the entropy estimation to compute the saliency metric scores across datasets. This is primarily because the AUCs are not in agreement with the visual saliency results nor the Shannon entropy-based approach

Aggregated SICs comparing the performance of various saliency methods on the Brain Tumor MRI image classification task. Vanilla IG achieves the highest AUC of 0.893, closely followed by SmoothGrad IG (0.884) and Guided IG (0.865), suggesting these methods are particularly effective in highlighting regions that influence the model’s class probabilities. ScoreCAM also performs well with an AUC of 0.768. GradCAM++ and GradCAM show moderate performance with AUC values of 0.634 and 0.620, respectively. XRAI shows an AUC of 0.530, and the Random saliency mask exhibits an AUC of 0.573, indicating some critical regions might be retained by chance. This comparison highlights the variability in this evaluation metric irrespective of the underlying approach to estimating image entropy

Aggregated AICs evaluating the performance of various saliency attribution methods on the Chest X-ray image classification task. ScoreCAM demonstrates the highest AUC of 0.077, suggesting it retains the most relevant image regions effectively. This is followed by XRAI with an AUC of 0.071, Vanilla IG with an AUC of 0.053, and Guided IG with an AUC of 0.042. Methods like SmoothGrad IG, GradCAM, and GradCAM++ show minimal to zero AUC values, indicating limited effectiveness in this evaluation. The overall trend highlights that some methods, particularly ScoreCAM and XRAI, provide better retention of relevant regions compared to others. This result is in line with the Shannon entropy-based approach

Aggregated SICs comparing the performance of various saliency methods on the Chest X-ray images. The overall trend shows that Vanilla IG achieves the highest AUC of 0.972, closely followed by SmoothGrad IG (0.970) and Guided IG (0.961). Random saliency exhibits a high AUC of 0.828, suggesting that some important regions might be retained by chance. Other methods, including XRAI (0.731), GradCAM (0.694), ScoreCAM (0.692), and GradCAM++ (0.660), show moderate performance. This detailed comparison highlights a somewhat inverse relation with the visual explainability results

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Brima, Y., Atemkeng, M. Saliency-driven explainable deep learning in medical imaging: bridging visual explainability and statistical quantitative analysis. BioData Mining 17 , 18 (2024). https://doi.org/10.1186/s13040-024-00370-4

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DOI : https://doi.org/10.1186/s13040-024-00370-4

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