assignment on qualitative and quantitative research

Qualitative vs Quantitative Research Methods & Data Analysis

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

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

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

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Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

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

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What is the difference between quantitative and qualitative?

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

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

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

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

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

What Is Qualitative Research?

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

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

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

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

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

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

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

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

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

Qualitative Methods

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

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

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

Here are some examples of qualitative data:

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

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

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

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

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

Qualitative Data Analysis

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

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

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

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

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

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Quantitative vs. Qualitative Research in Psychology

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.

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• Methodology

Research Design | Step-by-Step Guide with Examples

Published on 5 May 2022 by Shona McCombes . Revised on 20 March 2023.

A research design is a strategy for answering your research question  using empirical data. Creating a research design means making decisions about:

• Your overall aims and approach
• The type of research design you’ll use
• Your sampling methods or criteria for selecting subjects
• Your data collection methods
• The procedures you’ll follow to collect data
• Your data analysis methods

A well-planned research design helps ensure that your methods match your research aims and that you use the right kind of analysis for your data.

Table of contents

Step 1: consider your aims and approach, step 2: choose a type of research design, step 3: identify your population and sampling method, step 4: choose your data collection methods, step 5: plan your data collection procedures, step 6: decide on your data analysis strategies, frequently asked questions.

• Introduction

Before you can start designing your research, you should already have a clear idea of the research question you want to investigate.

There are many different ways you could go about answering this question. Your research design choices should be driven by your aims and priorities – start by thinking carefully about what you want to achieve.

The first choice you need to make is whether you’ll take a qualitative or quantitative approach.

Qualitative research designs tend to be more flexible and inductive , allowing you to adjust your approach based on what you find throughout the research process.

Quantitative research designs tend to be more fixed and deductive , with variables and hypotheses clearly defined in advance of data collection.

It’s also possible to use a mixed methods design that integrates aspects of both approaches. By combining qualitative and quantitative insights, you can gain a more complete picture of the problem you’re studying and strengthen the credibility of your conclusions.

Practical and ethical considerations when designing research

As well as scientific considerations, you need to think practically when designing your research. If your research involves people or animals, you also need to consider research ethics .

• How much time do you have to collect data and write up the research?
• Will you be able to gain access to the data you need (e.g., by travelling to a specific location or contacting specific people)?
• Do you have the necessary research skills (e.g., statistical analysis or interview techniques)?
• Will you need ethical approval ?

At each stage of the research design process, make sure that your choices are practically feasible.

Prevent plagiarism, run a free check.

Within both qualitative and quantitative approaches, there are several types of research design to choose from. Each type provides a framework for the overall shape of your research.

Types of quantitative research designs

Quantitative designs can be split into four main types. Experimental and   quasi-experimental designs allow you to test cause-and-effect relationships, while descriptive and correlational designs allow you to measure variables and describe relationships between them.

With descriptive and correlational designs, you can get a clear picture of characteristics, trends, and relationships as they exist in the real world. However, you can’t draw conclusions about cause and effect (because correlation doesn’t imply causation ).

Experiments are the strongest way to test cause-and-effect relationships without the risk of other variables influencing the results. However, their controlled conditions may not always reflect how things work in the real world. They’re often also more difficult and expensive to implement.

Types of qualitative research designs

Qualitative designs are less strictly defined. This approach is about gaining a rich, detailed understanding of a specific context or phenomenon, and you can often be more creative and flexible in designing your research.

The table below shows some common types of qualitative design. They often have similar approaches in terms of data collection, but focus on different aspects when analysing the data.

Your research design should clearly define who or what your research will focus on, and how you’ll go about choosing your participants or subjects.

In research, a population is the entire group that you want to draw conclusions about, while a sample is the smaller group of individuals you’ll actually collect data from.

Defining the population

A population can be made up of anything you want to study – plants, animals, organisations, texts, countries, etc. In the social sciences, it most often refers to a group of people.

For example, will you focus on people from a specific demographic, region, or background? Are you interested in people with a certain job or medical condition, or users of a particular product?

The more precisely you define your population, the easier it will be to gather a representative sample.

Sampling methods

Even with a narrowly defined population, it’s rarely possible to collect data from every individual. Instead, you’ll collect data from a sample.

To select a sample, there are two main approaches: probability sampling and non-probability sampling . The sampling method you use affects how confidently you can generalise your results to the population as a whole.

Probability sampling is the most statistically valid option, but it’s often difficult to achieve unless you’re dealing with a very small and accessible population.

For practical reasons, many studies use non-probability sampling, but it’s important to be aware of the limitations and carefully consider potential biases. You should always make an effort to gather a sample that’s as representative as possible of the population.

Case selection in qualitative research

In some types of qualitative designs, sampling may not be relevant.

For example, in an ethnography or a case study, your aim is to deeply understand a specific context, not to generalise to a population. Instead of sampling, you may simply aim to collect as much data as possible about the context you are studying.

In these types of design, you still have to carefully consider your choice of case or community. You should have a clear rationale for why this particular case is suitable for answering your research question.

For example, you might choose a case study that reveals an unusual or neglected aspect of your research problem, or you might choose several very similar or very different cases in order to compare them.

Data collection methods are ways of directly measuring variables and gathering information. They allow you to gain first-hand knowledge and original insights into your research problem.

You can choose just one data collection method, or use several methods in the same study.

Survey methods

Surveys allow you to collect data about opinions, behaviours, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews.

Observation methods

Observations allow you to collect data unobtrusively, observing characteristics, behaviours, or social interactions without relying on self-reporting.

Observations may be conducted in real time, taking notes as you observe, or you might make audiovisual recordings for later analysis. They can be qualitative or quantitative.

Other methods of data collection

There are many other ways you might collect data depending on your field and topic.

If you’re not sure which methods will work best for your research design, try reading some papers in your field to see what data collection methods they used.

Secondary data

If you don’t have the time or resources to collect data from the population you’re interested in, you can also choose to use secondary data that other researchers already collected – for example, datasets from government surveys or previous studies on your topic.

With this raw data, you can do your own analysis to answer new research questions that weren’t addressed by the original study.

Using secondary data can expand the scope of your research, as you may be able to access much larger and more varied samples than you could collect yourself.

However, it also means you don’t have any control over which variables to measure or how to measure them, so the conclusions you can draw may be limited.

As well as deciding on your methods, you need to plan exactly how you’ll use these methods to collect data that’s consistent, accurate, and unbiased.

Planning systematic procedures is especially important in quantitative research, where you need to precisely define your variables and ensure your measurements are reliable and valid.

Operationalisation

Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalisation means turning these fuzzy ideas into measurable indicators.

If you’re using observations , which events or actions will you count?

If you’re using surveys , which questions will you ask and what range of responses will be offered?

You may also choose to use or adapt existing materials designed to measure the concept you’re interested in – for example, questionnaires or inventories whose reliability and validity has already been established.

Reliability and validity

Reliability means your results can be consistently reproduced , while validity means that you’re actually measuring the concept you’re interested in.

For valid and reliable results, your measurement materials should be thoroughly researched and carefully designed. Plan your procedures to make sure you carry out the same steps in the same way for each participant.

If you’re developing a new questionnaire or other instrument to measure a specific concept, running a pilot study allows you to check its validity and reliability in advance.

Sampling procedures

As well as choosing an appropriate sampling method, you need a concrete plan for how you’ll actually contact and recruit your selected sample.

That means making decisions about things like:

• How many participants do you need for an adequate sample size?
• What inclusion and exclusion criteria will you use to identify eligible participants?
• How will you contact your sample – by mail, online, by phone, or in person?

If you’re using a probability sampling method, it’s important that everyone who is randomly selected actually participates in the study. How will you ensure a high response rate?

If you’re using a non-probability method, how will you avoid bias and ensure a representative sample?

Data management

It’s also important to create a data management plan for organising and storing your data.

Will you need to transcribe interviews or perform data entry for observations? You should anonymise and safeguard any sensitive data, and make sure it’s backed up regularly.

Keeping your data well organised will save time when it comes to analysing them. It can also help other researchers validate and add to your findings.

On their own, raw data can’t answer your research question. The last step of designing your research is planning how you’ll analyse the data.

Quantitative data analysis

In quantitative research, you’ll most likely use some form of statistical analysis . With statistics, you can summarise your sample data, make estimates, and test hypotheses.

Using descriptive statistics , you can summarise your sample data in terms of:

• The distribution of the data (e.g., the frequency of each score on a test)
• The central tendency of the data (e.g., the mean to describe the average score)
• The variability of the data (e.g., the standard deviation to describe how spread out the scores are)

The specific calculations you can do depend on the level of measurement of your variables.

Using inferential statistics , you can:

• Make estimates about the population based on your sample data.
• Test hypotheses about a relationship between variables.

Regression and correlation tests look for associations between two or more variables, while comparison tests (such as t tests and ANOVAs ) look for differences in the outcomes of different groups.

Your choice of statistical test depends on various aspects of your research design, including the types of variables you’re dealing with and the distribution of your data.

Qualitative data analysis

In qualitative research, your data will usually be very dense with information and ideas. Instead of summing it up in numbers, you’ll need to comb through the data in detail, interpret its meanings, identify patterns, and extract the parts that are most relevant to your research question.

Two of the most common approaches to doing this are thematic analysis and discourse analysis .

There are many other ways of analysing qualitative data depending on the aims of your research. To get a sense of potential approaches, try reading some qualitative research papers in your field.

A sample is a subset of individuals from a larger population. Sampling means selecting the group that you will actually collect data from in your research.

For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

Statistical sampling allows you to test a hypothesis about the characteristics of a population. There are various sampling methods you can use to ensure that your sample is representative of the population as a whole.

Operationalisation means turning abstract conceptual ideas into measurable observations.

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

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

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

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

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Quantitative and Qualitative Research Methods

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Quantitative research uses methods that seek to explain phenomena by collecting numerical data, which are then analysed mathematically, typically by statistics. With quantitative approaches, the data produced are always numerical; if there are no numbers, then the methods are not quantitative. Many phenomena lend themselves to quantitative methods because the relevant information is already available numerically. Qualitative methods provide a mechanism to provide answers based on the collection of non-numerical data ‘i.e words, actions, behaviours’. Both quantitative and qualitative methodologies are important in medical imaging and radiation therapy.   In some instances, both quantitative and qualitative approaches can be combined into a mixed-methods approach. This chapter discusses all methodological approaches to research from both medical imaging and radiation therapy perspectives.  

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Qualitative and Quantitative Research

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

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

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

References:

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

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

Quick Overview

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

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It's Not Always One or the Other!

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

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Qualitative Vs Quantitative Research – A Comprehensive Guide

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

What is Quantitative Research?

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

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

Characteristics of Quantitative Research

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

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

What is Qualitative Research?

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

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

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

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

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Characteristics of Qualitative Research

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

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

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

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When to Use Qualitative and Quantitative Research Model?

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

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

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

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

Limitations of Quantitative and Qualitative Research

What is quantitative research, what is qualitative research.

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

Qualitative or quantitative, which research type should I use?

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

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What are the different research strategies you can use in your dissertation? Here are some guidelines to help you choose a research strategy that would make your research more credible.

Struggling to figure out “whether I should choose primary research or secondary research in my dissertation?” Here are some tips to help you decide.

Descriptive research is carried out to describe current issues, programs, and provides information about the issue through surveys and various fact-finding methods.

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Essay on Qualitative vs. Quantitative Research

Both qualitative and quantitative researches are valued in the research world and are often used together under a single project. This is despite the fact that they have significant differences in terms of their theoretical, epistemological, and methodological formations. Qualitative research is usually in form of words while quantitative research takes the numerical approach. This paper discusses the similarities, differences, advantages, and disadvantages of qualitative and quantitative research and provides a personal stand.

Similarities

Both qualitative and quantitative research approaches begin with a problem on which scholars seek to find answers. Without a research problem or question, there would be no reason for carrying out the study. Once a problem is formulated, researchers at their own discretion and depending on the nature of the question choose the appropriate type of research to employ. Just like in qualitative research, data obtained from quantitative analysis need to be analyzed (Miles & Huberman, 1994). This step is crucial for helping researchers to gain a deeper understanding of the issue under investigation. The findings of any research enjoy confirmability after undergoing a thorough examination and auditing process (Miles & Huberman, 1994).

Both types of research approaches require a concise plan before they are carried out. Once researchers formulate the study question, they must come up with a plan for investigating the matter (Yilmaz, 2013). Such plans include deciding the appropriate research technique to implement, estimating budgets, and deciding on the study areas. Failure to plan before embarking on the research project may compromise the research findings. In addition, both qualitative and quantitative research are dependent on each other and can be used for a single research project (Miles & Huberman, 1994). Quantitative data helps the qualitative research in finding a representative study sample and obtaining the background data. In the same way, qualitative research provides the quantitative side with the conceptual development and instrumentation (Miles & Huberman, 1994).

Differences

Qualitative research seeks to explain why things are the way they seem to be. It provides well-grounded descriptions and explanations of processes in identifiable local contexts (Miles & Huberman, 1994). Researchers use qualitative research to dig deeper into the problem and develop a relevant hypothesis for potential quantitative research. On the other hand, Quantitative research uses numerical data to state and quantify the problem (Yilmaz, 2013). Researchers in quantitative research use measurable data in formulating facts and uncovering the research pattern.

Quantitative research approach involves a larger number of participants for the purpose of gathering as much information as possible to summarize characteristics across large groups. This makes it a very expensive research approach. On the contrary, qualitative research approach describes a phenomenon in a more comprehensive manner. A relatively small number of participants take part in this type of research. This makes the overall process cheaper and time friendly.

Data collection methods differ significantly in the two research approaches. In quantitative research, scholars use surveys, questionnaires, and systematic measurements that involve numbers (Yilmaz, 2013). Moreover, they report their findings in impersonal third person prose by using numbers. This is different from the qualitative approach where only the participants’ observation and deep document analysis is necessary for conclusions to be drawn. Findings are disseminated in the first person’s narrative with sufficient quotations from the participants.

Advantages and Disadvantages of Qualitative Research

Qualitative data is based on human observations. Respondent’s observations connect the researcher to the most basic human experiences (Rahman, 2016). It gives a detailed production of participants’ opinions and feelings and helps in efficient interpretation of their actions (Miles & Huberman, 1994). Moreover, this research approach is interdisciplinary and entails a wide range of research techniques and epistemological viewpoints. Data collection methods in qualitative approach are both detailed and subjective (Rahman, 2016). Direct observations, unstructured interviews, and participant observation are the most common techniques employed in this type of research. Researchers have the opportunity to mingle directly with the respondents and obtain first-hand information.

On the negative side, the smaller population sample used in qualitative research raises credibility concerns (Rahman, 2016). The views of a small group of respondents may not necessarily reflect those of the entire population. Moreover, conducting this type of research on certain aspects such as the performance of students may be more challenging. In such instances, researchers prefer to use the quantitative approach instead (Rahman, 2016). Data analysis and interpretation in qualitative research is a more complex process. It is long, has elusive data, and has very stringent requirements for analysis (Rahman, 2016). In addition, developing a research question in this approach is a challenging task as the refining question mostly becomes continuous throughout the research process.

Advantages and Disadvantages of Quantitative Research

The findings of a quantitative research can be generalized to a whole population as it involves larger samples that are randomly selected by researchers (Rahman, 2016). Moreover, the methods used allows for use of statistical software in test taking (Rahman, 2016). This makes the approach time effective and efficient for tackling complex research questions. Quantitative research allows for objectivity and accuracy of the study results. This approach is well designed to provide essential information that supports generalization of a phenomenon under study. It involves few variables and many cases that guarantee the validity and credibility of the study results.

This research approach, however, has some limitations. There is a limited direct connection between the researcher and respondents. Scholars who adopt this approach measure variables at specific moments in time and disregards the past experiences of the respondents (Rahman, 2016). As a result, deep information is often ignored and only the overall picture of the variables is represented. The quantitative approach uses standard questions set and administered by researchers (Rahman, 2016). This might lead to structural bias by respondents and false representation. In some instances, data may only reflect the views of the sample under study instead of revealing the real situation. Moreover, preset questions and answers limit the freedom of expression by the respondents.

Preferred Method

I would prefer quantitative research method over the qualitative approach. Data management in this technique is much familiar and more accessible to researchers’ contexts (Miles & Huberman, 1994). It is a more scientific process that involves the collection, analysis, and interpretation of large amounts of data. Researchers have more control of the manner in which data is collected. Unlike qualitative data that requires descriptions, quantitative approach majors on numerical data (Yilmaz, 2013). With this type of data, I can use the various available software for classification and analyzes. Moreover, researchers are more flexible and free to interact with respondents. This gives an opportunity for obtaining first-hand information and learning more about other behavioral aspects of the population under study.

As highlighted above, qualitative and quantitative techniques are the two research approaches. Both seek to dig deeper into a particular problem, analyze the responses of a selected sample and make viable conclusions. However, qualitative research is much concerned with the description of peoples’ opinions, motivations, and reasons for a particular phenomenon. On the other hand, Quantitative research uses numerical data to state and explain research findings. Use of numerical data allows for objectivity and accuracy of the research results. However structural biases are common in this approach. Data collection and sampling in qualitative research is more detailed and subjective. Considering the different advantages and disadvantages of the two research approaches, I would go for the quantitative over qualitative research.

Miles, M., & Huberman, A. (1994).  Qualitative data analysis  (2nd Ed.). Beverly Hills: Sage.

Rahman, M. (2016). The Advantages and Disadvantages of Using Qualitative and Quantitative Approaches and Methods in Language “Testing and Assessment” Research: A Literature Review.  Journal of Education and Learning , 6(1), 102.

Yilmaz, K. (2013). Comparison of Quantitative and Qualitative Research Traditions: epistemological, theoretical, and methodological differences.  European Journal of Education , 48(2), 311-325.

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Qualitative and Quantitative Research: Differences and Similarities

Qualitative research and quantitative research are two complementary approaches for understanding the world around us.

Qualitative research collects non-numerical data , and the results are typically presented as written descriptions, photographs, videos, and/or sound recordings.

In contrast, quantitative research collects numerical data , and the results are typically presented in tables, graphs, and charts.

Debates about whether to use qualitative or quantitative research methods are common in the social sciences (i.e. anthropology, archaeology, economics, geography, history, law, linguistics, politics, psychology, sociology), which aim to understand a broad range of human conditions. Qualitative observations may be used to gain an understanding of unique situations, which may lead to quantitative research that aims to find commonalities.

Within the natural and physical sciences (i.e. physics, chemistry, geology, biology), qualitative observations often lead to a plethora of quantitative studies. For example, unusual observations through a microscope or telescope can immediately lead to counting and measuring. In other situations, meaningful numbers cannot immediately be obtained, and the qualitative research must stand on its own (e.g. The patient presented with an abnormally enlarged spleen (Figure 1), and complained of pain in the left shoulder.)

For both qualitative and quantitative research, the researcher's assumptions shape the direction of the study and thereby influence the results that can be obtained. Let's consider some prominent examples of qualitative and quantitative research, and how these two methods can complement each other.

Qualitative research example

In 1960, Jane Goodall started her decades-long study of chimpanzees in the wild at Gombe Stream National Park in Tanzania. Her work is an example of qualitative research that has fundamentally changed our understanding of non-human primates, and has influenced our understanding of other animals, their abilities, and their social interactions.

Dr. Goodall was by no means the first person to study non-human primates, but she took a highly unusual approach in her research. For example, she named individual chimpanzees instead of numbering them, and used terms such as "childhood", "adolescence", "motivation", "excitement", and "mood". She also described the distinct "personalities" of individual chimpanzees. Dr. Goodall was heavily criticized for describing chimpanzees in ways that are regularly used to describe humans, which perfectly illustrates how the assumptions of the researcher can heavily influence their work.

The quality of qualitative research is largely determined by the researcher's ability, knowledge, creativity, and interpretation of the results. One of the hallmarks of good qualitative research is that nothing is predefined or taken for granted, and that the study subjects teach the researcher about their lives. As a result, qualitative research studies evolve over time, and the focus or techniques used can shift as the study progresses.

Qualitative research methods

Dr. Goodall immersed herself in the chimpanzees' natural surroundings, and used direct observation to learn about their daily life. She used photographs, videos, sound recordings, and written descriptions to present her data. These are all well-established methods of qualitative research, with direct observation within the natural setting considered a gold standard. These methods are time-intensive for the researcher (and therefore monetarily expensive) and limit the number of individuals that can be studied at one time.

When studying humans, a wider variety of research methods are available to understand how people perceive and navigate their world—past or present. These techniques include: in-depth interviews (e.g. Can you discuss your experience of growing up in the Deep South in the 1950s?), open-ended survey questions (e.g. What do you enjoy most about being part of the Church of Latter Day Saints?), focus group discussions, researcher participation (e.g. in military training), review of written documents (e.g. social media accounts, diaries, school records, etc), and analysis of cultural records (e.g. anything left behind including trash, clothing, buildings, etc).

Qualitative research can lead to quantitative research

Qualitative research is largely exploratory. The goal is to gain a better understanding of an unknown situation. Qualitative research in humans may lead to a better understanding of underlying reasons, opinions, motivations, experiences, etc. The information generated through qualitative research can provide new hypotheses to test through quantitative research. Quantitative research studies are typically more focused and less exploratory, involve a larger sample size, and by definition produce numerical data.

Dr. Goodall's qualitative research clearly established periods of childhood and adolescence in chimpanzees. Quantitative studies could better characterize these time periods, for example by recording the amount of time individual chimpanzees spend with their mothers, with peers, or alone each day during childhood compared to adolescence.

For studies involving humans, quantitative data might be collected through a questionnaire with a limited number of answers (e.g. If you were being bullied, what is the likelihood that you would tell at least one parent? A) Very likely, B) Somewhat likely, C) Somewhat unlikely, D) Unlikely).

Quantitative research example

One of the most influential examples of quantitative research began with a simple qualitative observation: Some peas are round, and other peas are wrinkled. Gregor Mendel was not the first to make this observation, but he was the first to carry out rigorous quantitative experiments to better understand this characteristic of garden peas.

As described in his 1865 research paper, Mendel carried out carefully controlled genetic crosses and counted thousands of resulting peas. He discovered that the ratio of round peas to wrinkled peas matched the ratio expected if pea shape were determined by two copies of a gene for pea shape, one inherited from each parent. These experiments and calculations became the foundation of modern genetics, and Mendel's ratios became the default hypothesis for experiments involving thousands of different genes in hundreds of different organisms.

The quality of quantitative research is largely determined by the researcher's ability to design a feasible experiment, that will provide clear evidence to support or refute the working hypothesis. The hallmarks of good quantitative research include: a study that can be replicated by an independent group and produce similar results, a sample population that is representative of the population under study, a sample size that is large enough to reveal any expected statistical significance.

Quantitative research methods

The basic methods of quantitative research involve measuring or counting things (size, weight, distance, offspring, light intensity, participants, number of times a specific phrase is used, etc). In the social sciences especially, responses are often be split into somewhat arbitrary categories (e.g. How much time do you spend on social media during a typical weekday? A) 0-15 min, B) 15-30 min, C) 30-60 min, D) 1-2 hrs, E) more than 2 hrs).

These quantitative data can be displayed in a table, graph, or chart, and grouped in ways that highlight patterns and relationships. The quantitative data should also be subjected to mathematical and statistical analysis. To reveal overall trends, the average (or most common survey answer) and standard deviation can be determined for different groups (e.g. with treatment A and without treatment B).

Typically, the most important result from a quantitative experiment is the test of statistical significance. There are many different methods for determining statistical significance (e.g. t-test, chi square test, ANOVA, etc.), and the appropriate method will depend on the specific experiment.

Statistical significance provides an answer to the question: What is the probably that the difference observed between two groups is due to chance alone, and the two groups are actually the same? For example, your initial results might show that 32% of Friday grocery shoppers buy alcohol, while only 16% of Monday grocery shoppers buy alcohol. If this result reflects a true difference between Friday shoppers and Monday shoppers, grocery store managers might want to offer Friday specials to increase sales.

After the appropriate statistical test is conducted (which incorporates sample size and other variables), the probability that the observed difference is due to chance alone might be more than 5%, or less than 5%. If the probability is less than 5%, the convention is that the result is considered statistically significant. (The researcher is also likely to cheer and have at least a small celebration.) Otherwise, the result is considered statistically insignificant. (If the value is close to 5%, the researcher may try to group the data in different ways to achieve statistical significance. For example, by comparing alcohol sales after 5pm on Friday and Monday.) While it is important to reveal differences that may not be immediately obvious, the desire to manipulate information until it becomes statistically significant can also contribute to bias in research.

So how often do results from two groups that are actually the same give a probability of less than 5%? A bit less than 5% of the time (by definition). This is one of the reasons why it is so important that quantitative research can be replicated by different groups.

Which research method should I choose?

Choose the research methods that will allow you to produce the best results for a meaningful question, while acknowledging any unknowns and controlling for any bias. In many situations, this will involve a mixed methods approach. Qualitative research may allow you to learn about a poorly understood topic, and then quantitative research may allow you to obtain results that can be subjected to rigorous statistical tests to find true and meaningful patterns. Many different approaches are required to understand the complex world around us.

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• Key Differences

Know the Differences & Comparisons

Difference Between Qualitative and Quantitative Research

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

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

Content: Qualitative Research Vs Quantitative Research

Comparison chart, definition of qualitative research.

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

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

Definition of Quantitative Research

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

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

Key Differences Between Qualitative And Quantitative Research

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

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

Video: Qualitative Vs Quantitative Research

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

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Combining qualitative and quantitative research within mixed method research designs: A methodological review

Ulrika Östlund.

a Division of Nursing, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden

b Institute for Applied Health Research/School of Health, Glasgow Caledonian University, United Kingdom

Yvonne Wengström

c Division of Nursing, Department or Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden

Neneh Rowa-Dewar

d Public Health Sciences, University of Edinburgh, United Kingdom

It has been argued that mixed methods research can be useful in nursing and health science because of the complexity of the phenomena studied. However, the integration of qualitative and quantitative approaches continues to be one of much debate and there is a need for a rigorous framework for designing and interpreting mixed methods research. This paper explores the analytical approaches (i.e. parallel, concurrent or sequential) used in mixed methods studies within healthcare and exemplifies the use of triangulation as a methodological metaphor for drawing inferences from qualitative and quantitative findings originating from such analyses.

This review of the literature used systematic principles in searching CINAHL, Medline and PsycINFO for healthcare research studies which employed a mixed methods approach and were published in the English language between January 1999 and September 2009.

In total, 168 studies were included in the results. Most studies originated in the United States of America (USA), the United Kingdom (UK) and Canada. The analytic approach most widely used was parallel data analysis. A number of studies used sequential data analysis; far fewer studies employed concurrent data analysis. Very few of these studies clearly articulated the purpose for using a mixed methods design. The use of the methodological metaphor of triangulation on convergent, complementary, and divergent results from mixed methods studies is exemplified and an example of developing theory from such data is provided.

A trend for conducting parallel data analysis on quantitative and qualitative data in mixed methods healthcare research has been identified in the studies included in this review. Using triangulation as a methodological metaphor can facilitate the integration of qualitative and quantitative findings, help researchers to clarify their theoretical propositions and the basis of their results. This can offer a better understanding of the links between theory and empirical findings, challenge theoretical assumptions and develop new theory.

What is already known about the topic?

• • Mixed methods research, where quantitative and qualitative methods are combined, is increasingly recognized as valuable, because it can potentially capitalize on the respective strengths of quantitative and qualitative approaches.
• • There is a lack of pragmatic guidance in the research literature as how to combine qualitative and quantitative approaches and how to integrate qualitative and quantitative findings.
• • Analytical approaches used in mixed-methods studies differ on the basis of the sequence in which the components occur and the emphasis given to each, e.g. parallel, sequential or concurrent.

What this paper adds

• • A trend for conducting parallel analysis on quantitative and qualitative data in healthcare research is apparent within the literature.
• • Using triangulation as a methodological metaphor can facilitate the integration of qualitative and quantitative findings and help researchers to clearly present both their theoretical propositions and the basis of their results.
• • Using triangulation as a methodological metaphor may also support a better understanding of the links between theory and empirical findings, challenge theoretical assumptions and aid the development of new theory.

1. Introduction

Mixed methods research has been widely used within healthcare research for a variety of reasons. The integration of qualitative and quantitative approaches is an interesting issue and continues to be one of much debate ( Bryman, 2004 , Morgan, 2007 , Onwuegbuzie and Leech, 2005 ). In particular, the different epistemological and ontological assumptions and paradigms associated with qualitative and quantitative research have had a major influence on discussions on whether the integration of the two is feasible, let alone desirable ( Morgan, 2007 , Sale et al., 2002 ). Proponents of mixed methods research suggest that the purist view, that quantitative and qualitative approaches cannot be merged, poses a threat to the advancement of science ( Onwuegbuzie and Leech, 2005 ) and that while epistemological and ontological commitments may be associated with certain research methods, the connections are not necessary deterministic ( Bryman, 2004 ). Mixed methods research can be viewed as an approach which draws upon the strengths and perspectives of each method, recognising the existence and importance of the physical, natural world as well as the importance of reality and influence of human experience ( Johnson and Onquegbuzie, 2004 ). Rather than continue these debates in this paper, we aim to explore the approaches used to integrate qualitative and quantitative data within healthcare research to date. Accordingly, this paper focuses on the practical issues of conducting mixed methods studies and the need to develop a rigorous framework for designing and interpreting mixed methods studies to advance the field. In this paper, we will attempt to offer some guidance for those interested in mixed methods research on ways to combine qualitative and quantitative methods.

The concept of mixing methods was first introduced by Jick (1979) , as a means for seeking convergence across qualitative and quantitative methods within social science research ( Creswell, 2003 ). It has been argued that mixed methods research can be particularly useful in healthcare research as only a broader range of perspectives can do justice to the complexity of the phenomena studied ( Clarke and Yaros, 1988 , Foss and Ellefsen, 2002 , Steckler et al., 1992 ). By combining qualitative and quantitative findings, an overall or negotiated account of the findings can be forged, not possible by using a singular approach ( Bryman, 2007 ). Mixed methods can also help to highlight the similarities and differences between particular aspects of a phenomenon ( Bernardi et al., 2007 ). Interest in, and expansion of, the use of mixed methods designs have most recently been fuelled by pragmatic issues: the increasing demand for cost effective research and the move away from theoretically driven research to research which meets policymakers’ and practitioners’ needs and the growing competition for research funding ( Brannen, 2009 , O’Cathain et al., 2007 ).

Tashakkori and Creswell (2007) broadly define mixed methods research as “research in which the investigator collects and analyses data, integrates the findings and draws inferences using both qualitative and quantitative approaches” (2007:3). In any mixed methods study, the purpose of mixing qualitative and quantitative methods should be clear in order to determine how the analytic techniques relate to one another and how, if at all, the findings should be integrated ( O’Cathain et al., 2008 , Onwuegbuzie and Teddlie, 2003 ). It has been argued that a characteristic of truly mixed methods studies are those which involve integration of the qualitative and quantitative findings at some stage of the research process, be that during data collection, analysis or at the interpretative stage of the research ( Kroll and Neri, 2009 ). An example of this is found in mixed methods studies which use a concurrent data analysis approach, in which each data set is integrated during the analytic stage to provide a complete picture developed from both data sets after data has been qualitised or quantitised (i.e. where both forms of data have been converted into either qualitative or quantitative data so that it can be easily merged) ( Onwuegbuzie and Teddlie, 2003 ). Other analytic approaches have been identified including; parallel data analysis, in which collection and analysis of both data sets is carried out separately and the findings are not compared or consolidated until the interpretation stage, and finally sequential data analysis, in which data are analysed in a particular sequence with the purpose of informing, rather than being integrated with, the use of, or findings from, the other method ( Onwuegbuzie and Teddlie, 2003 ). An example of sequential data analysis might be where quantitative findings are intended to lead to theoretical sampling in an in depth qualitative investigation or where qualitative data is used to generate items for the development of quantitative measures.

When qualitative and quantitative methods are mixed in a single study, one method is usually given priority over the other. In such cases, the aim of the study, the rationale for employing mixed methods, and the weighting of each method determine whether, and how, the empirical findings will be integrated. This is less challenging in sequential mixed methods studies where one approach clearly informs the other, however, guidance on combining qualitative and quantitative data of equal weight, for example, in concurrent mixed methods studies, is rather less clear ( Foss and Ellefsen, 2002 ). This is made all the more challenging by a common flaw which is to insufficiently and inexplicitly identify the relationships between the epistemological and methodological concepts in a particular study and the theoretical propositions about the nature of the phenomena under investigation ( Kelle, 2001 ).

One approach to combining different data of equal weight and which facilitate clear identification of the links between the different levels of theory, epistemology, and methodology could be to frame triangulation as a ‘methodological metaphor’, as argued by Erzberger and Kelle (2003) . This can help to; describe the logical relations between the qualitative and quantitative findings and the theoretical concepts in a study; demonstrate the way in which both qualitative and quantitative data can be combined to facilitate an improved understanding of particular phenomena; and, can also be used to help generate new theory ( Erzberger and Kelle, 2003 ) (see Fig. 1 ). The points of the triangle represent theoretical propositions and empirical findings from qualitative and quantitative data while the sides of the triangle represent the logical relationships between these propositions and findings. The nature and use of the triangle depends upon the outcome from the analysis, whether that be convergent , where qualitative and quantitative findings lead to the same conclusion; complementary, where qualitative and quantitative results can be used to supplement each other or; divergent , where the combination of qualitative and quantitative results provides different (and at times contradictory) findings. Each of these outcomes requires a different way of using the triangulation metaphor to link theoretical propositions to empirical findings ( Erzberger and Kelle, 2003 ).

Illustrating the triangulation triangle ( Erzberger and Kelle, 2003 )

1.1. Purpose of this paper

In the following paper, we identify the analytical approaches used in mixed methods healthcare research and exemplify the use of triangulation ( Erzberger and Kelle, 2003 ) as a methodological metaphor for drawing inferences from qualitative and quantitative findings. Papers reporting on mixed methods studies within healthcare research were reviewed to (i) determine the type of analysis approach used, i.e. parallel, concurrent, or sequential data analysis and, (ii) identify studies which could be used to illustrate the use of the methodological metaphor of triangulation suggested by Erzberger and Kelle (2003) . Four papers were selected to illustrate the application of the triangulation metaphor on complementary, convergent and divergent outcomes and to develop theory.

This literature review has used systematic principles ( Cochrane, 2009 , Khan, 2001 ) to search for mixed methods studies within healthcare research. The first search was conducted in September 2009 in the data bases CINAHL, Medline and PsycINFO on papers published in English language between 1999 and 2009. To identify mixed methods studies, the search terms (used as keywords and where possible as MeSH terms) were: “mixed methods”, “mixed research methods”, “mixed research”, “triangulation”, “complementary methods”, “concurrent mixed analysis” and “multi-strategy research.” These terms were searched individually and then combined (with OR). This resulted in 1896 hits in CINAHL, 1177 in Medline and 1943 in PsycINFO.

To focus on studies within, or relevant to, a healthcare context the following search terms were used (as keywords or as MeSH terms and combined with OR): “health care research”; “health services research”; and “health”. These limits applied to the initial search (terms combined with AND) resulted in 205 hits in Medline and 100 hits in PsycINFO. Since this combination in CINAHL only limited the search results to 1017; a similar search was conducted but without using the search term triangulation to capture mixed methods papers; resulting in 237 hits. In CINAHL the search result on 1017 papers was further limited by using “interventions” as a keyword resulting in 160 papers also selected to be reviewed. Moreover; in Medline the mixed methods data set was limited by the MeSH term “research” resulting in 218 hits and in PsycINFO with “intervention” as keyword or MeSH term resulting in 178 hits.

When duplicates were removed the total numbers of papers identified were 843. The abstracts were then reviewed by each author and those identified as relevant to the review were selected to be retrieved and reviewed in full text. Papers were selected based on the following inclusion criteria: empirical studies; published in peer review journals; healthcare research (for the purpose of this paper defined as any study focussing on participants in receipt, or involved in the delivery, of healthcare or a study conducted within a healthcare setting, e.g. different kinds of care, health economics, decision making, and professionals’ role development); and using mixed methods (defined as a study in which both qualitative and quantitative data were collected and analysed ( Halcomb et al., 2009b ). To maintain rigour, a random sample (10%) of the full text papers was reviewed jointly by two authors. Any disagreements or uncertainties that arose between the reviewers regarding their inclusion or in determining the type of analytic approach used were resolved through discussion between the authors.

In addition to the criteria outlined above, papers were excluded if the qualitative element constituted a few open-ended questions in a questionnaire, as we would agree with previous authors who have argued such studies do not strictly constitute a mixed methods design ( Kroll and Neri, 2009 ). Papers were also excluded if they could not be retrieved in full text via the library services at the University of Edinburgh, Glasgow Caledonian University or the Karolinska Institutet, or did not adequately or clearly describe their analytic strategy, for example, failing to report how the qualitative and quantitative data sets were analysed individually and, where relevant, how these were integrated. See Table 1 for reasons for the exclusion of subsequent papers.

Reasons for exclusion.

A second search was conducted within the databases of Medline, PsychInfo and Cinahl to identify studies which have specifically used Erzberger and Kelle's (2003) triangulation metaphor to frame the description and interpretation of their findings. The term ‘triangulation metaphor’ (as keywords) and author searches on ‘Christian Erzberger’ and ‘Udo Kelle’ were conducted. Three papers, published by Christian Erzberger and Udo Kelle, were identified in the PsychInfo databases but none of these were relevant to the purpose of this review. There were no other relevant papers identified in the other two databases.

168 Papers were included in the final review and reviewed to determine the type of mixed analysis approach used, i.e. parallel, concurrent, or sequential mixed analysis. Four of these papers (identified from the first search on mixed methods studies and healthcare research) were also used to exemplify the use of the methodological metaphor of triangulation ( Erzberger and Kelle, 2003 ). Data was extracted from included papers accordingly in relation to these purposes.

In total, 168 papers were included in our review. The majority of these studies originated in the USA ( n  = 63), the UK ( n  = 39) and Canada ( n  = 19), perhaps reflecting the considerable interest and expertise in mixed methods research within these countries. The focus of the studies included in the review varied significantly and the populations studied included both patients and healthcare professionals.

3.1. Analytic approaches

Table 2 illustrates the types of analytic approaches adopted in each of the studies included in the review. The most widely used analytic approach ( n  = 98) was parallel analysis ( Creswell and Plano Clark, 2007 ). However, very few of the studies employing parallel analysis clearly articulate their purpose for mixing qualitative and quantitative data, the weighting (or priority) given to the qualitative and quantitative data or the expected outcomes from doing so, mirroring previous research findings ( O’Cathain et al., 2008 ). The weighting, or priority, of the qualitative and quantitative data in a mixed methods study is dependent upon various factors including; the aims of the study and whether the purpose is, for example, to contextualise quantitative data using qualitative data or to use qualitative data to inform a larger quantitative approach such as a survey. Nonetheless, the omission of this statement makes it difficult to determine which data set the conclusions have been drawn from and the role of, or emphasis on, each approach. Therefore, is of importance for authors to clearly state this in their papers ( Creswell and Plano Clark, 2007 ). A number of studies had also used sequential data analysis ( n  = 46), where qualitative approaches were visibly used to inform the development of both clinical tools (e.g. Canales and Rakowski, 2006 ) and research measures and surveys (e.g. Beatty et al., 2004 ) or where quantitative surveys were supplemented by and issues further explored using qualitative approaches (e.g. Abadia and Oviedo, 2009 , Cheng, 2004 , Halcomb et al., 2008 ).

Included papers illustrating their analytical approach and country of origin.

Most notably, with only 20 included studies using a concurrent approach to data analysis, this was the least common design employed. Compared to the studies using a parallel or sequential approach, the authors of concurrent studies more commonly provided an explanation for their purpose of using a mixed methods design in their study, e.g. how it addressed a gap or would facilitate and advance the state of knowledge (e.g. Bussing et al., 2005 , Kartalova-O’Doherty and Tedstone Doherty, 2009 ). Despite this, there remained a lack of clarity within these studies about the weighting given to, and priority of, each method. Consequently, the importance and relevance of the findings produced by each approach and how these have informed their conclusions and interpretation is lacking. In four of the included papers a combination of approaches to data analysis (i.e. sequential and concurrent, parallel and concurrent, or sequential and parallel) were used. In the next section, we have selected papers to illustrate the methodological metaphor of triangulation ( Erzberger and Kelle, 2003 ).

3.2. Using the methodological metaphor of triangulation

We have selected four papers from our review ( Lukkarinen, 2005 , Midtgaard et al., 2006 , Shipman et al., 2008 , Skilbeck et al., 2005 ) to illustrate how the methodological metaphor of triangulation ( Erzberger and Kelle, 2003 ) can be applied to mixed methods studies. Each of these studies has been used to illustrate how the metaphor of triangulation can be applied to studies producing: (i) complementary findings, (ii) convergent findings, and (iii) divergent findings. In the following section, we demonstrate how the application of the metaphor can be used as a framework both to develop theory and to facilitate the interpretation of the findings from mixed methods studies and their conclusions in each of these scenarios, and how using the metaphor can help to promote greater clarity of the study's purpose, its theoretical propositions, and the links between data sets.

3.2.1. Triangulating complementary results

To exemplify the use of the methodological metaphor of triangulation ( Erzberger and Kelle, 2003 ) for drawing inferences from complementary results, we have drawn on the results of a UK based study by Shipman et al. (2008) ( Fig. 2 ). In the UK, members of district nursing teams (DNs) provide most nursing care to people at home in the last year of life. Following concerns that inadequate education might limit the confidence of some DNs to support patients and their carers’ at home, and that low home death rates may in part be related to this, the Department of Health (DH) identified good examples of palliative care educational initiatives for DNs and invested in a 3-year national education and support programme in the principles and practice of palliative care. Shipman et al.’s study evaluates whether the programme had measurable effects on DN knowledge and confidence in competency in the principles and practice of palliative care. The study had two parts, a summative (concerned with outcomes) quantitative component which included ‘before and after’ postal questionnaires which measured effects on DNs’ ( n  = 1280) knowledge, confidence and perceived competence in the principles and practice of palliative care and a formative (concerned with process) qualitative component which included semi-structured focus groups and interviews with a sub-sample of DNs ( n  = 39).

Illustrating the use of triangulation ( Erzberger and Kelle, 2003 ) on complementary results in the study by Shipman et al. (2008) .

While their theoretical proposition may not be explicitly stated by the authors, there is clearly an implicit theoretical proposition that the educational intervention would improve DNs knowledge and confidence (theoretical proposition 1, Fig. 2 ). This was supported by the quantitative findings which showed significant improvement in the district nurses confidence in their professional competence post intervention. Qualitative results supported and complemented the quantitative findings as the district nurses described several benefits from the program including greater confidence in tackling complex problems and better communication with patient and carers’ because of greater understanding of the reasons for symptoms. Thus, a complementary theoretical proposition (theoretical proposition 2, Fig. 2 ) can be deduced from the qualitative findings: the DN's better understanding of factors contributing to complex problems and underlying reasons for symptoms led to improved confidence in competence raised from district nurses increased understanding.

Fig. 2 illustrates the theoretical propositions, the empirical findings from qualitative and quantitative data and the logical relationships between these. Theoretical proposition 1 is supported by the quantitative findings. From qualitative findings, a complementary theoretical proposition (theoretical proposition 2) can be stated explaining the process that led to the DNs improved confidence in competence.

3.2.2. Triangulating convergent results

To illustrate how the methodological metaphor of triangulation can be used to draw inferences from convergent findings, we have drawn on the example of a Danish study by Midtgaard et al. (2006) ( Fig. 3 ). This study was conducted to explore experiences of group cohesion and changes in quality of life (QoL) among people ( n  = 55) who participated in a weekly physical exercise intervention (for six weeks) during treatment for cancer. The study, conducted in a Danish hospital, involved the use of structured QoL questionnaires, administered at baseline and post intervention (at six weeks) to determine changes in QoL and health status, and qualitative focus groups, conducted post intervention (at six weeks), to explore aspects of cohesion within the group. With regards to the theoretical proposition of the study ( Fig. 3 ), group cohesion was seen as essential to understand the processes within the group that facilitated the achievement of desired outcomes and the satisfaction of affective needs as well as promoting a sense of belonging to the group itself.

Illustrating the use of triangulation ( Erzberger and Kelle, 2003 ) on convergent results in the study by Midtgaard et al. (2006) .

This proposition was deductively tested in an intervention where patients exercised in mixed gender groups of seven to nine members during a nine hour weekly session over a six week period and was supported by both the empirical quantitative and qualitative findings. The quantitative data showed significant improvements in peoples’ emotional functioning, social functioning and mental health. The qualitative data showed how the group setting motivated the individuals to pursue personal endeavors beyond physical limitations, that patients used each others as role models during ‘down periods’ and how exercising in a group made individuals feel a sense of obligation to train and to do their best. This subsequently helped to improve their social functioning which in turn satisfied their affective needs, improving their improved emotional functioning and mental health.

Fig. 3 illustrates the theoretical propositions, empirical findings from qualitative and quantitative data and the logical relationships between these. Both the quantitative and qualitative findings, demonstrating improvements in participants’ emotional and social functioning and their mental health, can be attributed to the nature of group cohesion within the programme as expected.

3.2.3. Triangulating divergent results

Qualitative and quantitative results that seem to contradict each other are often explained as resulting from methodological error. However, instead of a methodological flaw, a divergent result could be a consequence of the inadequacy of the theoretical concepts used. This may indicate the need for changing or developing the theoretical concepts involved ( Erzberger and Kelle, 2003 ). The following example of using the methodological metaphor of triangulation ( Erzberger and Kelle, 2003 ) for drawing inferences from divergent results is intended as an example rather than an attempt to change the theoretical concept involved. In a study by Skilbeck et al. (2005) ( Fig. 4 ), some results were found to be divergent which was explained as resulting from the use of inadequate questionnaires. We do not wish to critique their conclusion; rather we intend to simply offer an alternative interpretation for their findings.

Illustrating the use of triangulation ( Erzberger and Kelle, 2003 ) on divergent results using the study by Skilbeck et al. (2005) .

The study aimed to explore family carers’ expectations and experiences of respite services provided by one independent hospice in North England. This hospice provides inpatient respite beds specifically for planned respite admission for a two-week period. Referrals were predominated from general practitioners and patients and their carers were offered respite care twice a year, during the study this was reduced to once a year for each patient. Data was collected prior to respite admission and post respite care by semi-structured interviews and using the Relative Stress Scale inventory (RSSI), a validated scale to measure relative distress in relation to caring. Twenty-five carers were included but pre- and post-data were completed by 12 carers. Qualitative data was analysed by using a process of constant comparison and quantitative data by descriptive and comparative statistical analysis.

No clear theoretical proposition was stated by the authors, but from the definition of respite care it is possible to deduce that ‘respite care is expected to provide relief from care-giving to the primary care provider’ (theoretical proposition 1, Fig. 4 ). This proposition was tested quantitatively by pre- and post-test using the RSSI showing that the majority of carers experienced either a negative or no change in scores following the respite stay (no test of significance was stated). Accordingly, the theoretical proposition was not supported by the quantitative empirical data. The qualitative empirical results, however, were supportive in showing that most of the carers considered respite care to be important as it enabled them to have a break and a rest from ongoing care-responsibilities. From this divergent empirical data it could be suggested changing or developing the original theoretical proposition. It seems that respite care gave the carers relief from their care-responsibilities but not from the distress carers experienced in relation to caring (measured by the used scale). We would therefore suggest that in order to lessen distress related to caring, other types of support is also needed which would change the theoretical proposition as suggested (theoretical proposition 2).

Fig. 4 illustrates the theoretical propositions, empirical findings from qualitative and quantitative data and the logical relationships between these. Theoretical proposition 1 was not supported by the quantitative findings (indicated in Fig. 4 by the broken arrow), but the qualitative findings supported this proposition. From these divergent empirical findings, the theoretical proposition could accordingly be changed and developed. Respite care seemed to provide relief from carers’ on-going care-responsibilities, but other types of support need to be added to provide relief from distress experienced (theoretical proposition 2).

3.2.4. Triangulation to produce theoretical propositions

Methodological triangulation has also been applied to illustrate how theoretical propositions can be produced by drawing on the findings from a Finnish study by Lukkarinen (2005) ( Fig. 5 ). The purpose of this longitudinal study was to describe, explain and understand the subjective health related quality of life (QoL) and life course of people with coronary artery disease (CAD). A longitudinal quantitative study was undertaken during the year post treatment and 19 individuals also attended thematic interviews one year after treatment. This study is one of the few studies that clearly defines theoretical underpinnings for both the selected methods and their purpose, namely “to obtain quantitatively abundant average information about the QoL of CAD patients and the changes in it as well as the patients’ individual, unique experiences of their respective life situations” ( Lukkarinen, 2005 :622).

Illustrating the use of triangulation ( Erzberger and Kelle, 2003 ) to develop theory from the study by Lukkarinen (2005) .

The results of the quantitative analysis showed that the male and female CAD patients in the youngest age group had the poorest QoL. While patients’ QoL improved in the dimensions of pain, energy and mobility it deteriorated on dimensions of social isolation, sleep and emotional reactions. From the viewpoint of methodological triangulation used in the study the aim of the quantitative approach was to observe changes in QoL at the group level and also explore correlations of background factors to QoL. The qualitative approach generated information concerning both QoL in the individuals’ life situation and life course and the individuals’ rehabilitation. Both the quantitative and the qualitative analysis showed the youngest CAD patients to have the poorest psychosocial QoL. The results obtained using qualitative methods explained the quantitative findings and offered new insight into the factors related to poor psychosocial QoL, which could be used to help develop theoretical propositions around these. Patients at risk of poorer QoL were those with an acute onset of illness at a young age that led to an unexpected termination of career, resulting in financial problems, and worries about family. This group also experienced lack of emotional support (especially the females with CAD) and were concerned for the illness that was not alleviated by treatment. The interviews and the method of phenomenological psychology therefore helped to gain insight into the participants’ situational experience of QoL and life course, not detectable by the use of a questionnaire.

Fig. 5 illustrates the theoretical propositions, empirical findings from qualitative and quantitative data and the relationships between these. The use of the mixed methods approach enabled a clearer understanding to emerge in relation to the lived experience of CAD patients and the factors that were related to poor QoL. This understanding allows new theoretical propositions about these issues to be developed and further explored, as depicted at the theoretical level.

4. Discussion

As the need for, and use of, mixed methods research continues to grow, the issue of quality within mixed methods studies is becoming increasingly important ( O’Cathain et al., 2008 , O’Cathain et al., 2007 ). Similarly, the need for guidance on the analysis and integration of qualitative and quantitative data is a prominant issue ( Bazeley, 2009 ). This paper firstly intended to review the types of analytic approaches (parallel, concurrent or sequential data analysis) that have been used in mixed methods studies within healthcare research. As identified in previous research ( O’Cathain et al., 2008 ), we found that the majority of studies included in our review employed parallel data analysis in which the different analyses are not compared or consolidated until the full analysis of both data sets have been completed. A trend to conduct separate analysis on quantitative and qualitative data is apparent in mixed methods healthcare studies, despite the fact that if the data were correlated, a more complete picture of a particular phenomenon may be produced ( Onwuegbuzie and Teddlie, 2003 ). If qualitative and quantitative data are not integrated during data collection or analysis, the findings may be integrated at the stage of interpretation and conclusion.

Although little pragmatic guidance exists within the wider literature, Erzberger and Kelle (2003) have published some practical advice, on the integration of mixed methods findings. For mixed methodologists, the ‘triangulation metaphor’ offers a framework to facilitate a description of the relationships between data sets and theoretical concepts and can also assist in the integration of qualitative and quantitative data ( Erzberger and Kelle, 2003 ). Yet despite the fact that the framework was published in 2003 within Tashakkori and Teddlie's (2003) seminal work, the Handbook for Mixed Methods in Social and Behavioural Research, our search revealed that it has received little application within the published body of work around mixed methods studies since its publication. This is surprising since mixed methodologists are acutely aware of the lack of guidance with regards to the pragmatics and practicalities of conducting mixed methods research ( Bryman, 2006 , Leech et al., 2010 ). Furthermore, there have been frequent calls to move the field of mixed methods away from the “should we or shouldn’t we” debate towards the practical application, analysis and integration of mixed methods and its’ findings and what we can learn from each other's work and advice. Consequently, we have a state of ambiguity and instability in the field of mixed methods in which mixed methodologists find themselves lacking appropriate sources or evidence to draw upon with which to facilitate the future design, conduct and interpretation of mixed methods studies. It is for these reasons that we, in this paper, also intended to identify and select studies that could be used as examples for the application of Erzberger and Kelle's (2003) triangulation metaphor.

When reviewing the studies it was clear that the majority of theoretical assumptions were implicit, rather than explicitly stated by authors. Wu and Volker (2009) previously acknowledged that while studies undoubtedly have a theoretical basis in their literature reviews and the nature of their research questions, they often fail to clearly articulate a particular theoretical framework. This is unfortunate as theory can help researchers to clarify their ideas and also help data collection, analysis and to improve the study's rigour ( Wu and Volker, 2009 ). When using triangulation as a methodological metaphor ( Erzberger and Kelle, 2003 ), researchers are encouraged to articulate their theoretical propositions and to validate their conclusions in relation to the chosen theories. Theory can also guide researchers when defining outcome measures . Should the findings not support the chosen theory, as shown in our examples on complementary and divergent results, researchers can modify or expand their theory accordingly and new theory may be developed ( Wu and Volker, 2009 ). It is therefore our belief that using triangulation as a methodological metaphor in mixed methods research can also benefit the design of mixed method studies.

Like other researchers ( O’Cathain et al., 2008 ), we have also found that most of the papers reviewed lacked clarity in whether the reported results primarily stemmed from qualitative or quantitative findings. Many of the papers were even less clear when discussing their results and the basis of their conclusions. The reporting of mixed methods studies is notoriously challenging, but clarity and transparency are, at the very least, crucial in such reports ( O’Cathain, 2009 ). Using triangulation as a methodological metaphor ( Erzberger and Kelle, 2003 ) may be one way of addressing this lack of clarity by explicitly showing the types of data that researchers have based their interpretations on. It may even help address some of the issues raised in the debate on the feasibility of integrating research methods and results stemming from different epistemological and ontological assumptions and paradigms ( Morgan, 2007 , Sale et al., 2002 ). In order to carry out methodological triangulation researchers also need to identify and observe the consistency and adequacy of the two methods, positivistic and phenomenological regarding the research questions, data collection, methods of analysis and conclusions.

While we used systematic principles in our search for mixed methods studies in healthcare research, we cannot claim to have included all such studies. In many cases, reports of mixed methods studies are subjected to ‘salami slicing’ by researchers and hence the conduct of, and findings from, individual approaches are addressed in separate papers. Since these papers are often not indexed as a ‘mixed method’ study, they are undoubtedly more difficult to identify. Furthermore, different terminologies are used to describe and index mixed methods studies within the electronic databases ( Halcomb and Andrew, 2009a ), making it challenging to be certain that all relevant studies were captured in this review. However, the studies included in this review should give a sufficient overview of the use of mixed analysis in healthcare research and most importantly, they enable us to make suggestions for the future design, conduct, interpretation and reporting of mixed methods studies. It is also important to emphasise that we have based our triangulation examples on the data published but have no further knowledge of the analysis and findings undertaken by the authors. The examples should thus be taken as examples and not alternative explanations or interpretations.

Mixed methods research within healthcare remains an emerging field and its use is subject to much debate. It is therefore particularly important that researchers clearly describe their use of the approach and the conclusions made to improve transparency and quality within mixed methods research. The use of triangulation as a methodological metaphor ( Erzberger and Kelle, 2003 ) can help researchers not only to present their theoretical propositions but also the origin of their results in an explicit way and to understand the links between theory, epistemology and methodology in relation to their topic area. Furthermore it has the potential to make valid inferences, challenge existing theoretical assumptions and to develop or create new ones.

Conflict of interest

None declared.

Ethical approval

Not required.

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