quantitative and qualitative research social work

Social Work Research Methods That Drive the Practice

Social workers advocate for the well-being of individuals, families and communities. But how do social workers know what interventions are needed to help an individual? How do they assess whether a treatment plan is working? What do social workers use to write evidence-based policy?

Social work involves research-informed practice and practice-informed research. At every level, social workers need to know objective facts about the populations they serve, the efficacy of their interventions and the likelihood that their policies will improve lives. A variety of social work research methods make that possible.

Data-Driven Work

Data is a collection of facts used for reference and analysis. In a field as broad as social work, data comes in many forms.

Quantitative vs. Qualitative

As with any research, social work research involves both quantitative and qualitative studies.

Quantitative Research

Answers to questions like these can help social workers know about the populations they serve — or hope to serve in the future.

• How many students currently receive reduced-price school lunches in the local school district?
• How many hours per week does a specific individual consume digital media?
• How frequently did community members access a specific medical service last year?

Quantitative data — facts that can be measured and expressed numerically — are crucial for social work.

Quantitative research has advantages for social scientists. Such research can be more generalizable to large populations, as it uses specific sampling methods and lends itself to large datasets. It can provide important descriptive statistics about a specific population. Furthermore, by operationalizing variables, it can help social workers easily compare similar datasets with one another.

Qualitative Research

Qualitative data — facts that cannot be measured or expressed in terms of mere numbers or counts — offer rich insights into individuals, groups and societies. It can be collected via interviews and observations.

• What attitudes do students have toward the reduced-price school lunch program?
• What strategies do individuals use to moderate their weekly digital media consumption?
• What factors made community members more or less likely to access a specific medical service last year?

Qualitative research can thereby provide a textured view of social contexts and systems that may not have been possible with quantitative methods. Plus, it may even suggest new lines of inquiry for social work research.

Mixed Methods Research

Combining quantitative and qualitative methods into a single study is known as mixed methods research. This form of research has gained popularity in the study of social sciences, according to a 2019 report in the academic journal Theory and Society. Since quantitative and qualitative methods answer different questions, merging them into a single study can balance the limitations of each and potentially produce more in-depth findings.

However, mixed methods research is not without its drawbacks. Combining research methods increases the complexity of a study and generally requires a higher level of expertise to collect, analyze and interpret the data. It also requires a greater level of effort, time and often money.

The Importance of Research Design

Data-driven practice plays an essential role in social work. Unlike philanthropists and altruistic volunteers, social workers are obligated to operate from a scientific knowledge base.

To know whether their programs are effective, social workers must conduct research to determine results, aggregate those results into comprehensible data, analyze and interpret their findings, and use evidence to justify next steps.

Employing the proper design ensures that any evidence obtained during research enables social workers to reliably answer their research questions.

Research Methods in Social Work

The various social work research methods have specific benefits and limitations determined by context. Common research methods include surveys, program evaluations, needs assessments, randomized controlled trials, descriptive studies and single-system designs.

Surveys involve a hypothesis and a series of questions in order to test that hypothesis. Social work researchers will send out a survey, receive responses, aggregate the results, analyze the data, and form conclusions based on trends.

Surveys are one of the most common research methods social workers use — and for good reason. They tend to be relatively simple and are usually affordable. However, surveys generally require large participant groups, and self-reports from survey respondents are not always reliable.

Program Evaluations

Social workers ally with all sorts of programs: after-school programs, government initiatives, nonprofit projects and private programs, for example.

Crucially, social workers must evaluate a program’s effectiveness in order to determine whether the program is meeting its goals and what improvements can be made to better serve the program’s target population.

Evidence-based programming helps everyone save money and time, and comparing programs with one another can help social workers make decisions about how to structure new initiatives. Evaluating programs becomes complicated, however, when programs have multiple goal metrics, some of which may be vague or difficult to assess (e.g., “we aim to promote the well-being of our community”).

Needs Assessments

Social workers use needs assessments to identify services and necessities that a population lacks access to.

Common social work populations that researchers may perform needs assessments on include:

• People in a specific income group
• Everyone in a specific geographic region
• A specific ethnic group
• People in a specific age group

In the field, a social worker may use a combination of methods (e.g., surveys and descriptive studies) to learn more about a specific population or program. Social workers look for gaps between the actual context and a population’s or individual’s “wants” or desires.

For example, a social worker could conduct a needs assessment with an individual with cancer trying to navigate the complex medical-industrial system. The social worker may ask the client questions about the number of hours they spend scheduling doctor’s appointments, commuting and managing their many medications. After learning more about the specific client needs, the social worker can identify opportunities for improvements in an updated care plan.

In policy and program development, social workers conduct needs assessments to determine where and how to effect change on a much larger scale. Integral to social work at all levels, needs assessments reveal crucial information about a population’s needs to researchers, policymakers and other stakeholders. Needs assessments may fall short, however, in revealing the root causes of those needs (e.g., structural racism).

Randomized Controlled Trials

Randomized controlled trials are studies in which a randomly selected group is subjected to a variable (e.g., a specific stimulus or treatment) and a control group is not. Social workers then measure and compare the results of the randomized group with the control group in order to glean insights about the effectiveness of a particular intervention or treatment.

Randomized controlled trials are easily reproducible and highly measurable. They’re useful when results are easily quantifiable. However, this method is less helpful when results are not easily quantifiable (i.e., when rich data such as narratives and on-the-ground observations are needed).

Descriptive Studies

Descriptive studies immerse the researcher in another context or culture to study specific participant practices or ways of living. Descriptive studies, including descriptive ethnographic studies, may overlap with and include other research methods:

• Informant interviews
• Census data
• Observation

By using descriptive studies, researchers may glean a richer, deeper understanding of a nuanced culture or group on-site. The main limitations of this research method are that it tends to be time-consuming and expensive.

Single-System Designs

Unlike most medical studies, which involve testing a drug or treatment on two groups — an experimental group that receives the drug/treatment and a control group that does not — single-system designs allow researchers to study just one group (e.g., an individual or family).

Single-system designs typically entail studying a single group over a long period of time and may involve assessing the group’s response to multiple variables.

For example, consider a study on how media consumption affects a person’s mood. One way to test a hypothesis that consuming media correlates with low mood would be to observe two groups: a control group (no media) and an experimental group (two hours of media per day). When employing a single-system design, however, researchers would observe a single participant as they watch two hours of media per day for one week and then four hours per day of media the next week.

These designs allow researchers to test multiple variables over a longer period of time. However, similar to descriptive studies, single-system designs can be fairly time-consuming and costly.

Learn More About Social Work Research Methods

Social workers have the opportunity to improve the social environment by advocating for the vulnerable — including children, older adults and people with disabilities — and facilitating and developing resources and programs.

Learn more about how you can earn your  Master of Social Work online at Virginia Commonwealth University . The highest-ranking school of social work in Virginia, VCU has a wide range of courses online. That means students can earn their degrees with the flexibility of learning at home. Learn more about how you can take your career in social work further with VCU.

From M.S.W. to LCSW: Understanding Your Career Path as a Social Worker

How Palliative Care Social Workers Support Patients With Terminal Illnesses

How to Become a Social Worker in Health Care

Gov.uk, Mixed Methods Study

MVS Open Press, Foundations of Social Work Research

Open Social Work Education, Scientific Inquiry in Social Work

Open Social Work, Graduate Research Methods in Social Work: A Project-Based Approach

Routledge, Research for Social Workers: An Introduction to Methods

SAGE Publications, Research Methods for Social Work: A Problem-Based Approach

Theory and Society, Mixed Methods Research: What It Is and What It Could Be

READY TO GET STARTED WITH OUR ONLINE M.S.W. PROGRAM FORMAT?

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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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The Handbook of Social Work Research Methods

• Edited by: Bruce A. Thyer
• Publisher: SAGE Publications, Inc.
• Publication year: 2001
• Online pub date: January 01, 2011
• Discipline: Social Work
• Methods: Measurement , Case study research , Theory
• DOI: https:// doi. org/10.4135/9781412986182
• Keywords: clients , handbooks , knowledge , population , social problems , social welfare , social work practice Show all Show less
• Print ISBN: 9780761919063
• Online ISBN: 9781412986182
• Buy the book icon link

Subject index

"`Not so much a handbook, but an excellent source of reference' - British Journal of Social Work This volume is the definitive resource for anyone doing research in social work. It details both quantitative and qualitative methods and data collection, as well as suggesting the methods appropriate to particular types of studies. It also covers issues such as ethics, gender and ethnicity, and offers advice on how to write up and present your research."

Front Matter

• Acknowledgments
• Overview of Quantitative Research Methods
• Probability and Sampling
• Reliability and Validity in Quantitative Measurement
• Locating Instruments
• Statistics for Social Workers
• Types of Studies
• Descriptive Studies
• Needs Assessments
• Randomized Controlled Trials
• Program Evaluation
• Using Cost → Procedure → Process → Outcome Analysis
• Single-System Designs
• Overview of Qualitative Research Methods
• Reliability and Validity in Qualitative Research
• Narrative Case Studies
• In-Depth Interviews
• Ethnographic Research Methods
• Participant Observation
• Grounded Theory and Other Inductive Research Methods
• Theory Development
• Historical Research
• Literature Reviews
• Critical Analyses
• Ethical Issues
• Gender, Ethnicity, and Race Matters
• Comparative International Research
• Integrating Qualitative and Quantitative Research Methods
• Applying for Research Grants
• Disseminating Research Findings

Back Matter

• About the Editor
• About the Contributors

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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.

Learn about our Editorial Process

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.

On This Page:

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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• Qualitative vs. Quantitative Research | Differences, Examples & Methods

Qualitative vs. Quantitative Research | Differences, Examples & Methods

Published on April 12, 2019 by Raimo Streefkerk . Revised on June 22, 2023.

When collecting and analyzing data, quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings. Both are important for gaining different kinds of knowledge.

Common quantitative methods include experiments, observations recorded as numbers, and surveys with closed-ended questions.

Quantitative research is at risk for research biases including information bias , omitted variable bias , sampling bias , or selection bias . Qualitative research Qualitative research is expressed in words . It is used to understand concepts, thoughts or experiences. This type of research enables you to gather in-depth insights on topics that are not well understood.

Common qualitative methods include interviews with open-ended questions, observations described in words, and literature reviews that explore concepts and theories.

Table of contents

The differences between quantitative and qualitative research, data collection methods, when to use qualitative vs. quantitative research, how to analyze qualitative and quantitative data, other interesting articles, frequently asked questions about qualitative and quantitative research.

Quantitative and qualitative research use different research methods to collect and analyze data, and they allow you to answer different kinds of research questions.

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

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

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

Quantitative data collection methods

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

Qualitative data collection methods

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

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

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

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

Quantitative research approach

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

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

Qualitative research approach

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

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

Mixed methods approach

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

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

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

Analyzing quantitative data

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

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

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

Analyzing qualitative data

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

Some common approaches to analyzing qualitative data include:

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

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

• Chi square goodness of fit test
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Inclusion and exclusion criteria

Research bias

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

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

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

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

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

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

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

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

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

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

A research project is an academic, scientific, or professional undertaking to answer a research question . Research projects can take many forms, such as qualitative or quantitative , descriptive , longitudinal , experimental , or correlational . What kind of research approach you choose will depend on your topic.

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4.4 Mixed methods

Learning objectives.

• Define sequence and emphasis and describe how they work in qualitative research
• List the five reasons why researchers use mixed methods

So far in this textbook, we have talked about quantitative and qualitative methods as an either/or choice—you can choose quantitative methods or qualitative methods. However, researchers often use both methods inside of their research projects. Take for example a recent study of the possibility of having optometrists refer their older patients to group exercise programs (Miyawaki, Mauldin, & Carman, 2019).  In this study, a short, written survey was distributed to optometrists across Texas.  The survey asked closed-ended questions about their practice, knowledge about fall prevention, and attitudes about prescribing group exercise programs to their patients.  While the study could have just surveyed optometrists for a descriptive quantitative analysis, it was designed to capture more rich details about the perspectives of older optometry patients through conducting focus groups with them.  In the focus groups, the older adults were asked about their perceptions of being prescribed group exercise classes by their optometrist.  The study used both qualitative and quantitative methods, or a mixed methods design.

Sequence and emphasis

There are many different mixed methods designs, each with their own strengths. However, a more simplified synthesis of mixed methods approaches is provided by Engel and Schutt (2016) using two key terms. Sequence refers to the order that each method is used. Researchers can use both methods at the same time or concurrently . Or, they can use one and then the other, or sequentially . The optometry study used a concurrent design in which data were collected and analyzed concurrently. The researchers could have used a sequential design in which one part of the study was conducted first, data analyzed, and then used to inform the researchers about how to conduct the second part of the study.

The other key term in mixed methods research is emphasis . In some studies, the qualitative data may be the most important, with the quantitative data providing secondary or background information.  In this case  qualitative methods are prioritized . Other times, however, quantitative methods are emphasized. In these studies, qualitative data are used mainly to provide context for the quantitative findings. For example, demonstrating quantitatively that a particular therapy works is important. By adding a qualitative component, researchers could find out how the participants experienced the intervention, how they understood its effects, and the meaning it had on their lives. These data would add depth and context to the findings of the study and allow researchers to improve the therapeutic technique in the future.

A similar practice is when researchers use qualitative methods to solicit feedback on a quantitative scale or measure. The experiences of individuals allow researchers to refine the measure before they do the quantitative component of their study. Finally, it is possible that researchers are equally interested in qualitative and quantitative information. In studies of equal emphasis , researchers consider both methods as the focus of the research project.

Why researchers use mixed methods

Mixed methods research is more than just sticking an open-ended question at the end of a quantitative survey. Mixed methods researchers use mixed methods for both pragmatic and synergistic reasons. That is, they use both methods because it makes sense with their research questions and because they will get the answers they want by combining the two approaches.

Mixed methods also allows you to use both inductive and deductive reasoning. As we’ve discussed, qualitative research follows inductive logic, moving from data to empirical generalizations or theory. In a mixed methods study, a researcher could use the results from a qualitative component to inform a subsequent quantitative component. The quantitative component would use deductive logic, using the theory derived from qualitative data to create and test a hypothesis. In this way, mixed methods use the strengths of both research methods, using each method to understand different parts of the same phenomenon. Quantitative allows the researcher to test new ideas. Qualitative allows the researcher to create new ideas.

With these two concepts in mind, we can start to see why researchers use mixed methods in the real world.  Mixed methods are often to initiate ideas with one method to study with another. For example, researchers could begin a mixed methods project by using qualitative methods to interview or conduct a focus group with participants. Based on their responses, the researchers could then formulate a quantitative project to follow up on the results.

In addition to providing information for subsequent investigation, using both quantitative and qualitative information provides additional context for the data. For example, in the optometry/group exercise study, most optometrists expressed that they would be willing to prescribe exercise classes to their patients.  In the focus groups, the patients were able to describe how they would respond to receiving a prescription from their optometrist and the barriers they faced to going to exercise classes.  The context provided by the qualitative focus group data provides important context for practitioners building clinical-community partnerships to help prevent falls among older adults.

Finally, another purpose of mixed methods research is corroborating data from both quantitative and qualitative sources. Ideally, your qualitative and quantitative results should support each other. For example, if interviews with participants showed a relationship between two concepts, that relationship should also be present in the qualitative data you collected. Differences between quantitative and qualitative data require an explanation. Perhaps there are outliers or extreme cases that pushed your data in one direction or another, for example.

In summary, these are a few of the many reasons researchers use mixed methods. They are summarized below:

• Triangulation or convergence on the same phenomenon to improve validity
• Complementarity, which aims to get at related but different facets of a phenomenon
• Development or the use of results from one phase or a study to develop another phase
• Initiation or the intentional analysis of inconsistent qualitative and quantitative findings to derive new insights
• Expansion or using multiple components to extend the scope of a study (Burnett, 2012, p. 77).

A word of caution

The use of mixed methods has many advantages. However, researchers should approach mixed methods with caution. Conducting a mixed methods study may mean doubling or even tripling your work. You must conceptualize how to use one method, another method, and how they fit together. This may mean operationalizing and creating a questionnaire, then writing an interview guide, and thinking through how the data on each measure relate to one another—more work than using one quantitative or qualitative method alone. Similarly, in sequential studies, the researcher must collect and analyze data from one component and then conceptualize and conduct the second component. This may also impact how long a project may take. Before beginning a mixed methods project, you should have a clear vision for what the project will entail and how each methodology will contribute to that vision.

Key Takeaways

• Mixed methods studies vary in sequence and emphasis.
• Mixed methods allow the research to corroborate findings, provide context, follow up on ideas, and use the strengths of each method.
• Emphasis- in a mixed methods study, refers to the priority that each method is given
• Sequence- in a mixed methods study, refers to the order that each method is used, either concurrently or sequentially

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In This Article Expand or collapse the "in this article" section Qualitative Research

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Qualitative Research by James Drisko LAST REVIEWED: 01 May 2017 LAST MODIFIED: 25 May 2011 DOI: 10.1093/obo/9780195389678-0047

“Qualitative research” is a term that encompasses a wide variety of research types and methods. Its great variety makes it difficult to define and describe succinctly. This bibliography will offer a general introduction but will inevitably be incomplete. Qualitative research in the social sciences has deep roots in sociology and anthropology. For example, fieldwork and ethnography continue to be pivotal methods in these and other disciplines. The professions have also drawn extensively on qualitative research, though emphasis on quantitative research in the academy after World War II and the current ideology of evidence-based approaches among academics and service funders devalue it. Qualitative research is widely found and widely taught in nursing and in education. It is quite evident, but less prominent, in social work, in medicine, in psychology, and in occupational therapy.

In social work, Jane Addams’s portrayals of the circumstances of immigrant populations in Chicago ( Addams 1895 ) are public qualitative research works that are still highly valued. Indeed, Addams is sometimes claimed as a role model by scholars outside the profession as well as within social work. Mary Richmond’s 1917 Social Diagnosis ( Richmond 1955 ) details a method for learning the psychosocial needs of clients and families in context, drawing on qualitative interviews, observations, and documents. These social work contributions emerged as sociology began to define its research methods ( Znaniecki 1934 ). The widely used traditional case study is one well-known form of qualitative research ( Gilgun 1994 ), though case study methods, purposes, and reporting vary, as does its quality. Social work education has long included both formal and informal training in qualitative data collection methods, including interviewing and participant observation, described by Zimbalist 1977 . Further, the traditional method of process recording has provided both a technique and active training in recording interview data. Beyond documentation, process recording also provided an introduction to active reflection on the participant and on the self that is a key element of professional practice as well as of qualitative research. Since 1994 qualitative research has been required content in the Council on Social Work Education’s accreditation standards for all bachelor’s and master’s level programs.

Addams, Jane, Agnes Sinclair Holbrook Florence Kelley, Alzina P. Stevens, Isabel Eaton, Charles Zeublin, Josefa Humpal Zeman, Alessandro Mastro-Valerio, Julia C. Lathrop, and Ellen Gates Starr . 1895. Hull House maps and papers . New York: Crowell.

Addams sought to document and publicize the living conditions of immigrant populations in Chicago. Her goal was to raise public awareness and to catalyze social change. Both Addams’s methods, which draw on fieldwork from sociology, and her goals, which affirm social justice, are widely evident in qualitative research across disciplines in the early 21st century. Seminal, groundbreaking work from a social work pioneer.

Gilgun, Jane F. 1994. A case for case studies in social work research. Social Work 39:371–380.

Gilgun argues for the wide applicability of the case study method to social work research and to social work practice. The article offers an overview of the case study method and takes stock of the method’s strengths and limitations. A very widely known, classic article.

Richmond, Mary Ellen. 1955. Social diagnosis . New York: Russell Sage Foundation.

First published in 1917. The originator of the psychosocial perspective, Richmond details a qualitative method of diagnosis that balances attention to macro-level social issues with micro-level family and individual concerns. Several case studies portray people-in-environments in great detail and with broad perspective. An early example of social work case studies based on planned interviews and observations—key tools in qualitative research as well.

Zimbalist, Sidney. 1977. Historic themes and landmarks in social welfare research . New York: Harper & Row.

A unique book on the history of social work research. Chronological in plan, the book shows the development of social work research models in context. Extensive use of qualitative methods is documented, and the forces that have promoted quantitative research as a dichotomous alternative to qualitative research are noted. Lacks contemporary perspective, however, given its publication date.

Znaniecki, Florian. 1934. The method of sociology . New York: Farrar & Rinehart.

In this early, classic work in sociology, Znaniecki details the method of analytic induction. Analytic indication seeks deductively to frame new concepts and preliminary theory while maintaining clear connections to its evidence base. This method is clearly the foundation of grounded theory, which followed it in the 1960s.

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Causality and Causal Inference in Social Work: Quantitative and Qualitative Perspectives

Lawrence a. palinkas.

1 School of Social Work, University of Southern California, Los Angeles, CA, USA

Achieving the goals of social work requires matching a specific solution to a specific problem. Understanding why the problem exists and why the solution should work requires a consideration of cause and effect. However, it is unclear whether it is desirable for social workers to identify cause and effect, whether it is possible for social workers to identify cause and effect, and, if so, what is the best means for doing so. These questions are central to determining the possibility of developing a science of social work and how we go about doing it. This article has four aims: (1) provide an overview of the nature of causality; (2) examine how causality is treated in social work research and practice; (3) highlight the role of quantitative and qualitative methods in the search for causality; and (4) demonstrate how both methods can be employed to support a “science” of social work.

In defining the mission of the profession of social work to enhance human well-being and help meet the basic needs of all people, the Preamble of the National Association of Social Workers Code of Ethics (2013) places great emphasis on the environmental forces that create, contribute to, and address problems in living. Implied in this emphasis is the assumption of a causal link between these environmental forces and the problems they create or contribute to. For instance, when faced with the challenge of providing care to a client with a depressive disorder, we first attempt to identify the factors that contributed to the onset of the disorder. Furthermore, to address these problems, we must appropriately and effectively match a specific solution to a specific problem. This, too, requires us to consider a causal link between the solution and its outcome (elimination of the problem or mitigation and treatment of its impacts). Thus, a client with a depressive disorder may benefit from treatment that addresses the symptoms, which may involve pharmacotherapy and/or psychotherapy.

However, the complexity of the issues we face as social workers forces us to consider whether it is desirable, much less even possible, to identify cause and effect, and if so, what is the best means for doing so. The issue of desirability has been raised in conjunction with criticism of the value of the scientific method in general and scientifically based evidencebased practice in particular ( Heineman, 1981 ; Karger, 1983 ; Otto & Ziegler, 2008 ; Tyson, 1995 ). The issue of feasibility has been raised in conjunction with the claim that the complexity of social phenomena renders the use of scientific methods as problematic and incomplete ( Otto & Ziegler, 2008 ; Rosen, 2003 ). These questions are by no means limited to social work, but they are central to our consideration of whether it is possible to develop a science of social work and, if so, how we go about doing it.

This article has four aims: (1) provide an overview of the nature of causality and causal inference; (2) examine how causality and causal inference are treated in social work research and practice; (3) highlight the role of quantitative and qualitative methods in the search for causality; and (4) demonstrate how both methods can be employed to support a “science” of social work.

The Nature of Causality and Causal Inference

The human sciences, including social work, place great emphasis on understanding the causes and effects of human behavior, yet there is a lack of consensus as to how cause and effect can and should be linked ( Parascandola & Weed, 2001 ; Salmon, 1998 ; Susser, 1973 ). What little consensus exists seems to be that effects are assumed to be consequences of causes. Causes and effects may be singular in nature (e.g., cigarette smoking causes cancer) or they may be multifactoral (e.g., cancer is caused by genetic predisposition, certain health behaviors like cigarette smoking and diet, and exposure to environmental hazards like toxic chemicals; cigarette smoking causes cancer, hypertension, diabetes, and emphysema). This relationship can be viewed both spatially and temporally. For instance, the presence of a depressive disorder in an individual may have some determinants that are distal (genetic predisposition, childhood experience) and some determinants that are proximal (e.g., recent life events like loss of employment, death of spouse) to the current episode of depressive symptoms. A link between one or more causes and one or more effects may also be viewed as direct and indirect (mediating, moderating; Koeske, 1993 ; Kramer, 1988 ; Susser, 1973 ). Thus, while the death of a spouse may contribute to the onset of a depressive disorder, it may do so directly or indirectly by virtue that it deprives the survivor of an important source of social support. Likewise, the death of a spouse may contribute differentially to the risk of a depressive disorder depending on whether the survivor is a male or female. Causal inference, in turn, may be viewed as the process of establishing the link between the perceived cause or causes and the perceived effect or effects.

Causation may also be viewed from the perspective of the distinction between necessary and sufficient causes. For instance, “a given exposure is considered a necessary cause of an outcome if the outcome does not occur in its absence. It is a sufficient cause if it always (i.e., in all individuals) leads to an outcome without requiring the presence or absence of any other factors” ( Kramer, 1988 , p. 256). However, causes may also be multifactorial, in which case causes are neither necessary nor sufficient for any given individual. The necessary and sufficient cause definitions assume that all causes are deterministic, while a probabilistic view of causation is one in which a cause increases the probability or chance that its effect will occur but may be neither necessary nor sufficient for its occurrence ( Parascandola & Weed, 2001 ). Kramer (1988) and others ( Kleinbaum, Kupper, & Morgenstern, 1982 ; Parascandola & Weed, 2001 ) argue that a probabilistic definition of causation is more consistent with the aims of applied human sciences like public health.

Our current notions of causation and causal inference generally owe their intellectual origins to the British social philosopher David Hume (1738/1975) . Hume's criteria of causation emphasize the importance of a temporal priority in which causes must necessarily occur or exist prior to the occurrence or existence of an effect (e.g., the cause and effect must be contiguous in space and time, the cause must be prior to the effect, and the relationship between cause and effect must be constant). Hume's criteria also stresses the one-to-one relationship between cause and effect (e.g., the same cause always produces the same effect, and the same effect only occurs in the presence of the same cause; where several different objects produce the same effect, it must be the result of some characteristic the causes have in common. However, Hume's criteria do not specify the tools used to describe that relationship—in other words, it does not provide any guidance on the methods used to determine if a relationship exists between two variables or phenomena and if the nature of that relationship is causal, correlational, or coincidental. A more contemporary version of these criteria was developed by the British biostatistician Austin Bradford Hill that is widely used in the field of public health ( Hill, 1965 ; see Table 1 ). Like Hume, these criteria give priority to the temporal relationship between a cause and effect (i.e., the first must precede the second) and to specificity (i.e., a single cause produces a specific effect), but also suggest the importance of measurement or quantification of the relationship (i.e., strength of association and existence of a doseresponse relationship) and experimental designs (They also suggest that support for the causal inference requires confirmation using other types of information or knowledge (i.e., consistency, plausibility, and coherence).

Hill's Criteria of Causation.

Source: Hill (1965) .

Causality in Social Work Research and Practice

Lewis (1975) argues that causal inference is an essential part of social work practice as well as social work research. However, the association between causality and causal inference in the field of social work and logical positivism and critical rationalism with its emphasis on universal laws has subjected the search for causal linkages to criticism from those who view it as deterministic, limited in its ability to address the complexity of social phenomena, and inconsistent with the goals of the profession ( Otto & Ziegler, 2008 ). As Padgett (2008 , p. 168) observes, “anti-positivistic skeptics question whether the search for causation is plausible or desirable, given the postmodern premise that facts are ‘fictitious’ ( Lofland & Lofland, 1995 ).” Nevertheless, embedded in much of social work research is an implicit understanding that actions have consequences and that most of the characteristics of the human condition can be linked directly or indirectly to one or more factors or events that are in some way responsible for that condition.

In social work research, randomized controlled trials (RCTs) have been used primarily to demonstrate causal linkages between specific interventions that are treated as independent variables and specific outcomes that are treated as dependent variables. For instance, Ell and colleagues (2010) assessed the effectiveness of an evidence-based, socioculturally adapted, collaborative depression care intervention for treatment of depression and diabetes in a group of 387 predominately Hispanic primary care patients recruited from two safety net clinics. The causal chain tested in this study was that the intervention (which included problem-solving therapy and/or antidepressant medication based on a stepped-care algorithm; first-line treatment choice; telephone treatment response, adherence, and relapse prevention follow-up over 12 months; plus systems navigation assistance) resulted in an improvement in mood (or a reduction in depressive symptoms), which, in turn, resulted in improvement in Hemoglobin A1C levels. In this instance, improvement in H1C levels was a direct effect of the reduction in depressive symptoms and an indirect effect of the depression treatment intervention. In another example, Glisson and colleagues (2010) conducted a RCT of the effectiveness of Multisystemic Therapy (MST) and the Availability, Responsiveness, and Continuity (ARC) organizational intervention in reducing problem behavior in delinquent youth residing in 14 rural counties in Tennessee, using a 2 × 2 design in which youth were randomized into receiving MST or treatment as usual, and counties were randomized into receiving the ARC intervention. A multilevel mixed effects regression analysis of 6-month treatment outcomes found that total youth problem behavior in the MST plus ARC condition was at a nonclinical level and significantly lower than in other conditions. The causal chain tested in this study was that the ARC intervention resulted in the successful implementation of MST, which, in turn, resulted in a reduction of youth problem behavior. In this instance, reduction of youth problem behaviors was a direct effect of the MST intervention and an indirect effect of the ARC organizational intervention.

However, qualitative methods have also been used in social work research to make causal inferences linking two sets of phenomena. For instance, Gutierrez, GlenMaye, and DeLois (1995) conducted interviews with administrators and staff at six different agencies to identify elements of the organizational context of empowerment practice. Using a modified grounded theory approach, they identified four sets of factors (funding sources, social environment, intrapersonal issues, and interpersonal issues) that constitute barriers to maintaining and implementing an empowerment-based approach in social work practice. For instance, “differing philosophies or politics of more traditional service providers (cause) negatively affected the willingness or ability of empowerment-based agencies to refer clients to other services (effect)” Gutierrez, GlenMaye, & DeLois, 1995 , p. 252, parentheses added). Alaggia and Millington (2008) conducted a phenomenological analysis of the lived experience of 14 men who were sexually abused in childhood to “generate knowledge … on the effects of boyhood sexual abuse on the present lives of men, and to understand how those effects found expression in men's everyday lives” (p. 267). In this instance, sexual abuse during childhood is treated as the cause and anger and rage, sexual disturbance and ambivalence, and loss and hope were identified as effects. The attempt to examine effects of childhood sexual abuse using a phenomenological approach is especially noteworthy because the focus on interpretative understanding or verstehen is often seen as a rejection of causal understanding (cf. Otto & Ziegler, 2008 ).

Qualitative and Quantitative Perspectives on Causality

Although these two studies are representative of the use of different qualitative methodological approaches to identify connections between certain phenomena and certain outcome, in social work as in other fields, priority in the determination of causality is given to quantitative methods in general and RCTs in particular. Otto and Ziegler (2008) note that RCTs are considered the best form of evidence of practice effectiveness ( McNeece & Thyer, 2004 ) and, therefore, of causality. “These designs serve to control or cancel out and differences that are effects of other Events (Z) to assess whether Event X (cause)—as independent variable—is nonspuriously conjunct with Event Y (effect) in the context of a controlled ceteris paribus condition” ( Otto & Ziegler, 2008 , p. 275). They further argue that the criteria of using the RCT design to determine causal connections between an intervention and its outcomes can hardly be applied to qualitative research such as ethnographic studies or deep hermeneutical interviews ( Otto & Ziegler, 2008 , p. 275). Consequently, qualitative studies are placed on a lower rank of evidence of causality ( McNeece & Thyer, 2004 ), and below what Cook and Campbell (1979) considered as the minimum interpretable design necessary and adequate for drawing valid conclusions about the effectiveness of treatments ( Otto & Ziegler, 2008 , p. 275).

However, there are inherent limitations to relying on RCTs to determine causality in social work research. Circumstances may preclude the use of the RCT design, including small sample sizes, especially in multilevel studies where single individuals are embedded in organizations like schools or agencies; concerns about external validity; the ethics of providing service to one group and denying the same service to another group of clients; the expense and logistics involved in conducting such research; the unwillingness of participants or organizations to accept randomization; and the expense and logistical challenges in conducting longitudinal follow-up assessments ( Glasgow, Magid, Beck, Ritzwoller, & Estabrooks, 2005 ; Landsverk, Brown, Chamberlain, Palinkas, & Horwitz, 2012 ; Palinkas & Soydan, 2012 ).

Furthermore, causal models can be constructed using quantitative or qualitative data. In the example presented in Figure 1 , the model of social capital effects on psychosocial adjustment of Chinese migrant children was developed by Wu, Palinkas, and He (2010) using structural equation modeling. On the other hand, using qualitative data collected from leaders of county-level child welfare, mental health and juvenile justice systems in California, Palinkas and colleagues (2014) also developed a model of interorganizational collaboration that posited causal linkages between characteristics of the outer context (availability of funding, legislative mandates, size of jurisdiction, and extent of responsibility for same client population), inner context (characteristics of the participating organizations and individual members of those organizations), and characteristics of the collaboration itself (focus on a single vs. multiple initiatives, formality, frequency of interaction) and the structure of social networks that, in turn, are linked to the pace and progress of implementation of evidence-based practices (see Figure 2 ).

Standardized solutions for the structural model of social capital effects on the psychosocial adjustment of Chinese migrant children. Source: Wu, Palinkas, and Xe (2010) .

Heuristic model of interorganizational collaboration for implementation of evidence-based practices. Source: Palinkas et al. (2014) .

Finally, not all qualitative methodologists have rejected the notion that the construction of causal inferences is both desirable and possible. Miles and Huberman (1994 , p. 4), for instance, “aim to account for events, rather than simply to document their sequence. We look for an individual or a social process, a mechanism, a structure at the core of events that can be captured to provide a causal description of the forces at work” (italics in original). Sayer (2000) argues that causal explanation is not only legitimate in qualitative research, but a particular strength of this approach, although it uses a different strategy from quantitative research, based on a process rather than a variance concept of causality. Ragin's (1987) qualitative comparative analysis involves representing each case as a combination of causes and effects that can then be compared with each other. Another qualitative comparative method, analytic induction, is described as an “exhaustive examination of cases in order to prove universal, causal generalizations” ( Vidich & Lyman, 2000 , p. 57). Denzin (1978) considered analytic induction to be one of three major strategies for establishing the existence of a causal relationship, the other two being the statistical method and the experimental method. Even Lofland (1971) , considered a skeptic of the search for causation, argued that the strong suit of the qualitative researcher is the ability to provide order, rich descriptive detail, stating that “it is perfectly appropriate that one be curious about causes, so long as one recognizes that whatever account or explanation he develops is conjecture” (p. 62).

It would seem, therefore, that quantitative and qualitative methods each present certain advantages and disadvantages in making causal inferences whether one identifies with a logical positivist or postpositivist or a postmodernist, social constructivist view of human nature, or is more at ease with the process of counting quantitative data or interpreting qualitative data. However, as no single method is adequate to the challenge of linking cause and effect in a deterministic or probabilistic fashion, it is perhaps prudent to heed the advice of Campbell (1999) , who maintained that because proving causality with certainty in explaining social phenomena is problematic and because all methods for proving causality are imperfect, multiple methods, both quantitative and qualitative, are needed to generate and test theory, improve understanding over time of how the world operates, and support informed policy making and social program decision making.

Causality and the Science of Social Work

The path to causality can be viewed as moving across a series of steps that begin with identification and proceed to description, explanation generation, explanation testing, and prescription or control. Identification first occurs through reports or studies that point to the existence of a previously unknown or unrecognized phenomenon. Description of the phenomenon may involve qualitative (narratives, case studies) and/or quantitative (frequencies, percentages) data. Both methodological approaches may be employed in the next step, which is the identification of associations between variables and the generation of hypotheses to be tested that can help to explain why the variables are in association with one another. The next step is then to test the hypotheses and the validity of the presumed explanation. This step usually requires the use of prospective longitudinal designs and the use of quantitative methods. The final step is the construction of experimental conditions that enable the investigator to simultaneously control for the possibility of alternate explanations for the observed association between one variable presumed to be the cause and the other variable or variables presumed to be the effect. This step usually requires the use of the RCT design and the use of quantitative methods.

One can conceive of two separate arguments that link these discrete steps in a meaningful way. In the first argument, the further we proceed along the path of scientific inquiry, the more we rely on quantitative methods to make causal inferences and support the existence of a causal link/relationship. However, as noted previously, there are inherent limitations to relying on RCTs to determine causality in social work research. In the second argument, qualitative and quantitative methods each make distinct contributions to the task of proving causality. Thus, in using quantitative methods, priority is placed on confirmation of hypothesis through experimentation and a narrow or segmented focus on potential causal explanations, while in using qualitative methods, priority is placed on exploration of phenomenon and generation of hypotheses through observation and a broad or holistic focus on the social context in which causal links occur.

Although they may differ with respect to the value placed on each set of methods (with the quantitative methods being considered dominant in the first argument and coequal with qualitative methods in the second argument), both arguments posit a relationship between qualitative and quantitative methods and both assume that each set of methods has a role to play in understanding causality and in making causal inferences. Relationships between the two sets of methods have been increasingly articulated using the terminology of mixed methods, defined as the integrated use of quantitative and qualitative methods in ways that provide greater understanding or insight into a phenomenon that might be obtainable from either method used alone ( Palinkas, Horwitz, Chamberlain, Hurlburt, & Landsverk, 2011 ). Cresswell and Plano Clark (2011) identify five different types of mixed methods designs. A Triangulation design is used when there is a need to compare results from different sources of information regarding the hypothesized same phenomenon or parameter to seek corroboration. An Explanatory or complementary design is used to understand a phenomenon more comprehensively or completely. An Exploratory design is used for instrument, taxonomy, or typology development , where qualitative data serve as an initial exploration to identify variables, constructs, taxonomies, or instruments for a subsequent quantitative study phase. An Embedded or Expansion design is used to assess hypothesized different phenomena or parameters using different methods. Finally, an Initiation or Transformative design is used to understand a phenomenon more insightfully, discovering new ideas, perspectives, and meanings. Each of these designs may be used to identify, describe, explain, verify, and control the relationships linking one phenomenon or set of phenomena to another phenomenon or set of phenomena in a causal fashion. This combined use of quantitative and qualitative methods may occur simultaneously, in which one method usually drives the project theoretically with the supplemental project designed to elicit information that the base method cannot achieve or for the results to inform in greater detail about one part of the dominant project, or sequentially, in which the method that theoretically drives the project is used first, with the second method designed to resolve problems/issues uncovered by the first study or to provide a logical extension from the findings of the first study.

An illustration of the use of mixed method designs to examine causality and causal inference can be found in the Child STEPS Effectiveness Trial (CSET), carried out by the Research Network on Youth Mental Health and funded by the John D. and Catherine T. MacArthur Foundation ( Chorpita et al., 2013 ; Weisz et al., 2012 ). The CSET focused on children aged 8–13 who had been referred for treatment of problems involving disruptive conduct, depression, anxiety, or any combination of these. Ten clinical service organizations in Honolulu and Boston, 84 therapists, and 174 youths participated in the project. Youth participants were treated with the usual treatment procedures in their settings or with one or more of three selected evidence-based treatments (EBTs): cognitive-behavioral therapy (CBT) for anxiety, CBT for depression, and behavioral parent training (BPT) for conduct problems. These evidence-based treatments were tested in two forms: standard manual treatment (standard), using full treatment manuals; and modular treatment (modular) in which therapists learn all the component practices of the evidence-based treatments but individualize the use of the components for each child, guided by a clinical algorithm and measurement feedback on practices and clinical progress. A cluster randomization design was employed with therapists assigned to one of three conditions (usual care, standard, and modular) and youth who met study criteria randomized to treatment delivered by one of these three groups of therapists.

Mixed effects regression analyses showed significantly superior outcome trajectories for modular treatment (cause) relative to usual care on weekly measures of a standardized Brief Problem Checklist and a patient-generated Top Problems Assessment (effect), and youths receiving modular treatment had significantly fewer diagnoses than usual care youths at posttreatment ( Chorpita et al., 2013 ; Weisz et al., 2012 ). In contrast, none of these outcomes showed significant differences between standard treatment and usual care. Follow-up tests also showed significantly better outcomes for modular treatment than standard treatment on the weekly trajectory measures. In general, the modular approach outperformed usual care and the standard approach on the clinical outcome measures, and the standard approach did not outperform usual care.

Although the use of the modular approach to evidence-based treatment was assumed to have caused an improvement in behavioral health outcomes in this population, the quantitative data alone could not explain why the modular approach was more successful than the standard approach. To address that question, a qualitative study of the process of EBT dissemination and implementation was embedded in the RCT. Semistructured interviews and focus groups were conducted with included 38 therapists, six project supervisors, and eight clinical organization directors or senior administrators to identify patterns of use of the EBTs once the randomized trial had been concluded. Twenty-six of the 28 therapists (93%) who had been assigned to the standard or the modular conditions reported using the techniques with nonstudy cases subsequent to the conclusion of the trial. However, the pattern of use among all therapists, including those in the standard manualized condition, was more consistent with the modular approach. While all of the therapists in these two conditions thought the EBTs were helpful, what distinguished the two groups of therapists was the perception that the modular approach (cause) allowed for more flexibility, accommodation, and control over the therapeutic alliance with clients (effects) than the standard approach. Both therapists and supervisors felt that the modular approach gave them more “license” to negotiate with researchers with respect to circumstances in which the modules could themselves be modified or, more often than not supplemented with additional materials and techniques acquired through experience with working with similar clients ( Palinkas et al., 2013 ).

We began by asking three questions. The first was whether it is desirable for social workers to identify cause and effect. It is desirable if we believe social work to be an applied, empirically grounded social and cultural science aiming at both causal explanation and interpretative understanding ( Otto & Ziegler, 2008 , p. 273), one that includes elements of logical positivism and postmodernist social constructivism. It is also desirable if the foundation of our profession is to change the lives of our clients for the better. As Kramer (1988 , p. 255) makes a similar argument for examining causality in public health, stating that “an understanding of cause is essential for change … A deliberate intervention (change in exposure) will be successful in altering outcome only to the extent that the exposure is a true cause of that outcome.” Alternatively, we might question whether it is possible to develop and implement a solution without a comprehensive understanding of the problem one is trying to solve (Can we achieve y without understanding x ?). To answer that question, we would have to determine whether that understanding can be comprehensive without understanding the cause of a problem (Is an understanding of x necessary to produce y ?). Further, even if the solution mitigated the consequences of the problem (e.g., reducing symptoms of depression or anxiety), is it truly an effective solution if the cause remains unaddressed (Can we produce y without changing x )?

The second question we addressed was whether it is possible for social workers to determine causality. Social workers face inherent challenges in adopting exclusively positivist criteria for determining causality. Making connections between a cause and an effect is possible whether one adheres to a positivist or a social constructivist view of society and behavior. If understanding cause and effect is the foundation of any science, then that understanding is possible if it is seen as a process and not as a specific outcome, especially if the process and outcome are both context-specific.

Finally, we asked about the best means of determining causality or making causal inferences if it is both possible and desirable for social workers to do so. The answer is that both qualitative and quantitative methods can and should be used to fulfill specific roles in that process. Qualitative methods would be especially important in the early exploratory stages of scientific inquiry and for providing in-depth understanding of the causal chain and the context in which it exists. Quantitative methods would be especially important in the later confirmatory stages of scientific inquiry and for generalizing findings to other populations in other settings. Both methods are fundamental to a science of social work.

The integrated use of quantitative and qualitative methods is certainly not a novel concept. Haight (2010) , for instance, called for the integration of postpositivist perspectives of critical realism, with an emphasis on quantitative methods and research designs, and interpretative perspectives with an emphasis on qualitative or mixed research designs and methods. While “postpositivist research using quantitative methods can help to identify generally effective interventions and eliminate the use of harmful or ineffective interventions, … interpretist research using qualitative methods can enhance understanding of the ways in which cultural context (cause) interact with interventions, resulting in diverse outcomes (effects): ( Haight, 2010 , p. 102). Epstein's (2009) model of “evidence-informed practice” calls for the integrated use of evidence-based practice with its emphasis on standardized quantitative measures and RCT designs and reflective practice with its emphasis on qualitative observation. What is novel here is that the process of making causal inferences is not limited to quantitative methods or RCT designs.

Perhaps the greatest challenge we face in creating a science of social work is being faithful to the principles of scientific inquiry on one hand while simultaneously being responsive to the needs, activities, traditions, and multiple perspectives of our discipline. The diversity of these needs, activities, traditions, and perspectives reflect the complexity of the problems we seek to solve and the underlying factors that are responsible for those problems. This complexity makes it difficult to identify single or specific causes of single or specific effects. However, while this complexity may be viewed as an obstacle to the creation of a science of social work, it also represents a unique opportunity to create a science that acknowledges the importance of qualitative as well as quantitative methods, of practice-based evidence as well as evidence-based practice, and explanation grounded in social constructivism as well as logical positivism.

Acknowledgments

Funding : The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Support for this article was provided by grants from the William T. Grant Foundation (Grant no. 10648: L. Palinkas, PI), National Institute of Mental Health (P30-MH074678: J. Landsverk, PI; and R01MH076158: P. Chamberlain, PI), and National Institute on Drug Abuse (P30 DA027828-01-A1: C. Hendricks Brown, PI).

Declaration of Conflicting Interests : The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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S371 Social Work Research - Jill Chonody: What is Quantitative Research?

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Quantitative Research in the Social Sciences

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Quantitative methods emphasize objective measurements and the statistical, mathematical, or numerical analysis of data collected through polls, questionnaires, and surveys, or by manipulating pre-existing statistical data using computational techniques . Quantitative research focuses on gathering numerical data and generalizing it across groups of people or to explain a particular phenomenon.

Babbie, Earl R. The Practice of Social Research . 12th ed. Belmont, CA: Wadsworth Cengage, 2010; Muijs, Daniel. Doing Quantitative Research in Education with SPSS . 2nd edition. London: SAGE Publications, 2010.

Characteristics of Quantitative Research

Your goal in conducting quantitative research study is to determine the relationship between one thing [an independent variable] and another [a dependent or outcome variable] within a population. Quantitative research designs are either descriptive [subjects usually measured once] or experimental [subjects measured before and after a treatment]. A descriptive study establishes only associations between variables; an experimental study establishes causality.

Quantitative research deals in numbers, logic, and an objective stance. Quantitative research focuses on numberic and unchanging data and detailed, convergent reasoning rather than divergent reasoning [i.e., the generation of a variety of ideas about a research problem in a spontaneous, free-flowing manner].

Its main characteristics are :

• The data is usually gathered using structured research instruments.
• The results are based on larger sample sizes that are representative of the population.
• The research study can usually be replicated or repeated, given its high reliability.
• Researcher has a clearly defined research question to which objective answers are sought.
• All aspects of the study are carefully designed before data is collected.
• Data are in the form of numbers and statistics, often arranged in tables, charts, figures, or other non-textual forms.
• Project can be used to generalize concepts more widely, predict future results, or investigate causal relationships.
• Researcher uses tools, such as questionnaires or computer software, to collect numerical data.

The overarching aim of a quantitative research study is to classify features, count them, and construct statistical models in an attempt to explain what is observed.

  Things to keep in mind when reporting the results of a study using quantiative methods :

• Explain the data collected and their statistical treatment as well as all relevant results in relation to the research problem you are investigating. Interpretation of results is not appropriate in this section.
• Report unanticipated events that occurred during your data collection. Explain how the actual analysis differs from the planned analysis. Explain your handling of missing data and why any missing data does not undermine the validity of your analysis.
• Explain the techniques you used to "clean" your data set.
• Choose a minimally sufficient statistical procedure ; provide a rationale for its use and a reference for it. Specify any computer programs used.
• Describe the assumptions for each procedure and the steps you took to ensure that they were not violated.
• When using inferential statistics , provide the descriptive statistics, confidence intervals, and sample sizes for each variable as well as the value of the test statistic, its direction, the degrees of freedom, and the significance level [report the actual p value].
• Avoid inferring causality , particularly in nonrandomized designs or without further experimentation.
• Use tables to provide exact values ; use figures to convey global effects. Keep figures small in size; include graphic representations of confidence intervals whenever possible.
• Always tell the reader what to look for in tables and figures .

NOTE:   When using pre-existing statistical data gathered and made available by anyone other than yourself [e.g., government agency], you still must report on the methods that were used to gather the data and describe any missing data that exists and, if there is any, provide a clear explanation why the missing datat does not undermine the validity of your final analysis.

Babbie, Earl R. The Practice of Social Research . 12th ed. Belmont, CA: Wadsworth Cengage, 2010; Brians, Craig Leonard et al. Empirical Political Analysis: Quantitative and Qualitative Research Methods . 8th ed. Boston, MA: Longman, 2011; McNabb, David E. Research Methods in Public Administration and Nonprofit Management: Quantitative and Qualitative Approaches . 2nd ed. Armonk, NY: M.E. Sharpe, 2008; Quantitative Research Methods . Writing@CSU. Colorado State University; Singh, Kultar. Quantitative Social Research Methods . Los Angeles, CA: Sage, 2007.

Basic Research Designs for Quantitative Studies

Before designing a quantitative research study, you must decide whether it will be descriptive or experimental because this will dictate how you gather, analyze, and interpret the results. A descriptive study is governed by the following rules: subjects are generally measured once; the intention is to only establish associations between variables; and, the study may include a sample population of hundreds or thousands of subjects to ensure that a valid estimate of a generalized relationship between variables has been obtained. An experimental design includes subjects measured before and after a particular treatment, the sample population may be very small and purposefully chosen, and it is intended to establish causality between variables. Introduction The introduction to a quantitative study is usually written in the present tense and from the third person point of view. It covers the following information:

• Identifies the research problem -- as with any academic study, you must state clearly and concisely the research problem being investigated.
• Reviews the literature -- review scholarship on the topic, synthesizing key themes and, if necessary, noting studies that have used similar methods of inquiry and analysis. Note where key gaps exist and how your study helps to fill these gaps or clarifies existing knowledge.
• Describes the theoretical framework -- provide an outline of the theory or hypothesis underpinning your study. If necessary, define unfamiliar or complex terms, concepts, or ideas and provide the appropriate background information to place the research problem in proper context [e.g., historical, cultural, economic, etc.].

Methodology The methods section of a quantitative study should describe how each objective of your study will be achieved. Be sure to provide enough detail to enable the reader can make an informed assessment of the methods being used to obtain results associated with the research problem. The methods section should be presented in the past tense.

• Study population and sampling -- where did the data come from; how robust is it; note where gaps exist or what was excluded. Note the procedures used for their selection;
• Data collection – describe the tools and methods used to collect information and identify the variables being measured; describe the methods used to obtain the data; and, note if the data was pre-existing [i.e., government data] or you gathered it yourself. If you gathered it yourself, describe what type of instrument you used and why. Note that no data set is perfect--describe any limitations in methods of gathering data.
• Data analysis -- describe the procedures for processing and analyzing the data. If appropriate, describe the specific instruments of analysis used to study each research objective, including mathematical techniques and the type of computer software used to manipulate the data.

Results The finding of your study should be written objectively and in a succinct and precise format. In quantitative studies, it is common to use graphs, tables, charts, and other non-textual elements to help the reader understand the data. Make sure that non-textual elements do not stand in isolation from the text but are being used to supplement the overall description of the results and to help clarify key points being made. Further information about how to effectively present data using charts and graphs can be found here .

• Statistical analysis -- how did you analyze the data? What were the key findings from the data? The findings should be present in a logical, sequential order. Describe but do not interpret these trends or negative results; save that for the discussion section. The results should be presented in the past tense.

Discussion Discussions should be analytic, logical, and comprehensive. The discussion should meld together your findings in relation to those identified in the literature review, and placed within the context of the theoretical framework underpinning the study. The discussion should be presented in the present tense.

• Interpretation of results -- reiterate the research problem being investigated and compare and contrast the findings with the research questions underlying the study. Did they affirm predicted outcomes or did the data refute it?
• Description of trends, comparison of groups, or relationships among variables -- describe any trends that emerged from your analysis and explain all unanticipated and statistical insignificant findings.
• Discussion of implications – what is the meaning of your results? Highlight key findings based on the overall results and note findings that you believe are important. How have the results helped fill gaps in understanding the research problem?
• Limitations -- describe any limitations or unavoidable bias in your study and, if necessary, note why these limitations did not inhibit effective interpretation of the results.

Conclusion End your study by to summarizing the topic and provide a final comment and assessment of the study.

• Summary of findings – synthesize the answers to your research questions. Do not report any statistical data here; just provide a narrative summary of the key findings and describe what was learned that you did not know before conducting the study.
• Recommendations – if appropriate to the aim of the assignment, tie key findings with policy recommendations or actions to be taken in practice.
• Future research – note the need for future research linked to your study’s limitations or to any remaining gaps in the literature that were not addressed in your study.

Black, Thomas R. Doing Quantitative Research in the Social Sciences: An Integrated Approach to Research Design, Measurement and Statistics . London: Sage, 1999; Gay,L. R. and Peter Airasain. Educational Research: Competencies for Analysis and Applications . 7th edition. Upper Saddle River, NJ: Merril Prentice Hall, 2003; Hector, Anestine.  An Overview of Quantitative Research in Compostion and TESOL . Department of English, Indiana University of Pennsylvania; Hopkins, Will G. “Quantitative Research Design.” Sportscience 4, 1 (2000); A Strategy for Writing Up Research Results . The Structure, Format, Content, and Style of a Journal-Style Scientific Paper. Department of Biology. Bates College; Nenty, H. Johnson. "Writing a Quantitative Research Thesis." International Journal of Educational Science 1 (2009): 19-32; Ouyang, Ronghua (John). Basic Inquiry of Quantitative Research . Kennesaw State University.

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Fieldwork in Social Work pp 119–141 Cite as

Data Collection for Field Reports in Social Work Practice

• M. Rezaul Islam   ORCID: orcid.org/0000-0002-2217-7507 2  
• First Online: 22 March 2024

This chapter equips social work students with essential skills for gathering and utilizing data effectively. It begins by providing an overview of both qualitative and quantitative data collection techniques, ensuring that students are well-versed in diverse methods. The chapter then focuses on the practical aspect of data collection, emphasizing the use of data collection tools and instruments to streamline the process and enhance data quality. Through this chapter, social work students gain the knowledge and skills necessary to collect, manage, and utilize data to inform their practice, enhancing their ability to make data-driven decisions in the field.

• Data collection methods
• Qualitative data
• Quantitative data
• Data collection tools
• Field practices

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Akhter, S. (2022). Key informants’ interviews. In M. R. Islam, N. A. Khan, & R. Baikady (Eds.), Principles of social research methodology. Springer. Principles of social research methodology (pp. 389–403). Springer Nature Singapore.

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Khan, N. A., & Abedin, S. (2022). Focus group discussion. In M. R. Islam, N. A. Khan, & R. Baikady (Eds.), Principles of social research methodology. Springer. Principles of social research methodology (pp. 377–387). Springer Nature Singapore.

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Review Questions

What is the primary purpose of data collection in social work field practice?

Name two qualitative data collection techniques discussed in this chapter and briefly explain their applications.

Briefly outline the ethical considerations related to participant autonomy and privacy in data collection.

Why is it beneficial to integrate mixed-methods approaches in social work field research?

Discuss the role of technology in data collection for social work field practices, highlighting its advantages and potential ethical considerations.

Multiple Choice Questions

What is the main advantage of utilizing mixed-methods approaches in social work field research?

Simplicity in data analysis

Increased depth and breadth of understanding

Limited perspectives on the research question

Narrow scope of data collection

Which of the following is an example of a qualitative data collection technique?

Statistical analysis

Content analysis

Standardized tests

What is a key ethical consideration in technology-mediated data collection?

Limited access to data

Participant anonymity

Informed consent

Avoidance of data encryption

In quantitative data collection, what method involves asking participants to respond to a series of predetermined questions?

Participant observation

Focus group discussions

Surveys and questionnaires

Key informant interviews

Why is ensuring participant autonomy important in social work field research?

It protects the participants’ rights and choices.

It simplifies the research process.

It reduces the need for informed consent.

It limits the diversity of collected data.

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Islam, M.R. (2024). Data Collection for Field Reports in Social Work Practice. In: Fieldwork in Social Work. Springer, Cham. https://doi.org/10.1007/978-3-031-56683-7_9

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Social Work Research and Evaluation: Quantitative and Qualitative Approaches, Seventh Edition , Edited by Richard M. Grinnell, Jr and Yvonne A. Unrau, New York, Oxford University Press, 2005, pp. xxii + 532, ISBN 0195179498, £42.00

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Difference Between Qualitative and Quantitative Social Research

Quantitative and Qualitative Social Research: This article defines the purpose of social research and its impact on day-to-day life. It also explains various research methodologies and tools that are used to analyse the different aspects of society and the research topic. Since the inception of Sociology as a discipline, research has been used at every step to frame theories and explain phenomena with the help of observations, interactions and experiments. The article expounds on the concept of Quantitative and Qualitative Social Research methodologies, their usage and their difference. Further, it explores different tools and methods used in the respective research.

At various points in our lives, we have come across the word Research. It is the systematic or methodical study of a subject. Different research aims can serve different purposes, such as proving a hypothesis or understanding complex phenomena.

Many of us conduct research in our daily activities without even realizing it. As an example, when we need to purchase a new mobile phone. Thanks to technology, everyone nowadays searches for it on Google first. We review different models and try to select the most promising one. But before we finalize it, we examine its features, qualities, battery life, customer reviews, etc. At the end of all this, we conclude. The answer can be yes, no, or still to be decided.

Research is done in every sector and it has a different purpose. Technical research is conducted while inventing an entirely new device or technology, and medical or scientific research is carried out before producing a novel medicine. A significant amount of research was conducted during the Covid-19 pandemic to develop a vaccine.

How does social research work?

To understand the structure of society, professionals conduct social research. The purpose of this research is to better comprehend human behaviour and interactions in society. Through it, one can better understand the human perspective. Individuals, groups, and communities can be more deeply studied through social research. Organisations and governments use it to analyze societal trends and dynamics. Conducting social research is the first and most prominent step when it comes to the formulation of policies or schemes which are focused on society or stakeholders. It gives an understanding of how people think. There are multiple types of tools and methodologies which a researcher follows according to the need and requirements of his topic.

There are various types of research methods which one can take into consideration while designing their research methodology. To begin, it is necessary to collect the data that will play an indispensable role in it. The collection of data is further divided into two parts- primary and secondary research.

Primary Research-

A primary research approach involves interacting directly with stakeholders and is based on no previous data. The purpose of fieldwork is to obtain first-hand data that is relevant and original. Interacting with people enables us to understand the dynamics of the situation much better. Primary research gives the researcher leverage over the data and information collected since it is not limited. It is conducted by every organisation or business enterprise before launching a newly developed product or introducing a novel service. Primary research helps to understand the needs and requirements of the customer and helps the researchers to understand how to fulfil them. It is time-consuming since it is conducted with the help of tools like surveys, interviews or focus groups. Observations are essential to primary research since they show what no document can do. The findings are also useful for directly addressing and solving the problem at hand.

Secondary Research –

Secondary research is when no new data is collected and the findings from already published documents or journals are taken into consideration. Generally, it is done to understand the relations between topics and problems or the history of the problem at hand. It is less expensive than primary research since no direct involvement or fieldwork is involved in it. The data from secondary research assists the researcher to get a grasp of various factors involved but it doesn’t necessarily tackle the problem at hand. Data is collected from various sources like books, newspapers, published magazines, periodicals or reports from various organisations. The internet also gives easy access to such data. Sometimes they are free of charge, while other times the researcher might have to pay for them. But it is solely the researcher’s responsibility to make sure the data are accurate and relevant for the research. The researcher needs to verify the data before proceeding with the research to avoid any fallacy

There are multiple research methodologies from which a researcher can choose. But the researcher needs to understand the problem at hand before choosing a methodology. There are two main types of research methodologies, quantitative social research method and qualitative social research method. A researcher uses primary and secondary data to collect information and data. However, the research method is also determined by the study’s hypothesis and purpose. One must choose wisely since there is a stark contrast between the two types of research. Let us understand the difference between quantitative and qualitative social research.

Difference between Quantitative and Qualitative Social Research-

Quantitative research –.

The quantitative research method is often used to interpret the data in numbers and statistics. It is conducted to prove a hypothesis or an outcome with the help of data that is collected. It is interpreted with the help of numbers, bar graphs and charts. Quantitative research analyses the data collected from the participants with the help of maths and statistics.

Sample Set-

• Quantitative social research is used when the sample set at hand is big. That means the number of participants in the research is more and quantitative research helps to analyse the huge amount of responses easily
• Quantitative research is conducted when the researcher wants to prove the hypothesis.
•  It is conducted to get the desired result and outcome. It can be assertive or negative concerning the hypothesis.
•  A hypothesis is a sentence framed by the researcher based on existing and limited information. It is neither true nor false unless and until it is proved.
• For instance, if the hypothesis at hand is- the excess use of social media is impacting the youth , then the researcher can use this method to derive answers.
• The researcher can take a huge sample set in consideration. For example, 100 participants ranging from 16 to 25 years of age can be interviewed or surveyed to understand the impact of social media on them.
• From the findings, the researcher can understand the variable and prove whether the hypothesis is right or wrong. And to find it, various tools are used.
• This research method is particularly used when there is a prediction or hypothesis which needs to be tested, proved or confirmed.
•  A hypothesis is a statement or supposition which is based on the existing amount of knowledge the researcher has of a particular topic. This hypothesis is neither true nor false unless and until it is proven.
• For instance , “mobile phone addiction has increased in children post-covid-19 induced pandemic ” can be a hypothesis. It is a generic hypothesis and a large sample set will help the researcher to understand a greater section of society
• Qualitative research gives the result and suggestions for the issue at hand. Its purpose is to prove the hypothesis and that data can be used for further purposes.

Tools used-

• As the answers to the hypothesis are limited (yes/no/maybe), objective questions are asked so that the numerical data can be derived from it
• These questions are close-ended. It has limited options to choose from so that the answers and conclusions derived are clear and definite.
• . The researchers mostly use tools like surveys or questionnaires to interact with people and collect data.
• It can be an on-ground survey which enables interaction with the subjects, if it’s a remote area then online surveys can be used or telephonic surveys and interviews can be conducted.
• Researchers can also conduct experiments and derive their data through observations.
• If the research topic is, “hybrid education mode is better than the completely online mode of education”, then a questionnaire can be formulated to understand the viewpoints of participants.
• Sometimes the questions are crafted according to the age group of the sample set. The age group is also decided based on the requirements of the research. In this case, participants who are currently pursuing education in any mode would be more helpful in finding the conclusion. The findings are easier to present in numbers and statistics.

Example of how Quantitative Research takes place :

With the help of quantitative social research, researchers can drive the research in the direction they prefer. The findings in numbers help the researcher to understand a large section of society easily. If a researcher wants to learn more about the role of media in spreading false news, he/she will begin the research by formulating a hypothesis. For instance, if the hypothesis is, that “social media paves way for fake news” , it needs to be proved. For that, the researcher will prepare a survey or questionnaire for the sample set. It will have close-ended questions with options for the participants to choose from. There can be questions like, “do you think social media is an apt platform for news and information?” or “do you think people believe social media more than print media?”. Once the participants fill out the form, the answers are analysed with the help of math and statistics and it helps the researcher to arrive at a definite conclusion. The result can be supporting the hypothesis or completely against it.

Qualitative research-

Qualitative social research is done to avail findings in depth. It is not collected and presented with the help of numbers. It is analysed and categorised with the help of words. It is further interpreted in words, pictures and objects. Qualitative research allows the researcher to express thoughts and ideas which are discovered during the process. In-depth, discussions pave way for broad answers which help to interpret the topic.

• Qualitative social research is done to understand the context of a subject or topic or discover the complexity and subjectivity of a certain issue or phenomenon.
•  For this, a small amount of sample set is usually preferred to have an in-depth outlook of the topic.
•  A case in point . “Youth nowadays prefer online dating ” . This topic can be broadly understood only by interacting with the youth and understanding their perspectives.
• The researcher can interact with 10 to 15 people and ask them about their opinions and experiences. It can’t be defined in the terms of graphs or numbers but can be analysed and interpreted with the help of words. These findings can also help to formulate a research hypothesis or theory in future.
• Qualitative social research is conducted when the researcher wants to understand specific phenomena or processes or human behaviour in society under specific circumstances.
•  It is conducted to understand the thoughts and ideas of an individual or a group or the entire community. It is process oriented and a hypothesis is generated but it is not tested, it is analysed and interpreted.
•  For instance, if the hypothesis at hand is – Artificial Intelligence is impacting future careers, then the researcher can understand the significance of this topic with the help of this method. It will give the researcher a broader understanding of why things are happening. It gives an in-depth understanding of the hypothesis, it is not done just solely to prove the hypothesis but to explore it.
• It is conducted with the help of interviews or observations which are later interpreted into words.
• Literature reviews are studied to give a deeper insight.
• Researchers also attend focus groups which help to conduct an open discussion pertinent to a topic and even try to participate to understand the process better.
• There are open-ended questions which are asked during the qualitative research to understand the beliefs, thinking or mindset or perspective of individuals or society regarding a social issue or even a simple fact. The interviews can also be conducted with the help of voice calls or video calls.
• Group discussions are conducted and the researcher also participates in community programmes to understand various social aspects.
• Other tools like experiments and observations are used to study the subject in real-time and not in a fabricated or artificial nature
• The research is mostly conducted by a researcher in a known environment so that the participant doesn’t feel awkward. Discomfort or awkwardness in participants’ answers might impact the findings and in turn the research.
•  The researcher even tries to manipulate the participants sometimes to understand the cause-and-effect relationship. Once the research is done, the findings are presented with the help of various themes and are mostly in long-form rather than objective format. Research on the stigma revolving around “ex-inmates” and why they find it difficult to get a job after serving their sentence can be understood in depth with the help of quantitative social research.

Example of how Qualitative Research takes place:

If a researcher wants to discover and understand more about the “hesitancy among the parents towards adoption”, then he/she can follow the qualitative assessment route to learn more. There is no compulsion on the researcher to prove the hypothesis right or wrong. But the aim here is to understand the complexities and find answers to the “whys?”. The researcher can interact with a group of people who have adopted a child or those who haven’t but are thinking to. The interaction can involve various stakeholders so that the researcher can observe more. Open-ended questions will be asked, which will give the participants a chance to express themselves. Once the researcher gets a grasp of the topic and explores various segments, he/she presents the findings in a worldly and organised manner. These findings can even help the researcher to find another hypothesis or design a theory. Qualitative research develops an initial understanding of the topic and explains the topic in a broader sense that might help to postulate a hypothesis in the coming future.

Many researchers try to combine these two types of research methods to get a better understanding of their topic or hypothesis. Data interpretation and in-depth analysis of the topic help to figure out the topic in an efficient manner and sometimes lead to noteworthy discoveries.

References:

https://www.scribbr.com/methodology/research/

https://www.gcu.edu/blog/doctoral-journey/

Isha Rane is a sociology graduate with a keen interest in research and analysis, focusing on areas such as Corporate Social Responsibility, Human Resources, and Public Policy. She is an avid reader, particularly enjoying books about the history and political scenario of India. Isha also likes to write about pressing issues and topics that require a voice in the conversation. Her career aspirations lie in the development sector. Additionally, she has a passionate interest in mythology and calligraphy.