single subject design vs case study

CREd Library , Research Design and Method

Single-Subject Experimental Design: An Overview

Cred library, julie wambaugh, and ralf schlosser.

December, 2014

DOI: 10.1044/cred-cred-ssd-r101-002

Single-subject experimental designs – also referred to as within-subject or single case experimental designs – are among the most prevalent designs used in CSD treatment research. These designs provide a framework for a quantitative, scientifically rigorous approach where each participant provides his or her own experimental control.

An Overview of Single-Subject Experimental Design

What is single-subject design.

Transcript of the video Q&A with Julie Wambaugh. The essence of single-subject design is using repeated measurements to really understand an individual’s variability, so that we can use our understanding of that variability to determine what the effects of our treatment are. For me, one of the first steps in developing a treatment is understanding what an individual does. So, if I were doing a group treatment study, I would not necessarily be able to see or to understand what was happening with each individual patient, so that I could make modifications to my treatment and understand all the details of what’s happening in terms of the effects of my treatment. For me it’s a natural first step in the progression of developing a treatment. Also with the disorders that we deal with, it’s very hard to get the number of participants that we would need for the gold standard randomized controlled trial. Using single-subject designs works around the possible limiting factor of not having enough subjects in a particular area of study. My mentor was Dr. Cynthia Thompson, who was trained by Leija McReynolds from the University of Kansas, which was where a lot of single-subject design in our field originated, and so I was fortunate to be on the cutting edge of this being implemented in our science back in the late ’70s early ’80s. We saw, I think, a nice revolution in terms of attention to these types of designs, giving credit to the type of data that could be obtained from these types of designs, and a flourishing of these designs really through the 1980s into the 1990s and into the 2000s. But I think — I’ve talked with other single-subject design investigators, and now we’re seeing maybe a little bit of a lapse of attention, and a lack of training again among our young folks. Maybe people assume that people understand the foundation, but they really don’t. And more problems are occurring with the science. I think we need to re-establish the foundations in our young scientists. And this project, I think, will be a big plus toward moving us in that direction.

What is the Role of Single-Subject Design?

Transcript of the video Q&A with Ralf Schlosser. So what has happened recently, is with the onset of evidence-based practice and the adoption of the common hierarchy of evidence in terms of designs. As you noted the randomized controlled trial and meta-analyses of randomized controlled trials are on top of common hierarchies. And that’s fine. But it doesn’t mean that single-subject cannot play a role. For example, single-subject design can be implemented prior to implementing a randomized controlled trial to get a better handle on the magnitude of the effects, the workings of the active ingredients, and all of that. It is very good to prepare that prior to developing a randomized controlled trial. After you have implemented the randomized controlled trial, and then you want to implement the intervention in a more naturalistic setting, it becomes very difficult to do that in a randomized form or at the group level. So again, single-subject design lends itself to more practice-oriented implementation. So I see it as a crucial methodology among several. What we can do to promote what single-subject design is good for is to speak up. It is important that it is being recognized for what it can do and what it cannot do.

Basic Features and Components of Single-Subject Experimental Designs

Defining Features Single-subject designs are defined by the following features:

An individual “case” is the unit of intervention and unit of data analysis.
The case provides its own control for purposes of comparison. For example, the case’s series of outcome variables are measured prior to the intervention and compared with measurements taken during (and after) the intervention.
The outcome variable is measured repeatedly within and across different conditions or levels of the independent variable.

See Kratochwill, et al. (2010)

Structure and Phases of the Design Single-subject designs are typically described according to the arrangement of baseline and treatment phases.

The conditions in a single-subject experimental study are often assigned letters such as the A phase and the B phase, with A being the baseline, or no-treatment phase, and B the experimental, or treatment phase. (Other letters are sometimes used to designate other experimental phases.) Generally, the A phase serves as a time period in which the behavior or behaviors of interest are counted or scored prior to introducing treatment. In the B phase, the same behavior of the individual is counted over time under experimental conditions while treatment is administered. Decisions regarding the effect of treatment are then made by comparing an individual’s performance during the treatment, B phase, and the no-treatment. McReynolds and Thompson (1986)

Basic Components Important primary components of a single-subject study include the following:

The participant is the unit of analysis, where a participant may be an individual or a unit such as a class or school.
Participant and setting descriptions are provided with sufficient detail to allow another researcher to recruit similar participants in similar settings.
Dependent variables are (a) operationally defined and (b) measured repeatedly.
An independent variable is actively manipulated, with the fidelity of implementation documented.
A baseline condition demonstrates a predictable pattern which can be compared with the intervention condition(s).
Experimental control is achieved through introduction and withdrawal/reversal, staggered introduction, or iterative manipulation of the independent variable.
Visual analysis is used to interpret the level, trend, and variability of the data within and across phases.
External validity of results is accomplished through replication of the effects.
Social validity is established by documenting that interventions are functionally related to change in socially important outcomes.

See Horner, et al. (2005)

Common Misconceptions

Single-Subject Experimental Designs versus Case Studies

Transcript of the video Q&A with Julie Wambaugh. One of the biggest mistakes, that is a huge problem, is misunderstanding that a case study is not a single-subject experimental design. There are controls that need to be implemented, and a case study does not equate to a single-subject experimental design. People misunderstand or they misinterpret the term “multiple baseline” to mean that because you are measuring multiple things, that that gives you the experimental control. You have to be demonstrating, instead, that you’ve measured multiple behaviors and that you’ve replicated your treatment effect across those multiple behaviors. So, one instance of one treatment being implemented with one behavior is not sufficient, even if you’ve measured other things. That’s a very common mistake that I see. There’s a design — an ABA design — that’s a very strong experimental design where you measure the behavior, you implement treatment, and you then to get experimental control need to see that treatment go back down to baseline, for you to have evidence of experimental control. It’s a hard behavior to implement in our field because we want our behaviors to stay up! We don’t want to see them return back to baseline. Oftentimes people will say they did an ABA. But really, in effect, all they did was an AB. They measured, they implemented treatment, and the behavior changed because the treatment was successful. That does not give you experimental control. They think they did an experimentally sound design, but because the behavior didn’t do what the design requires to get experimental control, they really don’t have experimental control with their design.

Single-subject studies should not be confused with case studies or other non-experimental designs.

In case study reports, procedures used in treatment of a particular client’s behavior are documented as carefully as possible, and the client’s progress toward habilitation or rehabilitation is reported. These investigations provide useful descriptions. . . .However, a demonstration of treatment effectiveness requires an experimental study. A better role for case studies is description and identification of potential variables to be evaluated in experimental studies. An excellent discussion of this issue can be found in the exchange of letters to the editor by Hoodin (1986) [Article] and Rubow and Swift (1986) [Article]. McReynolds and Thompson (1986)

Other Single-Subject Myths

Transcript of the video Q&A with Ralf Schlosser. Myth 1: Single-subject experiments only have one participant. Obviously, it requires only one subject, one participant. But that’s a misnomer to think that single-subject is just about one participant. You can have as many as twenty or thirty. Myth 2: Single-subject experiments only require one pre-test/post-test. I think a lot of students in the clinic are used to the measurement of one pre-test and one post-test because of the way the goals are written, and maybe there’s not enough time to collect continuous data.But single-case experimental designs require ongoing data collection. There’s this misperception that one baseline data point is enough. But for single-case experimental design you want to see at least three data points, because it allows you to see a trend in the data. So there’s a myth about the number of data points needed. The more data points we have, the better. Myth 3: Single-subject experiments are easy to do. Single-subject design has its own tradition of methodology. It seems very easy to do when you read up on one design. But there are lots of things to consider, and lots of things can go wrong.It requires quite a bit of training. It takes at least one three-credit course that you take over the whole semester.

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Case Study vs. Single-Case Experimental Designs

What's the difference.

Case study and single-case experimental designs are both research methods used in psychology and other social sciences to investigate individual cases or subjects. However, they differ in their approach and purpose. Case studies involve in-depth examination of a single case, such as an individual, group, or organization, to gain a comprehensive understanding of the phenomenon being studied. On the other hand, single-case experimental designs focus on studying the effects of an intervention or treatment on a single subject over time. These designs use repeated measures and control conditions to establish cause-and-effect relationships. While case studies provide rich qualitative data, single-case experimental designs offer more rigorous experimental control and allow for the evaluation of treatment effectiveness.

Further Detail

Introduction.

When conducting research in various fields, it is essential to choose the appropriate study design to answer research questions effectively. Two commonly used designs are case study and single-case experimental designs. While both approaches aim to provide valuable insights into specific phenomena, they differ in several key attributes. This article will compare and contrast the attributes of case study and single-case experimental designs, highlighting their strengths and limitations.

Definition and Purpose

A case study is an in-depth investigation of a particular individual, group, or event. It involves collecting and analyzing qualitative or quantitative data to gain a comprehensive understanding of the subject under study. Case studies are often used to explore complex phenomena, generate hypotheses, or provide detailed descriptions of unique cases.

On the other hand, single-case experimental designs are a type of research design that focuses on studying a single individual or a small group over time. These designs involve manipulating an independent variable and measuring its effects on a dependent variable. Single-case experimental designs are particularly useful for examining cause-and-effect relationships and evaluating the effectiveness of interventions or treatments.

Data Collection and Analysis

In terms of data collection, case studies rely on various sources such as interviews, observations, documents, and artifacts. Researchers often employ multiple methods to gather rich and diverse data, allowing for a comprehensive analysis of the case. The data collected in case studies are typically qualitative in nature, although quantitative data may also be included.

In contrast, single-case experimental designs primarily rely on quantitative data collection methods. Researchers use standardized measures and instruments to collect data on the dependent variable before, during, and after the manipulation of the independent variable. This allows for a systematic analysis of the effects of the intervention or treatment on the individual or group being studied.

Generalizability

One of the key differences between case studies and single-case experimental designs is their generalizability. Case studies are often conducted on unique or rare cases, making it challenging to generalize the findings to a larger population. The focus of case studies is on providing detailed insights into specific cases rather than making broad generalizations.

On the other hand, single-case experimental designs aim to establish causal relationships and can provide evidence for generalizability. By systematically manipulating the independent variable and measuring its effects on the dependent variable, researchers can draw conclusions about the effectiveness of interventions or treatments that may be applicable to similar cases or populations.

Internal Validity

Internal validity refers to the extent to which a study accurately measures the cause-and-effect relationship between variables. In case studies, establishing internal validity can be challenging due to the lack of control over extraneous variables. The presence of multiple data sources and the potential for subjective interpretation may also introduce bias.

In contrast, single-case experimental designs prioritize internal validity by employing rigorous control over extraneous variables. Researchers carefully design the intervention or treatment, implement it consistently, and measure the dependent variable under controlled conditions. This allows for a more confident determination of the causal relationship between the independent and dependent variables.

Time and Resources

Case studies often require significant time and resources due to their in-depth nature. Researchers need to spend considerable time collecting and analyzing data from various sources, conducting interviews, and immersing themselves in the case. Additionally, case studies may involve multiple researchers or a research team, further increasing the required resources.

On the other hand, single-case experimental designs can be more time and resource-efficient. Since they focus on a single individual or a small group, data collection and analysis can be more streamlined. Researchers can also implement interventions or treatments in a controlled manner, reducing the time and resources needed for data collection.

Ethical Considerations

Both case studies and single-case experimental designs require researchers to consider ethical implications. In case studies, researchers must ensure the privacy and confidentiality of the individuals or groups being studied. Informed consent and ethical guidelines for data collection and analysis should be followed to protect the rights and well-being of the participants.

Similarly, in single-case experimental designs, researchers must consider ethical considerations when implementing interventions or treatments. The well-being and safety of the individual or group being studied should be prioritized, and informed consent should be obtained. Additionally, researchers should carefully monitor and evaluate the potential risks and benefits associated with the intervention or treatment.

Case studies and single-case experimental designs are valuable research approaches that offer unique insights into specific phenomena. While case studies provide in-depth descriptions and exploratory analyses of individual cases, single-case experimental designs focus on establishing causal relationships and evaluating interventions or treatments. Researchers should carefully consider the attributes and goals of their study when choosing between these two designs, ensuring that the selected approach aligns with their research questions and objectives.

Comparisons may contain inaccurate information about people, places, or facts. Please report any issues.

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10.1 Overview of Single-Subject Research

Learning objectives.

Explain what single-subject research is, including how it differs from other types of psychological research.
Explain what case studies are, including some of their strengths and weaknesses.
Explain who uses single-subject research and why.

What Is Single-Subject Research?

Single-subject research is a type of quantitative research that involves studying in detail the behavior of each of a small number of participants. Note that the term single-subject does not mean that only one participant is studied; it is more typical for there to be somewhere between two and 10 participants. (This is why single-subject research designs are sometimes called small- n designs, where n is the statistical symbol for the sample size.) Single-subject research can be contrasted with group research , which typically involves studying large numbers of participants and examining their behavior primarily in terms of group means, standard deviations, and so on. The majority of this book is devoted to understanding group research, which is the most common approach in psychology. But single-subject research is an important alternative, and it is the primary approach in some areas of psychology.

Before continuing, it is important to distinguish single-subject research from two other approaches, both of which involve studying in detail a small number of participants. One is qualitative research, which focuses on understanding people’s subjective experience by collecting relatively unstructured data (e.g., detailed interviews) and analyzing those data using narrative rather than quantitative techniques. Single-subject research, in contrast, focuses on understanding objective behavior through experimental manipulation and control, collecting highly structured data, and analyzing those data quantitatively.

It is also important to distinguish single-subject research from case studies. A case study is a detailed description of an individual, which can include both qualitative and quantitative analyses. (Case studies that include only qualitative analyses can be considered a type of qualitative research.) The history of psychology is filled with influential cases studies, such as Sigmund Freud’s description of “Anna O.” (see Note 10.5 “The Case of “Anna O.”” ) and John Watson and Rosalie Rayner’s description of Little Albert (Watson & Rayner, 1920), who learned to fear a white rat—along with other furry objects—when the researchers made a loud noise while he was playing with the rat. Case studies can be useful for suggesting new research questions and for illustrating general principles. They can also help researchers understand rare phenomena, such as the effects of damage to a specific part of the human brain. As a general rule, however, case studies cannot substitute for carefully designed group or single-subject research studies. One reason is that case studies usually do not allow researchers to determine whether specific events are causally related, or even related at all. For example, if a patient is described in a case study as having been sexually abused as a child and then as having developed an eating disorder as a teenager, there is no way to determine whether these two events had anything to do with each other. A second reason is that an individual case can always be unusual in some way and therefore be unrepresentative of people more generally. Thus case studies have serious problems with both internal and external validity.

The Case of “Anna O.”

Sigmund Freud used the case of a young woman he called “Anna O.” to illustrate many principles of his theory of psychoanalysis (Freud, 1961). (Her real name was Bertha Pappenheim, and she was an early feminist who went on to make important contributions to the field of social work.) Anna had come to Freud’s colleague Josef Breuer around 1880 with a variety of odd physical and psychological symptoms. One of them was that for several weeks she was unable to drink any fluids. According to Freud,

She would take up the glass of water that she longed for, but as soon as it touched her lips she would push it away like someone suffering from hydrophobia.…She lived only on fruit, such as melons, etc., so as to lessen her tormenting thirst (p. 9).

But according to Freud, a breakthrough came one day while Anna was under hypnosis.

[S]he grumbled about her English “lady-companion,” whom she did not care for, and went on to describe, with every sign of disgust, how she had once gone into this lady’s room and how her little dog—horrid creature!—had drunk out of a glass there. The patient had said nothing, as she had wanted to be polite. After giving further energetic expression to the anger she had held back, she asked for something to drink, drank a large quantity of water without any difficulty, and awoke from her hypnosis with the glass at her lips; and thereupon the disturbance vanished, never to return.

Freud’s interpretation was that Anna had repressed the memory of this incident along with the emotion that it triggered and that this was what had caused her inability to drink. Furthermore, her recollection of the incident, along with her expression of the emotion she had repressed, caused the symptom to go away.

As an illustration of Freud’s theory, the case study of Anna O. is quite effective. As evidence for the theory, however, it is essentially worthless. The description provides no way of knowing whether Anna had really repressed the memory of the dog drinking from the glass, whether this repression had caused her inability to drink, or whether recalling this “trauma” relieved the symptom. It is also unclear from this case study how typical or atypical Anna’s experience was.

Figure 10.2

“Anna O.” was the subject of a famous case study used by Freud to illustrate the principles of psychoanalysis.

Wikimedia Commons – public domain.

Assumptions of Single-Subject Research

Again, single-subject research involves studying a small number of participants and focusing intensively on the behavior of each one. But why take this approach instead of the group approach? There are several important assumptions underlying single-subject research, and it will help to consider them now.

First and foremost is the assumption that it is important to focus intensively on the behavior of individual participants. One reason for this is that group research can hide individual differences and generate results that do not represent the behavior of any individual. For example, a treatment that has a positive effect for half the people exposed to it but a negative effect for the other half would, on average, appear to have no effect at all. Single-subject research, however, would likely reveal these individual differences. A second reason to focus intensively on individuals is that sometimes it is the behavior of a particular individual that is primarily of interest. A school psychologist, for example, might be interested in changing the behavior of a particular disruptive student. Although previous published research (both single-subject and group research) is likely to provide some guidance on how to do this, conducting a study on this student would be more direct and probably more effective.

A second assumption of single-subject research is that it is important to discover causal relationships through the manipulation of an independent variable, the careful measurement of a dependent variable, and the control of extraneous variables. For this reason, single-subject research is often considered a type of experimental research with good internal validity. Recall, for example, that Hall and his colleagues measured their dependent variable (studying) many times—first under a no-treatment control condition, then under a treatment condition (positive teacher attention), and then again under the control condition. Because there was a clear increase in studying when the treatment was introduced, a decrease when it was removed, and an increase when it was reintroduced, there is little doubt that the treatment was the cause of the improvement.

A third assumption of single-subject research is that it is important to study strong and consistent effects that have biological or social importance. Applied researchers, in particular, are interested in treatments that have substantial effects on important behaviors and that can be implemented reliably in the real-world contexts in which they occur. This is sometimes referred to as social validity (Wolf, 1976). The study by Hall and his colleagues, for example, had good social validity because it showed strong and consistent effects of positive teacher attention on a behavior that is of obvious importance to teachers, parents, and students. Furthermore, the teachers found the treatment easy to implement, even in their often chaotic elementary school classrooms.

Who Uses Single-Subject Research?

Single-subject research has been around as long as the field of psychology itself. In the late 1800s, one of psychology’s founders, Wilhelm Wundt, studied sensation and consciousness by focusing intensively on each of a small number of research participants. Herman Ebbinghaus’s research on memory and Ivan Pavlov’s research on classical conditioning are other early examples, both of which are still described in almost every introductory psychology textbook.

In the middle of the 20th century, B. F. Skinner clarified many of the assumptions underlying single-subject research and refined many of its techniques (Skinner, 1938). He and other researchers then used it to describe how rewards, punishments, and other external factors affect behavior over time. This work was carried out primarily using nonhuman subjects—mostly rats and pigeons. This approach, which Skinner called the experimental analysis of behavior —remains an important subfield of psychology and continues to rely almost exclusively on single-subject research. For excellent examples of this work, look at any issue of the Journal of the Experimental Analysis of Behavior . By the 1960s, many researchers were interested in using this approach to conduct applied research primarily with humans—a subfield now called applied behavior analysis (Baer, Wolf, & Risley, 1968). Applied behavior analysis plays an especially important role in contemporary research on developmental disabilities, education, organizational behavior, and health, among many other areas. Excellent examples of this work (including the study by Hall and his colleagues) can be found in the Journal of Applied Behavior Analysis .

Although most contemporary single-subject research is conducted from the behavioral perspective, it can in principle be used to address questions framed in terms of any theoretical perspective. For example, a studying technique based on cognitive principles of learning and memory could be evaluated by testing it on individual high school students using the single-subject approach. The single-subject approach can also be used by clinicians who take any theoretical perspective—behavioral, cognitive, psychodynamic, or humanistic—to study processes of therapeutic change with individual clients and to document their clients’ improvement (Kazdin, 1982).

Key Takeaways

Single-subject research—which involves testing a small number of participants and focusing intensively on the behavior of each individual—is an important alternative to group research in psychology.
Single-subject studies must be distinguished from case studies, in which an individual case is described in detail. Case studies can be useful for generating new research questions, for studying rare phenomena, and for illustrating general principles. However, they cannot substitute for carefully controlled experimental or correlational studies because they are low in internal and external validity.
Single-subject research has been around since the beginning of the field of psychology. Today it is most strongly associated with the behavioral theoretical perspective, but it can in principle be used to study behavior from any perspective.
Practice: Find and read a published article in psychology that reports new single-subject research. (A good source of articles published in the Journal of Applied Behavior Analysis can be found at http://seab.envmed.rochester.edu/jaba/jabaMostPop-2011.html .) Write a short summary of the study.

Practice: Find and read a published case study in psychology. (Use case study as a key term in a PsycINFO search.) Then do the following:

Describe one problem related to internal validity.
Describe one problem related to external validity.
Generate one hypothesis suggested by the case study that might be interesting to test in a systematic single-subject or group study.

Baer, D. M., Wolf, M. M., & Risley, T. R. (1968). Some current dimensions of applied behavior analysis. Journal of Applied Behavior Analysis , 1 , 91–97.

Freud, S. (1961). Five lectures on psycho-analysis . New York, NY: Norton.

Kazdin, A. E. (1982). Single-case research designs: Methods for clinical and applied settings . New York, NY: Oxford University Press.

Skinner, B. F. (1938). The behavior of organisms: An experimental analysis . New York, NY: Appleton-Century-Crofts.

Watson, J. B., & Rayner, R. (1920). Conditioned emotional reactions. Journal of Experimental Psychology , 3 , 1–14.

Wolf, M. (1976). Social validity: The case for subjective measurement or how applied behavior analysis is finding its heart. Journal of Applied Behavior Analysis, 11 , 203–214.

Research Methods in Psychology Copyright © 2016 by University of Minnesota is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Knowledge Base

Methodology

What Is a Case Study? | Definition, Examples & Methods

What Is a Case Study? | Definition, Examples & Methods

Published on May 8, 2019 by Shona McCombes . Revised on November 20, 2023.

A case study is a detailed study of a specific subject, such as a person, group, place, event, organization, or phenomenon. Case studies are commonly used in social, educational, clinical, and business research.

A case study research design usually involves qualitative methods , but quantitative methods are sometimes also used. Case studies are good for describing , comparing, evaluating and understanding different aspects of a research problem .

When to do a case study, step 1: select a case, step 2: build a theoretical framework, step 3: collect your data, step 4: describe and analyze the case, other interesting articles.

A case study is an appropriate research design when you want to gain concrete, contextual, in-depth knowledge about a specific real-world subject. It allows you to explore the key characteristics, meanings, and implications of the case.

Case studies are often a good choice in a thesis or dissertation . They keep your project focused and manageable when you don’t have the time or resources to do large-scale research.

You might use just one complex case study where you explore a single subject in depth, or conduct multiple case studies to compare and illuminate different aspects of your research problem.

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Once you have developed your problem statement and research questions , you should be ready to choose the specific case that you want to focus on. A good case study should have the potential to:

Provide new or unexpected insights into the subject
Challenge or complicate existing assumptions and theories
Propose practical courses of action to resolve a problem
Open up new directions for future research

TipIf your research is more practical in nature and aims to simultaneously investigate an issue as you solve it, consider conducting action research instead.

Unlike quantitative or experimental research , a strong case study does not require a random or representative sample. In fact, case studies often deliberately focus on unusual, neglected, or outlying cases which may shed new light on the research problem.

Example of an outlying case studyIn the 1960s the town of Roseto, Pennsylvania was discovered to have extremely low rates of heart disease compared to the US average. It became an important case study for understanding previously neglected causes of heart disease.

However, you can also choose a more common or representative case to exemplify a particular category, experience or phenomenon.

Example of a representative case studyIn the 1920s, two sociologists used Muncie, Indiana as a case study of a typical American city that supposedly exemplified the changing culture of the US at the time.

While case studies focus more on concrete details than general theories, they should usually have some connection with theory in the field. This way the case study is not just an isolated description, but is integrated into existing knowledge about the topic. It might aim to:

Exemplify a theory by showing how it explains the case under investigation
Expand on a theory by uncovering new concepts and ideas that need to be incorporated
Challenge a theory by exploring an outlier case that doesn’t fit with established assumptions

To ensure that your analysis of the case has a solid academic grounding, you should conduct a literature review of sources related to the topic and develop a theoretical framework . This means identifying key concepts and theories to guide your analysis and interpretation.

There are many different research methods you can use to collect data on your subject. Case studies tend to focus on qualitative data using methods such as interviews , observations , and analysis of primary and secondary sources (e.g., newspaper articles, photographs, official records). Sometimes a case study will also collect quantitative data.

Example of a mixed methods case studyFor a case study of a wind farm development in a rural area, you could collect quantitative data on employment rates and business revenue, collect qualitative data on local people’s perceptions and experiences, and analyze local and national media coverage of the development.

The aim is to gain as thorough an understanding as possible of the case and its context.

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In writing up the case study, you need to bring together all the relevant aspects to give as complete a picture as possible of the subject.

How you report your findings depends on the type of research you are doing. Some case studies are structured like a standard scientific paper or thesis , with separate sections or chapters for the methods , results and discussion .

Others are written in a more narrative style, aiming to explore the case from various angles and analyze its meanings and implications (for example, by using textual analysis or discourse analysis ).

In all cases, though, make sure to give contextual details about the case, connect it back to the literature and theory, and discuss how it fits into wider patterns or debates.

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.

Normal distribution
Degrees of freedom
Null hypothesis
Discourse analysis
Control groups
Mixed methods research
Non-probability sampling
Quantitative research
Ecological validity

Research bias

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

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McCombes, S. (2023, November 20). What Is a Case Study? | Definition, Examples & Methods. Scribbr. Retrieved April 12, 2024, from https://www.scribbr.com/methodology/case-study/

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10.1: Overview of Single-Subject Research

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Learning Objectives

Explain what single-subject research is, including how it differs from other types of psychological research.
Explain who uses single-subject research and why.

What Is Single-Subject Research?

Single-subject research is a type of quantitative research that involves studying in detail the behavior of each of a small number of participants. Note that the term single-subject does not mean that only one participant is studied; it is more typical for there to be somewhere between two and 10 participants. (This is why single-subject research designs are sometimes called small- n designs, where n is the statistical symbol for the sample size.) Single-subject research can be contrasted with group research , which typically involves studying large numbers of participants and examining their behavior primarily in terms of group means, standard deviations, and so on. The majority of this textbook is devoted to understanding group research, which is the most common approach in psychology. But single-subject research is an important alternative, and it is the primary approach in some more applied areas of psychology.

Before continuing, it is important to distinguish single-subject research from case studies and other more qualitative approaches that involve studying in detail a small number of participants. As described in Chapter 6, case studies involve an in-depth analysis and description of an individual, which is typically primarily qualitative in nature. More broadly speaking, qualitative research focuses on understanding people’s subjective experience by observing behavior and collecting relatively unstructured data (e.g., detailed interviews) and analyzing those data using narrative rather than quantitative techniques. Single-subject research, in contrast, focuses on understanding objective behavior through experimental manipulation and control, collecting highly structured data, and analyzing those data quantitatively.

Assumptions of Single-Subject Research

A third assumption of single-subject research is that it is important to study strong and consistent effects that have biological or social importance. Applied researchers, in particular, are interested in treatments that have substantial effects on important behaviors and that can be implemented reliably in the real-world contexts in which they occur. This is sometimes referred to as social validity (Wolf, 1976) [1] . The study by Hall and his colleagues, for example, had good social validity because it showed strong and consistent effects of positive teacher attention on a behavior that is of obvious importance to teachers, parents, and students. Furthermore, the teachers found the treatment easy to implement, even in their often-chaotic elementary school classrooms.

Who Uses Single-Subject Research?

In the middle of the 20th century, B. F. Skinner clarified many of the assumptions underlying single-subject research and refined many of its techniques (Skinner, 1938) [2] . He and other researchers then used it to describe how rewards, punishments, and other external factors affect behavior over time. This work was carried out primarily using nonhuman subjects—mostly rats and pigeons. This approach, which Skinner called the experimental analysis of behavior —remains an important subfield of psychology and continues to rely almost exclusively on single-subject research. For excellent examples of this work, look at any issue of the Journal of the Experimental Analysis of Behavior . By the 1960s, many researchers were interested in using this approach to conduct applied research primarily with humans—a subfield now called applied behavior analysis (Baer, Wolf, & Risley, 1968) [3] . Applied behavior analysis plays an especially important role in contemporary research on developmental disabilities, education, organizational behavior, and health, among many other areas. Excellent examples of this work (including the study by Hall and his colleagues) can be found in the Journal of Applied Behavior Analysis .

Key Takeaways

Single-subject research—which involves testing a small number of participants and focusing intensively on the behavior of each individual—is an important alternative to group research in psychology.
Single-subject studies must be distinguished from qualitative research on a single person or small number of individuals. Unlike more qualitative research, single-subject research focuses on understanding objective behavior through experimental manipulation and control, collecting highly structured data, and analyzing those data quantitatively.
Single-subject research has been around since the beginning of the field of psychology. Today it is most strongly associated with the behavioral theoretical perspective, but it can in principle be used to study behavior from any perspective.
Practice: Find and read a published article in psychology that reports new single-subject research. (An archive of articles published in the Journal of Applied Behavior Analysis can be found at http://www.ncbi.nlm.nih.gov/pmc/journals/309/ ) Write a short summary of the study.
Wolf, M. (1976). Social validity: The case for subjective measurement or how applied behavior analysis is finding its heart. Journal of Applied Behavior Analysis, 11 , 203–214.
Skinner, B. F. (1938). T he behavior of organisms: An experimental analysis . New York, NY: Appleton-Century-Crofts.
Baer, D. M., Wolf, M. M., & Risley, T. R. (1968). Some current dimensions of applied behavior analysis. Journal of Applied Behavior Analysis, 1 , 91–97.
Kazdin, A. E. (1982). Single-case research designs: Methods for clinical and applied settings . New York, NY: Oxford University Press.

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Neag School of Education

Educational Research Basics by Del Siegle

Single subject research.

“ Single subject research (also known as single case experiments) is popular in the fields of special education and counseling. This research design is useful when the researcher is attempting to change the behavior of an individual or a small group of individuals and wishes to document that change. Unlike true experiments where the researcher randomly assigns participants to a control and treatment group, in single subject research the participant serves as both the control and treatment group. The researcher uses line graphs to show the effects of a particular intervention or treatment. An important factor of single subject research is that only one variable is changed at a time. Single subject research designs are “weak when it comes to external validity….Studies involving single-subject designs that show a particular treatment to be effective in changing behavior must rely on replication–across individuals rather than groups–if such results are be found worthy of generalization” (Fraenkel & Wallen, 2006, p. 318).

Suppose a researcher wished to investigate the effect of praise on reducing disruptive behavior over many days. First she would need to establish a baseline of how frequently the disruptions occurred. She would measure how many disruptions occurred each day for several days. In the example below, the target student was disruptive seven times on the first day, six times on the second day, and seven times on the third day. Note how the sequence of time is depicted on the x-axis (horizontal axis) and the dependent variable (outcome variable) is depicted on the y-axis (vertical axis).

Once a baseline of behavior has been established (when a consistent pattern emerges with at least three data points), the intervention begins. The researcher continues to plot the frequency of behavior while implementing the intervention of praise.

In this example, we can see that the frequency of disruptions decreased once praise began. The design in this example is known as an A-B design. The baseline period is referred to as A and the intervention period is identified as B.

Another design is the A-B-A design. An A-B-A design (also known as a reversal design) involves discontinuing the intervention and returning to a nontreatment condition.

Sometimes an individual’s behavior is so severe that the researcher cannot wait to establish a baseline and must begin with an intervention. In this case, a B-A-B design is used. The intervention is implemented immediately (before establishing a baseline). This is followed by a measurement without the intervention and then a repeat of the intervention.

Multiple-Baseline Design

Sometimes, a researcher may be interested in addressing several issues for one student or a single issue for several students. In this case, a multiple-baseline design is used.

“In a multiple baseline across subjects design, the researcher introduces the intervention to different persons at different times. The significance of this is that if a behavior changes only after the intervention is presented, and this behavior change is seen successively in each subject’s data, the effects can more likely be credited to the intervention itself as opposed to other variables. Multiple-baseline designs do not require the intervention to be withdrawn. Instead, each subject’s own data are compared between intervention and nonintervention behaviors, resulting in each subject acting as his or her own control (Kazdin, 1982). An added benefit of this design, and all single-case designs, is the immediacy of the data. Instead of waiting until postintervention to take measures on the behavior, single-case research prescribes continuous data collection and visual monitoring of that data displayed graphically, allowing for immediate instructional decision-making. Students, therefore, do not linger in an intervention that is not working for them, making the graphic display of single-case research combined with differentiated instruction responsive to the needs of students.” (Geisler, Hessler, Gardner, & Lovelace, 2009)

Regardless of the research design, the line graphs used to illustrate the data contain a set of common elements.

Generally, in single subject research we count the number of times something occurs in a given time period and see if it occurs more or less often in that time period after implementing an intervention. For example, we might measure how many baskets someone makes while shooting for 2 minutes. We would repeat that at least three times to get our baseline. Next, we would test some intervention. We might play music while shooting, give encouragement while shooting, or video the person while shooting to see if our intervention influenced the number of shots made. After the 3 baseline measurements (3 sets of 2 minute shooting), we would measure several more times (sets of 2 minute shooting) after the intervention and plot the time points (number of baskets made in 2 minutes for each of the measured time points). This works well for behaviors that are distinct and can be counted.

Sometimes behaviors come and go over time (such as being off task in a classroom or not listening during a coaching session). The way we can record these is to select a period of time (say 5 minutes) and mark down every 10 seconds whether our participant is on task. We make a minimum of three sets of 5 minute observations for a baseline, implement an intervention, and then make more sets of 5 minute observations with the intervention in place. We use this method rather than counting how many times someone is off task because one could continually be off task and that would only be a count of 1 since the person was continually off task. Someone who might be off task twice for 15 second would be off task twice for a score of 2. However, the second person is certainly not off task twice as much as the first person. Therefore, recording whether the person is off task at 10-second intervals gives a more accurate picture. The person continually off task would have a score of 30 (off task at every second interval for 5 minutes) and the person off task twice for a short time would have a score of 2 (off task only during 2 of the 10 second interval measures.

I also have additional information about how to record single-subject research data .

I hope this helps you better understand single subject research.

I have created a PowerPoint on Single Subject Research , which also available below as a video.

I have also created instructions for creating single-subject research design graphs with Excel .

Fraenkel, J. R., & Wallen, N. E. (2006). How to design and evaluate research in education (6th ed.). Boston, MA: McGraw Hill.

Geisler, J. L., Hessler, T., Gardner, R., III, & Lovelace, T. S. (2009). Differentiated writing interventions for high-achieving urban African American elementary students. Journal of Advanced Academics, 20, 214–247.

Del Siegle, Ph.D. University of Connecticut [email protected] www.delsiegle.info

Revised 02/02/2024

10.2 Single-Subject Research Designs

Learning objectives.

Describe the basic elements of a single-subject research design.
Design simple single-subject studies using reversal and multiple-baseline designs.
Explain how single-subject research designs address the issue of internal validity.
Interpret the results of simple single-subject studies based on the visual inspection of graphed data.

General Features of Single-Subject Designs

Before looking at any specific single-subject research designs, it will be helpful to consider some features that are common to most of them. Many of these features are illustrated in Figure 10.1, which shows the results of a generic single-subject study. First, the dependent variable (represented on the y -axis of the graph) is measured repeatedly over time (represented by the x -axis) at regular intervals. Second, the study is divided into distinct phases, and the participant is tested under one condition per phase. The conditions are often designated by capital letters: A, B, C, and so on. Thus Figure 10.1 represents a design in which the participant was tested first in one condition (A), then tested in another condition (B), and finally retested in the original condition (A). (This is called a reversal design and will be discussed in more detail shortly.)

Figure 10.2 Results of a Generic Single-Subject Study Illustrating Several Principles of Single-Subject Research

Figure 10.1 Results of a Generic Single-Subject Study Illustrating Several Principles of Single-Subject Research

Another important aspect of single-subject research is that the change from one condition to the next does not usually occur after a fixed amount of time or number of observations. Instead, it depends on the participant’s behavior. Specifically, the researcher waits until the participant’s behavior in one condition becomes fairly consistent from observation to observation before changing conditions. This is sometimes referred to as the steady state strategy (Sidman, 1960) [1] . The idea is that when the dependent variable has reached a steady state, then any change across conditions will be relatively easy to detect. Recall that we encountered this same principle when discussing experimental research more generally. The effect of an independent variable is easier to detect when the “noise” in the data is minimized.

Reversal Designs

The most basic single-subject research design is the reversal design , also called the ABA design . During the first phase, A, a baseline is established for the dependent variable. This is the level of responding before any treatment is introduced, and therefore the baseline phase is a kind of control condition. When steady state responding is reached, phase B begins as the researcher introduces the treatment. There may be a period of adjustment to the treatment during which the behavior of interest becomes more variable and begins to increase or decrease. Again, the researcher waits until that dependent variable reaches a steady state so that it is clear whether and how much it has changed. Finally, the researcher removes the treatment and again waits until the dependent variable reaches a steady state. This basic reversal design can also be extended with the reintroduction of the treatment (ABAB), another return to baseline (ABABA), and so on.

The study by Hall and his colleagues employed an ABAB reversal design. Figure 10.2 approximates the data for Robbie. The percentage of time he spent studying (the dependent variable) was low during the first baseline phase, increased during the first treatment phase until it leveled off, decreased during the second baseline phase, and again increased during the second treatment phase.

Figure 10.3 An Approximation of the Results for Hall and Colleagues’ Participant Robbie in Their ABAB Reversal Design

Figure 10.2 An Approximation of the Results for Hall and Colleagues’ Participant Robbie in Their ABAB Reversal Design

Why is the reversal—the removal of the treatment—considered to be necessary in this type of design? Why use an ABA design, for example, rather than a simpler AB design? Notice that an AB design is essentially an interrupted time-series design applied to an individual participant. Recall that one problem with that design is that if the dependent variable changes after the treatment is introduced, it is not always clear that the treatment was responsible for the change. It is possible that something else changed at around the same time and that this extraneous variable is responsible for the change in the dependent variable. But if the dependent variable changes with the introduction of the treatment and then changes back with the removal of the treatment (assuming that the treatment does not create a permanent effect), it is much clearer that the treatment (and removal of the treatment) is the cause. In other words, the reversal greatly increases the internal validity of the study.

There are close relatives of the basic reversal design that allow for the evaluation of more than one treatment. In a multiple-treatment reversal design , a baseline phase is followed by separate phases in which different treatments are introduced. For example, a researcher might establish a baseline of studying behavior for a disruptive student (A), then introduce a treatment involving positive attention from the teacher (B), and then switch to a treatment involving mild punishment for not studying (C). The participant could then be returned to a baseline phase before reintroducing each treatment—perhaps in the reverse order as a way of controlling for carryover effects. This particular multiple-treatment reversal design could also be referred to as an ABCACB design.

In an alternating treatments design , two or more treatments are alternated relatively quickly on a regular schedule. For example, positive attention for studying could be used one day and mild punishment for not studying the next, and so on. Or one treatment could be implemented in the morning and another in the afternoon. The alternating treatments design can be a quick and effective way of comparing treatments, but only when the treatments are fast acting.

Multiple-Baseline Designs

There are two potential problems with the reversal design—both of which have to do with the removal of the treatment. One is that if a treatment is working, it may be unethical to remove it. For example, if a treatment seemed to reduce the incidence of self-injury in a child with an intellectual delay, it would be unethical to remove that treatment just to show that the incidence of self-injury increases. The second problem is that the dependent variable may not return to baseline when the treatment is removed. For example, when positive attention for studying is removed, a student might continue to study at an increased rate. This could mean that the positive attention had a lasting effect on the student’s studying, which of course would be good. But it could also mean that the positive attention was not really the cause of the increased studying in the first place. Perhaps something else happened at about the same time as the treatment—for example, the student’s parents might have started rewarding him for good grades. One solution to these problems is to use a multiple-baseline design , which is represented in Figure 10.3. There are three different types of multiple-baseline designs which we will now consider.

Multiple-Baseline Design Across Participants

In one version of the design, a baseline is established for each of several participants, and the treatment is then introduced for each one. In essence, each participant is tested in an AB design. The key to this design is that the treatment is introduced at a different time for each participant. The idea is that if the dependent variable changes when the treatment is introduced for one participant, it might be a coincidence. But if the dependent variable changes when the treatment is introduced for multiple participants—especially when the treatment is introduced at different times for the different participants—then it is unlikely to be a coincidence.

Figure 10.4 Results of a Generic Multiple-Baseline Study. The multiple baselines can be for different participants, dependent variables, or settings. The treatment is introduced at a different time on each baseline.

Figure 10.3 Results of a Generic Multiple-Baseline Study. The multiple baselines can be for different participants, dependent variables, or settings. The treatment is introduced at a different time on each baseline.

As an example, consider a study by Scott Ross and Robert Horner (Ross & Horner, 2009) [2] . They were interested in how a school-wide bullying prevention program affected the bullying behavior of particular problem students. At each of three different schools, the researchers studied two students who had regularly engaged in bullying. During the baseline phase, they observed the students for 10-minute periods each day during lunch recess and counted the number of aggressive behaviors they exhibited toward their peers. After 2 weeks, they implemented the program at one school. After 2 more weeks, they implemented it at the second school. And after 2 more weeks, they implemented it at the third school. They found that the number of aggressive behaviors exhibited by each student dropped shortly after the program was implemented at his or her school. Notice that if the researchers had only studied one school or if they had introduced the treatment at the same time at all three schools, then it would be unclear whether the reduction in aggressive behaviors was due to the bullying program or something else that happened at about the same time it was introduced (e.g., a holiday, a television program, a change in the weather). But with their multiple-baseline design, this kind of coincidence would have to happen three separate times—a very unlikely occurrence—to explain their results.

Multiple-Baseline Design Across Behaviors

In another version of the multiple-baseline design, multiple baselines are established for the same participant but for different dependent variables, and the treatment is introduced at a different time for each dependent variable. Imagine, for example, a study on the effect of setting clear goals on the productivity of an office worker who has two primary tasks: making sales calls and writing reports. Baselines for both tasks could be established. For example, the researcher could measure the number of sales calls made and reports written by the worker each week for several weeks. Then the goal-setting treatment could be introduced for one of these tasks, and at a later time the same treatment could be introduced for the other task. The logic is the same as before. If productivity increases on one task after the treatment is introduced, it is unclear whether the treatment caused the increase. But if productivity increases on both tasks after the treatment is introduced—especially when the treatment is introduced at two different times—then it seems much clearer that the treatment was responsible.

Multiple-Baseline Design Across Settings

In yet a third version of the multiple-baseline design, multiple baselines are established for the same participant but in different settings. For example, a baseline might be established for the amount of time a child spends reading during his free time at school and during his free time at home. Then a treatment such as positive attention might be introduced first at school and later at home. Again, if the dependent variable changes after the treatment is introduced in each setting, then this gives the researcher confidence that the treatment is, in fact, responsible for the change.

Data Analysis in Single-Subject Research

In addition to its focus on individual participants, single-subject research differs from group research in the way the data are typically analyzed. As we have seen throughout the book, group research involves combining data across participants. Group data are described using statistics such as means, standard deviations, correlation coefficients, and so on to detect general patterns. Finally, inferential statistics are used to help decide whether the result for the sample is likely to generalize to the population. Single-subject research, by contrast, relies heavily on a very different approach called visual inspection . This means plotting individual participants’ data as shown throughout this chapter, looking carefully at those data, and making judgments about whether and to what extent the independent variable had an effect on the dependent variable. Inferential statistics are typically not used.

In visually inspecting their data, single-subject researchers take several factors into account. One of them is changes in the level of the dependent variable from condition to condition. If the dependent variable is much higher or much lower in one condition than another, this suggests that the treatment had an effect. A second factor is trend , which refers to gradual increases or decreases in the dependent variable across observations. If the dependent variable begins increasing or decreasing with a change in conditions, then again this suggests that the treatment had an effect. It can be especially telling when a trend changes directions—for example, when an unwanted behavior is increasing during baseline but then begins to decrease with the introduction of the treatment. A third factor is latency , which is the time it takes for the dependent variable to begin changing after a change in conditions. In general, if a change in the dependent variable begins shortly after a change in conditions, this suggests that the treatment was responsible.

In the top panel of Figure 10.4, there are fairly obvious changes in the level and trend of the dependent variable from condition to condition. Furthermore, the latencies of these changes are short; the change happens immediately. This pattern of results strongly suggests that the treatment was responsible for the changes in the dependent variable. In the bottom panel of Figure 10.4, however, the changes in level are fairly small. And although there appears to be an increasing trend in the treatment condition, it looks as though it might be a continuation of a trend that had already begun during baseline. This pattern of results strongly suggests that the treatment was not responsible for any changes in the dependent variable—at least not to the extent that single-subject researchers typically hope to see.

Figure 10.5 Results of a Generic Single-Subject Study Illustrating Level, Trend, and Latency. Visual inspection of the data suggests an effective treatment in the top panel but an ineffective treatment in the bottom panel.

Figure 10.4 Results of a Generic Single-Subject Study Illustrating Level, Trend, and Latency. Visual inspection of the data suggests an effective treatment in the top panel but an ineffective treatment in the bottom panel.

The results of single-subject research can also be analyzed using statistical procedures—and this is becoming more common. There are many different approaches, and single-subject researchers continue to debate which are the most useful. One approach parallels what is typically done in group research. The mean and standard deviation of each participant’s responses under each condition are computed and compared, and inferential statistical tests such as the t test or analysis of variance are applied (Fisch, 2001) [3] . (Note that averaging across participants is less common.) Another approach is to compute the percentage of non-overlapping data (PND) for each participant (Scruggs & Mastropieri, 2001) [4] . This is the percentage of responses in the treatment condition that are more extreme than the most extreme response in a relevant control condition. In the study of Hall and his colleagues, for example, all measures of Robbie’s study time in the first treatment condition were greater than the highest measure in the first baseline, for a PND of 100%. The greater the percentage of non-overlapping data, the stronger the treatment effect. Still, formal statistical approaches to data analysis in single-subject research are generally considered a supplement to visual inspection, not a replacement for it.

Key Takeaways

Single-subject research designs typically involve measuring the dependent variable repeatedly over time and changing conditions (e.g., from baseline to treatment) when the dependent variable has reached a steady state. This approach allows the researcher to see whether changes in the independent variable are causing changes in the dependent variable.
In a reversal design, the participant is tested in a baseline condition, then tested in a treatment condition, and then returned to baseline. If the dependent variable changes with the introduction of the treatment and then changes back with the return to baseline, this provides strong evidence of a treatment effect.
In a multiple-baseline design, baselines are established for different participants, different dependent variables, or different settings—and the treatment is introduced at a different time on each baseline. If the introduction of the treatment is followed by a change in the dependent variable on each baseline, this provides strong evidence of a treatment effect.
Single-subject researchers typically analyze their data by graphing them and making judgments about whether the independent variable is affecting the dependent variable based on level, trend, and latency.
Does positive attention from a parent increase a child’s tooth-brushing behavior?
Does self-testing while studying improve a student’s performance on weekly spelling tests?
Does regular exercise help relieve depression?
Practice: Create a graph that displays the hypothetical results for the study you designed in Exercise 1. Write a paragraph in which you describe what the results show. Be sure to comment on level, trend, and latency.
Sidman, M. (1960). Tactics of scientific research: Evaluating experimental data in psychology . Boston, MA: Authors Cooperative. ↵
Ross, S. W., & Horner, R. H. (2009). Bully prevention in positive behavior support. Journal of Applied Behavior Analysis, 42 , 747–759. ↵
Fisch, G. S. (2001). Evaluating data from behavioral analysis: Visual inspection or statistical models. Behavioral Processes, 54 , 137–154. ↵
Scruggs, T. E., & Mastropieri, M. A. (2001). How to summarize single-participant research: Ideas and applications. Exceptionality, 9 , 227–244. ↵

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Terms to Use for Articles

"reversal design" OR "withdrawal design" OR "ABAB design" OR "A-B-A-B design" OR "ABC design" OR "A-B-C design" OR "ABA design" OR "A-B-A design" OR "multiple baseline" OR "alternating treatments design" OR "multi-element design" OR "multielement design" OR "changing criterion design" OR "single case design" OR "single subject design" OR “single case series" or "single subject" or "single case"

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ProQuest Education Database This link opens in a new window ProQuest Education Database indexes, abstracts, and provides full-text to leading scholarly and trade publications as well as reports in the field of education. Content includes primary, secondary, higher education, special education, home schooling, adult education, and more.
PsycINFO This link opens in a new window PsycINFO, from the American Psychological Association's (APA), is a resource for abstracts of scholarly journal articles, book chapters, books, and dissertations across a range of disciplines in psychology. PsycINFO is indexed using APA's Thesaurus of Psychological Index Terms. Subscription ends 6/30/24.

Research Hints

Stimming – or self-stimulatory behaviour – is repetitive or unusual body movement or noises . Stimming might include:

hand and finger mannerisms – for example, finger-flicking and hand-flapping
unusual body movements – for example, rocking back and forth while sitting or standing
posturing – for example, holding hands or fingers out at an angle or arching the back while sitting
visual stimulation – for example, looking at something sideways, watching an object spin or fluttering fingers near the eyes
repetitive behaviour – for example, opening and closing doors or flicking switches
chewing or mouthing objects
listening to the same song or noise over and over.

How to Search for a Specific Research Methodology in JABA

Single Case Design (Research Articles)

Single Case Design (APA Dictionary of Psychology) an approach to the empirical study of a process that tracks a single unit (e.g., person, family, class, school, company) in depth over time. Specific types include the alternating treatments design, the multiple baseline design, the reversal design, and the withdrawal design. In other words, it is a within-subjects design with just one unit of analysis. For example, a researcher may use a single-case design for a small group of patients with a tic. After observing the patients and establishing the number of tics per hour, the researcher would then conduct an intervention and watch what happens over time, thus revealing the richness of any change. Such studies are useful for generating ideas for broader studies and for focusing on the microlevel concerns associated with a particular unit. However, data from these studies need to be evaluated carefully given the many potential threats to internal validity; there are also issues relating to the sampling of both the one unit and the process it undergoes. Also called N-of-1 design; N=1 design; single-participant design; single-subject (case) design.
Anatomy of a Primary Research Article Document that goes through a research artile highlighting evaluative criteria for every section. Document from Mohawk Valley Community College. Permission to use sought and given
Single Case Design (Explanation) Single case design (SCD), often referred to as single subject design, is an evaluation method that can be used to rigorously test the success of an intervention or treatment on a particular case (i.e., a person, school, community) and to also provide evidence about the general effectiveness of an intervention using a relatively small sample size. The material presented in this document is intended to provide introductory information about SCD in relation to home visiting programs and is not a comprehensive review of the application of SCD to other types of interventions.
Single-Case Design, Analysis, and Quality Assessment for Intervention Research The purpose of this article is to describe single-case studies, and contrast them with case studies and randomized clinical trials Lobo, M. A., Moeyaert, M., Baraldi Cunha, A., & Babik, I. (2017). Single-case design, analysis, and quality assessment for intervention research. Journal of neurologic physical therapy : JNPT, 41(3), 187–197. https://doi.org/10.1097/NPT.0000000000000187
The difference between a case study and single case designs There is a big difference between case studies and single case designs, despite them superficially sounding similar. (This is from a Blog posting)
Single Case Design (Amanda N. Kelly, PhD, BCBA-D, LBA-aka Behaviorbabe) Despite the aka Behaviorbabe, Dr. Amanda N. Kelly, PhD, BCBA-D, LBA] provides a tutorial and explanation of single case design in simple terms.
Lobo (2018). Single-Case Design, Analysis, and Quality Assessment for Intervention Research Lobo, M. A., Moeyaert, M., Cunha, A. B., & Babik, I. (2017). Single-case design, analysis, and quality assessment for intervention research. Journal of neurologic physical therapy: JNPT, 41(3), 187.. https://doi.org/10.1097/NPT.0000000000000187
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Single-Subject Experimental Design for Evidence-Based Practice

Breanne j. byiers.

a University of Minnesota, Minneapolis

Joe Reichle

Frank j. symons.

Single-subject experimental designs (SSEDs) represent an important tool in the development and implementation of evidence-based practice in communication sciences and disorders. The purpose of this article is to review the strategies and tactics of SSEDs and their application in speech-language pathology research.

The authors discuss the requirements of each design, followed by advantages and disadvantages. The logic and methods for evaluating effects in SSED are reviewed as well as contemporary issues regarding data analysis with SSED data sets. Examples of challenges in executing SSEDs are included. Specific exemplars of how SSEDs have been used in speech-language pathology research are provided throughout.

SSED studies provide a flexible alternative to traditional group designs in the development and identification of evidence-based practice in the field of communication sciences and disorders.

The use of single-subject experimental designs (SSEDs) has a rich history in communication sciences and disorders (CSD) research. A number of important studies dating back to the 1960s and 1970s investigated fluency treatments using SSED approaches (e.g., Hanson, 1978 ; Haroldson, Martin, & Starr, 1968 ; Martin & Siegel, 1966 ; Reed & Godden, 1977 ). Several reviews, tutorials, and textbooks describing and promoting the use of SSEDs in CSD were published subsequently in the 1980s and 1990s (e.g., Connell, & Thompson, 1986 ; Fukkink, 1996 ; Kearns, 1986 ; McReynolds & Kearns, 1983 ; McReynolds & Thompson, 1986 ; Robey, Schultz, Crawford, & Sinner, 1999 ). Despite their history of use within CSD, SSEDs are sometimes overlooked in contemporary discussions of evidence-based practice. This article provides a comprehensive overview of SSEDs specific to evidence-based practice issues in CSD that, in turn, could be used to inform disciplinary research as well as clinical practice.

In the current climate of evidence-based practice, the tools provided by SSEDs are relevant for researchers and practitioners alike. The American Speech-Language-Hearing Association ( ASHA; 2005 ) promotes the incorporation of evidence-based practice into clinical practice, defining evidence-based practice as “an approach in which current, high-quality research evidence is integrated with practitioner experience and client preferences and values into the process of making clinical decisions.” The focus on the individual client afforded by SSEDs makes them ideal for clinical applications. The potential strength of the internal validity of SSEDs allows researchers, clinicians, and educators to ask questions that might not be feasible or possible to answer with traditional group designs. Because of these strengths, both clinicians and researchers should be familiar with the application, interpretation, and relationship between SSEDs and evidence-based practice.

The goal of this tutorial is to familiarize readers with the logic of SSEDs and how they can be used to establish evidence-based practice. The basics of SSED methodology are described, followed by descriptions of several commonly implemented SSEDs, including their benefits and limitations, and a discussion of SSED analysis and evaluation issues. A set of standards for the assessment of evidence quality in SSEDs is then reviewed. Examples of how SSEDs have been used in CSD research are provided throughout. Finally, a number of current issues in SSEDs, including effect size calculations and the use of statistical techniques in the analysis of SSED data, are considered.

The Role of SSEDs in Evidence-Based Practice

Numerous criteria have been developed to identify best educational and clinical practices that are supported by research in psychology, education, speech-language science, and related rehabilitation disciplines. Some of the guidelines include SSEDs as one experimental design that can help identify the effectiveness of specific treatments (e.g., Chambless et al., 1998 ; Horner et al., 2005 ; Yorkston et al., 2001 ). Many research communities, however, hold the position that randomized control trials (RCTs) represent the “gold standard” for research methodology aimed at validating best intervention practices; therefore, RCTs de facto become the only valid research methodology that is necessary for establishing evidence-based practice.

RCTs do have many specific advantages related to understanding causal relations by addressing methodological issues that may compromise the internal validity of research studies. Kazdin (2010) , however, compellingly argued that certain characteristics of SSEDs make them an important addition and alternative to large-group designs. He argued that RCTs may not be feasible with many types of interventions, as resources for such large-scale studies may not be available to test the thousands of treatments likely in use in any given field. In addition, the carefully controlled conditions in which RCTs must be conducted to ensure that the results are interpretable may not be comparable and/or possible to implement in real-life (i.e., uncontrolled) conditions. SSEDs are an ideal tool for establishing the viability of treatments in real-life settings before attempts are made to implement them at the large scale needed for RCTs (i.e., scaling up). Ideally, several studies using a variety of methodologies will be conducted to establish an intervention as evidence-based practice. When a treatment is established as evidence based using RCTs, it is often interpreted as meaning that the intervention is effective with most or all individuals who participated. Unfortunately, this may not be the case (i.e., there are responders and nonresponders). Thus, systematic evaluation of the effects of a treatment at an individual level may be needed, especially within the context of educational or clinical practice. SSEDs can be helpful in identifying the optimal treatment for a specific client and in describing individual-level effects.

Analysis of Effects in SSEDs

Desirable qualities of baseline data.

The analysis of experimental control in all SSEDs is based on visual comparison between two or more conditions. The conditions tested typically include a baseline condition, during which no intervention is in place, as well as one or more intervention conditions. The baseline phase establishes a benchmark against which the individual's behavior in subsequent conditions can be compared. The data from this phase must have certain qualities to provide an appropriate basis for comparison. The first quality of ideal baseline data is stability, meaning that they display limited variability. With stable data, the range within which future data points will fall is predictable. The second quality of ideal baseline data is a lack of a clear trend of improvement. The difficulty posed by trends in baseline data is dictated by the direction of behavior change expected during the intervention phase: If the behavior reflected in the dependent measure is expected to increase as a result of the intervention, a decreasing trend during baseline does not pose a significant problem. If, on the other hand, the trend for the dependent measure is increasing during baseline, determining whether or not a continued increase during the intervention phase constitutes a treatment effect is likely to be compromised. By convention, a minimum of three baseline data points are required to establish dependent measure stability ( Kazdin, 2010 ), with more being preferable. If stability is not established in the initial sessions, additional measurements should be obtained until stability is achieved. Alternatively, steps can be taken to introduce additional controls (strengthening internal validity) into the baseline sessions that may contribute to variability.

Visual Data Inspection as a Data Reduction Strategy: Changes in Level, Trend, and Variability

Once the data in all conditions have been obtained, they are examined for changes in one or more of three parameters: level, trend (slope), and variability. Level refers to the average rate of performance during a phase. Panel A of Figure 1 shows hypothetical data demonstrating a change in level. In this case, the average rate of performance during the baseline phase is lower than the average rate of performance during the intervention phase. Figure 1 also illustrates that the change in level occurred immediately following the change in phase. The change in level is evident, in part, because there is no overlap between the phases, meaning that the lowest data point from the intervention phase is still higher than the highest data point from the baseline phase.

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Hypothetical data demonstrating unambiguous changes in level (Panel A), trend (Panel B), and variability (Panel C).

On the other hand, there is overlap between the baseline and intervention phases in Panel B of Figure 1 , and the overall level of the dependent variable does not differ much between the phases. There is, however, a change in trend, as there is a consistent decreasing trend during the baseline phase, which is reversed in the intervention phase.

Finally, in Panel C, there is no evidence for changes in level or trend. There is, however, a change in variability. During the baseline phase, performance in the dependent measure is highly variable, with a minimum of 0% and a maximum of 100%. In contrast, during the intervention phase, performance is stable, with a range of only 6%. All three of these types of changes may be used as evidence for the effects of an independent variable in an appropriate experimental design.

When such changes are large and immediate, visual inspection is relatively straightforward, as in all three graphs in Figure 1 . In many real-life data sets, however, effects are more ambiguous. Take, for example, the graphs in Figure 2 . If only the average performance during each phase is considered, each of these graphs includes a between-phase change in level. On closer inspection, however, each presents a problem that threatens the internal validity of the experiment and the ability of the clinical researcher to make a warranted causal inference about the relation between treatment (the independent variable) and effect (the dependent variable).

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Hypothetical data demonstrating demonstrations of non-effect: delayed latency to change (Panel A), trend in desired direction during baseline phase (Panel B), highly variable data with overlap between baseline and intervention phases (Panel C).

In Panel A of Figure 2 , no change is observed until the third session of the intervention phase. This latency brings into question the assumption that the manipulation of the independent variable is responsible for the observed changes in the dependent variable. It is possible that the observed change may be more appropriately attributed to some factor outside the control of the experimenter. To rule out the plausibility of an extraneous variable, the experimental effect must be replicated, thereby showing that although there may be a delay, changes in the dependent variable reliably occur following changes to the independent variable. This type of replication (within study) is a primary characteristic of SSEDs and is the primary basis for internally valid inferences.

By contrast, Panel B of Figure 2 shows a data set in which an increasing trend is present during the baseline phase. As a result, any increases observed during the intervention phase may simply be a continuation of that trend rather than the result of the manipulation of the independent variable. This underscores the importance of “good” baseline data, and, in particular, of the need to continue collecting baseline data to eliminate the possibility that any trends observed are likely to continue in the absence of an intervention.

Panel C also underscores the importance of “good” baseline data. Although no consistent trend is present in the baseline phase, the data are highly variable. As a result, there is an overlap between many of the sessions in the baseline and intervention phases, even though the average level of performance is higher in the intervention phase ( M = 37%) than in the baseline phase ( M = 57%). Because the determination of experimental effects in SSEDs is based on visual inspection of the results rather than statistical analyses, such an overlap obscures any potential effects. As a result, when baseline data such as these are collected, the researcher should attempt to eliminate possible sources of variability to help establish a clear pattern of responding.

Threats to the internal validity of SSEDs, such as those demonstrated in Figure 2 , are described as “demonstrations of noneffect” in the language of a panel assembled by the What Works Clearinghouse (WWCH), an initiative of the Institute for Education Sciences (IES) that was appointed to develop a set of criteria for determining whether the results of SSEDs provide evidence of sufficient quality to identify an intervention as evidence based ( Kratochwill et al., 2010 ). A description of the criteria developed by the panel as well as their application to evidence-based practice in CSD follows.

Criteria for Evidence Quality in SSEDs

A number of groups from different fields have developed criteria to assess the quality of evidence used to support the effectiveness of interventions and to facilitate the incorporation of research findings into practice. Among the most recent of these criteria focusing specifically on SSEDs are those developed by the WWCH panel. Considering the WWCH criteria, determining whether an intervention qualifies as evidence based involves a three-step sequence. The first step involves assessing the adequacy of the experimental design (see Table 1 ) to determine whether it meets the standards, with or without reservations. If the design is not found to be adequate, no further steps are needed. If the design meets the standards, the second step is to conduct a visual analysis of the results to determine whether the data suggest an experimental effect. If the visual analysis supports the presence of an effect, the data should be examined for demonstrations of noneffect, such as those depicted in Figure 2 . If no evidence of an experimental effect is found, the process is terminated. If the visual analysis suggests that the results support the effectiveness of the intervention, the reviewer can move on to the third step: assessing the overall state of the evidence in favor of an intervention by examining the number of times its effectiveness has been demonstrated, both within and across participants. The importance of replication in SSEDs is discussed in more detail in the next section. If the design meets the standards and the visual analysis indicates that there is an effect, with no demonstrations of noneffect, the study would be considered one that provides strong evidence. If it meets the standards and there is evidence of an effect, but the results include at least one demonstration of noneffect, then the study would be considered one that provides moderate evidence. The results of all studies that reported the effects of a particular intervention can then be examined for overall level of evidence in favor of the treatment.

Summary of What Works Clearinghouse criteria for experimental designs.

Replication for Internal and External Validity

Replication is one of the hallmarks of SSEDs. Experimental control is demonstrated when the effects of the intervention are repeatedly and reliably demonstrated within a single participant or across a small number of participants. The way in which the effects are replicated depends on the specific experimental design implemented. For many designs, each time the intervention is implemented (or withdrawn following an initial intervention phase), an opportunity to provide an instance of effect replication is created. This within-study replication is the basis of internal validity for SSEDs.

By replicating an investigation across different participants, or different types of participants, researchers and clinicians can examine the generality of the treatment effects and thus potentially enhance external validity. Kazdin (2010) distinguished between two types of replication. Direct replication refers to the application of an intervention to new participants under exactly, or nearly exactly, the same conditions as those included in the original study. This type of replication allows the researcher or clinician to determine whether the findings of the initial study were specific to the participant(s) who were involved. Systematic replication involves the repetition of the investigation while systematically varying one or more aspects of the original study. This might include applying the intervention to participants with more heterogeneous characteristics, conducting the intervention in a different setting with different dependent variables, and so forth. The variation inherent to systematic replication allows the researcher, educator, or clinician to determine the extent to which the findings will generalize across different types of participants, settings, or target behaviors. As noted by Johnston and Pennypacker (2009) , conducting direct replications of an effect tells us about the certainty of our knowledge, whereas conducting systematic replications can expand the extent of our knowledge.

An intervention or treatment cannot be considered evidence based following the results of a single study. The WWCH panel recommended that an intervention have a minimum of five supporting SSED studies meeting the evidence standards if the studies are to be combined into a single summary rating of the intervention's effectiveness. Further, these studies must have been conducted by at least three different research teams at three different geographical locations and must have included a combined number of at least 20 participants or cases (see O'Neill, McDonnell, Billingsley, & Jenson, 2011 , for a summary of different evidence-based practice guidelines on replication). The panel also suggested the use of some type of effect size to quantify intervention effects within each study, thereby facilitating the computation of a single summary rating of the evidence in favor of the invention (a discussion of the advantages and disadvantages of SSEDs and effects sizes follows later). In the next section, the specific types of SSEDs are described and reviewed.

Types of SSEDs

Six primary design types are discussed: the pre-experimental (or AB) design, the withdrawal (or ABA/ABAB) design, the multiple-baseline/multiple-probe design, the changing-criterion design, the multiple-treatment design, and the alternating treatments and adapted alternating treatments designs (see Table 2 ).

Summary of single-subject experimental designs (SSEDs).

Pre-Experimental (AB) Design

Although the AB design is often described as a SSED, it is more accurately considered a pre-experimental design because it does not sufficiently control for many threats to internal validity and, therefore, does not demonstrate experimental control. As a result, the AB design is best thought of as one that demonstrates correlation between the independent and dependent variables but not necessarily causation. Nevertheless, the AB design is an important building block for true experimental designs. It is made up of two phases: the A (baseline) phase and the B (intervention) phase. Several baseline sessions establish the pre-intervention level of performance. As previously noted, the purpose of the baseline phase is to establish the existing levels/patterns of the behavior(s) of interest, thus allowing for future performance predictions under the continued absence of intervention. Due to the lack of replication of the experimental effect in an AB design, however, it is impossible to say with certainty whether any observed changes in the dependent variable are a reliable, replicable result of the manipulation of the independent variable. As a result, it is possible that any number of external factors may be responsible for the observed changes. Nevertheless, these designs can provide preliminary objective data regarding the effects of an intervention when time and resources are limited (see Kazdin, 2010 ).

Withdrawal (ABA and ABAB) Designs

The withdrawal design is one option for answering research questions regarding the effects of a single intervention or independent variable. Like the AB design, the ABA design begins with a baseline phase (A), followed by an intervention phase (B). However, the ABA design provides an additional opportunity to demonstrate the effects of the manipulation of the independent variable by withdrawing the intervention during a second “A” phase. A further extension of this design is the ABAB design, in which the intervention is re-implemented in a second “B” phase. ABAB designs have the benefit of an additional demonstration of experimental control with the reimplementation of the intervention. Additionally, many clinicians/educators prefer the ABAB design because the investigation ends with a treatment phase rather than the absence of an intervention.

It is worth noting that although they are often used interchangeably in the literature, the terms withdrawal design and reversal design refer to two related but distinctly different research designs. In the withdrawal design, the third phase represents a change back to pre-intervention conditions or the withdrawal of the intervention. In contrast, the reversal design requires the active reversal of the intervention conditions. For example, reinforcement is provided contingent on the occurrence of a response incompatible with the response reinforced during the intervention (B) phases (see Barlow, Nock, & Hersen, 2009 , for a complete discussion of the mechanics and relative advantages of reversal designs).

A recent example of the withdrawal design was executed by Tincani, Crozier, and Alazetta (2006) . They implemented an ABAB design to demonstrate the effects of positive reinforcement for vocalizations within a Picture Exchange Communication System (PECS) intervention with school-age children with autism (see Figure 3 ). A visual analysis of the results reveals large, immediate changes in percentage of vocal approximations emitted by the student each time the independent variable is manipulated, and there are no overlapping data between the baseline and intervention phases. Finally, there are no demonstrations of a noneffect. As a result, this case would be considered strong evidence supporting the effectiveness of the intervention based on the WWCH evidence-based practice criteria. The study meets the standards (with reservations) because (a) the researchers actively manipulated the independent variable (presence/absence of vocal reinforcement), (b) data on the dependent variable were collected systematically over time, (c) a minimum of four data points were collected in each phase (at least five are needed to meet the standards without reservations), and (d) the effect was replicated three times (the intervention was implemented, withdrawn, and implemented again).

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Percentage of trials containing vocal approximations during no positive reinforcement of vocalization (baseline; see Panel A) and positive reinforcement of vocalization (see Panel B), using an ABAB design. Voc. = vocal; PR = positive reinforcement. From “The Picture Exchange Communication System: Effects on manding and speech development for school-aged children with autism,” by Tincani, Crozier, and Alazetta, 2006 , Education and Training in Developmental Disabilities, 41, p. 183. Copyright 2006 by Council for Exceptional Children, Division on Developmental Disabilities. Reprinted with permission.

Advantages and disadvantages of withdrawal designs

Withdrawal designs (e.g., ABA and ABAB) provide a high degree of experimental control while being relatively straightforward to plan and implement. However, a major assumption of ABAB designs is that the dependent variable being targeted is reversible (e.g., will return to pre-intervention levels when the intervention is withdrawn). If the individual continues to perform the behavior at the same level even though the intervention is withdrawn, a functional relationship between the independent and dependent variables cannot be demonstrated. When this happens, the study becomes susceptible to the same threats to internal validity that are inherent in the AB design.

Although many behaviors would be expected to return to pre-intervention levels when the conditions change, others would not. For example, if one's objective were to teach or establish a new behavior that an individual could not previously perform, returning to baseline conditions would not likely cause the individual to “unlearn” the behavior. Similarly, studies aiming to improve proficiency in a skill through practice may not experience returns to baseline levels when the intervention is withdrawn. In other cases, the behavior of the parents, teachers, or staff implementing the intervention may not revert to baseline levels with adequate fidelity. In other cases still, the behavior may come to be maintained by other contingencies not under the control of the experimenter.

Another potential disadvantage of these designs is the ethical issue associated with withdrawing an apparently effective intervention. Additionally, stakeholders may be unwilling (or unable) to return to baseline conditions, especially given the expectation that the behavior will return to baseline levels (or worse) when the intervention is withdrawn.

Overall, ABAB designs are one of the most straightforward and strongest SSED “treatment effect demonstration” strategies. Ethical considerations regarding the withdrawal of the intervention and the reversibility of the behavior need to be taken into account before the study begins. Further extensions of the ABAB design logic to comparisons between two or more interventions are discussed later in this article.

Multiple-Baseline and Multiple-Probe Designs

Multiple-baseline and multiple-probe designs are appropriate for answering research questions regarding the effects of a single intervention or independent variable across three or more individuals, behaviors, stimuli, or settings. On the surface, multiple-baseline designs appear to be a series of AB designs stacked on top of one another. However, by introducing the intervention phases in a staggered fashion, the effects can be replicated in a way that demonstrates experimental control. In a multiple-baseline study, the researcher selects multiple (typically three to four) conditions in which the intervention can be implemented. These conditions may be different behaviors, people, stimuli, or settings. Each condition is plotted in its own panel, or leg , that resembles an AB graph. Baseline data collection begins simultaneously across all the legs. The intervention is introduced systematically in one condition while baseline data collection continues in the others. Once responding is stable in the intervention phase in the first leg, the intervention is introduced in the next leg, and this continues until the AB sequence is complete in all the legs.

Figure 4 shows the results from a study using a multiple-baseline, across-participants design examining the collateral language effects of a question-asking training procedure for children with autism ( Koegel, Koegel, Green-Hopkins, & Barnes, 2010 ). The design meets the WWCH standards. The independent variable (the question-asking procedure) was actively manipulated, and the dependent variable (percentage of unprompted questions asked by each child) was measured systematically across time, with appropriate levels of interobserver agreement reported. Except for the generalization phase, at least five data points were collected in each phase. Because the generalization phase is not integral to the demonstration of the experimental control, this does not affect the sufficiency of the design: The effects were replicated across three activities.

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Percentage of unprompted questions asked by three participants in baseline, intervention, and generalization sessions using a multiple-baseline, across-participants design. From “Question-asking and collateral language acquisition in children with autism,” by Koegel, Koegel, Green-Hopkins, and Barnes (2010) , Journal of Autism and Developmental Disorders, 40, p. 512. Copyright 2009 by the authors. Reprinted with permission.

Visual analysis of the results supports the effectiveness of the intervention in that there was an immediate change in unprompted question-asking with the implementation of the intervention for all three children, with no overlap between the baseline and intervention phases. No indications of noneffect are present in the data. As a result, this study provides strong evidence that the question-asking intervention results in increases in collateral question-asking.

The data from the final phase of the study depicted in Figure 4 are worth noting because they show the continued performance of the dependent variable in the absence of the treatment. In some ways, this is akin to a return to baseline conditions, as in the second “A” condition of a withdrawal design. In this case, however, the behavior does not return to pre-intervention levels, suggesting that the behavior is nonreversible and that using a reversal design to demonstrate the effects of the intervention would have been inappropriate. For this study, the maintenance of the behavior after the intervention was withdrawn supports its long-term effectiveness without undermining the experimental control.

In some cases, the simultaneous and continuous data collection in all legs of multiple-baseline designs is not feasible or necessary. Multiple-probe designs are a common variation on multiple baselines in which continuous baseline assessment is replaced by intermittent probes to document performance in each of the conditions during baseline. Probes reduce the burden of data collection because they remove the need for continuous collection in all phases simultaneously (see Horner & Baer, 1978 , for a full description of multiple-probe designs). Pre-intervention probes in Condition 1 are obtained continuously until a stable pattern of performance is established. Meanwhile, single data collection sessions would be conducted in each of the other conditions to assess pre-intervention levels. Once responding has reached the criterion threshold in the intervention phase of the first leg, continuous measurement of pre-intervention levels is introduced in the second. When stable responding during the intervention phase is observed, intermittent probes can be implemented to demonstrate continued performance, and intervention is introduced in the second leg. This pattern is repeated until the effects of the intervention have been demonstrated across all the conditions.

Multiple-probe designs may not be appropriate for behaviors with significant variability because the intermittent probes may not provide sufficient data to demonstrate a functional relationship. If a stable pattern of responding is not clear during the baseline phase with probes, the continuous assessment of a multiple-baseline format may be necessary.

When selecting conditions for a multiple-baseline (or multiple-probe) design, it is important to consider both the independence and equivalence of the conditions. Independence means that changing behavior in one condition will not affect performance in the others. If the conditions are not independent, implementing the intervention in one condition may lead to changes in behavior in another condition while it remains in the baseline phase ( McReynolds & Kearns, 1983 ). This makes it challenging (if not impossible) to demonstrate convincingly that the intervention is responsible for changes in the behavior across all the conditions. When implementing the intervention across individuals, it may be necessary—to avoid diffusion of the treatment—to ensure that the participants do not interact with one another. When the intervention is implemented across behaviors, the behaviors must be carefully selected to ensure that any learning that takes place in one will not transfer to the next. Similarly, contexts or stimuli must be sufficiently dissimilar so as to minimize the likelihood of effect generalization.

Although an assumption of independence suggests that researchers should select conditions that are clearly dissimilar from one another, the conditions must be similar enough that the effects of the independent variable can be replicated across each of them. If the multiple baselines are conducted across participants, this means that all the participants must be comparable in their behaviors and other characteristics. If the multiple baselines are being conducted across behaviors, those behaviors must be similar in function, topography, and the effort required to produce them while remaining independent of one another.

Advantages and disadvantages of multiple-baseline/multiple-probe designs

Because replication of the experimental effect is across conditions in multiple-baseline/multiple-probe designs, they do not require the withdrawal of the intervention. This can make them more practical with behaviors for which a return to baseline levels cannot occur. Depending on the speed of the changes in the previous conditions, however, one or more conditions may remain in the baseline phase for a relatively long time. Thus, when multiple baselines are conducted across participants, one or more individuals may wait some time before receiving a potentially beneficial intervention.

The need for multiple conditions can make multiple-baseline/multiple-probe designs inappropriate when the intervention can be applied to only one individual, behavior, and setting. Also, potential generalization effects such as these must be considered and carefully controlled to minimize threats to internal validity when these designs are used. Nevertheless, multiple-baseline designs often are appealing to researchers and interventionists because they do not require the behavior to be reversible and do not require the withdrawal of an effective intervention.

Changing-Criterion Designs

Similar to withdrawal and multiple-baseline/multiple-probe designs, changing-criterion designs are appropriate for answering questions regarding the effects of a single intervention or independent variable on one or more dependent variables. In the previous designs, however, the assumption is that manipulating the independent variable will result in large, immediate changes to the dependent variable(s). In contrast, a major assumption of the changing-criterion is that the dependent variable can be increased or decreased incrementally with stepwise changes to the dependent variable. Typically, this is achieved by arranging a consequence (e.g., reinforcement) contingent on the participant meeting the predefined criterion. The changing-criterion design can be considered a special variation of multiple-baseline designs in that each phase serves as a baseline for the subsequent one ( Hartmann & Hall, 1976 ). However, rather than having multiple baselines across participants, settings, or behaviors, the changing-criterion design uses multiple levels of the independent variable. Experimental control is demonstrated when the behavior changes repeatedly to meet the new criterion (i.e., level of the independent variable).

Figure 5 shows the results of a study by Facon, Sahiri, and Riviere (2008) . In this study, a token reinforcement procedure was used to increase the speech volume of a child with selective mutism and mental retardation. During the baseline phase, the child's speech was barely audible, averaging 43 dB. For each new phase in the treatment condition, a criterion level for speech volume was set, which dictated what level of performance the child had to demonstrate to earn the reinforcement tokens. The horizontal lines on the graph represent the criterion set for each phase. To ensure the student's success during the intervention, the initial criterion was set at 43 dB. Researchers established a priori decision rules for changes to the criterion: The criterion would be increased when 80% of the child's utterances during three consecutive sessions were equal to or above the current criterion. Each new criterion value was equal to the mean loudness of the five best verbal responses during the last session of the previous phase.

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Speech volume during a token reinforcement intervention and follow-up using a changing-criterion design. From “A controlled single-case treatment of severe long-term selective mutism in a child with mental retardation,” by Facon, Sahiri, and Riviere, (2008) , Behavior Therapy, 39, p. 313. Copyright 2008 by Elsevier. Reprinted with permission.

The design of this study meets the WWCH standards, but with reservations. The independent variable (in this case, the token reinforcement system with the increasing dB criterion) was actively manipulated by the researchers, and the dependent variable was measured systematically over time. Each phase included a minimum of three data points (but not the five points required to meet the standards fully), and the number of phases with different criteria far exceeded the minimum three required.

Upon visual inspection, the results support the effectiveness of the intervention. There were few overlapping data points between the different criterion phases, and changes to the criterion usually resulted in immediate increases in the target behavior. These results would have been further strengthened by the inclusion of bidirectional changes, or mini-reversals, to the criterion ( Kazdin, 2010 ). Such temporary changes in the level of the dependent measure(s) in the direction opposite from that of the treatment effect enhance experimental control because they demonstrate that the dependent variable covaries with the independent variable. As such, bidirectional changes are much less likely to be the result of extraneous factors. Nevertheless, the results did not show any evidence of noneffect, and the results would be considered strong evidence in favor of the intervention.

Advantages and disadvantages of changing-criterion designs

Changing-criterion designs are ideal for behaviors for which it is unrealistic to expect large, immediate changes to coincide with manipulation of the independent variable. They do not require the withdrawal of treatment and, therefore, do not present any ethical concerns associated with removing potentially beneficial treatments. Unlike multiple-baseline/multiple-probe designs, changing-criterion studies require only one participant, behavior, and setting. Not all interventions, however, can be studied using a changing-criterion design; only interventions in which consequences for meeting or not meeting the established criterion levels of the behavior can be used. In addition, because the participant must be able to meet a certain criterion to contact the contingency, the participant must have some level of the target behavior in his or her repertoire before the study begins. Changing-criterion designs are not appropriate for behaviors that are severe or life threatening because they do not result in immediate, substantial changes. For teaching many complex tasks, however, shaping a behavior through a series of graduated steps is an appropriate strategy, and the changing-criterion design is a good option for a demonstrating the intervention's effectiveness.

Multiple-Treatment Designs

Thus far, the designs that we have described are only appropriate to answer questions regarding the effects of a single intervention or variable. In many cases, however, investigators—whether they are researchers, educators, or clinicians—are interested in not only whether an intervention works but also whether it works better than an alternative intervention. One strategy for comparing the effects of two interventions is to simply extend the logic of withdrawal designs to include more phases and more conditions. The most straightforward design of this type is the ABACAC design, which begins with an ABA design and is followed by a CAC design. The second “A” phase acts as both the withdrawal condition for the ABA portion of the experiment and the baseline phase for the ACAC portion. This design is ideal in situations where an ABA or ABAB study was planned but the effects of the intervention were not as sizable as had been hoped. Under these conditions, the intervention can be modified, or another intervention selected, and the effects of the new intervention can be demonstrated. The design has the same advantages and disadvantages of basic withdrawal designs but allows for a comparison of effects for two different treatments. A major drawback, however, is that the logic of SSEDs allows only for the comparison of adjacent conditions. This restriction helps to minimize threats to internal validity, such as maturation, that can lead to gradual changes in behavior over time, independent of study conditions. As a result, it is not appropriate to comment on the relative effects of the interventions (i.e., the “B” and “C” phases) in an ABACAC study because they never occur next to one another. Rather, one can only conclude that one, both, or neither intervention is effective relative to baseline. On the other hand, beginning with a full reversal or withdrawal design (ABAB), with it followed by a demonstration of the effects of the second intervention (CAC, resulting in ABABCAC), allows for the direct comparison of the two interventions. The BC comparison, however, is never repeated in this sequence, limiting the internal validity of the comparison.

Besides comparing the relative effects of two or more distinct interventions, multiple-treatment-phase designs can be used to assess the additive effects of treatment components. For example, if a treatment package consists of two separate components (components “B” and “C”), one can determine whether the intervention effects are due to one component alone or whether both are needed. Ward-Horner and Sturmey (2010) identified two methods for conducting component analyses: dropout , in which components were systematically removed from the treatment package to determine whether the treatment retained its effectiveness, and add-in , in which components were assessed individually before the implementation of the full treatment package. Each of these methods has its own advantages and disadvantages (see Ward-Horner & Sturmey, 2010 , for a full discussion), but taken together, component analyses can provide a great deal of information about the necessity and sufficiency of treatment components. In addition, they can inform strategies for fading out treatments while maintaining their effects.

Wacker and colleagues (1990) conducted dropout-type component analyses of functional communication training (FCT) procedures for three individuals with challenging behavior. The data presented in Figure 6 show the percentage of intervals with hand biting, prompts, and mands (signing) across functional analysis, treatment package, and component analysis phases. The functional analysis results indicated that the target behavior (hand biting) was maintained by access to tangibles as well as by escape from demands. In the second phase, a treatment package that included FCT and time-out was implemented. By the end of the phase, the target behavior was eliminated, prompting had decreased, and signing had increased. To identify the active components of the treatment package, a dropout component analysis was conducted. First, the time-out component of the intervention was removed, leaving the FCT component alone. A decreasing trend in signing and an increasing trend in hand biting were observed. This was reversed when the full treatment packaged was reimplemented. In the third phase of the component analysis, the FCTcomponent was removed, leaving time-out and differential reinforcement of other behavior (DRO). Again, a decreasing trend in signing and an increasing trend in hand biting were observed, which were again reversed when the full treatment package was applied.

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Percent of intervals with challenging behavior and mands during functional analysis, intervention demonstration, and component analysis. From “A component analysis of functional communication training across three topographies of severe behavior problems,” by Wacker et al., 1990 , Journal of Applied Behavior Analysis, 23, p. 424. Copyright 2008 by the Society for the Experimental Analysis of Behavior. Reprinted with permission.

Overall, visual inspection of these data provides a strong argument for the necessity of both the FCT and time-out components in the effectiveness of the treatment package, and no indications of noneffect are present in the data. The design, however, does not meet the standards set forth by the WWCH panel. This is because (a) the final two final treatment phases do not include the minimum of three data points and (b) the individual treatment component phases (FCT only and time-out/DRO) were implemented only once each. As a result, the data from this study could not be used to support the treatment package as an evidence-based practice by the IES standards. Additional data points within each phase, as well as replications of the phases, would strengthen the study results.

One disadvantage of all designs that involve two or more interventions or independent variables is the potential for multiple-treatment interference. This occurs when the same participant receives two or more treatments whose effects may not be independent. As a result, it is possible that the order in which the interventions are given will affect the results. For example, the effects of two interventions may be additive, so that the effects of Intervention 2 are enhanced beyond what they should be because Intervention 2 followed Intervention 1. In essence, this creates the potential for an order effect (or a carryover effect). Alternatively, Intervention 1 may have measurable but delayed effects on the dependent variable, making it appear that Intervention 2 is effective when the results should be attributed to Intervention 1. Such possibilities should be considered when multi-treatment studies are being planned (see Hains & Baer, 1989 , for a comprehensive discussion of multiple-treatment interference). A final, longer phase in which the final “winning” treatment is implemented for an extended time can help alleviate some of the concerns regarding multiple-treatment interference.

Advantages and disadvantages of multiple-treatment designs

Designs such as ABCABC and ABCBCA can be very useful when a researcher wants to examine the effects of two interventions. These designs provide strong internal validity evidence regarding the effectiveness of the interventions. External validity, however, may be compromised by the threat of multiple-treatment interference. Additionally, the same advantages and disadvantages of ABAB designs apply, including issues related to the reversibility of the target behavior. Despite their limitations, these designs can provide strong empirical data upon which to base decisions regarding the selection of treatments for an individual client. Although, in theory, these types of designs can be extended to compare any number of interventions or conditions, doing so beyond two becomes excessively cumbersome; therefore, the alternating treatments design should be considered.

Alternating Treatments and Adapted Alternating Treatments Designs

Alternating treatments design (atd).

The logic of the ATD is similar to that of multiple-treatment designs, and the types of research questions that it can address are also comparable. The major distinction is that the ATD involves the rapid alternation of two or more interventions or conditions ( Barlow & Hayes, 1979 ). Data collection typically begins with a baseline (A) phase, similar to that of a multiple-treatment study, but during the next phase, each session is randomly assigned to one of two or more intervention conditions. Because there are no longer distinct phases of each intervention, the interpretation of the results of ATD studies differs from that of the studies reviewed so far. Rather than comparing between phases, all the data points within a condition (e.g., all sessions of Intervention 1) are connected (even if they do not occur adjacently). Demonstration of experimental control is achieved by having differentiation between conditions, meaning that the data paths of the conditions do not overlap.

In ATDs, it is important that all potential “nuisance” variables be controlled or counterbalanced. For example, having different experimenters conduct sessions in different conditions, or running different session conditions at different times of day, may influence the results beyond the effect of the independent variables specified. Therefore, all experimental procedures must be analyzed to ensure that all conditions are identical except for the variable(s) of interest. Presenting conditions in random order can help eliminate issues regarding temporal cycles of behavior as well as ensure that there are equal numbers of sessions for each condition.

Lang and colleagues (2011) used an ATD to examine the effects of language of instruction on correct responding and inappropriate behavior (tongue clicks) with a student with autism from a Spanish-speaking family. To ensure that the conditions were equivalent, all aspects of the teaching sessions except for the independent variable (language of instruction) were held constant. Specifically, the same teacher, materials, task demands, reinforcers, and reinforcer schedules were used in both the English and Spanish sessions.

The results of this study (see Figure 7 ) demonstrated that the student produced a higher number of correct responses and engaged in fewer challenging behaviors when instruction was delivered in Spanish than in English. The superiority of the Spanish instruction was evident in this case because there was no overlap in correct responding or inappropriate behaviors between the English and Spanish conditions.

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Number of correct responses and tongue clicks during discrete trial training sessions in Spanish (Sp.) and English (Eng.) using an alternating treatments design. From “Effects of language instruction on response accuracy and challenging behavior in a child with autism,” by Lang et al., 2011 , Journal of Behavioral Education, 20, p. 256. Copyright 2001 by Springer Science+Business Media, LLC. Reprinted with permission.

Although visual analysis supported the inference that treatment effects were functionally related to the independent variable, the results of this study did not meet the design standards set out by the WWCH panel because the design consisted of only two treatments in comparison with each other. To meet the criterion of having at least three attempts to demonstrate an effect, studies using an ATD must include a direct comparison of three interventions, or two interventions compared with a baseline. To be considered as support for an evidence-based practice, this design would need to have incorporated a third intervention condition or to have begun with a baseline condition.

Adapted alternating treatments design (AATD)

One commonly used alternative to the ATD is called the adapted alternating treatments design (AATD; Sindelar, Rosenburg, & Wilson, 1985 ). Whereas the traditional ATD assesses the effects of different interventions or independent variables on a single outcome variable, in the AATD, a different set of responses is assigned to each intervention or independent variable. The resulting design is similar to a multiple-baseline, across-behaviors design with concurrent training for all behaviors. For example, Conaghan, Singh, Moe, Landrum, and Ellis (1992) assigned a different set of 10 phrases to each of three conditions (directed rehearsal, directed rehearsal plus positive reinforcement, and control). This strategy allowed the researchers to determine whether the acquisition of new signed phrases differed across the three conditions. Figure 8 shows one participant's correct responses during sessions across baseline phases, alternating treatments phases, and extended treatment phases.

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Number of phrases signed correctly during directed rehearsal, directed rehearsal with positive reinforcement, and control sessions using an adapted alternating treatments design. From “Acquisition and generalization of manual signs by hearing-impaired adults with mental retardation,” by Conaghan, Singh, Moe, Landrum, and Ellis, 1992 , Journal of Behavioral Education, 2, p. 192. Copyright 1992 by Human Sciences Press. Reprinted with permission.

Unlike the Lang et al. (2011) study, the design used in this study met the WWCH standards. This was because, in addition to meeting the minimum number of sessions per phase, it included a direct comparison between three conditions as well as a direct comparison with a baseline phase. The data from the baseline phase established that the participant did not respond correctly in the absence of the intervention. The data from the alternating treatments phase supported the effectiveness of the directed rehearsal and directed rehearsal plus positive reinforcement conditions compared with the control condition. They also supported the relative effectiveness of the directed rehearsal with reinforcement compared with directed rehearsal alone.

During the initial four sessions of the alternating treatments phase, responding remained at zero for all three word sets. Steadily increasing trends were observed in both of the directed rehearsal conditions beginning in the fifth session, whereas responding remained at zero in the control condition. The rate of acquisition in the directed rehearsal plus positive reinforcement condition was higher than in directed rehearsal alone throughout the alternating treatments phase. The latency in correct responding observed during the initial sessions of the alternating treatments was a demonstration of noneffect. The fact that no change in responding was observed in the control condition, however, is evidence that the changes were due to the intervention rather than a result of some factor outside of the study. As further demonstration of the experimental effect of directed rehearsal plus reinforcement, a final condition was implemented in which the treatment package was used to teach the phrases from the other two conditions. This condition further strengthened the evidence for the effectiveness of the intervention, as performance on all three words sets reached 100% by the end of the phase. In sum, the latency to change observed during the alternating treatments phase meant that this study merits a rating of moderate evidence in favor of the intervention.

Advantages and disadvantages of ATDs and AATDs

ATDs and AATDs can be useful in comparing the effects of two or more interventions or independent variables. Unlike multiple-treatment designs, these designs can allow multiple comparisons in relatively few sessions. The issues related to multiple-treatment interference are also relevant with the ATD because the dependent variable is exposed to each of the independent variables, thus making it impossible to disentangle their independent effects. To ensure that the selected treatment remains effective when implemented alone, a final phase demonstrating the effects of the best treatment is recommended ( Holcombe & Wolery, 1994 ), as was done in the study by Conaghan et al., 1992 . Many researchers pair an independent but salient stimulus with each treatment (i.e., room, color of clothing, etc.) to ensure that the participants are able to discriminate which intervention is in effect during each session ( McGonigle, Rojahn, Dixon, & Strain, 1987 ). Nevertheless, outcome behaviors must be readily reversible if differentiation between conditions is to be demonstrated.

The AATD eliminates some of the concerns regarding multiple-treatment interference because different behaviors are exposed to different conditions. As in the multiple-baseline/multiple-probe designs, the possibility of generalization across behaviors must be considered, and steps should be taken to ensure the independence of the behaviors selected. In addition, care must be taken to ensure equal difficulty of the responses assigned to different conditions.

Having reviewed the logic underlying SSED, the basic approach to analysis (visual inspection relying on observed changes in level, trend, and variability), and the core strategies for arranging conditions (i.e., design types), in the following section we briefly discuss a number of quantitative evaluation issues concerning SSED. The issues are germane because of the WWCH and related efforts to establish standard approaches for evaluating SSED data sets as well as the problem of whether and how to derive standardized effect sizes from SSED data sets for inclusion in quantitative syntheses (i.e., meta-analysis).

Evaluating Results in SSED Research

Statistical analysis and ssed.

The issue of when, if ever, the data generated from SSEDs should be statistically analyzed has a long and, at times, contentious history ( Iwata, Neef, Wacker, Mace, & Vollmer, 2000 ). We approach this issue by breaking it into four related but distinct parts that include detecting effects, determining their magnitude and the quality of the causal inference, and data-based decision making. Subsequently, relevant considerations for research and practice are delineated. Space considerations preclude treating any one aspect of this issue exhaustively (suggestions for further reading are provided).

Effect detection

Conventional approaches to single-subject data analysis rely on visual inspection (as reviewed earlier in this article). From the perspective of clinical significance, supporting a “visual inspection–only” approach is warranted because the practitioner (and, ultimately, the field of practice) is interested in identifying only those variables that lead to large, unambiguous changes in behavior. One argument against the exclusive reliance on visual inspection is that it is prone to Type 1 errors (inferring an effect when there is none), particularly if the effects are small to medium ( Franklin, Gorman, Beasley, & Allison, 1996 ; Todman & Dugard, 2001 ). Evidence for experimental control is not always as compelling from a visual analysis perspective. This was showcased in the Tincani et al. (2006) study discussed previously. In many cases, the clinical significance of behavior change between conditions is less clear and, therefore, is open to interpretation.

From the perspective of scientific significance, one can argue that statistical analysis may be warranted as a judgment aid for determining whether there were any effects, regardless of size, because knowing this would help determine whether to continue investigating the variable (i.e., intervention). If it is decided that, under some circumstances, it is scientifically sensible to use statistical analyses (e.g., t tests, analyses of variance [ANOVAs], etc.) as judgment aids for effect detection within single case data sets, the next question is a very practical one—can we? In other words, can parametric inferential statistical techniques be applied safely? In this context, the term safely refers to whether the outcome variables are sufficiently robust that they withstand violating the assumptions underlying the statistical test. The short answer seems to be “no,” with the qualifier “under almost all circumstances.” The key limitation and common criticism of generating statistics based on single-subject data is auto-correlation (any given data point is dependent or interacts with the data point preceding it). Because each data point is generated by the same person, the data points are not independent of one another (violating a core assumption of statistical analysis—technically, that the error terms are not independent of one another). Thus, performance represented in each data point may likely be influencing the next ( Todman & Dugard, 2001 ). Autocorrelated data will, in turn, artificially inflate p values and affect Type 1 error rates.

One argument for statistically analyzing single-subject data sets, mentioned above, is that visual inspection is prone to Type 1 error in the presence of medium to small effects ( Franklin et al., 1996 ). Unfortunately, the proposed solution of implementing conventional inferential statistical tests with single-subject data based on repeated measurement of the same subject is equally prone to Type 1 error because of autocorrelation. Traditional nonparametric approaches have been advocated, but they do not necessarily avoid the autocorrelation problem and, depending on the size of the data array, there are power issues. Alternatively, if single-subject data are regarded as time-series data, there have been some novel applications of bootstrapping methodologies relying on using the data set itself along with resampling approaches to determine exact probabilities rather than probability estimates ( Wilcox, 2001 ). For example, Borckardt et al. (2008) described a “simulation modeling analysis” for time-series data, which allows a statistical comparison between phases of a single-subject experiment while controlling for serial dependency in the data (i.e., quantifying the autocorrelation and modeling it in the analysis). In the end, effect detection is determined by data patterns in relation to the phases of the experimental design. It seems that the clearer one is about the logic of the design and the criteria that will be used to determine an effect in advance, the less one needs to rely on searching for a “just-in-case” test after the fact.

Magnitude of effect

An emphasis on accountability is embodied in the term evidence-based practice . One of the tools used to help answer the question of “what works” that forms the basis for the evidence in evidence-based practice is meta-analysis —the quantitative synthesis of studies from which standardized and weighted effect sizes can be derived. Meta-analysis methodology provides an objective estimate of the magnitude of an intervention's effect. One of the main problems of SSEDs is that the evidence generated is not always included in meta-analyses. Alternatively, if studies based on SSEDs are used in meta-analysis, there is no agreement on the correct metric to estimate and quantify the effect size.

An obvious corollary to the issue of effect magnitude is that visual inspection, per se—although sensitive to a range of holistic information embodied in a data display (trend, recurrent pattern, delayed/lagged response, variability, etc.; Parker & Hagan-Burke, 2007 )—does not generate a quantitative index of intervention strength (i.e., effect magnitude) that is recognizable to the broader scientific community. The determination of which practices and interventions are evidence based (and which will, therefore, be promoted and funded) increasingly involves quantitative synthesis of data and exposes the need for a single, agreed-upon effect size metric to reflect magnitude in SSEDs ( Parker, Hagan-Burke, & Vannest, 2007 ). Accordingly, the changing scientific standards across practice communities (e.g., ASHA, American Psychological Association, American Educational Research Association) are reflected in the organization's editorial policies and publication practices, which increasingly require effect sizes to be reported.

There has been a small but steady body of work addressing effect size calculation and interpretation for SSEDs. Space precludes an exhaustive review of all the metrics (for comprehensive reviews, see Parker & Hagan-Burke, 2007 , and related papers from this group). There are, however, a number of points that can be made regarding the use (derivation, interpretation) of effect size indices that are common to all. The simplest and most common effect size metric is the percentage of nonoverlapping data (PND; Scruggs, Mastropieri, & Casto, 1987 ). It is easy to calculate by hand and, therefore, is easily accessible to practitioners. The most extreme positive (the term positive is used in relation to the clinical problem being addressed; therefore, it could be the highest or lowest score) baseline data point is selected, from which a straight line is drawn across the intervention phase of the graph (for simplicity's sake, assume an AB-only design). Then, the number of data points that fall above (or below) the line is tallied and divided by the total number of intervention data points. If, for example, in a study of a treatment designed to improve (i.e., increase) communication fluency, eight of 10 data points in the intervention phase are greater in value than the largest baseline data point value, the resulting PND would equal 80%.

Although the clinical/educational appeal of such a metric seems obvious (easy to calculate, it is consistent with visual inspection of graphic data), there are potential problems with the approach. For example, there are ceiling effects for PND, making comparisons across or between interventions difficult ( Parker & Hagan-Burke, 2007 ; Parker et al., 2007 ), and PND is based on a single data point, making it sensitive to outliers ( Riley-Tillman & Burns, 2009 ). In addition, there is no known sampling distribution, making it impossible to derive a confidence interval (CI). CIs are important because they help create an interpretive context for the dependability of the effect by providing upper and lower bounds for the estimate. As a result, PND is a statistic of unknown reliability.

Most work on effect sizes for SSEDs has been conducted implicitly or explicitly to address the limits of PND. Some work has conserved the approach by continuing to calculate some form of descriptive degrees of overlap , including percentage of data points exceeding the median (PEM; Ma, 2006 ), percentage of zero data points (PZD; Johnson, Reichle, & Monn, 2009 ), and the percentage of all nonover-lapping data (PAND; Parker et al., 2007 ). Olive and Smith (2005) compared a set of descriptive effect size statistics (including a regression-based effect size, PND, standard mean difference, and mean baseline reduction) to visual analysis of several data sets and found that each consistently estimated relative effect size. Other investigators have attempted to integrate degree of overlap with general linear model approaches such as linear regression. The regression-based techniques (e.g., Gorman & Allison, 1996 , pp. 159–214) make use of predicted values derived from baseline data to remove the effects of trend (i.e., predicted values are subtracted from observed data). Subsequently, adjusted mean treatment scores can be used in calculating familiar effect size statistics (e.g., Cohen's d , Hedge's g ). This application may be more commonly accepted among those familiar with statistical procedures associated with group design.

As with each of the issues discussed in this section, there are advantages and disadvantages to the regression and non-regression methods for determining effect size for SSEDs. Nonregression methods involve simpler hand calculations, map on to visual inspection of the data, and are less biased in the presence of small numbers of observations ( Scruggs & Mastropieri, 1998 ). But, as recently argued by Wolery, Busick, Reichow, and Barton (2010) , the overlap approaches for calculating effect sizes do not produce metrics that adequately reflect magnitude (i.e., in cases where the intervention was effective and there is no overlap between baseline and treatment, the degree of the nonoverlap of the data—the magnitude—is not indexed by current overlap-based effect sizes). Regression methods are less sensitive to outliers, control for trend in the data, and may be more sensitive to detecting treatment effects in slope and intercept ( Gorman & Allison, 1996 ). As work in this area continues, novel effect size indices will likely emerge. Parker and Hagan-Burke (2007) , for example, demonstrated that the improvement rate difference metric (IRD—an index frequently used in evidenced-based medicine) was superior to both PND and PEM (it produces a CI and discriminates among cases [i.e., reduced floor/ceiling effects]) but conserved many of their clinically appealing features (hand calculation, based on nonoverlapping data) without requiring any major assumptions of the data.

Although effect sizes may not be a requirement for databased decision making for a given specific case—because the decision about effect is determined primarily by the degree of differentiation within the data set as ascertained through visual inspection and by the logical ordering of conditions (see also the Practice and data-based decisions section below)— their calculation and reporting may be worth considering with respect to changing publication standards and facilitating future meta-analyses. Note also that lost in the above discussion concerning effect size metrics is the issue of statistical versus clinical significance. Although one of the scientific goals of research is to discover functional relations between independent and dependent variables, the purpose of applied research is discovering the relations that lead to clinically meaningful outcomes (i.e., clinical significance; see Barlow & Hersen, 1973 ) or socially relevant behavior changes (i.e., social validity; see Wolf, 1978 ). From a practice perspective, one of the problems of statistical significance is that it can over- or underestimate clinical significance ( Chassan, 1979 ). In principle, the notion of quantifying how large (i.e., magnitude) of an effect was obtained is in keeping with the spirit of clinical significance and social validity, but the effect size interpretation should not blindly lead to assertions of clinically significant results divorced from judgments about whether the changes were clinically or socially meaningful.

Quality of inference

One of the great scientific strengths of SSEDs is the premium placed on internal validity and the reliance on effect replication within and across participants. One of the great clinical strengths of SSEDs is the ability to use a response-guided intervention approach such that phase or condition changes (i.e., changes in the independent variable) are made based on the behavior of the participant. This notion has a long legacy and reflects Skinner's (1948) early observation that the subject (“organism”) is always right. In contrast with these two strengths, there is a line of thinking that argues for incorporating randomization into SSEDs ( Kratochwill & Levin, 2009 ). This notion has a relatively long history ( Edgington, 1975 ) and continues to be mentioned in contemporary texts ( Todman & Dugard, 2001 ). The advantages and disadvantages of the practice are worth addressing (albeit briefly).

The argument for incorporating randomization into SSEDs is to further improve the quality of the causal inference (i.e., strengthening internal validity) by randomizing phase order or condition start times (there are numerous approaches to randomizing within SSEDs; see Kratochwill & Levin, 2009 , or almost any of Edgington's work). However, doing so comes at the cost of practitioner flexibility in making phase/condition changes based on patterns in the data (i.e., how the participant is responding). This cost, it is argued, is worth the expense because randomization is superior to replication for reducing plausible threats to internal validity. The within-series intervention conditions are compared in an unbiased (i.e., randomized) manner rather than in a manner that is researcher determined and, hence, prone to bias. The net effect is to further enhance the scientific credibility of the findings from SSEDs. At this point, it seems fair to conclude that it remains an open question about whether randomization is superior to replication with regard to producing clinically meaningful effects for any given participant in an SSED.

One potential additional advantage to incorporating randomization into an SSED is that the data series can be analyzed using randomization tests ( Bulte & Onghena, 2008 ; Edgington, 1996 ; Todman & Dugard, 2001 ) that leverage the ease and availability of computer-based resampling for likelihood estimation. Exact p values are generated, and the tests appear to be straightforward ways to supplement the visual analysis of single-subject data. It should be noted, however, that randomization tests in and of themselves do not necessarily address the problem of autocorrelation.

Practice and data-based decisions

Finally, related to several different comments in the preceding sections regarding practical significance, there is the issue of interpreting effects directly in relation to practice in terms of eventual empirically based decision making for a given client or participant. At issue here is not determining whether there was an effect and its standardized size but whether there is change in behavior or performance over time—and the rate of that change. Riley-Tillman and Burns (2009) argued that effect size estimates may make valuable contributions for future quantitative syntheses; however, for a given practitioner, data interpretation and subsequent practice decisions are driven more by slope changes, not by average effect sizes. Nontrivial practice issues, such as special education eligibility, entitlement decisions, and instructional modification, depend on repeated measurement of student growth (i.e., time series data) that is readily translatable into single-subject design logic with judgment aids in the form of numerical slope values and aim lines.

Key advantages of relying on visual inspection and quantifying slope are not only that student growth rates can be interpreted for an individual student in relation to an intervention but also that the growth rate values can be compared to a given student's respective grade or class (or other local norms). For a clear example, interested readers are referred to Silberglitt and Gibbons’ (2005) documentation of a slope-standard approach to identifying, intervening, and monitoring reading fluency and at-risk students. Of course, the approach (relying on slope values from serially collected single-subject data) is not without its problems. Depending on the frequency and duration of data collection, the standard error of the estimate for slope values can vary widely ( Christ, 2006 ), leading to interpretive problems for practice. Thus, consistent with all of the points made above, sound methodology (design, measurement) is the biggest determinant of valid decision making. Overall, the four issues discussed above—effect detection, magnitude of effect, quality of the inference, and practice decisions—reflect the critical dimensions involved in the analysis of SSED. The importance of any one dimension over the other will likely depend on the purpose of the study and the state of the scientific knowledge about the problem being addressed.

Conclusions

Unlike the research questions often addressed by studies using traditional group designs, studies employing SSEDs can address the effects that intervention strategies and environmental variables have on performance at the individual level. SSED methodology permits flexibility within a study to modify the independent variable when it does not lead to the desired or expected effect, and it does not compromise the integrity of the experimental design. As a result, SSED methodology provides a useful alternative to RCTs (and quasi-experimental group designs) for the goal of empirically demonstrating that an intervention is effective, or alternatively, determining the better of two or more potential interventions. SSEDs are ideal for both researchers and clinicians working with small or very heterogeneous populations in the development and implementation of evidence-based practice. The strong internal validity of well-implemented SSED studies allows for visual and, under some circumstances, statistical data analyses to support confident conclusions about—in the words of U.S. Department of Education—“what works.”

Kazdin (2010) , Horner et al. (2005) , and others have highlighted the issue of RCTs within traditional probabilistic group design research being favored among policymakers, granting agencies, and practitioners in the position of selecting interventions from the evidence base. They also highlight the important role that SSEDs can and should play in this process. The specific criteria developed by the WWCH panel emphasize the importance of strong experimental designs—and replication, if SSEDs are to be taken seriously as a tool within the establishment of evidence-based practice. Speech, language, and hearing interventions, by their nature, strive to improve outcomes for individual clients or research participants. Evaluating those interventions within SSEDs and associated visual and statistical data analyses lends rigor to clinical work, is logically and methodologically consistent with intervention research in the field, and can serve as a common framework for decision making with colleagues within and outside the CSD field.

American Speech-Language-Hearing Association Evidence-based practice in communication disorders [Position statement] 2005 Available from www.asha.org/policy .
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Case studies, single-subject research, and N of 1 randomized trials: comparisons and contrasts

Affiliation.

1 Department of Health Care & Epidemiology, School of Rehabilitation Sciences, The University of British Columbia, Vancouver, Canada.
PMID: 10088595
DOI: 10.1097/00002060-199903000-00022

Case studies, single-subject research designs, and N of 1 randomized clinical trials are methods of scientific inquiry applied to an individual or small group of individuals. A case study is a form of descriptive research that seeks to identify explanatory patterns for phenomena and generates hypotheses for future research. Single-subject research designs provide a quasi-experimental approach to investigating causal relationships between independent and dependent variables. They are characterized by repeated measures of an observable and clinically relevant target behavior throughout at least one pretreatment (baseline) and intervention phase. The N of 1 clinical trial is similar to the single-subject research design through its use of repeated measures over time but also borrows principles from the conduct of large, randomized controlled trials. Typically, the N of 1 trial compares a therapeutic procedure with placebo or compares two treatments by administering the two conditions in a predetermined random order. Neither the subject nor the clinician is aware of the treatment condition in any given period of time. All three approaches are relatively easy to integrate into clinical practice and are useful for documenting individualized outcomes and providing evidence in support of rehabilitation interventions.

Publication types

Double-Blind Method
Effect Modifier, Epidemiologic
Guidelines as Topic
Medical Records*
Randomized Controlled Trials as Topic / methods*
Rehabilitation
Reproducibility of Results
Research Design / standards*

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Chapter 10: Single-Subject Research

Overview of Single-Subject Research

Learning Objectives

Explain what single-subject research is, including how it differs from other types of psychological research.
Explain what case studies are, including some of their strengths and weaknesses.
Explain who uses single-subject research and why.

What Is Single-Subject Research?

Single-subject research is a type of quantitative research that involves studying in detail the behaviour of each of a small number of participants. Note that the term single-subject does not mean that only one participant is studied; it is more typical for there to be somewhere between two and 10 participants. (This is why single-subject research designs are sometimes called small- n designs, where n is the statistical symbol for the sample size.) Single-subject research can be contrasted with group research , which typically involves studying large numbers of participants and examining their behaviour primarily in terms of group means, standard deviations, and so on. The majority of this textbook is devoted to understanding group research, which is the most common approach in psychology. But single-subject research is an important alternative, and it is the primary approach in some areas of psychology.

Before continuing, it is important to distinguish single-subject research from two other approaches, both of which involve studying in detail a small number of participants. One is qualitative research, which focuses on understanding people’s subjective experience by collecting relatively unstructured data (e.g., detailed interviews) and analyzing those data using narrative rather than quantitative techniques. Single-subject research, in contrast, focuses on understanding objective behaviour through experimental manipulation and control, collecting highly structured data, and analyzing those data quantitatively.

It is also important to distinguish single-subject research from case studies. A case study is a detailed description of an individual, which can include both qualitative and quantitative analyses. (Case studies that include only qualitative analyses can be considered a type of qualitative research.) The history of psychology is filled with influential cases studies, such as Sigmund Freud’s description of “Anna O.” (see Note 10.5 “The Case of “Anna O.””) and John Watson and Rosalie Rayner’s description of Little Albert (Watson & Rayner, 1920) [1] , who learned to fear a white rat—along with other furry objects—when the researchers made a loud noise while he was playing with the rat. Case studies can be useful for suggesting new research questions and for illustrating general principles. They can also help researchers understand rare phenomena, such as the effects of damage to a specific part of the human brain. As a general rule, however, case studies cannot substitute for carefully designed group or single-subject research studies. One reason is that case studies usually do not allow researchers to determine whether specific events are causally related, or even related at all. For example, if a patient is described in a case study as having been sexually abused as a child and then as having developed an eating disorder as a teenager, there is no way to determine whether these two events had anything to do with each other. A second reason is that an individual case can always be unusual in some way and therefore be unrepresentative of people more generally. Thus case studies have serious problems with both internal and external validity.

The Case of “Anna O.”

Sigmund Freud used the case of a young woman he called “Anna O.” to illustrate many principles of his theory of psychoanalysis (Freud, 1961) [2] . (Her real name was Bertha Pappenheim, and she was an early feminist who went on to make important contributions to the field of social work.) Anna had come to Freud’s colleague Josef Breuer around 1880 with a variety of odd physical and psychological symptoms. One of them was that for several weeks she was unable to drink any fluids. According to Freud,

She would take up the glass of water that she longed for, but as soon as it touched her lips she would push it away like someone suffering from hydrophobia.…She lived only on fruit, such as melons, etc., so as to lessen her tormenting thirst. (p. 9)

But according to Freud, a breakthrough came one day while Anna was under hypnosis.

[S]he grumbled about her English “lady-companion,” whom she did not care for, and went on to describe, with every sign of disgust, how she had once gone into this lady’s room and how her little dog—horrid creature!—had drunk out of a glass there. The patient had said nothing, as she had wanted to be polite. After giving further energetic expression to the anger she had held back, she asked for something to drink, drank a large quantity of water without any difficulty, and awoke from her hypnosis with the glass at her lips; and thereupon the disturbance vanished, never to return. (p.9)

A woman in a floor-length dress with long sleeves. She holds a long white stick.

Assumptions of Single-Subject Research

Again, single-subject research involves studying a small number of participants and focusing intensively on the behaviour of each one. But why take this approach instead of the group approach? There are several important assumptions underlying single-subject research, and it will help to consider them now.

First and foremost is the assumption that it is important to focus intensively on the behaviour of individual participants. One reason for this is that group research can hide individual differences and generate results that do not represent the behaviour of any individual. For example, a treatment that has a positive effect for half the people exposed to it but a negative effect for the other half would, on average, appear to have no effect at all. Single-subject research, however, would likely reveal these individual differences. A second reason to focus intensively on individuals is that sometimes it is the behaviour of a particular individual that is primarily of interest. A school psychologist, for example, might be interested in changing the behaviour of a particular disruptive student. Although previous published research (both single-subject and group research) is likely to provide some guidance on how to do this, conducting a study on this student would be more direct and probably more effective.

A third assumption of single-subject research is that it is important to study strong and consistent effects that have biological or social importance. Applied researchers, in particular, are interested in treatments that have substantial effects on important behaviours and that can be implemented reliably in the real-world contexts in which they occur. This is sometimes referred to as social validity (Wolf, 1976) [3] . The study by Hall and his colleagues, for example, had good social validity because it showed strong and consistent effects of positive teacher attention on a behaviour that is of obvious importance to teachers, parents, and students. Furthermore, the teachers found the treatment easy to implement, even in their often-chaotic elementary school classrooms.

Who Uses Single-Subject Research?

In the middle of the 20th century, B. F. Skinner clarified many of the assumptions underlying single-subject research and refined many of its techniques (Skinner, 1938) [4] . He and other researchers then used it to describe how rewards, punishments, and other external factors affect behaviour over time. This work was carried out primarily using nonhuman subjects—mostly rats and pigeons. This approach, which Skinner called the experimental analysis of behaviour —remains an important subfield of psychology and continues to rely almost exclusively on single-subject research. For excellent examples of this work, look at any issue of the Journal of the Experimental Analysis of Behaviour . By the 1960s, many researchers were interested in using this approach to conduct applied research primarily with humans—a subfield now called applied behaviour analysis (Baer, Wolf, & Risley, 1968) [5] . Applied behaviour analysis plays an especially important role in contemporary research on developmental disabilities, education, organizational behaviour, and health, among many other areas. Excellent examples of this work (including the study by Hall and his colleagues) can be found in the Journal of Applied Behaviour Analysis .

Although most contemporary single-subject research is conducted from the behavioural perspective, it can in principle be used to address questions framed in terms of any theoretical perspective. For example, a studying technique based on cognitive principles of learning and memory could be evaluated by testing it on individual high school students using the single-subject approach. The single-subject approach can also be used by clinicians who take any theoretical perspective—behavioural, cognitive, psychodynamic, or humanistic—to study processes of therapeutic change with individual clients and to document their clients’ improvement (Kazdin, 1982) [6] .

Key Takeaways

Single-subject research—which involves testing a small number of participants and focusing intensively on the behaviour of each individual—is an important alternative to group research in psychology.
Single-subject studies must be distinguished from case studies, in which an individual case is described in detail. Case studies can be useful for generating new research questions, for studying rare phenomena, and for illustrating general principles. However, they cannot substitute for carefully controlled experimental or correlational studies because they are low in internal and external validity.
Single-subject research has been around since the beginning of the field of psychology. Today it is most strongly associated with the behavioural theoretical perspective, but it can in principle be used to study behaviour from any perspective.
Practice: Find and read a published article in psychology that reports new single-subject research. ( An archive of articles published in the Journal of Applied Behaviour Analysis can be found at http://www.ncbi.nlm.nih.gov/pmc/journals/309/) Write a short summary of the study.
Describe one problem related to internal validity.
Describe one problem related to external validity.
Generate one hypothesis suggested by the case study that might be interesting to test in a systematic single-subject or group study.

Media Attributions

Pappenheim 1882 by unknown is in the Public Domain .
Watson, J. B., & Rayner, R. (1920). Conditioned emotional reactions. Journal of Experimental Psychology, 3 , 1–14. ↵
Freud, S. (1961). Five lectures on psycho-analysis . New York, NY: Norton. ↵
Wolf, M. (1976). Social validity: The case for subjective measurement or how applied behaviour analysis is finding its heart. Journal of Applied Behaviour Analysis, 11 , 203–214. ↵
Skinner, B. F. (1938). T he behaviour of organisms: An experimental analysis . New York, NY: Appleton-Century-Crofts. ↵
Baer, D. M., Wolf, M. M., & Risley, T. R. (1968). Some current dimensions of applied behaviour analysis. Journal of Applied Behaviour Analysis, 1 , 91–97. ↵
Kazdin, A. E. (1982). Single-case research designs: Methods for clinical and applied settings . New York, NY: Oxford University Press. ↵

A type of quantitative research that involves studying the behaviour of each small number of participants in detail.

The study of large numbers of participants and examining their behaviour primarily in terms of group means, standard deviations, and so on.

A detailed description of an individual, which can include both qualitative and quantitative analyses.

The study of strong and consistent effects that can be implemented reliably in the real-world contexts in which they occur.

Laboratory methods that rely on single-subject research; based upon B. F. Skinner’s philosophy of behaviourism which posits that everything organisms do is behaviour.

Starting in the 1960s, researchers began using single-subject techniques to conduct applied research with human subjects.

Research Methods in Psychology - 2nd Canadian Edition Copyright © 2015 by Paul C. Price, Rajiv Jhangiani, & I-Chant A. Chiang is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Single-Case Designs

First Online: 30 April 2023

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Lodi Lipien 3 ,
Megan Kirby 3 &
John M. Ferron 3

Part of the book series: Autism and Child Psychopathology Series ((ACPS))

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Single-case design (SCD), also known as single-case experimental design, single-subject design, or N-of-1 trials, refers to a research methodology that involves examining the effect of an intervention on a single individual over time by repeatedly measuring a target behavior across different intervention conditions. These designs may include replication across cases, but the focus is on individual effects. Differences in the target behaviors and individuals studied, as well as differences in the research questions posed, have spurred the development of a variety of single-case designs, each with distinct advantages in specific situations. These designs include reversal designs, multiple baseline designs (MBD), alternating treatments designs (ATD), and changing criterion designs (CCD). Our purpose is to describe these designs and their application in behavioral research. In doing so, we consider the questions they address and the conditions under which they are well suited to answer those questions.

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Reversal designs, first described by Leitenberg ( 1973 ) and later reviewed by Wine et al. ( 2015 ), originally referred to a type of design in which the effects of one IV on two topographically distinct DVs (DV 1, DV 2) were repeatedly measured across time. The intervention, such as reinforcement, was presented in each phase but was in effect for either DV 1 or DV 2. The purpose of the use is to show changes in rates of responding when an IV is introduced to DV 1 and withdrawn from DV 2, as the rate of responding for each would change across phases when in the presence or absence of the IV. However, the reversal design as described is rarely used in contemporary behavior analytic literature and is often used interchangeably with withdrawal design .

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Lipien, L., Kirby, M., Ferron, J.M. (2023). Single-Case Designs. In: Matson, J.L. (eds) Handbook of Applied Behavior Analysis. Autism and Child Psychopathology Series. Springer, Cham. https://doi.org/10.1007/978-3-031-19964-6_20

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Case Study vs Single-Subject Design: Research Methods

Understanding the differences: case study vs single-subject design in research methods.

Research methods play a fundamental role in the field of social sciences, helping researchers uncover insights and draw conclusions about various phenomena. Two commonly utilized research methods in this domain are case study and single-subject design. While both methods aim to investigate and analyze specific subjects or situations, they differ in their approaches and purposes. In this article, we will delve into the intricacies of case study and single-subject design, highlighting their unique attributes and exploring the advantages and limitations of each.

Case Study: Investigating Complexity

Imagine you are an archeologist exploring a newly discovered ancient civilization. To unravel the mysteries surrounding this society, you decide to conduct a case study. Case studies are like in-depth detective investigations where researchers immerse themselves in a particular phenomenon, examining it from multiple angles and perspectives. They provide a comprehensive understanding of a specific case, capturing its complexities and nuances.

Just like a detective, the researcher collects vast amounts of data from various sources such as interviews, observations, documents, and artifacts. By analyzing and interpreting this rich data, they can build a detailed and holistic picture of the subject being studied. Case studies are particularly useful when exploring rare, unique, or complex cases, where a more generalized approach might not provide the depth of understanding required.

Consider the case of a child prodigy who exhibits exceptional musical abilities. A case study could be conducted to investigate the origins of their talent, exploring factors such as family background, early exposure to music, and their individual learning processes. This approach allows researchers to delve into the intricacies and particularities of the prodigy’s development, elucidating the factors that contribute to their extraordinary abilities.

Single-Subject Design: Focusing on Individual Experiences

Now, let’s shift our focus to single-subject design, which takes a more targeted and individualized approach. Single-subject design is like a magnifying glass that zooms in on the experiences of a single participant or a small group of participants. This method allows researchers to observe and analyze behavior, interventions, or treatments in a controlled and systematic manner.

In single-subject design, researchers carefully define specific behaviors or variables they wish to study and establish a baseline before implementing any interventions. This baseline serves as a comparison point, enabling researchers to measure the effectiveness of the intervention accurately. By examining behavior before, during, and after the intervention, researchers can determine whether the intervention had a significant impact.

Imagine a psychologist working with a child who has attention deficit hyperactivity disorder (ADHD). To assess the effectiveness of a new behavioral therapy, the psychologist might choose to utilize single-subject design. They would first observe the child’s behavior without any intervention, establishing a baseline of ADHD symptoms. They would then implement the behavioral therapy, closely observing and measuring the child’s behavior throughout the process. Finally, they would compare the post-intervention behavior with the baseline, evaluating the therapy’s efficacy.

Advantages and Limitations of Case Study

Case studies provide valuable insights and contribute to the existing body of knowledge in various fields. Let’s explore some of the advantages and limitations of this research method:

As we can see, case studies offer a profound understanding of complex cases, providing a detailed exploration of the subject matter. The researcher can collect a rich array of data, allowing for comprehensive analysis and interpretation. Additionally, case studies often uncover new research directions and possibilities for further investigation.

However, it is important to acknowledge the limitations of case studies. These studies are highly specific to the individual case being examined, making it challenging to generalize the findings to other cases or populations. Moreover, there is a risk of researcher bias influencing the interpretation of data. Case studies also require a significant investment of time and resources, restricting their feasibility in certain situations.

Advantages and Limitations of Single-Subject Design

Now, let’s turn our attention to single-subject design and explore its advantages and limitations:

Single-subject design offers a personalized approach, allowing researchers to assess behaviors and interventions at an individual level. The controlled and systematic nature of this method enhances the validity and reliability of the observations made. By measuring the impact of interventions, researchers can determine their effectiveness, tailoring approaches to individual needs.

However, generalizing findings from single-subject designs to larger populations can be challenging due to the limited sample size and unique characteristics of the participants. The possibility of observer bias must also be considered, as researchers’ subjective interpretations may influence results. Ethical concerns arise when interventions are withheld or withdrawn during the study, as researchers must prioritize the well-being of participants.

Choosing the Right Method: Context Matters

When deciding whether to employ a case study or single-subject design, researchers must consider the context and objectives of their study. Both methods have distinct advantages and limitations, and selecting the appropriate approach depends on the research question, available resources, and the level of depth required.

If the aim is to investigate a complex and unique case, uncovering detailed insights and understanding the various factors at play, a case study would be a suitable choice. On the other hand, if the focus is on individual experiences and assessing the effectiveness of interventions, single-subject design offers a more targeted and controlled approach.

As we have explored case study and single-subject design, it is clear that these two research methods bring their own strengths and considerations to the table. Case studies provide a holistic understanding of complex cases, whereas single-subject designs allow for the observation and analysis of individual experiences.

By recognizing the advantages and limitations of each method, researchers can make informed decisions that align with their research objectives. Ultimately, the choice between case study and single-subject design hinges on the specific context of the study. So, whether you embark on a case study or opt for a single-subject design, remember to embrace the unique insights these methods can offer on your journey to unravel the secrets of the social sciences.

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COMMENTS

PDF Single Subject Design vs Case Study
Single Subject Design Overview u A type of research that allows for making causal inference about the effects of an intervention compared to a baseline. u Based on observational data or behaviors that occur during a specific period of time. u Often involve more than one component in the experimental treatment. u A design where the participant serves as his or her own control.
Single-Subject Experimental Design: An Overview
Single-Subject Experimental Designs versus Case Studies Transcript of the video Q&A with Julie Wambaugh. One of the biggest mistakes, that is a huge problem, is misunderstanding that a case study is not a single-subject experimental design.
Case Study vs. Single-Case Experimental Designs
One of the key differences between case studies and single-case experimental designs is their generalizability. Case studies are often conducted on unique or rare cases, making it challenging to generalize the findings to a larger population. The focus of case studies is on providing detailed insights into specific cases rather than making ...
Single-Case Design, Analysis, and Quality Assessment for Intervention
Single-case studies can provide a viable alternative to large group studies such as randomized clinical trials. Single case studies involve repeated measures, and manipulation of and independent variable. They can be designed to have strong internal validity for assessing causal relationships between interventions and outcomes, and external ...
10.1 Overview of Single-Subject Research
Key Takeaways. Single-subject research—which involves testing a small number of participants and focusing intensively on the behavior of each individual—is an important alternative to group research in psychology. Single-subject studies must be distinguished from case studies, in which an individual case is described in detail.
Single-subject design
In design of experiments, single-subject curriculum or single-case research design is a research design most often used in applied fields of psychology, education, and human behaviour in which the subject serves as his/her own control, rather than using another individual/group. Researchers use single-subject design because these designs are sensitive to individual organism differences vs ...
Single-Subject Research Designs
Many of these features are illustrated in Figure 10.2, which shows the results of a generic single-subject study. First, the dependent variable (represented on the y -axis of the graph) is measured repeatedly over time (represented by the x -axis) at regular intervals. Second, the study is divided into distinct phases, and the participant is ...
What Is a Case Study?
Revised on November 20, 2023. A case study is a detailed study of a specific subject, such as a person, group, place, event, organization, or phenomenon. Case studies are commonly used in social, educational, clinical, and business research. A case study research design usually involves qualitative methods, but quantitative methods are ...
10.1: Overview of Single-Subject Research
Before continuing, it is important to distinguish single-subject research from case studies and other more qualitative approaches that involve studying in detail a small number of participants. As described in Chapter 6, case studies involve an in-depth analysis and description of an individual, which is typically primarily qualitative in nature.
Single Subject Research Design
Single subject research design is a type of research methodology characterized by repeated assessment of a particular phenomenon (often a behavior) over time and is generally used to evaluate interventions [].Repeated measurement across time differentiates single subject research design from case studies and group designs, as it facilitates the examination of client change in response to an ...
Single Subject Research
"Single subject research (also known as single case experiments) is popular in the fields of special education and counseling. ... .Studies involving single-subject designs that show a particular treatment to be effective in changing behavior must rely on replication-across individuals rather than groups-if such results are be found worthy ...
Single-Case Experimental Designs: A Systematic Review of Published
The single-case experiment has a storied history in psychology dating back to the field's founders: Fechner (1889), Watson (1925), and Skinner (1938).It has been used to inform and develop theory, examine interpersonal processes, study the behavior of organisms, establish the effectiveness of psychological interventions, and address a host of other research questions (for a review, see ...
Case Study Methodology of Qualitative Research: Key Attributes and
Multiple-case study design however has a distinct advantage over a single case study design. Multiple-case studies are generally considered more compelling and robust, and worthy of undertaking. This is because a multiple case study design has a greater chance of weeding out data collection errors and prejudices, and produces a more acceptable ...
10.2 Single-Subject Research Designs
Many of these features are illustrated in Figure 10.1, which shows the results of a generic single-subject study. First, the dependent variable (represented on the y -axis of the graph) is measured repeatedly over time (represented by the x -axis) at regular intervals. Second, the study is divided into distinct phases, and the participant is ...
Applied Behavior Analysis: Single Subject Research Design
Single case design (SCD), often referred to as single subject design, is an evaluation method that can be used to rigorously test the success of an intervention or treatment on a particular case (i.e., a person, school, community) and to also provide evidence about the general effectiveness of an intervention using a relatively small sample size.
The Case Report, Case Study, and Single Subject Design
The case report, case study, and single subject design are methods of inquiry by which physical therapists and occupational therapists can inform practice and provide foundation knowledge for clinical trials. These three methods of inquiry are unique in their focus on individual children and families and feasibility of implementation in ...
What is the difference between single subject design and a case study
Thank you, that cleared up a lot! They are wildly different. A single subject design is an experimental design that tests validity of a treatment using only one subject and a case study is an in depth evaluation of one case. No because of replication. Most experiments using single subject designs have been replicated many times.
Single-Subject Experimental Design for Evidence-Based Practice
Keywords: single-subject experimental designs, tutorial, research methods, evidence-based practice. The use of single-subject experimental designs (SSEDs) has a rich history in communication sciences and disorders (CSD) research. A number of important studies dating back to the 1960s and 1970s investigated fluency treatments using SSED ...
Case studies, single-subject research, and N of 1 randomized trials
Abstract. Case studies, single-subject research designs, and N of 1 randomized clinical trials are methods of scientific inquiry applied to an individual or small group of individuals. A case study is a form of descriptive research that seeks to identify explanatory patterns for phenomena and generates hypotheses for future research.
Overview of Single-Subject Research
Key Takeaways. Single-subject research—which involves testing a small number of participants and focusing intensively on the behaviour of each individual—is an important alternative to group research in psychology. Single-subject studies must be distinguished from case studies, in which an individual case is described in detail.
The Family of Single-Case Experimental Designs
Abstract. Single-case experimental designs (SCEDs) represent a family of research designs that use experimental methods to study the effects of treatments on outcomes. The fundamental unit of analysis is the single case—which can be an individual, clinic, or community—ideally with replications of effects within and/or between cases.
Single-Case Designs
Single-case design (SCD), also known as single-subject design, single-case experimental design, or N-of-1 trials, refers to a research methodology that involves examining the effect of an intervention on an individual or on each of multiple individuals. Unlike case studies, SCDs involve the systematic manipulation of an independent variable (IV ...
Case Study vs Single-Subject Design: Research Methods
Understanding the Differences: Case Study vs Single-Subject Design in Research Methods. Research methods play a fundamental role in the field of social sciences, helping researchers uncover insights and draw conclusions about various phenomena. Two commonly utilized research methods in this domain are case study and single-subject design.

Single-Subject Experimental Design: An Overview

DOI: 10.1044/cred-cred-ssd-r101-002

An Overview of Single-Subject Experimental Design

What is the Role of Single-Subject Design?

Basic Features and Components of Single-Subject Experimental Designs

Common Misconceptions

Further Reading: Components of Single-Subject Designs

Further Reading: Single-Subject Design Textbooks

Further Reading: Foundational Articles

Clinical Research Education

Case Study vs. Single-Case Experimental Designs

Further Detail

Definition and Purpose

Data Collection and Analysis

Generalizability

Internal Validity

Time and Resources

Ethical Considerations

10.1 Overview of Single-Subject Research

What Is Single-Subject Research?

The Case of “Anna O.”

Who Uses Single-Subject Research?

Key Takeaways

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What Is a Case Study? | Definition, Examples & Methods

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Shona McCombes

10.1: Overview of Single-Subject Research

What Is Single-Subject Research?

Assumptions of Single-Subject Research

Who Uses Single-Subject Research?

Key Takeaways

Educational Research Basics by Del Siegle

10.2 Single-Subject Research Designs

General Features of Single-Subject Designs

Reversal Designs

Multiple-Baseline Designs

Multiple-Baseline Design Across Participants

Multiple-Baseline Design Across Behaviors

Multiple-Baseline Design Across Settings

Data Analysis in Single-Subject Research

Key Takeaways

Share This Book

Applied Behavior Analysis: Single Subject Research Design

Terms to Use for Articles

Go To Databases

Research Hints

How to Search for a Specific Research Methodology in JABA

Single Case Design (Research Articles)

Single-Subject Experimental Design for Evidence-Based Practice

Joe Reichle

The Role of SSEDs in Evidence-Based Practice

Analysis of Effects in SSEDs

Visual Data Inspection as a Data Reduction Strategy: Changes in Level, Trend, and Variability

Criteria for Evidence Quality in SSEDs

Replication for Internal and External Validity

Types of SSEDs

Pre-Experimental (AB) Design

Withdrawal (ABA and ABAB) Designs

Advantages and disadvantages of withdrawal designs

Multiple-Baseline and Multiple-Probe Designs

Advantages and disadvantages of multiple-baseline/multiple-probe designs

Changing-Criterion Designs

Advantages and disadvantages of changing-criterion designs

Multiple-Treatment Designs

Advantages and disadvantages of multiple-treatment designs

Alternating Treatments and Adapted Alternating Treatments Designs

Adapted alternating treatments design (AATD)

Advantages and disadvantages of ATDs and AATDs

Evaluating Results in SSED Research

Effect detection

Magnitude of effect

Quality of inference

Practice and data-based decisions

Conclusions

Case studies, single-subject research, and N of 1 randomized trials: comparisons and contrasts