Making behavior analysis fun and accessible

The AllDayABA Blog

Join our mailing list.

If you want to be the first to read new blog posts, gain access to awesome resources, and hear about upcoming projects, then click "Sign Up" to become a part of our family today!

Copyright © 2022 AllDayABA - All Rights Reserved.

Powered by GoDaddy

Cookie Policy

This website uses cookies. By continuing to use this site, you accept our use of cookies.

Logo for BCcampus Open Publishing

Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices. -->

Chapter 10: Single-Subject Research

The Single-Subject Versus Group “Debate”

Learning Objectives

Single-subject research is similar to group research—especially experimental group research—in many ways. They are both quantitative approaches that try to establish causal relationships by manipulating an independent variable, measuring a dependent variable, and controlling extraneous variables. But there are important differences between these approaches too, and these differences sometimes lead to disagreements. It is worth addressing the most common points of disagreement between single-subject researchers and group researchers and how these disagreements can be resolved. As we will see, single-subject research and group research are probably best conceptualized as complementary approaches.

Data Analysis

One set of disagreements revolves around the issue of data analysis. Some advocates of group research worry that visual inspection is inadequate for deciding whether and to what extent a treatment has affected a dependent variable. One specific concern is that visual inspection is not sensitive enough to detect weak effects. A second is that visual inspection can be unreliable, with different researchers reaching different conclusions about the same set of data (Danov & Symons, 2008) [1] . A third is that the results of visual inspection—an overall judgment of whether or not a treatment was effective—cannot be clearly and efficiently summarized or compared across studies (unlike the measures of relationship strength typically used in group research).

In general, single-subject researchers share these concerns. However, they also argue that their use of the steady state strategy, combined with their focus on strong and consistent effects, minimizes most of them. If the effect of a treatment is difficult to detect by visual inspection because the effect is weak or the data are noisy, then single-subject researchers look for ways to increase the strength of the effect or reduce the noise in the data by controlling extraneous variables (e.g., by administering the treatment more consistently). If the effect is still difficult to detect, then they are likely to consider it neither strong enough nor consistent enough to be of further interest. Many single-subject researchers also point out that statistical analysis is becoming increasingly common and that many of them are using this as a supplement to visual inspection—especially for the purpose of comparing results across studies (Scruggs & Mastropieri, 2001) [2] .

Turning the tables, some advocates of single-subject research worry about the way that group researchers analyze their data. Specifically, they point out that focusing on group means can be highly misleading. Again, imagine that a treatment has a strong positive effect on half the people exposed to it and an equally strong negative effect on the other half. In a traditional between-subjects experiment, the positive effect on half the participants in the treatment condition would be statistically cancelled out by the negative effect on the other half. The mean for the treatment group would then be the same as the mean for the control group, making it seem as though the treatment had no effect when in fact it had a strong effect on every single participant!

But again, group researchers share this concern. Although they do focus on group statistics, they also emphasize the importance of examining distributions of individual scores. For example, if some participants were positively affected by a treatment and others negatively affected by it, this would produce a bimodal distribution of scores and could be detected by looking at a histogram of the data. The use of within-subjects designs is another strategy that allows group researchers to observe effects at the individual level and even to specify what percentage of individuals exhibit strong, medium, weak, and even negative effects.

External Validity

The second issue about which single-subject and group researchers sometimes disagree has to do with external validity—the ability to generalize the results of a study beyond the people and specific situation actually studied. In particular, advocates of group research point out the difficulty in knowing whether results for just a few participants are likely to generalize to others in the population. Imagine, for example, that in a single-subject study, a treatment has been shown to reduce self-injury for each of two developmentally disabled children. Even if the effect is strong for these two children, how can one know whether this treatment is likely to work for other developmentally disabled children?

Again, single-subject researchers share this concern. In response, they note that the strong and consistent effects they are typically interested in—even when observed in small samples—are likely to generalize to others in the population. Single-subject researchers also note that they place a strong emphasis on replicating their research results. When they observe an effect with a small sample of participants, they typically try to replicate it with another small sample—perhaps with a slightly different type of participant or under slightly different conditions. Each time they observe similar results, they rightfully become more confident in the generality of those results. Single-subject researchers can also point to the fact that the principles of classical and operant conditioning—most of which were discovered using the single-subject approach—have been successfully generalized across an incredibly wide range of species and situations.

And, once again turning the tables, single-subject researchers have concerns of their own about the external validity of group research. One extremely important point they make is that studying large groups of participants does not entirely solve the problem of generalizing to other  individuals . Imagine, for example, a treatment that has been shown to have a small positive effect on average in a large group study. It is likely that although many participants exhibited a small positive effect, others exhibited a large positive effect, and still others exhibited a small negative effect. When it comes to applying this treatment to another large  group , we can be fairly sure that it will have a small effect on average. But when it comes to applying this treatment to another  individual , we cannot be sure whether it will have a small, a large, or even a negative effect. Another point that single-subject researchers make is that group researchers also face a similar problem when they study a single situation and then generalize their results to other situations. For example, researchers who conduct a study on the effect of cell phone use on drivers on a closed oval track probably want to apply their results to drivers in many other real-world driving situations. But notice that this requires generalizing from a single situation to a population of situations. Thus the ability to generalize is based on much more than just the sheer number of participants one has studied. It requires a careful consideration of the similarity of the participants  and  situations studied to the population of participants and situations that one wants to generalize to (Shadish, Cook, & Campbell, 2002) [3] .

Single-Subject and Group Research as Complementary Methods

As with quantitative and qualitative research, it is probably best to conceptualize single-subject research and group research as complementary methods that have different strengths and weaknesses and that are appropriate for answering different kinds of research questions (Kazdin, 1982) [4] . Single-subject research is particularly good for testing the effectiveness of treatments on individuals when the focus is on strong, consistent, and biologically or socially important effects. It is also especially useful when the behaviour of particular individuals is of interest. Clinicians who work with only one individual at a time may find that it is their only option for doing systematic quantitative research.

Group research, on the other hand, is ideal for testing the effectiveness of treatments at the group level. Among the advantages of this approach is that it allows researchers to detect weak effects, which can be of interest for many reasons. For example, finding a weak treatment effect might lead to refinements of the treatment that eventually produce a larger and more meaningful effect. Group research is also good for studying interactions between treatments and participant characteristics. For example, if a treatment is effective for those who are high in motivation to change and ineffective for those who are low in motivation to change, then a group design can detect this much more efficiently than a single-subject design. Group research is also necessary to answer questions that cannot be addressed using the single-subject approach, including questions about independent variables that cannot be manipulated (e.g., number of siblings, extraversion, culture).

Finally, it is important to understand that the single-subject and group approaches represent different research traditions. This factor is probably the most important one affecting which approach a researcher uses. Researchers in the experimental analysis of behaviour and applied behaviour analysis learn to conceptualize their research questions in ways that are amenable to the single-subject approach. Researchers in most other areas of psychology learn to conceptualize their research questions in ways that are amenable to the group approach. At the same time, there are many topics in psychology in which research from the two traditions have informed each other and been successfully integrated. One example is research suggesting that both animals and humans have an innate “number sense”—an awareness of how many objects or events of a particular type have they have experienced without actually having to count them (Dehaene, 2011) [5] . Single-subject research with rats and birds and group research with human infants have shown strikingly similar abilities in those populations to discriminate small numbers of objects and events. This number sense—which probably evolved long before humans did—may even be the foundation of humans’ advanced mathematical abilities.

Key Takeaways

Research Methods in Psychology - 2nd Canadian Edition 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.

Share This Book

single case research design vs group design

Welcome!

D-4: Describe the advantages of single subject experimental designs compared to group design ©

Target terms (or phrases, in this case): single subject experimental designs, groups designs

single case research design vs group design

Research is about asking and answering questions. It’s really important that the way we find an answer matches the question that was asked! That’s basically what research methods are all about. It’s probably not helpful or productive to think of group designs and single subject designs as diametrically opposed in any philosophical way. They are two different ways of answering different kinds of research questions. Some people do feel that there are important and foundational divisions between the underlying assumptions of group designs and those of single subject designs. (This can be complex! Focus on learning the basics first.) It’s important to note that “groups design” does not inherently refer to very large numbers of participants, nor does “single subject design” refer to studies that necessarily contain a single participant. The names refer to the level at which the analysis is conducted, not the absolute number of participants involved in the study. That being said, most single case studies do involve fewer participants than group designs.

Groups designs 

Definition: Most people are familiar with research that involves two big groups of people. Researchers do something to group 1, and not group 2, and then they compare how both groups are doing (often by looking at an average of both groups) to see if the treatment made a difference. This is between subject research because the comparison is done (you guessed it) between different research subjects! (There is much more to between groups designs, of course. They are not simple. This is just a very, very basic overview of the main idea.) 

Example in clinical context : Leonardo is in charge of assembling a team to evaluate the effectiveness of different reading programs on students who attend special education programs in major U.S. cities. The goal is to find out which program is most effective, so that the Department of Education can provide more targeted support to special education programs in urban areas. This research question is not about a particular individual. It is about which program is the best, on average, for urban special education students as a whole. This is a question that should be answered by assembling several very large groups of students, assigning them randomly to different programs, then comparing the dependent variables (in this case, reading scores) for each group. This design acknowledges that the results may not capture each individual’s experience. It is about what has the greatest impact on the dependent variable overall. 

Why it matters : Between groups designs are not “bad,” nor do they yield inferior results when compared to within subjects designs. They simply provide answers to different questions, and those answers should be applied in different ways . It is important for behavior analysts to understand the basics of groups designs for several reasons, including the following: (1) most other fields, professionals, and stakeholders are more familiar with groups designs than with single subject methodology, and it is important to speak the same language when collaborating, (2) much of the broader conversation about “evidence based practices” in the helping professions are informed by groups research, and (3) a behavior analyst will probably encounter questions in their career that are best answered by consulting the between groups design literature. 

Single subject experimental designs

Definition: Single subject experimental designs are different in several fundamental ways. The most obvious way is that comparisons are made within each subject. This kind of research design is therefore called within subjects research . The most obvious (and most experimentally weak) way to use an individual as their own control is to take data at baseline, then introduce an intervention and take data, and then compare the two sets of data to see if there is a difference. (See D-5 to explore more sophisticated ways to do this!)

Example in clinical context: DeShawn is in charge of selecting reading programming for special education students within an urban public school in the U.S. He knows that his students have different learning profiles and different learning contexts. DeShawn evaluates the relative benefit of multiple reading interventions at the individual level by conducting a multi-element design, and makes his decision based on how individual students respond to each intervention. 

Why it matters: It is a foundational responsibility of behavior analysts to make decisions based on data and for the benefit of each individual client. Using single case design methodology helps us demonstrate a functional relationship between our intervention and the behavior change the client needs. At this point in human history, nothing can answer client specific questions regarding environmental interventions better than single subject methodologies. Pretty cool, right?  

Share this:

Forgive our mess, we are currently reworking our site to give you the best experience possible. We will try to keep the entire site functional throughout our work!

single case research design vs group design

10.3 The Single-Subject Versus Group “Debate”

Learning objectives.

Single-subject research is similar to group research—especially experimental group research—in many ways. They are both quantitative approaches that try to establish causal relationships by manipulating an independent variable, measuring a dependent variable, and controlling extraneous variables. But there are important differences between these approaches too, and these differences sometimes lead to disagreements. It is worth addressing the most common points of disagreement between single-subject researchers and group researchers and how these disagreements can be resolved. As we will see, single-subject research and group research are probably best conceptualized as complementary approaches.

Data Analysis

One set of disagreements revolves around the issue of data analysis. Some advocates of group research worry that visual inspection is inadequate for deciding whether and to what extent a treatment has affected a dependent variable. One specific concern is that visual inspection is not sensitive enough to detect weak effects. A second is that visual inspection can be unreliable, with different researchers reaching different conclusions about the same set of data (Danov & Symons, 2008) [1] . A third is that the results of visual inspection—an overall judgment of whether or not a treatment was effective—cannot be clearly and efficiently summarized or compared across studies (unlike the measures of relationship strength typically used in group research).

In general, single-subject researchers share these concerns. However, they also argue that their use of the steady state strategy, combined with their focus on strong and consistent effects, minimizes most of them. If the effect of a treatment is difficult to detect by visual inspection because the effect is weak or the data are noisy, then single-subject researchers look for ways to increase the strength of the effect or reduce the noise in the data by controlling extraneous variables (e.g., by administering the treatment more consistently). If the effect is still difficult to detect, then they are likely to consider it neither strong enough nor consistent enough to be of further interest. Many single-subject researchers also point out that statistical analysis is becoming increasingly common and that many of them are using this as a supplement to visual inspection—especially for the purpose of comparing results across studies (Scruggs & Mastropieri, 2001) [2] .

Turning the tables, some advocates of single-subject research worry about the way that group researchers analyze their data. Specifically, they point out that focusing on group means can be highly misleading. Again, imagine that a treatment has a strong positive effect on half the people exposed to it and an equally strong negative effect on the other half. In a traditional between-subjects experiment, the positive effect on half the participants in the treatment condition would be statistically cancelled out by the negative effect on the other half. The mean for the treatment group would then be the same as the mean for the control group, making it seem as though the treatment had no effect when in fact it had a strong effect on every single participant!

But again, group researchers share this concern. Although they do focus on group statistics, they also emphasize the importance of examining distributions of individual scores. For example, if some participants were positively affected by a treatment and others negatively affected by it, this would produce a bimodal distribution of scores and could be detected by looking at a histogram of the data. The use of within-subjects designs is another strategy that allows group researchers to observe effects at the individual level and even to specify what percentage of individuals exhibit strong, medium, weak, and even negative effects. Finally, factorial designs can be used to examine whether the effects of an independent variable on a dependent variable differ in different groups of participants (introverts vs. extraverts).

External Validity

The second issue about which single-subject and group researchers sometimes disagree has to do with external validity—the ability to generalize the results of a study beyond the people and specific situation actually studied. In particular, advocates of group research point out the difficulty in knowing whether results for just a few participants are likely to generalize to others in the population. Imagine, for example, that in a single-subject study, a treatment has been shown to reduce self-injury for each of two children with intellectual disabilities. Even if the effect is strong for these two children, how can one know whether this treatment is likely to work for other children with intellectual delays?

Again, single-subject researchers share this concern. In response, they note that the strong and consistent effects they are typically interested in—even when observed in small samples—are likely to generalize to others in the population. Single-subject researchers also note that they place a strong emphasis on replicating their research results. When they observe an effect with a small sample of participants, they typically try to replicate it with another small sample—perhaps with a slightly different type of participant or under slightly different conditions. Each time they observe similar results, they rightfully become more confident in the generality of those results. Single-subject researchers can also point to the fact that the principles of classical and operant conditioning—most of which were discovered using the single-subject approach—have been successfully generalized across an incredibly wide range of species and situations.

And, once again turning the tables, single-subject researchers have concerns of their own about the external validity of group research. One extremely important point they make is that studying large groups of participants does not entirely solve the problem of generalizing to other  individuals . Imagine, for example, a treatment that has been shown to have a small positive effect on average in a large group study. It is likely that although many participants exhibited a small positive effect, others exhibited a large positive effect, and still others exhibited a small negative effect. When it comes to applying this treatment to another large  group , we can be fairly sure that it will have a small effect on average. But when it comes to applying this treatment to another  individual , we cannot be sure whether it will have a small, a large, or even a negative effect. Another point that single-subject researchers make is that group researchers also face a similar problem when they study a single situation and then generalize their results to other situations. For example, researchers who conduct a study on the effect of cell phone use on drivers on a closed oval track probably want to apply their results to drivers in many other real-world driving situations. But notice that this requires generalizing from a single situation to a population of situations. Thus the ability to generalize is based on much more than just the sheer number of participants one has studied. It requires a careful consideration of the similarity of the participants  and  situations studied to the population of participants and situations to which one wants to generalize (Shadish, Cook, & Campbell, 2002) [3] .

Single-Subject and Group Research as Complementary Methods

As with quantitative and qualitative research, it is probably best to conceptualize single-subject research and group research as complementary methods that have different strengths and weaknesses and that are appropriate for answering different kinds of research questions (Kazdin, 1982) [4] . Single-subject research is particularly good for testing the effectiveness of treatments on individuals when the focus is on strong, consistent, and biologically or socially important effects. It is also especially useful when the behavior of particular individuals is of interest. Clinicians who work with only one individual at a time may find that it is their only option for doing systematic quantitative research.

Group research, on the other hand, is ideal for testing the effectiveness of treatments at the group level. Among the advantages of this approach is that it allows researchers to detect weak effects, which can be of interest for many reasons. For example, finding a weak treatment effect might lead to refinements of the treatment that eventually produce a larger and more meaningful effect. Group research is also good for studying interactions between treatments and participant characteristics. For example, if a treatment is effective for those who are high in motivation to change and ineffective for those who are low in motivation to change, then a group design can detect this much more efficiently than a single-subject design. Group research is also necessary to answer questions that cannot be addressed using the single-subject approach, including questions about independent variables that cannot be manipulated (e.g., number of siblings, extraversion, culture).

Finally, it is important to understand that the single-subject and group approaches represent different research traditions. This factor is probably the most important one affecting which approach a researcher uses. Researchers in the experimental analysis of behavior and applied behavior analysis learn to conceptualize their research questions in ways that are amenable to the single-subject approach. Researchers in most other areas of psychology learn to conceptualize their research questions in ways that are amenable to the group approach. At the same time, there are many topics in psychology in which research from the two traditions have informed each other and been successfully integrated. One example is research suggesting that both animals and humans have an innate “number sense”—an awareness of how many objects or events of a particular type have they have experienced without actually having to count them (Dehaene, 2011) [5] . Single-subject research with rats and birds and group research with human infants have shown strikingly similar abilities in those populations to discriminate small numbers of objects and events. This number sense—which probably evolved long before humans did—may even be the foundation of humans’ advanced mathematical abilities.

The Principle of Converging Evidence

Now that you have been introduced to many of the most commonly used research methods in psychology it should be readily apparent that no design is perfect. Every research design has strengths and weakness. True experiments typically have high internal validity but may have problems with external validity, while non-experimental research (e.g., correlational research) often has good external validity but poor internal validity. Each study brings us closer to the truth but no single study can ever be considered definitive. This is one reason why, in science, we say there is no such thing as scientific proof, there is only scientific evidence.

While the media will often try to reach strong conclusions on the basis of the findings of one study, scientists focus on evaluating a body of research. Scientists evaluate theories not by waiting for the perfect experiment but by looking at the overall trends in a number of partially flawed studies. The idea of converging evidence tells us to examine the pattern of flaws running through the research literature because the nature of this pattern can either support or undermine the conclusions we wish to draw. Suppose the findings from a number of different studies were largely consistent in supporting a particular conclusion. If all of the studies were flawed in a similar way, for example, if all of the studies were correlational and contained the third variable problem and the directionality problem, this would undermine confidence in the conclusions drawn because the consistency of the outcome may simply have resulted from a particular flaw that all of the studies shared. On the other hand, if all of the studies were flawed in different ways and the weakness of some of the studies were the strength of others (the low external validity of a true experiment was balanced by the high external validity of a correlational study), then we could be more confident in our conclusions.

While there are fundamental tradeoffs in different research methods, the diverse set of approaches used by psychologists have complementary strengths that allow us to search for converging evidence. We can reach meaningful conclusions and come closer to understanding truth by examining a large number of different studies each with different strengths and weakness. If the result of a large number of studies all conducted using different designs converge on the same conclusion then our confidence in that conclusion can be increased dramatically. In science, we strive for progress, not perfection.

Key Takeaways

Creative Commons License

Share This Book

Our websites may use cookies to personalize and enhance your experience. By continuing without changing your cookie settings, you agree to this collection. For more information, please see our University Websites Privacy Notice .

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).

image002

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.

image004

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.

image006

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.

image008

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.

image010

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)

image012

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

image014

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 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 02/28/2015

ASHA_org_pad

Single-Subject Experimental Design: An Overview

Cred library, julie wambaugh, and ralf schlosser.

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:

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:

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.

Further Reading: Components of Single-Subject Designs

Kratochwill, T. R., Hitchcock, J., Horner, R. H., Levin, J. R., Odom, S. L., Rindskopf, D. M. & Shadish, W. R. (2010). Single-case designs technical documentation. From the What Works Clearinghouse. http://ies.ed.gov/ncee/wwc/documentsum.aspx?sid=229

Further Reading: Single-Subject Design Textbooks

Kazdin, A. E. (2011). Single-case research designs: Methods for clinical and applied settings. Oxford University Press.

McReynolds, L. V. & Kearns, K. (1983). Single-subject experimental designs in communicative disorders. Baltimore: University Park Press.

Further Reading: Foundational Articles

Julie Wambaugh University of Utah

Ralf Schlosser Northeastern University

The content of this page is based on selected clips from video interviews conducted at the ASHA National Office.

Additional digested resources and references for further reading were selected and implemented by CREd Library staff.

Copyright © 2015 American Speech-Language-Hearing Association

logoCREDHeader

Clinical Research Education

More from the cred library, innovative treatments for persons with dementia, implementation science resources for crisp, when the ears interact with the brain, follow asha journals on twitter.

logoAcademy_Revised_2

© 1997-2022 American Speech-Language-Hearing Association Privacy Notice Terms of Use

People also looked at

Review article, single-subject research in psychiatry: facts and fictions.

single case research design vs group design

Scientific evidence in the field of psychiatry is mainly derived from group-based (“nomothetic”) studies that yield group-aggregated results, while often the need is to answer questions that apply to individuals. Particularly in the presence of great inter-individual differences and temporal complexities, information at the individual-person level may be valuable for personalized treatment decisions, individual predictions and diagnostics. The single-subject study design can be used to make inferences about individual persons. Yet, the single-subject study is not often used in the field of psychiatry. We believe that this is because of a lack of awareness of its value rather than a lack of usefulness or feasibility. In the present paper, we aimed to resolve some common misconceptions and beliefs about single-subject studies by discussing some commonly heard “facts and fictions.” We also discuss some situations in which the single-subject study is more or less appropriate, and the potential of combining single-subject and group-based study designs into one study. While not intending to plea for single-subject studies at the expense of group-based studies, we hope to increase awareness of the value of single-subject research by informing the reader about several aspects of this design, resolving misunderstanding, and providing references for further reading.

Introduction

Scientific evidence in the field of psychiatry mainly relies on studies that evaluate what is true on average in the population or a group. In many instances these studies yield valuable information, but particularly when the goal is to improve patient care we need to answer questions that apply to individual patients. For instance, if we want to know whether an antidepressant drug is effective in a particular patient, it will not suffice to know that this drug results in an average reduction of 0.31 SD in depressive symptoms in the population ( 1 ). Also, knowing that at the group level depressive symptoms are associated with increased levels of inflammatory markers ( 2 ) will not inform us whether for a specific patient depressive symptoms will increase when levels of inflammatory markers increase. It is increasingly being recognized that there are great inter-individual differences in causes, risk factors, and course over time of psychiatric disorders and their symptoms, and their response to treatments [e.g., ( 3 , 4 )].

To illustrate the potential magnitude of this heterogeneity, Figure 1 shows the course over time of depressive symptoms weekly assessed over a period of 3 years in 267 persons who were depressed at baseline [see for study details ( 5 )]. The Figure shows that there are great differences between persons in the trajectories and that most persons show substantial fluctuations in symptom levels over time. It seems that for very few persons the average trajectory (left panel) applies, even were it to some extent. So, one may wonder to what extent such group-level results will give us information about what happens in most individual persons studied in that group.

www.frontiersin.org

Figure 1 . Weekly assessed depressive symptom severity over a period of 3 years in 267 persons who were initially depressed. Left : mean (95% CI) symptom severity. Right : trajectories of the individual persons.

What the Figure shows is not an extraordinary pattern, and many authors have noted the problem of relying on averages when no subject is average [e.g., ( 6 – 12 )]. Many phenomena we study in the field of psychiatry are very heterogeneous across people, and most phenomena are not static but are highly dynamic (for example mood regulation and stress physiology). In the presence of such great inter- and intra-individual variability, information at the individual-person level may be of great value for making personalized treatment decisions or identifying personal predictors of changes in symptoms. Furthermore, in order to grasp the highly dynamic nature of certain phenomena we would need multiple repeated assessments across time. The single-subject study is a useful study design that can be used to make inferences about individual persons and to uncover the highly dynamic nature of our variables of interest. Nevertheless, this design is rarely used in the field of psychiatry. We think this may be due to a lack of awareness of its value, which may be partly due to a number of persistent misconceptions regarding single-subject studies. In the present paper, we aim to increase the recognition of the value of single-subject studies in the field of psychiatry by discussing some major facts and fictions of single-subject research.

Single-subject studies are characterized by their focus on single persons. This is in contrast to most traditional group-based (“nomothetic”) study designs, which focus on group averages and compare (groups of) individuals with other individuals (such as RCTs, cohort studies or case-control studies). In single-subject studies, data of each individual are analyzed separately and individuals are compared with themselves ( 13 , 14 ). By virtue of multiple assessments collected within one individual, an individual can serve as his or her own control over time. This allows to quantitatively examine whether changes in one variable are systematically related to changes in another variable within an individual (observational single-subject design), or whether an experimental manipulation is related to a consistent change within this individual (experimental single-subject design; see Figure 2 ).

www.frontiersin.org

Figure 2 . Schematic illustration of different sorts of single-subject studies and some examples of aims of the two types of single-subject studies.

More generalizable conclusions can be obtained by replicating multiple single-subject studies on a specific topic. In the presence of great inter- and intra-individual variability, this will only answer questions that apply to individual patients when each participant is analyzed at the intra-individual level. If the same effect is found in a series of single-subject studies, this could potentially be the basis for a generalizable conclusion. That is, the association might be true for the majority of persons [i.e., true in general; ( 15 )]. In case of large heterogeneity, the chance of finding such commonalities for processes underlying psychiatric disorders might not be great. In that case, single-subject studies can be used to link individual-level results to certain person characteristics, or may be used in clinical practice to inform the treatment process.

The single-subject study might be rare nowadays, it has been used much in earlier centuries and has yielded important information about human behavior, physiology and pathology (see Box 1 ). The use of this design began to decline at the beginning of the 20th century, when people became interested in the improvement of species or races ( 27 ). In that time, scientists (and eugenicists) like Pearson and Fisher introduced statistical techniques focusing on group averages, therewith fueling a paradigm shift toward group-aggregated results. This shift toward statistics based on group averages was a logical step to make if the interest is in improving species or plant varieties. For example, if a farmer wants to know which factors improve the growth of lettuce plants, he is not interested in the growth of the individual lettuce plant, but rather in the average yield of the whole field of lettuce plants. However, as we illustrated in the first paragraph of this introduction, many questions in the field of psychiatry apply to individual patients. The almost complete disappearance of the single-subject study at the beginning of the 20th century therefore seems incompatible with the demand for information that applies to individual persons in this field.

Box 1 . The single-subject study has a long history.

Single-subject research is rather unpopular nowadays, except in some specific subfields of psychology ( 16 ). However, it used to be very common. At the end of the 19th century, “the intensive study of individuals” (also called “Idiographic research”) was the most popular scientific approach ( 15 ). The term “nomothetic” had a different meaning in those days: nomothetic research was research aimed at establishing general laws and theories ( 10 ). It was thought that establishing general laws cannot be done without describing and explaining particular events and individual processes. That is, in order to find out whether something is true for all or the majority of persons, one must first describe and explain what holds for single individuals. Accordingly, nomothetic and idiographic research were seen as complementary. This changed in the beginning of the 20th century. Inspired by famous statisticians and eugenicists like Pearson and Fisher, who introduced techniques like the correlation coefficient and the normal distribution, the research focus shifted from the intensive study of individuals to the study of aggregates from large groups. The label “nomothetic” came to stand for group-based research, and instead of focusing on what is common to all, analyses became focused on what is true “on average” ( 10 , 15 ). 1 Although seemingly old-fashioned, several useful findings have sprouted from the study of individuals, for example from quantitative research by Ebbinghaus, Pavlov, Thorndike, Watson, and Shapiro, and qualitative research by Broca, James, Freud, Alzheimer, and Piaget, which has been described elegantly elsewhere ( 10 , 17 , 18 ). One of the most famous adepts of the single-subject approach was Burrhus Skinner, who said that he would rather study one rat for a thousand times than a thousand rats for 1 h each ( 19 ). Skinner studied how animal subjects (such as pigeons or rats) acquired certain behaviors in response to stimuli by rewarding or punishing the animal ( 20 ). This work on operant conditioning revealed important knowledge of human behavior that is still applied nowadays for addressing clinical problems, such as the treatment of addiction ( 21 ), and the development of cognitive behavioral and operant behavioral therapies ( 22 ). In medical sciences, single-subject studies are still used occasionally, for example to examine the benefits or side effects of a drug in individual patients. Such “n-of-1 trials” have shown their potential in terms of deciding whether a specific intervention works for a specific individual patient [e.g., ( 23 – 26 )].

Although they are still relatively rare, in recent years single-subject studies have been more frequently used in the field of psychiatry, possibly due to innovations in ecological momentary assessment (EMA), analytic methods, and technologies ( 28 , 29 ). Single-subject studies in the field of psychiatry have been applied for several reasons (see Figure 2 ). First, observational single-subject studies have been done to evaluate temporal associations between variables that may be important in processes underlying psychiatric disorders [e.g., ( 30 – 33 )]. Such studies can yield very useful information about potential risk and protective factors at the individual level, which can increase scientific as well as clinical insight. Some recent studies have elaborated on this, and used single-subject research for developing personalized diagnostics ( 34 – 40 ) and person-tailored treatment advice ( 41 – 44 ). Another applicability of observational single-subject studies is the examination of the temporal dynamics of single variables, such as the variability or inertia (or autocorrelation, i.e., the degree to which successive observations are related to each other). For instance, Wichers et al. revealed that an increase in autocorrelation in negative affect preceded a relapse of depression in a single patient ( 45 ). Thus, single-subject studies can yield information that can be used for detecting psychopathological changes or early warning signals. Another application of the single-subject study is to evaluate the effects and side effects of interventions in single individuals. In the field of psychiatry such experimental single-subject studies have evaluated, for example, the person-specific effects of individualized cognitive therapy for depression in women with metastatic cancer ( 46 ), pharmacological treatment for depression ( 47 ), stimulants for ADHD in children ( 48 ), and treatments for schizophrenia ( 49 ). Taken together, single-subject studies are and can be used for different reasons in the field of psychiatry.

Despite a small recent increase in the use of the single-subject study design, it is still relatively scarce in the field of psychiatry. This is remarkable in a field that typically has to deal with a lot of inter-individual heterogeneity and intra-individual variability, and in which there is a high demand for results that apply to individual patients. We believe that this is because of a lack of awareness of the value of single-subject studies rather than a lack of usefulness or feasibility. We will discuss several facts and fictions regarding single-subject research, in order to resolve some existing misconceptions about single-subject studies and make the reader more aware of their value.

Facts and Fictions

We will now describe several statements that are often heard from, for example, reviewers, members of ethical boards, funding agencies, and colleague researchers. For each statement we will explain whether we think it is a fact or fiction, and elaborate on this.

Statement 1. Single-Subject Studies Are Case Reports and Therefore Have No Scientific Value

Although single-subject and case reports both focus on individuals (i.e., are both idiographic; see Figure 2 ), there are some major differences. A case report is the presentation of an interesting observation on a patient by the treating specialist that lacks a pre-conceived design and systematic assessments. Although case reports may be very informative for generating hypotheses, the lack of systematic design elements makes them prone to bias and invalid inference ( 14 , 50 ). For instance, a clinician may observe improvement in a depressed patient after a certain therapy [e.g., ( 51 )] and may attribute this improvement to the therapy, while in reality it was due to something else or a spontaneous recovery. Experimental single-subject studies have specific design elements that help elucidate whether the improvement is really due to the therapy. They have a pre-conceived design with different cross-over periods (intervention and control) and pre-planned assessments, often done with validated instruments by an independent researcher [for guidelines see ( 14 , 17 , 50 , 52 )].

Observational single-subject studies are also characterized by a pre-planned design and systematic assessments of outcomes, making them valuable for scientific research and clinical diagnostics. For example, a patient might want to know whether he generally feels more depressed after seeing his mother in law. A single-subject observational study can answer questions about such dynamic associations between variables within one individual, which may go undetected in the clinical care setting.

Thus, if a single-subject study is set up properly and has enough observations to allow for statistical inference, it can yield valid and reliable scientific and clinical evidence ( 13 , 17 , 23 ).

Statement 2. The Sample Size of Single-Subject Studies Is Too Small to Yield Enough Statistical Power

An often-heard objection is that the sample size of single-subject studies is too small. However, in single-subject studies time-series data are analyzed for each individual separately. Because of this, the power in single-subject studies depends on the number of repeated observations within a person instead of the number of persons. Thus, if the number of repeated observations within the individual is large enough for the planned statistical analysis, the power is sufficient. A variety of statistical methods exists for the analysis of single-subject data [e.g., ( 29 , 53 , 54 )].

Statement 3. The Sample Size of Single-Subject Studies Is Often Too Small to Generalize Findings to the Population

While power of a single-subject study can be sufficient even if n = 1 (see Statement 2), the sample size of single-subject studies is still relevant for generalizability to the population. One can only generalize findings to a population if it can be demonstrated that the principle holds in all, or a large majority, of a representative sample, which is not possible if n = 1. However, in order to generalize the results of single-subject studies, multiple single-subject studies can be performed in individuals of the same population (direct replication), or in different settings or populations (systematic replication) ( 55 , 56 ). Results of multiple single-subject studies may subsequently be summarized using for instance meta-analysis ( 24 , 57 , 58 ). If the ultimate goal is to gather scientific evidence concerning questions that apply to individual persons, researchers can build a body of single-case work, eventually leading to a large sample.

Statement 4. Group-Based Studies Are More Suitable Than Single-Subject Studies to Find Out What Is True in General

Results from group-based studies yield information about what is “true on average” and typically end up in standardized treatment guidelines. However, in the presence of large inter- and intra-individual differences, average effects are not informative on whether a result is “true in general” (i.e., present in the majority of the sample) ( 15 ). For instance, mood disorders are on average associated with higher cortisol levels at the group level ( 59 ), but this does not necessarily mean that worse mood is associated with higher cortisol levels in the majority of individuals. A single-subject study repeated in 30 individuals found great individual differences in the within-subject association between mood and cortisol levels ( 31 ). Thus, group-aggregated, averaged results are not necessarily or very likely true for each individual in that group. Moreover, the average may also be a poor reflection of what is true for most individuals in the group, for example if the distribution of parameters is bimodal or trimodal ( 60 ). Also creating subgroups may not solve that problem, because we often do not know by which characteristics we must define subgroups.

More formally, it has been shown that results obtained from group-based studies can only be generalized to individuals when the assumption of “ergodicity” is met. Ergodicity implies that the average, variance, covariance and lagged covariance between variables should be the same for all individuals (homogeneity), and that no changes over time in these statistical characteristics should be present (stationarity) ( 9 , 61 , 62 ). In the absence of ergodicity, effects calculated at the group level, or even at a more homogeneous subgroup level, will not generalize to the individual level ( 7 , 9 , 12 , 15 , 63 – 65 ). An association found at the (sub) group level may be weaker, stronger, absent or even reversed in an individual ( 61 ). In fact, group-aggregated results may sometimes not even apply to a single individual in that group ( 10 , 15 ).

Statement 5. Confounding in Single-Subject Studies Is the Same as in Group-Based Studies

A point frequently raised by reviewers is that analyses of single-subject studies should be adjusted for relevant demographic or clinical variables. Indeed, these type of variables may confound associations in group-based studies because they may differ between individuals (between-subjects confounding). However, in single-subject studies all variance in the outcome is due to within-person variance in other variables, which may include changes in environmental variables, events, lifestyle- or other behaviors, treatments, etc. ( 61 , 62 ). Therefore, in single-subject studies variables can only confound an association if they vary within the individual over time (within-subject confounding). Variables that do not show fluctuations over time, such as sex or a stable somatic condition, need (and can) not be adjusted for in single-subject studies. While in group-based studies both between- and within-subjects confounding may occur, in single-subject studies only within-subject confounding may occur. Only if one reverts to a group approach, for example by combining results from multiple single-subject studies, group-level covariates will become applicable again.

Statement 6. A Single-Subject Study Cannot Establish Causality

It is not possible to establish causality using a single-subject design. Moreover, this is also true for group-based study designs. Hypothetically, the ideal experiment to determine the causal effect of a certain treatment would be to expose an individual to this treatment, observe what happens, and then go back in time and expose the same individual to another condition (i.e., no treatment or placebo), all other things being equal ( 25 , 66 ). Of course, such a design is not possible as we cannot go back in time. But interestingly, certain forms of single-subject studies come close to this ideal experimental design: the n-of-1 randomized controlled trials (n-of-1 RCTs). In these trials, various conditions (e.g., treatment and placebo, or medicines with varying dosages) are alternated over time and the order of exposure is determined randomly. In this way, each individual serves as his or her own control. The strength of the design increases if multiple cross-over periods are used and patients as well as clinicians and researchers are blinded to the treatment order. A few studies in the field of psychiatry have used such designs successfully to examine the impact of interventions in single patients [e.g., ( 47 – 49 , 67 , 68 )]. The deviation of this n-of-1 RCT from the ideal counterfactual experiment is small and concerns only the fact that the individual may have changed over time. In the traditional group-based RCT, groups of people are compared with each other, assuming these groups are similar. This latter assumed similarity is a stronger assumption, and generally not true ( 69 ) than the assumption that an individual is similar to him- or herself somewhat earlier in time ( 66 ). Furthermore, n-of-1 RCTs often include patients that do not meet the highly selective inclusion criteria of group-based RCTs, and thus provide information for patients for whom there is currently a lack of evidence for treatment efficacy ( 47 ). Thus, the n-of-1 RCT appears at least as ideal as the group-based RCT to establish causality, and therefore deserves more attention in the scientific field ( 25 , 66 , 70 ).

In addition to the n-of-1 RCT, other forms of single-subject studies, such as ABA-designs or observational single-subject studies can also contribute information that is important for establishing causality, including strength of the association, consistency of the association in different contexts and times, specificity of the association, temporal precedence, and dose-response relationship ( 71 , 72 ). Because an individual serves as his or her own control in all single-subject studies, it is possible to determine in a systematic way the strength, consistency, specificity and dose-response relation of an association within that individual. Furthermore, because of the multitude of repeated assessments in single-subject studies, the temporal order of associations can be revealed ( 30 , 31 , 73 – 75 ). For example, it can be revealed that changes in certain factors systematically precede changes in other variables (i.e., temporal precedence). Even though causality can probably never be completely established, these aspects of single-subject studies are particularly helpful to approach valid causal inference. Also sophisticated approaches to establish causal inference via Directed Acyclic Graphs and Structural Causal models can be applied to single-subject models ( 76 , 77 ), although these have very strict assumptions that are very hard to meet in practice ( 77 ).

Statement 7. Single-Subject Research Is a Lot of Work

An often-heard statement about single-subject research is that it needs a lot of effort, which is true. Single-subject research involves frequent/repeated assessment during a relatively long period of time, which is time consuming and effortful for both the participant, the researcher, and for therapists if they are involved. However, due to recent technical innovations and increased smartphone and sensor use, collecting ambulatory time-series data has become more feasible. The feasibility has been shown for healthy individuals ( 37 ), older adults ( 78 , 79 ), and also for patients with psychiatric disorders such as severe depression or bipolar disorder ( 80 ), panic disorder ( 36 ), psychosis ( 81 ), eating disorders ( 82 ) or ADHD ( 83 ). Data collection via a smartphone is more convenient for the participant than paper-and-pencil methods and makes laborious and error-prone data entry unnecessary. The researcher mainly has to focus on data cleaning, statistical analysis and (optionally) feedback generation, for which nowadays more and more automated algorithms are being developed [e.g., ( 84 , 85 )].

Single-subject research is also more feasible when participating in a single-subject study is rewarding for participants. For instance, revealing the personal treatment effects or optimal dosage of a certain drug ( 47 ), or giving diagnostic information through a personalized feedback report ( 37 , 41 , 43 ) appeared particularly motivating to increase compliance in clinical samples. Feedback reports can contain descriptive feedback [e.g., ( 41 )] or information about potential triggers of symptoms based on statistical models [e.g., ( 36 , 37 )]. Although there are still many challenges that need to be resolved ( 86 ), personalized feedback may reveal valuable insights for patients and thus help to motivate them to complete the study.

Statement 8. One Can Just as Well Use Multilevel Modeling in Order to Analyze Data of a Group of Individuals

In single-subject research, time series of each individual are analyzed separately. But why not use multilevel modeling instead? Multilevel modeling is a well-known tool for analyzing longitudinal data collected in multiple persons, that also allows to study within-person associations. While this is true, multilevel methods still yield group-aggregated results. The fixed effects, which are usually the main outcome of interest, represent the average effect in the group. The fixed effects are a mix of within- and between-person effects, but person-mean centering of the predictors can be applied if we are interested in within-person associations[( 87 ); for examples of such studies, see ( 88 – 94 )]. However, such analyses still yield average within-person associations. As discussed in Statement 4, the average may not reflect what is true in general, i.e., for the majority of individuals. If there is large heterogeneity in the sample, for example if the distribution of parameters is bimodal or trimodal, or if the functional form of the model differs across individuals, the fixed effects will be a poor reflection of what holds for individuals ( 60 , 95 , 96 ). As a corollary, also the random effects (the inter-individual differences in the effects) may not be appropriate. Random effects are post-hoc estimated deviations from the average effects, and are assumed to be normally distributed around these averages. If the latter are inaccurate, so will be the random effects ( 60 , 95 , 96 ).

Furthermore, while person-mean centering is useful for disaggregating between- and within-person effects of the predictors, with respect to other model features within- and between-person variance is more difficult to separate (for example, the error covariance matrix) ( 97 , 98 ). Recently, new models like Dynamic Structural Equation Modeling ( 99 ), or Bayesian Dynamic Modeling ( 96 , 100 , 101 ) do offer increased possibilities to adequately model other model features within a multilevel framework. However, with increased model complexity, for example with multiple interactions, feedback loops, or non-linear effects, the problem of disaggregating within- and between-person variance in multilevel models becomes quite difficult. Analyzing data at the individual level leaves more room for modeling such complexities ( 56 , 95 , 96 ).

Nevertheless, there are situations in which multilevel modeling is the preferred statistical method. Multilevel models have the advantage that they can “borrow strength” from the data of other individuals ( 95 , 101 ). This may be a great advantage if individual time-series data are noisy, the number of repeated measures is low, or the sample is homogeneous. In such cases, multilevel models will yield better estimates than single-subject analyses. A replicated single-subjects approach may be the statistical method of choice in case of large heterogeneity, many repeated observations, or high complexity ( 33 , 56 , 60 , 95 , 96 , 101 ).

Despite the relatively high demand for information applying to individual persons, the single-subject study is not often used in the field of psychiatry. We believe that this is because of a lack of awareness of their value rather than a lack of usefulness or feasibility. In the present paper we aimed to resolve some common misconceptions and beliefs about single-subject studies by discussing some commonly heard facts and fictions.

Single-subject studies can be particularly useful and have additional value in several situations. For example, when there are large inter-individual differences in the processes under study, when these processes are very complex or nonlinear, or can change over time. Additionally, single-subject studies have the advantage that they may include any patient (also those with complex or rare diseases), and (in the case of experimental single-subject studies) are able to adjust the treatment when deemed necessary ( 102 , 103 ). This increases the ecological validity of the single-subject study, and makes it particularly useful in situations when large-group studies are not feasible; for instance because the disease or event under study is rare, the patient is complex (e.g., many comorbidities), the setting is complex (e.g., palliative care), the intervention is highly expensive or controversial, or the intervention contains person-specific elements ( 46 , 56 , 102 , 104 , 105 ). Furthermore, single-subject studies may involve patients more in their treatment process, thereby increasing patient empowerment and shared decision making ( 68 ). The single-subject study may also contribute in the diagnostic process, to evaluate factors contributing to treatment responses, or to evaluate efficacy of certain treatments ( 34 , 38 – 41 , 43 , 44 ). Practically, this has led to the recent development of algorithms and clinical care applications that implement single-subject analyses in the diagnostic process in clinical care settings [( 42 , 44 , 106 ) conference abstract ]. Single-subject studies can also be used to obtain a detailed description of a particular approach applied to an individual in order to test an existing clinical theory [theory exemplification; ( 107 )]. While group-based studies often only study a limited number of aspects belonging to a theory, a single-subject study can map in detail all its aspects together in one person. For example, a single-subject study could detail all processes underlying response to cognitive therapy in a specific patient [e.g. ( 108 )], thereby showing how to optimally apply an existing theory underlying cognitive therapy. Complex statistical models can be linked to processes underlying psychiatric disorders [for example how a panic attack evolves in a specific patient, ( 109 )], which may help in understanding their mechanism in individual patients.

Despite these advantages of single-subject studies, group-based studies are more appropriate if one wants to make inferences about average tendencies in the population, such as the prevalence, incidence or average risk of a disorder, or the average effect of certain treatments in the whole population or a certain subpopulation. For instance, if one wants to know whether legalizing cannabis helps in reducing the prevalence or incidence of psychotic disorders in the population. Moreover, group-based studies are more appropriate if individual time-series data are very short or noisy, or the process under study is homogeneous across individuals ( 33 , 95 , 101 ). Thus, group-based and single-subject studies can both be useful and are appropriate in different situations.

In some circumstances, the group-based and single-subject approach may be combined. A first reason for combining these designs is to identify commonalities across persons, in order to increase generalizability to the population. Practically this can be done by combining group- and individual-level analyses in one model, for example using Group Iterative Multiple Model Estimation [GIMME ( 60 )], or meta-analytical techniques for pooling data from multiple single-subject studies ( 57 , 58 , 110 ). Related to this, pooling data from multiple single-subject studies can be used to link individual-level results to certain between-subjects characteristics. For instance, results from multiple experimental single-subject studies may be pooled and linked to patient characteristics in order to identify which patient characteristics are associated with better outcomes of a certain treatment ( 103 ). Likewise, data from multiple observational single-subject studies may be pooled in order to identify whether person-specific associations between variables can be linked to certain patient characteristics such as the presence of a depressive disorder [e.g. ( 31 , 73 , 111 )], or different severity of depressive symptoms ( 112 ). Another reason to combine the single-subject with the group-based approach would be to examine the group-level effectiveness of an individualized treatment or lifestyle advice that is based on single-subject analyses of diary observations [e.g., ( 43 , 44 )]. In this way, the single-subject study design can be of added value to the increasing urge for personalized patient care in mental health care settings ( 29 , 113 – 115 ).

In the field of psychiatry, single-subject studies are still relatively scarce. We hope that we have resolved some misunderstandings surrounding single-subject studies and have increased the reader's awareness of possibilities and impossibilities of the single-subject design. Although single-subject studies are definitely not suitable in all circumstances, we believe that they deserve more attention in the field of psychiatry, especially in view of the current urge for personalized patient care, increased importance of shared decision making, increased availability of electronic devices and sensors, and recent advancements in analytic methods for time-series data.

Author Contributions

MZ, HR, ES, SB, MW, and EB: conceptualization, review, and editing. ES, SB, and MW wrote sections of the manuscript. MZ, HR, and EB: wrote first draft of major parts of the manuscript. All authors contributed to the article and approved the submitted version.

MW received an ERC consolidator grant (project No. 681466; TRANS-ID, 2015). HR received a grant of the Foundation VCVGZ (Stichting tot Steun VCVGZ, grant no. 239). HR and MW received a grant of the Innovatiefonds De Friesland (grant no. DS81). The sponsors had no role in the design and content of the present paper.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

We thank Dr. Conradi and Prof. Dr. de Jonge for using the data of the INSTEL study. This project was initiated by the iLab of the Department of Psychiatry, University Medical Center Groningen, Groningen, Netherlands ( http://ilab-psychiatry.nl ).

1. ^ In view of the ambiguous meaning of the word “nomothetic,” we use the term “group-based” throughout the paper when we refer to a study that focuses on group-aggregated results.

1. Turner EH, Matthews AM, Linardatos E, Tell RA, Rosenthal R. Selective publication of antidepressant trials and its influence on apparent efficacy. N Engl J Med . (2008) 358:252–60. doi: 10.1056/NEJMsa065779

PubMed Abstract | CrossRef Full Text | Google Scholar

2. Howren MB, Lamkin DM, Suls J. Associations of depression with C-reactive protein, IL-1, and IL-6: a meta-analysis. Psychosom Med . (2009) 71:171–86. doi: 10.1097/PSY.0b013e3181907c1b

CrossRef Full Text | Google Scholar

3. Ozomaru U, Wahlestedt C, Nemeroff CB. Personalized medicine in psychiatry: problems and promises. BMC Med . (2013) 11:132. doi: 10.1186/1741-7015-11-132

4. Fernandes BS, Williams LM, Steiner J, Leboyer M, Carvalho AF, Berk M. The new field of ‘precision psychiatry'. BMC Med . (2017) 15:80. doi: 10.1186/s12916-017-0849-x

5. Conradi HJ, de Jonge P, Kluiter H, Smit A, Van der Meer K, Jenner JA, et al. Enhanced treatment for depression in primary care: long-term outcomes of a psycho-educational prevention program alone and enriched with psychiatric consultation or cognitive behavioral therapy. Psychol Med . (2007) 37:849–62. doi: 10.1017/S0033291706009809

6. Estes WK, Maddox WT. Risks of drawing inferences about cognitive processes from model fits to individual versus average performance. Psychonomic Bull Rev. (2005) 12:403–8. doi: 10.3758/BF03193784

7. Hamaker EL, Dolan CV, Molenaar PCM. Statistical modeling of the individual: Rationale and application of multivariate stationary time series analysis. Multivariate Behav Res . (2005) 40:207–33. doi: 10.1207/s15327906mbr4002_3

8. Navarro DJ, Griffiths TL, Steyvers M, Lee MD. Modeling individual differences using Dirichlet Processes. J Math Psychol. (2006) 50:101–22. doi: 10.1016/j.jmp.2005.11.006

9. Molenaar PCM, Campbell CG. The new person-specific paradigm in psychology. Curr Dir Psychol Sci . (2009) 18:112–7. doi: 10.1111/j.1467-8721.2009.01619.x

10. Robinson OC. The idiographic/nomothetic dichotomy: tracing historical origins of contemporary confusions. History Philos Psychol . (2011) 13:32–9.

Google Scholar

11. Liew SX, Howe PDL, Little DR. The appropriacy of averaging in the study of context effects. Psychon Bull Rev. (2016) 23:1639–46. doi: 10.3758/s13423-016-1032-7

12. Fisher AJ, Medaglia JD, Jeronimus BF. Lack of group-to-individual generalizability is a threat to human subjects research. Proc Natl Acad Sci USA . (2018) 115:E6106–15. doi: 10.1073/pnas.1711978115

13. Hilliard RB. Single-case methodology in psychotherapy process and outcome research. J Consult Clin Psychol . (1993) 61:373–80. doi: 10.1037/0022-006X.61.3.373

14. Kazdin AE. Single-Case Research Designs . 2nd ed. New York, NY: Oxford University Press (2011).

15. Lamiell JT. “Nomothetic” and “idiographic”: Contrasting Windelband's understanding with contemporary usage. Theory Psychol . (1998) 8:23–38. doi: 10.1177/0959354398081002

16. Little DR, Smith PL. Replication is already mainstream: Lessons from small- N designs. Behav Brain Sci. (2018) 41:e141. doi: 10.1017/S0140525X18000766

17. Cohen LL, Feinstein A, Masuda A, Vowles KE. Single-case research design in pediatric psychology: considerations regarding data analysis. J Pediatr Psychol . (2014) 39:124–37. doi: 10.1093/jpepsy/jst065

18. Barlow DH, Nock MK. Why can't we be more idiographic in our research? Perspect Psychol Sci . (2009) 4:19–21. doi: 10.1111/j.1745-6924.2009.01088.x

19. Skinner BF. Operant behavior. In: Honig WK, editor. Operant Behavior: Areas of Research and Application . New York, NY: Appleton-Century-Crofts (1966). p.12–32.

20. Skinner BF. Science and Human Behavior . New York, NY: Macmillan (1953).

21. Silverman K. Exploring the limits and utility of operant conditioning in the treatment of drug addiction. Behavior Analyst . (2004) 27:209–30. doi: 10.1007/BF03393181

22. Rachman S. The evolution of behaviour therapy and cognitive behaviour therapy. Behav Res Ther . (2015) 64:1–8. doi: 10.1016/j.brat.2014.10.006

23. Schork NJ. Time for one-person trials. Nature . (2015) 520:609–11. doi: 10.1038/520609a

24. Stunnenberg BC, Woertman W, Raaphorst J, Statland JM, Griggs RC, Timmermans J. Combined n-of-1 trials to investigate mexiletine in non-dystrophic myotonia using a Bayesian approach; study rationale and protocol. BMC Neurol . (2015) 15:43. doi: 10.1186/s12883-015-0294-4

25. Madhok V, Fahey T. N-of-1 trials: an opportunity to tailor treatment in individual patients. Br J Gen Pract . (2005) 55:171–2.

PubMed Abstract | Google Scholar

26. Mahon J, Laupacis A, Donner A, Wood T. Randomised study of n of 1 trials versus standard practice. BMJ . (1996) 312:1069–74. doi: 10.1136/bmj.312.7038.1069

27. Pearson K. The scope of Biometrika. Biometrika . (1901) 1:1–2. doi: 10.1093/biomet/1.1.1

CrossRef Full Text

28. Hamaker EL, Wichers M. No time like the present: discovering the hidden dynamics in intensive longitudinal data. Curr Dir Psycholog Sci . (2017) 26:10–5. doi: 10.1177/0963721416666518

29. Wright AGC, Woods WC. Personalized models of psychopathology. Annu Rev Clin Psychol. (2020) 16:49–74. doi: 10.1146/annurev-clinpsy-102419-125032

30. Rosmalen JGM, Wenting AM, Roest AM, De Jonge P, Bos EH. Revealing causal heterogeneity using time series analysis of ambulatory assessments: application to the association between depression and physical activity after myocardial infarction. Psychosom Med . (2012) 74:377–86. doi: 10.1097/PSY.0b013e3182545d47

31. Booij SH, Bos EH, de Jonge P, Oldehinkel AJ. The temporal dynamics of cortisol and affective states in depressed and non-depressed individuals. Psychoneuroendocrinology . (2016) 69:16–25. doi: 10.1016/j.psyneuen.2016.03.012

32. Cheung YK, Hsueh PS, Qian M, Yoon S, Meli L, Diaz KM, et al. Are nomothetic or ideographic approaches superior in predicting daily exercise behaviors? Methods Inf Med . (2017) 56:452–60. doi: 10.3414/ME16-02-0051

33. Rozet A, Kronish IM, Schwartz JE, Davidson KW. Using machine learning to derive just-in-time personalized predictors of stress: observational study bridging the gap between nomothetic and ideographic approaches. J Med Internet Res . (2019) 21:e12910. doi: 10.2196/12910

34. Bak M, Drukker M, Hasmi L, van Os J. An n=1 clinical network analysis of symptoms and treatment in psychosis. PLoS ONE . (2016) 11:e0162811. doi: 10.1371/journal.pone.0162811

35. Fisher AJ, Reeves JW, Lawyer G, Medaglia JD, Rubel JA. Exploring the idiographic dynamics of mood and anxiety via network analysis. J Abnorm Psychol . (2017) 126:1044–56. doi: 10.1037/abn0000311

36. Kroeze R, van der Veen DC, Servaas MN, Bastiaansen JA, Oude Voshaar RC, Borsboom D, et al. Personalized feedback on symptom dynamics of psychopathology: a proof-of-principle study. J Person Oriented Res . (2017) 3:1–10. doi: 10.17505/jpor.2017.01

37. Van der Krieke L, Blaauw FJ, Emerencia AC, Schenk HM, Slaets JP, Bos EH, et al. Temporal dynamics of health and well-being: a crowdsourcing approach to momentary assessments and automated generation of personalized feedback. Psychosom Med . (2017) 79:213–23. doi: 10.1097/PSY.0000000000000378

38. David SJ, Marshall AJ, Evanovich EK, Mumma GH. Intraindividual dynamic network analysis – implications for clinical assessment. J Psychopathol Behav Assess . (2018) 40:235–48. doi: 10.1007/s10862-017-9632-8

39. Epskamp S, van Borkulo CD, van der Veen DC, Servaas MN, Isvoranu AM, Riese H, et al. Personalized network modeling in psychopathology: the importance of contemporaneous and temporal connections. Clin Psychol Sci . (2018) 6:416–27. doi: 10.1177/2167702617744325

40. Voigt ALA, Kreiter DJ, Jacobs CJ, Revenich EGM, Serafras N, Wiersma M, et al. Clinical network analysis in a bipolar patient using an experience sampling mobile health tool: An n=1 study. Bipolar Disord . (2018) 4:1. doi: 10.4172/2472-1077.1000121

41. Kramer I, Simons CJ, Hartmann JA, Menne-Lothmann C, Viechtbauer W, Peeters F, et al. A therapeutic application of the experience sampling method in the treatment of depression: a randomized controlled trial. World Psychiatry . (2014) 13:68–77. doi: 10.1002/wps.20090

42. Fernandez KC, Fisher AJ, Chi C. Development and initial implementation of the Dynamic Assessment Treatment Algorithm (DATA). PLoS ONE . (2017) 12:e0178806. doi: 10.1371/journal.pone.0178806

43. Van Roekel E, Vrijen C, Heininga VE, Masselink M, Bos EH, Oldehinkel AJ. An exploratory randomized controlled trial of personalized lifestyle advice and tandem skydives as means to reduce anhedonia. Behav Ther . (2017) 48:76–96. doi: 10.1016/j.beth.2016.09.009

44. Fisher AJ, Bosley HG, Fernandez KC, Reeves JW, Soyster PD, Diamond AE, et al. Open trial of a personalized modular treatment for mood and anxiety. Behav Res Ther . (2019) 116:69–79. doi: 10.1016/j.brat.2019.01.010

45. Wichers M, Groot PC, Psychosystems, ESM Group EWS Group. (2016). Critical slowing down as a personalized early warning signal for depression. Psychother. Psychosom . 85:114–6. doi: 10.1159/000441458

46. Lévesque M, Savard J, Simard S, Gauthier JG, Ivers H. Efficacy of cognitive therapy for depression among women with metastatic cancer: a single-case experimental study. J Beh Ther Exp Psychiatry . (2004) 35:287–305. doi: 10.1016/j.jbtep.2004.05.002

47. Kronish IM, Hampsey M, Falzon L, Konrad B, Davidson KW. Personalized (N-of-1) trials for depression. A systematic review. Clin Psychopharmacol . (2018) 38:218–25. doi: 10.1097/JCP.0000000000000864

48. Mordijck E, Danckaerts M, Onghena P. [N-of-1 trials in child and adolescent psychiatry: a closer look at stimulants]. Tijdschrift Voor Psychiatrie . (2018) 60:315–25.

49. Marwick KFM, Stevenson AJ, Davies C, Lawrie SM. Application of n-of-1 treatment trials in schizophrenia: systematic review. Br J Psychiatry . (2018) 213:398–403. doi: 10.1192/bjp.2018.71

50. Kravitz RL, Duan N, (eds) the DEcIDE Methods Center N-of-1 Guidance Panel (Duan N Eslick I Gabler NB Kaplan HC Kravitz RL Larson EB.). Design and Implementation of N-of-1 Trials: A User's Guide . AHRQ Publication No. 13(14)-EHC122-EF. Rockville, MD: Agency for Healthcare Research and Quality (2014).

51. Tavormina R, Tavormina MGM. Overcoming depression with dance movement therapy: a case report. Psychiatria Danubina . (2018) 30:515–20.

52. Vohra S, Shamseer L, Sampson M, Bukutu C, Schmid CH, Tate R, et al. CONSORT extension for reporting N-of 1 trials (CENT) 2015: explanation and elaboration. J Clin Epidemiol . (2016) 76:9–17. doi: 10.1016/j.jclinepi.2015.05.004

53. Hamaker EL, Ceulemans E, Grasman RPPP, Tuerlinckx F. Modeling affect dynamics: state of the art and future challenges. Emot Rev . (2015) 7:316–22. doi: 10.1177/1754073915590619

54. Velicer WF, Molenaar P. Time series analysis. Research methods in psychology. In: Schinka J, Velicer WF, editors. Volume 2 of Handbook of Psychology (I. B. Weiner, Editor-in-Chief) . 2nd ed. New York, NY: John Wiley & Sons (2013). p. 628–60.

55. Ottenbacher KJ, Hinderer SR. Evidence-based practice: methods to evaluate individual patient improvement. Am J Phys Med Rehabil . (2001) 80:786–96. doi: 10.1097/00002060-200110000-00014

56. Smith PL, Little DR. Small is beautiful: in defense of the small-N design. Psychonomic Bull Rev . (2018) 25:2083–101. doi: 10.3758/s13423-018-1451-8

57. Zucker DR, Schmid CH, McIntosh MW, D'Agostino RB, Selker HP, Lau J. Combining single patient (N-of-1) trials to estimate population treatment effects and to evaluate individual patient responses to treatment. J Clin Epidemiol . (1997) 50:401–10. doi: 10.1016/S0895-4356(96)00429-5

58. Huber AM, Tomlinson GA, Koren G, Feldman BM. Amitriptyline to relieve pain in juvenile idiopathic arthritis: a pilot study using Bayesian meta-analysis of multiple n-of-1 clinical trials. J Rheumatol . (2007) 34:1125–32.

59. Stetler C, Miller GE. Depression and hypothalamic-pituitary-adrenal activation: a quantitative summary of four decades of research. Psychosom Med . (2011) 73:114–26. doi: 10.1097/PSY.0b013e31820ad12b

60. Gates KM, Molenaar PC. Group search algorithm recovers effective connectivity maps for individuals in homogeneous and heterogeneous samples. Neuroimage . (2012) 63:310–9. doi: 10.1016/j.neuroimage.2012.06.026

61. Hamaker EL. Why researchers should think ‘within-person': a paradigmatic rationale. In: Mehl MR, Conner TS, editors. Handbook of Research Methods for Studying Daily Life. New York, NY: Guilford Press (2012). p. 43–61.

62. Molenaar PCM. A manifesto on psychology as idiographic science: Bringing the person back into scientific psychology, this time forever. Measurement . (2004) 2:201–18. doi: 10.1207/s15366359mea0204_1

63. Cattell RB. Patterns of change: measurement in relation to state-dimension, trait change, lability, and process concepts. In: Handbook of Multivariate Experimental Psychology . Chicago: Rand McNally (1966). p. 355–402.

64. Nesselroade JR. Interindividual differences in intraindividual change. In Collins LM, Horns JL, editors. Best Methods for the Analysis of Change: Recent Advances, Unanswered Questions, Future Directions . Washington DC: American Psychological Association (1991). p. 92–105.

65. Kievit RA, Frankenhuis WE, Waldorp LJ, Borsboom D. Simpson's paradox in psychological science: a practical guide. Front Psychol . (2013) 4:513. doi: 10.3389/fpsyg.2013.00513

66. Vandenbroucke JP. De N = 1 trial, de meest ideale onderzoeksopzet, die te weinig wordt gebruikt. Ned Tijdschr Geneeskd . (2006) 150:2794–5.

67. Kent MA, Camfield CS, Camfield PR. Doube-blind methylphenidate trials – practical, useful, and highly endorsed by families. Arch Pediatr Adolesc Med . (1999) 153:1292–6. doi: 10.1001/archpedi.153.12.1292

68. Nikles JC, Mitchell GK, DelMar CB, Clavarino A, McNairn N. An n-of-1 trial service in clinical practice: testing the effectiveness of stimulants for attention-deficit/ hyperactivity disorder. Pediatrics . (2006) 117:2040–6. doi: 10.1542/peds,.2005-1328

69. Saint-Mont U. Randomization does not help much, comparability does. PLoS ONE . (2015) 10:e0132102. doi: 10.1371/journal.pone.0132102

70. OCEBM Levels of Evidence Working Group. The Oxford 2011 Levels of Evidence. Oxford Centre for Evidence-Based Medicine . Available online at: https://www.cebm.net/wp-content/uploads/2014/06/CEBM-Levels-of-Evidence-2.1.pdf (assessed January 13, 2020).

71. Hill AB. The environment and disease: association or causation? Proc R Soc Med . (1965) 58:295–300. doi: 10.1177/003591576505800503

72. Kraemer HC, Kazdin AE, Offord DR, Kessler RC, Jensen PS, Kupfer DJ. Coming to terms with the terms of risk. Arch Gen Psychiatry . (1997) 54:337–43. doi: 10.1001/archpsyc.1997.01830160065009

73. Stavrakakis N, Booij SH, Roest AM, De Jonge P, Oldehinkel AJ, Bos EH. Temporal dynamics of physical activity and affect in depressed and nondepressed individuals. Health Psychol . (2015) 34S:1268–77. doi: 10.1037/hea0000303

74. Snippe E, Bos EH, Van der Ploeg K, Sanderman R, Fleer J, Schroevers MJ. Time-series analysis of daily changes in mindfulness, repetitive thinking, and depressive symptoms during a mindfulness-based treatment. Mindfulness . (2015) 6:1053–62. doi: 10.1007/s12671-014-0354-7

75. Hoenders HR, Bos EH, De Jong JT, De Jonge P. Temporal dynamics of symptom and treatment variables in a lifestyle-oriented approach to anxiety disorder: a single-subject time-series analysis. Psychother Psychosom . (2012) 81:253–5. doi: 10.1159/000335928

76. Pearl J, Glymour M, Jewell NP. Causal Inference in Statistics: A Primer . Chichester: John Wiley & Sons (2016).

77. Dablander F. An Introduction to Causal Inference . (2019). Available online at: https://fabiandablander.com/r/Causal-Inference.html (accessed October 20, 2020).

78. Ramsey AT, Wetherell JL, Depp C, Dixon D, Lenze E. Feasibility and acceptability of smartphone assessment in older adults with cognitive and emotional difficulties. J Technol Hum Serv . (2016) 34:209–23. doi: 10.1080/15228835.2016.1170649

79. Maher JP, Rebar AL, Dunton GF. Ecological momentary assessment is a feasible and valid methodological tool to measure older adults' physical activity and sedentary behavior. Front Psychol . (2018) 9:1485. doi: 10.3389/fpsyg.2018.01485

80. Aan Het Rot M, Hogenelst K, Schoevers RA. Mood disorders in everyday life: a systematic review of experience sampling and ecological momentary assessment studies. Clin Psychol Rev . (2012) 32:510–23. doi: 10.1016/j.cpr.2012.05.007

81. Niendam TA, Tully LM, Iosif AM, Kumar D, Nye KE, Denton JC, et al. Enhancing early psychosis treatment using smartphone technology: a longitudinal feasibility and validity study. J Psychiatr Res . (2018) 96:239–46. doi: 10.1016/j.jpsychires.2017.10.017

82. Haedt-Matt AA, Keel PK. Revisiting the affect regulation model of binge eating: a meta-analysis of studies using ecological momentary assessment. Psychol Bull . (2011) 137:660–81. doi: 10.1037/a0023660

83. Miguelez-Fernandez C, de Leon SJ, Baltasar-Tello I, Penuelas-Calvo I, Barrignon ML, Capdevila AS, et al. Evaluating attention-deficit/hyperactivity disorder using ecological momentary assessment: a systematic review. Atten. Defic. Hyperact. Disord . (2018) 10:247–65. doi: 10.1007/s12402-018-0261-1

84. Van der Krieke L, Emerencia AC, Bos EH, Rosmalen JGM, Riese H, Aiello M, et al. Ecological momentary assessments and automated time series analysis to promote tailored health care: a proof-of-principle study. JMIR Res Protoc . (2015) 4:e100. doi: 10.2196/resprot.4000

85. Blaauw FJ, Schenk HM, Jeronimus BF, Van der Krieke L, De Jonge P, Aiello M, et al. Let's get Physiqual – An intuitive and generic method to combine sensor technology with ecological momentary assessments. J of Biomed Inform . (2016) 63:141–9. doi: 10.1016/j.jbi.2016.08.001

86. Wichers M, Snippe E, Riese H, Bos FM. De netwerkbenadering bij depressie: veel noten op de zang of heilige graal? Gedragstherapie . (2019) 52:43−68.

87. Curran PJ, Bauer DJ. The disaggregation of within-person and between-person effects in longitudinal models of change. Annu Rev Psychol . (2011) 62:583–619. doi: 10.1146/annurev.psych.093008.100356

88. Houtveen JH, Hamaker EL, Van Doornen LJ. Using multilevel path analysis in analyzing 24-h ambulatory physiological recordings applied to medically unexplained symptoms. Psychophysiology . (2010) 47:570–8. doi: 10.1111/j.1469-8986.2009.00951.x

89. Pe ML, Kircanski K, Thompson RJ, Bringmann LF, Tuerlinckx F, Mestdagh M, et al. Emotion network density in major depressive disorder. Clin Psychol Sci . (2015) 3:292–300. doi: 10.1177/2167702614540645

90. Nezlek JB, Holas P, Rusanowska M, Krejtz I. Being present in the moment: event-level relationships between mindfulness and stress, positivity, and importance. Pers Individ Dif . (2016) 93:1–5. doi: 10.1016/j.paid.2015.11.031

91. Bos FM, Snippe E, de Vos S, Hartmann JA, Simons CJP, van der Krieke L, et al. Can we jump from cross-sectional to dynamic interpretations of networks? Implications for the network perspective in psychiatry. Psychother Psychosom . (2017) 86:175–7. doi: 10.1159/000453583

92. Schenk HM, Bos EH, Slaets JP, De Jonge P, Rosmalen JG. Differential association between affect and somatic symptoms at the between- and within-individual level. Br J Health Psychol . (2017) 22:270–80. doi: 10.1111/bjhp.12229

93. Bouwmans MEJ, Oude Oosterik NAM, Bos EH, de Groot IW, Oldehinkel AJ, de Jonge P. The temporal order of changes in physical activity and subjective sleep in depressed versus nondepressed individuals: findings from the MOOVD study. Behav Sleep Med . (2018) 16:154–68. doi: 10.1080/15402002.2016.1180521

94. Klippel A, Viechtbauer W, Reininghaus U, Wigman J, van Borkulo C, Myin-Germeys I, et al. The cascade of stress: a network approach to explore differential dynamics in populations varying in risk for psychosis. Schizophr. Bull . (2018) 44:328–37. doi: 10.1093/schbul/sbx037

95. Liu S. Person-specific versus multilevel autoregressive models: accuracy in parameter estimates at the population and individual levels. Br J Math Stat Psychol . (2017) 70:480–98. doi: 10.1111/bmsp.12096

96. Krone T, Albers CJ, Kuppens P, Timmerman ME. A multivariate statistical model for emotion dynamics. Emotion . (2018) 18:739–54. doi: 10.1037/emo0000384

97. Verbeke G, Molenberghs G. Estimation of the marginal model. In Verbeke G, Molenberghs G, editors. Linear mixed models for longitudinal data . New York, NY: Springer (2000). p. 41–54.

98. Hoffman L. Multilevel models for examining individual differences in within-person variation and covariation over time. Multivariate Behav Res . (2007) 42:609–29. doi: 10.1080/00273170701710072

99. Asparouhov T, Hamaker EL, Muthén B. Dynamic structural equation models. Struct Eq Model . (2018) 25:359–88. doi: 10.1080/10705511.2017.1406803

100. Oravecz Z, Tuerlinckx F, Vandekerckhove J. Bayesian data analysis with the bivariate hierarchical Ornstein-Uhlenbeck process model. Multivariate Behav Res. (2016) 51:106–19. doi: 10.1080/00273171.2015.1110512

101. Driver CC, Voelkle MC. Hierarchical Bayesian continuous time dynamic modeling. Psychol Methods . (2018) 23:774–99. doi: 10.1037/met0000168

102. Persons JB, Silberschatz G. Are results of randomized controlled trials useful to psychotherapists? J Consult Clin Psychol . (1998) 66:126. doi: 10.1037/0022-006X.66.1.126

103. Lillie EO, Patay B, Diamant J, Issell B, Topol EJ, Schork NJ. The n-of-1 clinical trial: the ultimate strategy for individualizing medicine? Personalized Med . (2011) 8:161–73. doi: 10.2217/pme.11.7

104. Zhan S, Ottenbacher KJ. Single subject research designs for disability research. Disabil Rehabil . (2001) 23:1–8. doi: 10.1080/09638280150211202

105. Ter Kuile MM, Bulte I, Weijenborg PTM, Beekman A, Melles R, Onghena P. Therapist-aided exposure for women with lifelong vaginismus: a replicated single-case design. J Consul Clin Psychol . (2009) 77:149–59. doi: 10.1037/a0014273

106. Bos FM, Wichers M, Emerencia A, Veling W, Haarman B, Van der Veen DC, et al. Developing a flexible interface to generate personalized diaries in mental health care. Support Health Technol . (2019) 9:3.

107. Robinson OC, McAdams DP. Four functional roles for case studies in emerging adulthood research. Emerg Adulth. (2015) 3:413–21. doi: 10.1177/2167696815592727

108. Bishop S, Miller IW, Norman W, Buda M, Foulke M. Cognitive therapy of psychotic depression: a case report. Psychotherapy . (1986) 23:167–73. doi: 10.1037/h0085584

109. Burger J, van der Veen DC, Robinaugh DJ, Quax R, Riese H, Schoevers RA, et al. Bridging the gap between complexity science and clinical practice by formalizing idiographic theories: a computational model of functional analysis. BMC Med. (2020) 18:99. doi: 10.1186/s12916-020-01558-1

110. West SG, Hepworth JT. Statistical issues in the study of temporal data: daily experiences. J Pers . (1991) 59:609–62. doi: 10.1111/j.1467-6494.1991.tb00261.x

111. Bouwmans MEJ, Beltz AM, Bos EH, Oldehinkel AJ, de Jonge P, Molenaar PCM. The person-specific interplay of melatonin, affect, and fatigue in the context of sleep and depression. Pers Ind Diff . (2018) 123:163–70. doi: 10.1016/j.paid.2017.11.022

112. Yang X, Ram N, Gest SD, Lydon-Staley DM, Conroy DE, Pincus AL, et al. Socioemotional dynamics of emotion regulation and depressive symptoms: a person-specific network approach. Complexity . (2018) 2018:5094179. doi: 10.1155/2018/5094179

113. Simon GE, Perlis RH. Personalized medicine for depression: can we match patients with treatments? Am J Psychiatry . (2010) 167:1445–55. doi: 10.1176/appi.ajp.2010.09111680

114. Cuijpers P, Reynolds CF, Donker T, Li J, Andersson G, Beekman A. Personalized treatment of adult depression: medication, psychotherapy, or both? A systematic review. Depress Anxiety . (2012) 29:855–64. doi: 10.1002/da.21985

115. Schneider RL, Arch JJ, Wolitzky-Taylor KB. The state of personalized treatment for anxiety disorders: a systematic review of treatment moderators. Clin Psychol Rev . (2015) 38:39–54. doi: 10.1016/j.cpr.2015.02.004

Keywords: psychiatry, single-subject, idiographic, nomothetic, N-of-1, intra-individual

Citation: Zuidersma M, Riese H, Snippe E, Booij SH, Wichers M and Bos EH (2020) Single-Subject Research in Psychiatry: Facts and Fictions. Front. Psychiatry 11:539777. doi: 10.3389/fpsyt.2020.539777

Received: 02 March 2020; Accepted: 08 October 2020; Published: 13 November 2020.

Reviewed by:

Copyright © 2020 Zuidersma, Riese, Snippe, Booij, Wichers and Bos. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Marij Zuidersma, m.zuidersma@umcg.nl

IMAGES

  1. (PDF) Mixed Methods Single Case Research: State of the Art and Future Directions

    single case research design vs group design

  2. What is a multiple case study design

    single case research design vs group design

  3. PPT

    single case research design vs group design

  4. PPT

    single case research design vs group design

  5. Single Case Research Designs

    single case research design vs group design

  6. (PDF) Single-Case Experimental Designs in Educational Research: A Methodology for Causal

    single case research design vs group design

VIDEO

  1. Colour बर्बाद करके बनाई सुंदर Painting 😍 #short #viral #shorts #trending #facts #hindi #yt #ytshorts

  2. Multiple Roles and Multiple Researchers in Design-based Research

  3. Case Study 3: Group Sequential Design

  4. Gumball Old vs New Design #fyp #theamazingworldofgumball #tawog #gumball #darwin #anais #edit

  5. Research Design

  6. Research Co-Design Webinar 4

COMMENTS

  1. Single-Subject vs. Group Research Designs

    Group design involves randomly assigning participants to two (or more) groups with at least one treatment group and one control group. Data from

  2. The Single-Subject Versus Group “Debate”

    Single-subject research is similar to group research—especially experimental group research—in many ways. They are both quantitative approaches that try to

  3. D-4: Describe the advantages of single subject experimental

    It's important to note that “groups design” does not inherently refer to very large numbers of participants, nor does “single subject design” refer to studies

  4. Single Subject and Group Research Design

    given setting that requires at least three consecutive phases. – An initial baseline where the independent variable is not present.

  5. Comparing Group and Single-Case Designs

    Throughout the behavioral and health sciences, correlational and experimental studies dominate the research design landscape. Although differing from one.

  6. 10.3 The Single-Subject Versus Group “Debate”

    Single-subject research is similar to group research—especially experimental group research—in many ways. They are both quantitative approaches that try to

  7. 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

  8. Single Subject Research

    Unlike true experiments where the researcher randomly assigns participants to a control and treatment group, in single subject research the participant serves

  9. Single-Subject Experimental Design: An Overview

    Single-subject experimental designs – also referred to as within-subject or single case experimental designs – are among the most prevalent designs used in

  10. Single-Subject Research in Psychiatry: Facts and Fictions

    Single-subject studies are characterized by their focus on single persons. This is in contrast to most traditional group-based (“nomothetic”)