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Chapter 10: Single-Subject Research

Single-Subject Research Designs

Learning Objectives

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

General Features of Single-Subject Designs

Before looking at any specific single-subject research designs, it will be helpful to consider some features that are common to most of them. Many of these features are illustrated in Figure 10.2, which shows the results of a generic single-subject study. First, the dependent variable (represented on the  y -axis of the graph) is measured repeatedly over time (represented by the  x -axis) at regular intervals. Second, the study is divided into distinct phases, and the participant is tested under one condition per phase. The conditions are often designated by capital letters: A, B, C, and so on. Thus Figure 10.2 represents a design in which the participant was tested first in one condition (A), then tested in another condition (B), and finally retested in the original condition (A). (This is called a reversal design and will be discussed in more detail shortly.)

A subject was tested under condition A, then condition B, then under condition A again.

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

Reversal Designs

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

The study by Hall and his colleagues was an ABAB reversal design. Figure 10.3 approximates the data for Robbie. The percentage of time he spent studying (the dependent variable) was low during the first baseline phase, increased during the first treatment phase until it leveled off, decreased during the second baseline phase, and again increased during the second treatment phase.

A graph showing the results of a study with an ABAB reversal design. Long description available.

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

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

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

Multiple-Baseline Designs

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

One solution to these problems is to use a  multiple-baseline design , which is represented in Figure 10.4. In one version of the design, a baseline is established for each of several participants, and the treatment is then introduced for each one. In essence, each participant is tested in an AB design. The key to this design is that the treatment is introduced at a different  time  for each participant. The idea is that if the dependent variable changes when the treatment is introduced for one participant, it might be a coincidence. But if the dependent variable changes when the treatment is introduced for multiple participants—especially when the treatment is introduced at different times for the different participants—then it is extremely unlikely to be a coincidence.

Three graphs depicting the results of a multiple-baseline study. Long description available.

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

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

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

Data Analysis in Single-Subject Research

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

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

In the top panel of Figure 10.5, there are fairly obvious changes in the level and trend of the dependent variable from condition to condition. Furthermore, the latencies of these changes are short; the change happens immediately. This pattern of results strongly suggests that the treatment was responsible for the changes in the dependent variable. In the bottom panel of Figure 10.5, however, the changes in level are fairly small. And although there appears to be an increasing trend in the treatment condition, it looks as though it might be a continuation of a trend that had already begun during baseline. This pattern of results strongly suggests that the treatment was not responsible for any changes in the dependent variable—at least not to the extent that single-subject researchers typically hope to see.

Results of a single-subject study showing level, trend and latency. Long description available.

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

Key Takeaways

  • Single-subject research designs typically involve measuring the dependent variable repeatedly over time and changing conditions (e.g., from baseline to treatment) when the dependent variable has reached a steady state. This approach allows the researcher to see whether changes in the independent variable are causing changes in the dependent variable.
  • In a reversal design, the participant is tested in a baseline condition, then tested in a treatment condition, and then returned to baseline. If the dependent variable changes with the introduction of the treatment and then changes back with the return to baseline, this provides strong evidence of a treatment effect.
  • In a multiple-baseline design, baselines are established for different participants, different dependent variables, or different settings—and the treatment is introduced at a different time on each baseline. If the introduction of the treatment is followed by a change in the dependent variable on each baseline, this provides strong evidence of a treatment effect.
  • Single-subject researchers typically analyze their data by graphing them and making judgments about whether the independent variable is affecting the dependent variable based on level, trend, and latency.
  • Does positive attention from a parent increase a child’s toothbrushing behaviour?
  • Does self-testing while studying improve a student’s performance on weekly spelling tests?
  • Does regular exercise help relieve depression?
  • Practice: Create a graph that displays the hypothetical results for the study you designed in Exercise 1. Write a paragraph in which you describe what the results show. Be sure to comment on level, trend, and latency.

Long Descriptions

Figure 10.3 long description: Line graph showing the results of a study with an ABAB reversal design. The dependent variable was low during first baseline phase; increased during the first treatment; decreased during the second baseline, but was still higher than during the first baseline; and was highest during the second treatment phase. [Return to Figure 10.3]

Figure 10.4 long description: Three line graphs showing the results of a generic multiple-baseline study, in which different baselines are established and treatment is introduced to participants at different times.

For Baseline 1, treatment is introduced one-quarter of the way into the study. The dependent variable ranges between 12 and 16 units during the baseline, but drops down to 10 units with treatment and mostly decreases until the end of the study, ranging between 4 and 10 units.

For Baseline 2, treatment is introduced halfway through the study. The dependent variable ranges between 10 and 15 units during the baseline, then has a sharp decrease to 7 units when treatment is introduced. However, the dependent variable increases to 12 units soon after the drop and ranges between 8 and 10 units until the end of the study.

For Baseline 3, treatment is introduced three-quarters of the way into the study. The dependent variable ranges between 12 and 16 units for the most part during the baseline, with one drop down to 10 units. When treatment is introduced, the dependent variable drops down to 10 units and then ranges between 8 and 9 units until the end of the study. [Return to Figure 10.4]

Figure 10.5 long description: Two graphs showing the results of a generic single-subject study with an ABA design. In the first graph, under condition A, level is high and the trend is increasing. Under condition B, level is much lower than under condition A and the trend is decreasing. Under condition A again, level is about as high as the first time and the trend is increasing. For each change, latency is short, suggesting that the treatment is the reason for the change.

In the second graph, under condition A, level is relatively low and the trend is increasing. Under condition B, level is a little higher than during condition A and the trend is increasing slightly. Under condition A again, level is a little lower than during condition B and the trend is decreasing slightly. It is difficult to determine the latency of these changes, since each change is rather minute, which suggests that the treatment is ineffective. [Return to Figure 10.5]

  • Sidman, M. (1960). Tactics of scientific research: Evaluating experimental data in psychology . Boston, MA: Authors Cooperative. ↵
  • Ross, S. W., & Horner, R. H. (2009). Bully prevention in positive behaviour support. Journal of Applied Behaviour Analysis, 42 , 747–759. ↵
  • Fisch, G. S. (2001). Evaluating data from behavioural analysis: Visual inspection or statistical models.  Behavioural Processes, 54 , 137–154. ↵
  • Scruggs, T. E., & Mastropieri, M. A. (2001). How to summarize single-participant research: Ideas and applications.  Exceptionality, 9 , 227–244. ↵

The researcher waits until the participant’s behaviour in one condition becomes fairly consistent from observation to observation before changing conditions. This way, any change across conditions will be easy to detect.

A study method in which the researcher gathers data on a baseline state, introduces the treatment and continues observation until a steady state is reached, and finally removes the treatment and observes the participant until they return to a steady state.

The level of responding before any treatment is introduced and therefore acts as a kind of control condition.

A baseline phase is followed by separate phases in which different treatments are introduced.

Two or more treatments are alternated relatively quickly on a regular schedule.

A baseline is established for several participants and the treatment is then introduced to each participant at a different time.

The plotting of individual participants’ data, examining the data, and making judgements about whether and to what extent the independent variable had an effect on the dependent variable.

Whether the data is higher or lower based on a visual inspection of the data; a change in the level implies the treatment introduced had an effect.

The gradual increases or decreases in the dependent variable across observations.

The time it takes for the dependent variable to begin changing after a change in conditions.

The percentage of responses in the treatment condition that are more extreme than the most extreme response in a relevant control condition.

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.

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Applied Behavior Analysis: Single Subject Research Design

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Terms to Use for Articles

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

Go To Databases

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

Research Hints

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

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

How to Search for a Specific Research Methodology in JABA

Single Case Design (Research Articles)

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

Cred library, julie wambaugh, and ralf schlosser.

  • December, 2014

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

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

An Overview of Single-Subject Experimental Design

What is single-subject design.

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

What is the Role of Single-Subject Design?

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

Basic Features and Components of Single-Subject Experimental Designs

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

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

See Kratochwill, et al. (2010)

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

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

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

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

See Horner, et al. (2005)

Common Misconceptions

Single-Subject Experimental Designs versus Case Studies

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

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

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

Other Single-Subject Myths

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

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.

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Using Single Subject Experimental Designs

single subject experimental designs applied behavior analysis

What are the Characteristics of Single Subject Experimental Designs?

Single-subject designs are the staple of applied behavior analysis research. Those preparing for the BCBA exam or the BCaBA exam must know single subject terms and definitions. When choosing a single-subject experimental design, ABA researchers are looking for certain characteristics that fit their study. First, individuals serve as their own control in single subject research. In other words, the results of each condition are compared to the participant’s own data. If 3 people participate in the study, each will act as their own control. Second, researchers are trying to predict, verify, and replicate the outcomes of their intervention. Prediction, replication, and verification are essential to single-subject design research and help prove experimental control. Prediction: the hypothesis related to what the outcome will be when measured Verification : showing that baseline data would remain consistent if the independent variable was not manipulated Replication: repeating the independent variable manipulation to show similar results across multiple phases Some experimental designs like withdrawal designs are better suited for demonstrating experimental control than others, but each design has its place. We will now look at the different types of single subject experimental designs and the core features of each.

Reversal Design/Withdrawal Design/A-B-A

Arguably the simplest single subject design, the reversal/withdrawal design is excellent at identifying experimental control. First, baseline data is recorded. Then, an intervention is introduced and the effects are recorded. Finally, the intervention is withdrawn and the experiment returns to baseline. The researcher or researchers then visually analyze the changes from baseline to intervention and determine whether or not experimental control was established. Prediction, verification, and replication are also clearly demonstrated in the withdrawal design. Below is a simple example of this A-B-A design.

reversal design withdrawal design

Advantages: Demonstrate experimental control Disadvantages: Ethical concerns, some behaviors cannot be reversed, not great for high-risk or dangerous behaviors

Multiple Baseline Design/Multiple Probe Design

Multiple baseline designs are used when researchers need to measure across participants, behaviors, or settings. For instance, if you wanted to examine the effects of an independent variable in a classroom, in a home setting, and in a clinical setting, you might use a multiple baseline across settings design. Multiple baseline designs typically involve 3-5 subjects, settings, or behaviors. An intervention is introduced into each segment one at a time while baseline continues in the other conditions. Below is a rough example of what a multiple baseline design typically looks like:

multiple baseline design single subject design

Multiple probe designs are identical to multiple baseline designs except baseline is not continuous. Instead, data is taken only sporadically during the baseline condition. You may use this if time and resources are limited, or you do not anticipate baseline changing. Advantages: No withdrawal needed, examine multiple dependent variables at a time Disadvantages : Sometimes difficult to demonstrate experimental control

Alternating Treatment Design

The alternating treatment design involves rapid/semirandom alternating conditions taking place all in the same phase. There are equal opportunities for conditions to be present during measurement. Conditions are alternated rapidly and randomly to test multiple conditions at once.

alternating treatment design applied behavior analysis

Advantages: No withdrawal, multiple independent variables can be tried rapidly Disadvantages : The multiple treatment effect can impact measurement

Changing Criterion Design

The changing criterion design is great for reducing or increasing behaviors. The behavior should already be in the subject’s repertoire when using changing criterion designs. Reducing smoking or increasing exercise are two common examples of the changing criterion design. With the changing criterion design, treatment is delivered in a series of ascending or descending phases. The criterion that the subject is expected to meet is changed for each phase. You can reverse a phase of a changing criterion design in an attempt to demonstrate experimental control.

changing criterion design aba

Summary of Single Subject Experimental Designs

Single subject designs are popular in both social sciences and in applied behavior analysis. As always, your research question and purpose should dictate your design choice. You will need to know experimental design and the details behind single subject design for the BCBA exam and the BCaBA exam. For BCBA exam study materials check out our BCBA exam prep. For a full breakdown of the BCBA fifth edition task list, check out our YouTube :

A Meta-Analysis of Single-Case Research on Applied Behavior Analytic Interventions for People With Down Syndrome

Affiliation.

  • 1 Nicole Neil, Ashley Amicarelli, Brianna M. Anderson, and Kailee Liesemer, Western University, Canada.
  • PMID: 33651891
  • DOI: 10.1352/1944-7558-126.2.114

This systematic review evaluates single-case research design studies investigating applied behavior analytic (ABA) interventions for people with Down syndrome (DS). One hundred twenty-five studies examining the efficacy of ABA interventions on increasing skills and/or decreasing challenging behaviors met inclusion criteria. The What Works Clearinghouse standards and Risk of Bias in N-of-1 Trials scale were used to analyze methodological characteristics, and Tau-U effect sizes were calculated. Results suggest the use of ABA-based interventions are promising for behavior change in people with DS. Thirty-six high-quality studies were identified and demonstrated a medium overall effect. A range of outcomes was targeted, primarily involving communication and challenging behavior. These outcomes will guide future research on ABA interventions and DS.

Keywords: Down syndrome; Tau-U; applied behavior analysis; single-case research.

Publication types

  • Meta-Analysis
  • Systematic Review
  • Behavior Therapy
  • Communication
  • Down Syndrome* / therapy

3 Dimensions of a Single-case Study Design

data is scientific

Prediction, verification and replication.

Prediction involves anticipating what you think will happen in the future.  Verification is showing that dependent variables (DVs) would not change without intervention (independent variables: IVs).  Replication involves taking away the intervention, reintroducing it, and obtaining similar outcomes.

Level, trend and variability have to do with the visual analysis of graphed data.

Studies across participants, settings or behaviors are ways to set up multiple baseline designs.

Stimulus, response and consequence make up the “three-term contingency.”

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single case study design aba

Applied Behavior Analysis

  • Find Articles on a Topic

Two Ways to Find Single Subject Research Design (SSRD) Articles

Finding ssrd articles via the browsing method, finding ssrd articles via the searching method.

  • Search by Article Citation in OneSearch
  • Find Reading Lists (AKA 'Course Reserves')
  • Get Articles We Don't Have through Interlibrary Loan
  • Browse ABA Journals
  • APA citation style
  • Install LibKey Nomad

Types of Single Subject Research Design

 Types of SSRDs to look for as you skim abstracts:

  • reversal design
  • withdrawal design
  • ABAB design
  • A-B-A-B design
  • A-B-C design
  • A-B-A design
  • multiple baseline
  • alternating treatments design
  • multi-element design
  • changing criterion design
  • single case design
  • single subject design
  • single case series

Behavior analysts recognize the advantages of single-subject design for establishing intervention efficacy.  Much of the research performed by behavior analysts will use SSRD methods.

When you need to find SSRD articles, there are two methods you can use:

single case study design aba

  • Click on a title from the list of ABA Journal Titles .
  • Scroll down on the resulting page to the View Online section.
  • Choose a link which includes the date range you're interested in.
  • Click on a link to an issue (date) you want to explore.
  • From the resulting Table of Contents, explore titles of interest, reading the abstract carefully for signs that the research was carried out using a SSRD.  (To help, look for the box on this page with a list of SSRD types.)
  • APA PsycInfo This link opens in a new window When you search in APA PsycInfo, you are searching through abstracts and descriptions of articles published in these ABA Journals in addition to thousands of other psychology-related journals. more... less... Description: PsycInfo is a key database in the field of psychology. Includes information of use to psychologists, students, and professionals in related fields such as psychiatry, management, business, and education, social science, neuroscience, law, medicine, and social work. Time Period: 1887 to present Sources: Indexes more than 2,500 journals. Subject Headings: Education, Mobile, Psychology, Social Sciences (Psychology) Scholarly or Popular: Scholarly Primary Materials: Journal Articles Information Included: Abstracts, Citations, Linked Full Text FindIt@BALL STATE: Yes Print Equivalent: None Publisher: American Psychological Association Updates: Monthly Number of Simultaneous Users: Unlimited

icon for database searching

First , go to APA PsycInfo.

Second , copy and paste this set of terms describing different types of SSRDs into an APA PsycInfo search box, and choose "Abstract" in the drop-down menu.

Drop-down menu showing "AB Abstract"

Third , copy and paste this list of ABA journals into another search box in APA PsycInfo, and choose "SO Publication Name" in the drop-down menu.

Drop-down menu showing: "SO Publication Name"

Fourth , type in some keywords in another APA PsycInfo search box (or two) describing what you're researching.  Use OR and add synonyms or related words for the best results.

Hit SEARCH, and see what kind of results you get!

Here's an example of a search for SSRDs in ABA journals on the topic of fitness:

APA PsycInfo search with 3 boxes.  1st box: "reversal design" OR "withdrawal design" etc. 2nd box: "Analysis of Verbal Behavior" OR "Behavior Analyst" OR etc. 3rd box: exercise or physical activity or fitness

Note that the long list of terms in the top two boxes gets cut off in the screenshot - - but they're all there!

The reason this works:

  • To find SSRD articles, we can't just search on the phrase "single subject research" because many studies which use SSRD do not include that phrase anywhere in the text of the article; instead such articles typically specify in the abstract (and "Methods" section) what type of SSRD method was used (ex. withdrawal design, multiple baseline, or ABA design).  That's why we string together all the possible descriptions of SSRD types with the word OR in between -- it enables us to search for any sort of SSRD, regardless of how it's described.  Choosing "Abstract" in the drop-down menu ensures that we're focusing on these terms being used in the abstract field (not just popping up in discussion in the full-text).
  • To search specifically for studies carried out in the field of Applied Behavior Analysis, we enter in the titles of the ABA journals, strung together, with OR in between.  The quotation marks ensure each title is searched as a phrase.  Choosing "SO Publication Name" in the drop-down menu ensures that results will be from articles published in those journals (not just references to those journals).
  • To limit the search to a topic we're interested in, we type in some keywords in another search box.  The more synonyms you can think of, the better; that ensures you'll have a decent pool of records to look through, including authors who may have described your topic differently.

Search ideas:

To limit your search to just the top ABA journals, you can use this shorter list in place of the long one above:

"Behavior Analysis in Practice" OR "Journal of Applied Behavior Analysis" OR "Journal of Behavioral Education" OR "Journal of Developmental and Physical Disabilities" OR "Journal of the Experimental Analysis of Behavior"

To get more specific, topic-wise, add another search box with another term (or set of terms), like in this example:

Four search boxes in PsycInfo.  Same as above, but with a 4th box: autism OR "developmental disorders"

To search more broadly and include other psychology studies outside of ABA journals, simply remove the list of journal titles from the search, as shown here:

Search in PsycInfo without list of journal terms.

  • << Previous: Find Articles on a Topic
  • Next: Search by Article Citation in OneSearch >>
  • Last Updated: Feb 15, 2024 10:24 AM
  • URL: https://bsu.libguides.com/appliedbehavioranalysis

Creating Single-Subject Research Design Graphs with Google Applications

  • Technical and Tutorials
  • Published: 29 November 2021
  • Volume 15 , pages 295–311, ( 2022 )

Cite this article

  • Bryan J. Blair   ORCID: orcid.org/0000-0002-1787-1676 1 &
  • Paul J. Mahoney 2  

5097 Accesses

2 Citations

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The visual analysis of graphically displayed data is an essential skill for behavior analysts across a range of settings. Although there are several computer-based applications that facilitate the production of simple, consistent, and visually coherent graphs, these applications have several limitations, including cost. An alternative to using these applications is using free and widely available Google Sheets and Google Slides to produce high-quality clinical and research graphs. We provide a step-by-step pictorially supported task analysis for a system for creating graphs for a variety of single-subject research designs and clinical applications using Sheets and Slides. We also discuss the advantages and limitations of using Google applications to create graphs for use in the practice of applied behavior analysis.

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Study Protocol Representation

Avoid common mistakes on your manuscript.

One of the defining characteristics of applied behavior analysis (ABA) is maintaining direct contact with program data to facilitate appropriate and effective clinical and research decision making. To maintain such contact with data, several computer applications have been used by researchers and practitioners. However, many of these applications are limited by cost, accessibility, customization flexibility, and the fact that in general they restrict users to local usage. Applications like Microsoft Excel, GraphPad Prism, Apple Numbers, and Systat SigmaPlot are used by behavior analysts, however, given our experiences, Google applications have not enjoyed such widespread usage. Google offers access to several cloud-based productivity applications, storage options, and interactive capabilities that permit users to collaborate in real-time. Google Sheets, a spreadsheet application similar to Excel and Numbers, provides a platform in which ABA researchers and practitioners may summarize, analyze, and share applicable data in real time. For the ABA service provider, where cost may be a concern that drives which means of data collection and analysis is employed, Google offers several applications and features free of charge; however, payment-based subscriptions are available depending on security and storage needs (e.g., when managing protected health information).

Although some agencies have recently begun to use software applications that automatically summarize clinical data to produce graphs, in general the graphs that are automatically generated are not suitable for professional publication and thus the raw data must still be exported and manipulated in applications like Excel, Prism, Numbers, and now even Sheets. Excel, Prism, Numbers, SigmaPlot, and Sheets all include features that allow users to graph clinical data for manuscript submission or for conference presentations in ways that many other applications do not.

Several technical articles for the development of single-subject research design graphs have been published to assist ABA practitioners and researchers in their usage. For example, Carr and Burkholder ( 1998 ), Dixon et al. ( 2009 ), and Pritchard ( 2008 ) published tutorials on generating graphs with Microsoft Excel whereas Berkman et al. ( 2018 ) published a tutorial on training staff to create graphs using GraphPad Prism. Vanselow and Bourret ( 2012 ) also provided the ABA community with online interactive tutorials for creating a variety of graphs in Excel. In addition, other authors have published pictorially supported task-analyzed technical tutorials for using free/low-cost web-based collaborative applications to develop behavior analytic instructional activities (Blair & Shawler, 2019 ; Mattson et al., 2020 ). Likewise, the purpose of this technical article is to provide a tutorial specific to generating graphs common to ABA practice and research using web-based Google applications.

Why Google Sheets and Slides? Why Now?

Data analysis and graphing applications like Excel, Prism, Numbers, and SigmaPlot are fully featured applications that are well-suited to the needs of behavior analysts. But all of these applications present users with specific limitations (see Table 1 ). For example, the only native application that is free is Numbers, but only for macOS users. Another limitation is where users can use the applications, for example, SigmaPlot can only be used on the Windows operating system (OS). Although Excel and Prism can be used on both macOS and Windows OS, Prism cannot be used on Chrome OS and Excel can only be used with many limitations on Chrome OS through the Excel web application. Numbers can also be used as a web application on macOS, Windows OS, and Chrome OS, but, like Excel, the web application is missing some critical editing features when compared to its native application. A final limitation is the lack of ability to view graphs across operating systems without exporting or converting to a PDF, JPG, or GIF file. Prism and SigmaPlot are fully featured and comprehensive graph production applications, but they are both limited because graphs must first be converted/exported before being shared with others who might not have access to those applications.

The newest version of Sheets addresses all of these specific limitations. When compared to other data summary, data analysis, and graph production applications, Sheets presents several advantages, including the fact that it is free in almost all general use cases, it can be used on nearly any computer, graphs can be easily viewed on nearly any device, it can be used on mobile devices, and it allows for synchronous collaboration across users. In addition, when using a graph development and presentation system with Sheets and Slides, clinicians and researchers can present high-quality, clear, illustrative, minimalistic, and visually coherent graphs across a range of settings and presentation modalities (e.g., in-person, synchronous video meeting, asynchronous sharing). In particular, Google applications allow users to link data and graphs from within Sheets to Slides so that any changes made within Sheets are automatically updated within Slides as well.

These collaborative and synchronization features allow clinicians and researchers to be confident that all members of a team have access to the most recent data and the most recent visual presentation of that data. In contrast to more static applications with collaborative editing and visual consistency limitations like Excel, Prism, Numbers, or SigmaPlot, in particular during times when remote collaboration is even more critical, the dynamically linked and visually consistent Sheets and Slides system can be advantageous across a range of practices. The COVID-19 pandemic has possibly accelerated this need for remote collaboration. The pandemic imposed several restrictions that have significantly impacted traditional behavior analytic research and practice, and these restrictions have resulted in an increased demand for remote-based services and have altered the way in which behavior analysts conduct their research and clinical practice.

Despite the advantages of using Sheets and Slides related to collaboration and data synchronization, many ABA practitioners may not have previously used Sheets given a history of several design and customized formatting limitations when compared to other applications. However, on June 29, 2020, Google updated Sheets to allow for more precise design and formatting options that are relevant to single-subject research design graphs (Cooper et al., 2020 ; Kubina et al., 2017 ) and clinical data presentations (Google LLC, 2020 ). In particular, users can now add tick marks and tick mark labels, floating zeros, and visible axis lines to their graphs. In addition, users can now align data markers with tick marks. Table 1 lists several critical features added to the Sheets application with the update in June. Figure 1 provides examples of several new features included in the most recent version of the application. Many BCBAs might use Google applications in their clinical and/or research practice; however, they might not be aware of these new capabilities in Sheets. We believe that these updates make Sheets much more suitable for use in many ABA settings and provide a viable alternative to paid applications and/or standalone applications that can only be used effectively and consistently with specific operating systems.

figure 1

An Example of a Graph with the Identified Preferred Characteristics (View Together with Table 1 ). Note: This figure is an illustration of recommended preferred characteristics of a single-subject research design graph. The numbers correspond with the written descriptions of the preferred characteristics in Table 1

The following pictorially supported step-by-step task analysis provides ABA practitioners and researchers with easy-to-follow instructions to create and collaboratively maintain, in real time, commonly used single-subject research design line graphs that can also be automatically updated for access across colleagues, parents, caretakers, and other stakeholders across geographical locations. This tutorial allows for the creation of simple common line graphs but is not intended for graphs with detailed formatting requirements (e.g., editing individual tick mark labels, creating axis breaks, implementing discontinuous axis values, complex legends). These instructions result in graphs that are high-quality, visually consistent, and suitable for the majority of clinical settings with a quality that rivals graphs published in widely read behavior-analytic journals.

The tutorial begins with instructions for how to create a simple multiple condition/phase (e.g., withdrawal research design) line graph. The general steps for the development of the line graphs are as follows:

Create the data table in Sheets;

Create the graph from the data in Sheets;

Finalize formatting of the graph in Sheets;

Make a copy of the graph in Sheets to reuse formatted graph for future graphs;

Copy and link graph to Slides;

Perform final editing of graphs in Slides.

These basic steps can then be used to create multielement (alternating treatments) and multiple baseline research design graphs because the basic formatting is the same for each. Tiered multiple baseline research design graphs can be created by making multiple A-B graphs and then using Slides for alignment and formatting, multielement research design graphs can be created by using multiple columns of data in the data table and by making formatting changes in Sheets and Slides (e.g., adding a legend and changing data point shapes), and changing criterion research design graphs can be created by adding specific formatting (e.g., criterion lines) in Slides.

Some General Comments and Observations on the Behavior of Google Sheets

Regarding template graphs.

Although we do recommend creating template graphs in Sheets and then duplicating tabs within an individual spreadsheet, or duplicating an entire Sheets file for future graphs, we also suggest that if Sheets does not behave as expected (i.e., given a large number of possible permutations and combinations of connected settings and formatting options), it is probably easier and quicker to simply create a new blank Sheets file/tab and to follow the task-analyzed steps below.

Regarding Data Series Line Connectedness and Plotting Null Values

Given that Sheets does not have a feature for formatting individual data series lines, if the graph requires connected data paths even when data do not appear in subsequent rows in a data table (e.g., multielement design graphs) select Plot null values in the Chart style menu. Plotting null values (i.e., connecting data across periods of time of no data collection) can also be considered in order to skip data for particular sessions (e.g., multiple probe research design) or dates (e.g., weekends) whereas the session number/date and tick marks appear on the horizontal (x) axis. To keep lines disconnected even when null values are plotted (e.g., skipped dates in an ABAB withdrawal research design), instead of inputting data from the same condition in the same column after a gap in data collection for a certain condition (e.g., when switching between baseline and intervention multiple times), new columns with consistent data series names should be created in the data table for every condition/phase change , and data marker shapes must be changed to ensure conformity within conditions. Provided these considerations, the following sections outline easy-to-follow steps to construct commonly used graphs ABA research and practice.

Creating a Simple Withdrawal Design Graph

Data Table and Graph Insert (Fig. 2 )

Create a data table in Sheets with session/date, baseline, and treatment/intervention column headers (or other applicable data series/condition name);

Enter data by condition and by session number or date;

Highlight all rows and columns ;

Click Insert > Chart - (it should default to Line chart but if it doesn’t, change chart type to Line chart in the Chart editor to the right) Footnote 1 ;

figure 2

Insert a Line Chart from a Data Table. Note. This figure illustrates how to insert a new chart (i.e., line graph) in Sheets.

General Formatting (Figs. 3 , 4 , 5 and 6 )

Double-click on legend and press delete on keyboard Footnote 2 ;

Double-click on graph title and press delete on keyboard;

Double-click on vertical (y) axis on graph Footnote 3 (Fig. 3 );

Click on Gridlines and ticks;

Deselect Major gridlines ;

Select Major ticks ;

Set Ticks position to outside ;

Set Ticks length to 6 ;

Set Line color to black .

Repeat step 3 for horizontal (x) axis.

Double-click on vertical (x) axis on graph (Fig. 4 );

Select Show axis line (if applicable);

Set Text color to black;

Repeat step 5 for horizontal axis (if necessary).

Double-click on Baseline data series line on the graph (Figure 5 );

In Series dropdown select Apply to all series;

Set Line thickness to 2 ;

Set Point size to 10 .

In Series dropdown select Baseline (or first series) (Fig. 6 );

Set Color to Black;

Repeat previous step (7-2) for all other data series.

figure 3

Remove the Gridlines and Add Tick Marks. Note . This figure illustrates the steps for removing gridlines and adding axis tick marks in Sheets

figure 4

Format the Axis Lines. Note . This figure illustrates the steps for formatting the vertical (y) and horizontal (x) axes

figure 5

Format the Data Series Lines and Data Points (Part 1 of 2). Note . This figure illustrates the steps for formatting data series lines and data points (Part 1 of 2) in Sheets

figure 6

Format the Data Series Lines and Data Points (Part 2 of 2). Note. This figure illustrates the steps for formatting data series lines and data points (Part 2 of 2) in Sheets

Horizontal (X) Axis Tick Mark Labels as Session Numbers (Choose One of the Following)

Every Session Tick Mark Appears and Has Labels Footnote 4 ( Fig. 7 ).

Double-click on horizontal (x) axis;

Select Treat labels as text ;

figure 7

Make Tick Marks and Labels Appear for Every Session. Note. This figure illustrates the steps to ensure that horizontal (x) axis tick marks and labels appear for every session in Sheets

Only Some Session Tick Marks Appear and Have Labels ( Fig. 8 ).

Double-click on horizontal (x) axis ;

figure 8

Make Tick Marks and Labels Appear for Only Some Sessions. Note. This figure illustrates the steps to ensure that horizontal (x) axis tick marks and labels only appear for some sessions in Sheets.

Deselect Treat labels as tex t;

Set Min . to 0;

Deselect Allow bounds to hide data Footnote 5

Click Gridlines and ticks ;

Set Major spacing type to Step ;

Set Major step to preferred tick mark label interval/period ;

Cover up the 0 session number in Slides.

Horizontal (X) Axis Tick Mark Labels as Dates (Choose One of the Following)

Every Date Tick Mark Appears and Has Labels Footnote 6 (Fig. 9 ).

Enter dates in Session column of data table (instead of session numbers);

Set Slant labels to Auto . Footnote 7

figure 9

Make Tick Marks and Labels Appear for Every Date. Note. This figure illustrates the steps to ensure that horizontal (x) axis tick marks and labels appear for every date in Sheets

Only Some Date Tick Marks Appear and Have Labels ( Fig. 10 ).

figure 10

Make Tick Marks and Labels Appear for Only Some Dates. Note. This figure illustrates the steps to ensure that horizontal (x) axis tick marks and labels appear for only for some dates in Sheets

Deselect Treat labels as text ;

Set Slant labels to Auto ;

Click Gridlines and ticks;

Set Major count to preferred value (adjust based on number of dates in data table).

Choose One of the Following

Every Vertical (Y) Axis Tick Mark Appears and Has a Label (In General, This Moves the Floating Zero Farther from the Horizontal (X) Axis) ( Fig. 11 )

Double-click on vertical (y) axis ;

Set Min to a negative number (will vary depending on range—adjust until “floating” zero appears on graph) and set Max to desired maximum value;

Deselect Allow bounds to hide data .

figure 11

Make Tick Marks and Labels Appear for Every Vertical (Y) Axis Increment. Note: This figure illustrates the steps to ensure that vertical (y) axis tick marks and labels appear for every increment in Sheets

Click Gridlines and ticks

Set Major step to 1 (might need to return to step 1.2 to set the Min value again for “floating” zero to appear);

Hide/cover up the negative y value in Slides (described later).

Only Some Vertical (Y) Axis Tick Marks Appear and Have Labels (In General, This Allows the Floating Zero to Be Closer to the Horizontal (X) Axis) ( Fig. 12 )

figure 12

Make Tick Marks and Labels Appear for Only Some Vertical (Y) Axis Increments. Note. This figure illustrates the steps to ensure that vertical (y) axis tick marks and labels only appear for some increments in Sheets

Select Treat labels as text;

Set Min to a negative number (will vary depending on range—adjust until “floating” zero appears on graph) Footnote 8 and set Max to desired maximum value;

Deselect Allow bounds to hide data.

Set Major spacing type to Count;

Set Major count to Auto (might need to return to step 1.2 to set the Min value again for “floating” zero to appear).

Adding More Data

To add more data, add new rows to the bottom of the data table. In most cases, Sheets will not automatically add the data to the chart simply by adding a new row. To add the new data from the table to the graph:

Double-click on the graph then click on Setup in the Chart editor .

Change the Data range to include the newly added rows.

Adding a New Data Series Column (e.g., condition/phase) to Preexisting Graph

To add a new condition/phase (i.e., data series) to a preexisting graph, add the new columns of data, with column headers, to the preexisting data table. Adding a new column of data will most likely not automatically add the data to the graph and you will need to add the new data series manually. To add the new data from the table to the graph:

Change the Data range to include the newly added columns;

Click on Add series and select the new series to be added Footnote 9

Format the line and data points for the newly added data series.

Specific Formatting for Multiple Baseline Design Graphs (Fig. 13 ) Footnote 10

Duplicate graphs.

To maintain formatting across all tiers in a multiple baseline research design graph, consider creating the bottom/last tier first then duplicate that completed graph (with all desired formatting, sizing, etc.) for subsequent upper tiers.

figure 13

An Example of a Multiple Baseline Research Design Graph Created in Sheets and Slides. Note. This figure presents a multiple baseline design graph with preferred characteristics and formatting created in Sheets and Slides

Font Sizes in Sheets

Given that two or more graphs will be stacked on a single portrait-oriented page in Slides, and the graphs themselves will likely appear smaller than other graphs, consider increasing the size of the font for the vertical (y) and horizontal (x) axes tick mark labels at this point.

Same Number of Sessions/Dates and Vertical (Y) Axis Range

Ensure that the horizontal (x) axes for each tier in a connected multiple baseline design graph have the same number of sessions or the same dates and ensure that the vertical (y) axis has the same range, step count, etc. (if necessary).

No Horizontal (X) Axis Tick Mark Labels for Top Tier(s) of Multiple Baseline Design Graphs (Fig. 14 )

Double-click on horizontal axis ;

Change Text color to White .

figure 14

Remove the Horizontal (X) Axis Tick Mark Labels. Note. This figure illustrates the steps to remove the horizontal (x) axis labels in Sheets for a multiple baseline design graph

Specific Formatting for Multielement (Alternating Treatments) Design Designs

To create a multielement design graph:

If the data series markers are not different shapes, change them by double-clicking on each data series line and selecting different point shapes in the Chart editor).

Click Customize in the Chart editor and then click Legend and insert the legend where desired.

Final Formatting Before Making Copies of Graphs and/or Linking Graphs to Slides

As a reminder, it is advised to use the completed graph as a template for subsequent graphs in Sheets; therefore, ensure that all formatting has been finalized. In addition, given that a dynamic image of the graph will be linked to Slides, whereas the data in the graph will change, the formatting will no longer be editable within Slides itself and thus must be conducted now. At this point, consider changes to font styles and sizes as well as the following:

If the maximum vertical (y) axis or horizontal (x) axis data point isn’t showing or is partially hidden:

Set the max for that axis to greater than the desired maximum value and then hide the excess axis line in Slides (discussed later).

If either axis line is now too long, reduce maximum value in the range (if possible without hiding maximum data point[s]).

Decide whether the axis titles will be included or removed (you can add these titles later in Slides or delete the titles to use the individual graphs in multiple baseline design graphs). To remove the axis titles, click on the title and press delete.

Ensure that the aspect ratio of the graph is suitable for copying, downloading, presenting, adding to submitted manuscript, printing, and/or resizing in other applications. Ensure same ratio/size for each tier in linked multiple baseline graphs.

Making a Copy of a Graph for Reuse within Sheets

To preserve the formatting, size, and aspect ratio of the first graph across multiple similar graphs, duplicate the current tab in sheets (right-click on current tab and select Duplicate ) and then edit the data table in the new sheet. This method can be used for creating a multiple baseline design graph or for creating several withdrawal or multielement graphs for the same research project or manuscript. As previously noted, use caution when copying or duplicating graphs within sheets and if desired formatting cannot be obtained, we suggest creating a new tab/file and starting from scratch for new graphs.

Using Slides for Final Formatting and Presenting

Several of the following formatting elements can be accomplished in Sheets, however, doing so is more challenging because Sheets has limited graphical editing and formatting capabilities. For example, creating, inserting, and moving objects like text boxes and phase/condition lines in Sheets is much more cumbersome and less functional than performing the same task in Slides. Another example where formatting is far easier in Slides is when creating multiple baseline design graphs where precise formatting and visual alignment are required. In addition, Slides is a visual presentation application with a primary function of displaying images in a visually appealing way, whereas this function is secondary in Sheets. The additional step of copying, pasting, and linking a graph from Sheets to Slides allows for a much more usable and professional final product in Slides. Similar steps can be conducted in Google Docs, however, like Sheets, Docs is not primarily a graphical editing or presentation application, and thus the final product will not be as visually appealing as the product created in Slides.

Copy and Link Chart Footnote 11 from Sheets to Slides ( Fig. 15 )

In Sheets, click the three-dot menu in upper right of chart and select Copy chart ;

Open new Slides file ;

In Slides, on an empty slide, right-click and click Paste .

figure 15

Copy and Link Graph from Sheets to Slides. Note. This figure illustrates the steps to copy and paste a graph from Sheets to Slides, and how to link the graph to Slides from Sheets

Select Link to spreadsheet then click Paste .

To have multiple graphs with the same dimensions and aspect ratios, after making final edits to the graph in Sheets, do NOT change the chart size in Sheets before copying to Slides. Set the size once in Sheets, then perform all final resizing in Slides, treating the linked graph as a single image in Slides without changing the aspect ratio in Slides (i.e., resize the graph in Slides by dragging the corner of the image or with Format options ).

General Formatting of Linked Chart in Slides

Pro tip: Use formatting guides in Slides to help align objects. Click View , hover on Guides , click Show Guides .

Phase/Condition, Axes, and Data Series Labels (Figure 16 )

Insert condition, axes, or data series labels by clicking the insert text box tool and then type the name of the label. Center the condition label above the condition on the graph. Use the automated red/blue alignment lines to align the text boxes for different conditions with each other. Place the data series label text box near a data point and insert an arrow pointing from the text box to the data point (if applicable).

figure 16

Add Labels to the Linked Graph in Slides. Note. This figure illustrates the steps for inserting phase/condition and axis labels to a graph in Slides

Phase/Condition Lines (Fig. 17 )

Insert phase/condition lines using the insert line tool and place the line on the graph in the appropriate location.

figure 17

Insert Phase/Condition Lines. Note. This figure illustrates the steps for adding phase/condition change lines to a graph in Slides

Hiding (Covering) Extraneous Parts of a Graph (e.g., Negative Axis Values, Extra Axis Line Length) (Figure 18 )

Hide/cover-up negative axis values or extraneous axis line lengths by creating a white box with no borders and place the white box where needed on the graph. Click the insert shapes tool and place the box over the area of the graph that needs to be hidden. Edit the box to change the color of the box to white and change the color of the border to transparent/white.

figure 18

Hide Parts of Graphs in Slides. Note . This figure illustrates how to hide unnecessary parts of a graph in Slides

Specific Formatting for Multiple Baseline Design Graphs

Change slide page orientation to portrait.

To create a vertically stacked composite multiple baseline design graph it is advised to change the orientation of the slide to Portrait (as opposed to the default landscape):

Click File and select Page setup;

Click Custom ;

Set dimensions to 8.5 in by 11 in.

Distribute Graphs Vertically Footnote 13

To ensure that each tier is spaced the same distance from each other vertically, highlight all three graphs, right-click and click Distribute vertically.

Tier Labels

Create tier labels as you did for other labels by creating and placing text boxes. However, for tier labels, set the border color to black. Place the tier label in the desired location on one graph and place the other tier labels in the same place on their respective graphs and use the alignment guides in Slides to align the text boxes across graphs.

Centered Vertical (Y) Axis Label

Create a single vertical (y) axis label as you have done before by creating a borderless text box. Rotate the text box so that it is vertical and place the text box centered on the composite multiple baseline design graph.

Phase/Condition Change Lines

Create phase/condition change lines as you have done before. However, for multiple baseline design graphs, the phase change lines must be connected across tiers. Inserting and aligning phase change and connecting lines might be the most challenging step in the process of creating multiple baseline design graphs and practice, a lot of trial and error, and plenty of patience will most likely be necessary to align the lines as desired. To assist with line sizing and alignment, zoom in on sections of the graph: click View then Zoom then 200%. In addition, to keep lines perfectly horizontal or vertical when resizing or rotating, hold down Shift when using the mouse to rotate or resize lines. Also, holding down Shift while moving the lines (or any other object) with the keyboard arrows will allow for a possibly more precise placement of the lines.

We hope that the preceding task analysis for creating a variety of single-subject research design graphs in Sheets and Slides will prove to be helpful to clinicians and researchers who are looking for a simple, minimalistic, free, and easy-to-use graph production system. Although the system described here meets many of the needs of ABA practitioners, there are several limitations to note.

One limitation that might be frustrating for some behavior analysts is the inability to create unfilled data markers (e.g., open circles, open squares) in Sheets. We agree that this is a substantial limitation when compared to other graph creation applications. A limited workaround would be to change the color of every individual data point to white (the connecting lines will still appear) in the graph Chart editor in Sheets, link the graph to Slides, then create unfilled shapes in Slides and place those shapes on top of the linked graph where the data markers would normally have appeared. This is not an ideal solution, but currently unfilled data markers are not an option in Sheets.

Unlike other graphing tutorials (Dubuque, 2015 ; Fuller & Dubuque, 2018 ), our tutorial does not provide instructions for incorporating automatically generated and updated phase change lines in Sheets. Given the intended audience of this introductory tutorial, we elected to not include this option; however, manually inserting and updating phase change lines in Slides is fairly simple and not prohibitively time-consuming.

A series of related limitations to the system that we described include the fact that it requires the use of two applications, requires the user to be somewhat fluent with both Sheets and Slides, and requires the user to be able to interact with both in order to create simple graphs. Although publication-quality graphs can be created with other single-graphing applications (e.g., Excel, Prism, Numbers, SigmaPlot), those more advanced and robust applications are not free or are only available for use with specific operating systems, whereas Sheets and Slides are both free, are web-based, and graphs can be created, edited, and viewed in Sheets on macOS, Windows OS, and Chrome OS. In addition, the use of the linked system allows clinicians and researchers to use Slides in order to present a constantly updated graph as a properly formatted image across a variety of screens and media.

A limitation to any cloud-based storage and document collaboration system is how privacy is managed and protected—regulations dictated by FERPA and HIPAA laws, in particular. This concern is not specific to the graph creation and presentation system that we have described; however, a note of caution is warranted to ensure that all users are aware that there are inherent data privacy and security risks. Similar risks exist whenever one sends an email attachment of an Excel graph with FERPA/HIPAA protected data. Although users can access Sheets and Slides for no cost, it is important to note that the no-cost access to these applications does not allow for any type of additional HIPAA protections. If a user wishes to use Google applications with HIPAA protections, additional costs and contractual agreements with Google are likely necessary, depending on individual circumstances. Regardless of the software application that a person uses, it is the user’s responsibility to be aware of potential security risks and to comply with all regulations.

The preceding tutorial provided easy-to-follow steps to generate graphs for use in ABA research and practice. It is important to note that, over time, it is likely that Google may make adjustments to the Sheets and Slides applications and corresponding features and options; however, we hope that the instructions herein will provide the reader with sufficient skills to not only generate high-quality graphs but also, with providing familiarity with the many features included in the applications, generalize those skills to future versions of Sheets and Slides, and prepare them for any updates to come.

You might need to select Use row 1 as headers and Use column A as labels in Chart editor > Setup .

A legend might be required for multielement graphs. This is discussed later and this step can be skipped if you want to keep the legend.

The Chart editor should appear whenever you double-click on parts of the graph and this is where the vast majority of formatting will occur.

This allows the first session to be closer to the vertical (y) axis.

Directions for hiding the excess axis line and the zero session number are explained in a subsequent section.

When creating a new date-based graph it is recommended to create a new graph from scratch to ensure that all formats and settings are set to default values before proceeding.

Depending on the number of dates you may now need to enlarge the graph to ensure that all dates are displayed horizontally

Google Sheets appears to behave inconsistently here and you might need to readjust the Max value at this point—you might need to increase the maximum value then revert back to original preferred max for the floating zero to “look good.”

Make sure you added a column header prior to adding the data to the graph.

See Carr ( 2005 ) for further design considerations when creating multiple baseline design graphs.

Linking charts from Sheets to Slides ensures that the chart in Slides is automatically updated based on changes made in Sheets.

It is advised to perform this step before adding phase lines and condition labels.

Berkman, S. J., Roscoe, E. M., & Bourret, J. C. (2018). Comparing self-directed methods for training staff to create graphs using Graphpad Prism. Journal of Applied Behavior Analysis, 52 (1), 188–204. https://doi.org/10.1002/jaba.522 .

Blair, B. J., & Shawler, L. A. (2019). Developing and implementing emergent responding training systems with available and low-cost computer-based learning tools: Some best practices and a tutorial. Behavior Analysis in Practice, 13 (2), 509–520. https://doi.org/10.1007/s40617-019-00405-x .

Article   PubMed   PubMed Central   Google Scholar  

Carr, J. E. (2005). Recommendations for reporting multiple‐baseline designs across participants. Behavioral Interventions, 20 (3), 219–224. https://doi.org/10.1002/bin.191 .

Carr, J. E., & Burkholder, E. O. (1998). Creating single-subject design graphs with Microsoft Excel™. Journal of Applied Behavior Analysis, 31 (2), 245–251. https://doi.org/10.1901/jaba.1998.31-245 .

Article   PubMed Central   Google Scholar  

Cooper, J. O., Heron, T. E., & Heward, W. L. (2020). Applied behavior analysis . Pearson Education.

Dixon, M. R., Jackson, J. W., Small, S. L., Horner-King, M. J., Lik, N. M. K., Garcia, Y., & Rosales, R. (2009). Creating single-subject design graphs in Microsoft Excel™ 2007. Journal of Applied Behavior Analysis, 42 (2), 277–293. https://doi.org/10.1901/jaba.2009.42-277 .

Dubuque, E. M. (2015). Inserting phase change lines into Microsoft Excel® graphs. Behavior Analysis in Practice, 8 (2), 207–211. https://doi.org/10.1007/s40617-015-0078-8 .

Fuller, T. C., & Dubuque, E. M. (2018). Integrating phase change lines and labels into graphs in Microsoft Excel®. Behavior Analysis in Practice, 12 (1), 293–299. https://doi.org/10.1007/s40617-018-0248-6 .

Google LLC. (2020). G Suite updates: New chart axis customization in Google Sheets: Tick marks, tick spacing, and axis lines. https://gsuiteupdates.googleblog.com/2020/06/chart-axis-customization-google-sheets-tick-marks-lines.html

Kubina, R. M., Kostewicz, D. E., Brennan, K. M., & King, S. A. (2017). A critical review of line graphs in behavior analytic journals. Educational Psychology Review, 29 (3), 583–598. https://doi.org/10.1007/s10648-015-9339-x .

Article   Google Scholar  

Mattson, S. L., Higbee, T. S., Aguilar, J., Nichols, B., Campbell, V. E., Nix, L. D., Reinart, K. S., Peck, S., & Lewis, K. (2020). Creating and sharing digital ABA instructional activities: A practical tutorial. Behavior Analysis in Practice, 13 , 772–798. https://doi.org/10.1007/s40617-020-00440-z .

Pritchard, J. K. (2008). A decade later: Creating single-subject design graphs with Microsoft Excel 2007™. Behavior Analyst Today, 9 (3–4), 153–161. https://doi.org/10.1037/h0100655 .

Vanselow, N. R., & Bourret, J. C. (2012). Online interactive tutorials for creating graphs with Excel 2007 or 2010. Behavior Analysis in Practice, 5 (1), 40–46. https://doi.org/10.1007/BF03391816 .

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Blair, B.J., Mahoney, P.J. Creating Single-Subject Research Design Graphs with Google Applications. Behav Analysis Practice 15 , 295–311 (2022). https://doi.org/10.1007/s40617-021-00604-5

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10.2 Single-Subject Research Designs

Learning objectives.

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

General Features of Single-Subject Designs

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

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

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

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

Reversal Designs

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

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

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

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

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

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

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

Multiple-Baseline Designs

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

Multiple-Baseline Design Across Participants

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

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

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

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

Multiple-Baseline Design Across Behaviors

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

Multiple-Baseline Design Across Settings

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

Data Analysis in Single-Subject Research

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

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

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

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

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

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

Key Takeaways

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

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  • v.37(1); 2014 May

The Evidence-Based Practice of Applied Behavior Analysis

Timothy a. slocum.

Utah State University, Logan, UT USA

Ronnie Detrich

Wing Institute, Oakland, CA USA

Susan M. Wilczynski

Ball State University, Muncie, IN USA

Trina D. Spencer

Northern Arizona University, Flagstaff, AZ USA

Oregon State University, Corvallis, OR USA

Katie Wolfe

University of South Carolina, Columbia, SC USA

Evidence-based practice (EBP) is a model of professional decision-making in which practitioners integrate the best available evidence with client values/context and clinical expertise in order to provide services for their clients. This framework provides behavior analysts with a structure for pervasive use of the best available evidence in the complex settings in which they work. This structure recognizes the need for clear and explicit understanding of the strength of evidence supporting intervention options, the important contextual factors including client values that contribute to decision making, and the key role of clinical expertise in the conceptualization, intervention, and evaluation of cases. Opening the discussion of EBP in this journal, Smith ( The Behavior Analyst, 36 , 7–33, 2013 ) raised several key issues related to EBP and applied behavior analysis (ABA). The purpose of this paper is to respond to Smith’s arguments and extend the discussion of the relevant issues. Although we support many of Smith’s ( The Behavior Analyst, 36 , 7–33, 2013 ) points, we contend that Smith’s definition of EBP is significantly narrower than definitions that are used in professions with long histories of EBP and that this narrowness conflicts with the principles that drive applied behavior analytic practice. We offer a definition and framework for EBP that aligns with the foundations of ABA and is consistent with well-established definitions of EBP in medicine, psychology, and other professions. In addition to supporting the systematic use of research evidence in behavior analytic decision making, this definition can promote clear communication about treatment decisions across disciplines and with important outside institutions such as insurance companies and granting agencies.

Almost 45 years ago, Baer et al. ( 1968 ) described a new discipline—applied behavior analysis (ABA). This discipline was distinguished from the experimental analysis of behavior by its focus on social impact (i.e., solving socially important problems in socially important settings). ABA has produced remarkably powerful interventions in fields such as education, developmental disabilities and autism, clinical psychology, behavioral medicine, organizational behavior management, and a host of other fields and populations. Behavior analysts have long recognized that developing interventions capable of improving client behavior solves only one part of the problem. The problem of broad social impact must be solved by having interventions implemented effectively in socially important settings and at scales of social importance (Baer et al. 1987 ; Horner et al. 2005b ; McIntosh et al. 2010 ). This latter set of challenges has proved to be more difficult. In many cases, demonstrations of effectiveness are not sufficient to produce broad adoption and careful implementation of these procedures. Key decision makers may be more influenced by variables other than the increases and decreases in the behaviors of our clients. In addition, even when client behavior is a very powerful factor in decision making, it does not guarantee that empirical data will be the basis for treatment selection; anecdotes, appeals to philosophy, or marketing have been given priority over evidence of outcomes (Carnine 1992 ; Polsgrove 2003 ).

Across settings in which behavior analysts work, there has been a persistent gap between what is known from research and what is actually implemented in practice. Behavior analysts have been concerned with the failed adoption of research-based practices for years (Baer et al. 1987 ). Even in the fields in which behavior analysts have produced powerful interventions, the vast majority of current practice fails to take advantage of them.

Behavior analysts have not been alone in recognizing serious problems with the quality of interventions used employed in practice settings. In the 1960s, many within the medical field recognized a serious research-to-practice gap. Studies suggested that a relatively small percentage (estimates range from 10 to 25 %) of medical treatment decisions were based on high-quality evidence (Goodman 2003 ). This raised the troubling question of what basis was used for the remaining decisions if it was not high-quality evidence. These concerns led to the development of evidence-based practice (EBP) of medicine (Goodman 2003 ; Sackett et al. 1996 ).

The research-to-practice gap appears to be universal across professions. For example, Kazdin ( 2000 ) has reported that less than 10 % of the child and adolescent mental health treatments reported in the professional literature have been systematically evaluated and found to be effective and those that have not been evaluated are more likely to be adopted in practice settings. In recognition of their own research-to-practice gaps, numerous professions have adopted an EBP framework. Nursing and other areas of health care, social work, clinical and educational psychology, speech and language pathology, and many others have adopted this framework and adapted it to the specific needs of their discipline to help guide decision-making. Not only have EBP frameworks been helping to structure professional practice, but they have also been used to guide federal policy. With the passage of No Child Left Behind ( 2002 ) and the reauthorization of the Individuals with Disabilities Education Improvement Act ( 2005 ), the federal department of education has aligned itself with the EBP movement. A recent memorandum from the federal Office of Management and Budget instructed agencies to consider evidence of effectiveness when awarding funds, increase the use of evidence in competitions, and to encourage widespread program evaluation (Zients 2012 ). The memo, which used the term evidence-based practice extensively, stated: “Where evidence is strong, we should act on it. Where evidence is suggestive, we should consider it. Where evidence is weak, we should build the knowledge to support better decisions in the future” (Zients 2012 , p. 1).

EBP is more broadly an effort to improve decision-making in applied settings by explicitly articulating the central role of evidence in these decisions and thereby improving outcomes. It addresses one of the long-standing challenges for ABA; the need to effectively support and disseminate interventions in the larger social systems in which our work is embedded. In particular, EBP addresses the fact that many decision-makers are not sufficiently influenced by the best evidence that is relevant to important decisions. EBP is an explicit statement of one of ABA’s core tenets—a commitment to evidence-based decision-making. Given that the EBP framework is well established in many disciplines closely related to ABA and in the larger institutional contexts in which we operate (e.g., federal policy and funding agencies), aligning ABA with EBP offers an opportunity for behavior analysts to work more effectively within broader social systems.

Discussion of issues related to EBP in ABA has taken place across several years. Researchers have extensively discussed methods for identifying well-supported treatments (e.g., Horner et al. 2005a ; Kratochwill et al. 2010 ), and systematically reviewed the evidence to identify these treatments (e.g., Maggin et al. 2011 ; National Autism Center 2009 ). However, until recently, discussion of an explicit definition of EBP in ABA has been limited to conference papers (e.g., Detrich 2009 ). Smith ( 2013 ) opened a discussion of the definition and critical features of EBP of ABA in the pages of The Behavior Analyst . In his thought-provoking article, Smith raised many important points that deserve serious discussion as the field moves toward a clear vision of EBP of ABA. Most importantly, Smith ( 2013 ) argued that behavior analysts must carefully consider how EBP is to be defined and understood by researchers and practitioners of behavior analysis.

Definitions Matter

We find much to agree with in Smith’s paper, and we will describe these points of agreement below. However, we have a core disagreement with Smith concerning the vision of what EBP is and how it might enhance and expand the effective practice of ABA. As behavior analysts know, definitions matter. A well-conceived definition can promote conceptual understanding and set the context for effective action. Conversely, a poor definition or confusion about definitions hinders clear understanding, communication, and action.

In providing a basis for his definition of EBP, Smith refers to definitions in professions that have well-developed conceptions of EBP. He quotes the American Psychological Association (APA) ( 2005 ) definition (which we quote here more extensively than he did):

Evidence-based practice in psychology (EBPP) is the integration of the best available research with clinical expertise in the context of patient characteristics, culture, and preferences. This definition of EBPP closely parallels the definition of evidence-based practice adopted by the Institute of Medicine ( 2001 , p. 147) as adapted from Sackett et al. ( 2000 ): “Evidence-based practice is the integration of best research evidence with clinical expertise and patient values.” The purpose of EBPP is to promote effective psychological practice and enhance public health by applying empirically supported principles of psychological assessment, case formulation, therapeutic relationship, and intervention.

The key to understanding this definition is to note how APA and the Institute of Medicine use the word practice . Clearly, practice does not refer to an intervention; instead, it references one’s professional behavior. This is the sense in which one might speak of the professional practice of behavior analysis. American Psychological Association Presidential Task Force of Evidence-Based Practice ( 2006 ) further elaborates this point:

It is important to clarify the relation between EBPP and empirically supported treatments (ESTs)…. ESTs are specific psychological treatments that have been shown to be efficacious in controlled clinical trials, whereas EBPP encompasses a broader range of clinical activities (e.g., psychological assessment, case formulation, therapy relationships). As such, EBPP articulates a decision-making process for integrating multiple streams of research evidence—including but not limited to RCTs—into the intervention process. (p. 273)

In contrast, Smith defined EBP not as a decision-making process but as a set of interventions that have been shown to be efficacious through rigorous research. He stated:

An evidence-based practice is a service that helps solve a consumer’s problem. Thus it is likely to be an integrated package of procedures, operationalized in a manual, and validated in studies of socially meaningful outcomes, usually with group designs. (p. 27).

Smith’s EBP is what APA has clearly labeled an empirically supported treatment . This is a common misconception found in conversation and in published articles (e.g., Cook and Cook 2013 ) but at odds with formal definitions provided by many professional organizations; definitions which result from extensive consideration and debate by representative leaders of each professional field (e.g., APA 2005 ; American Occupational Therapy Association 2008 ; American Speech-Language Hearing Association 2005 ; Institute of Medicine 2001 ).

Before entering into the discussion of a useful definition of EBP of ABA, we should clarify the functions that we believe a useful definition of EBP should perform. First, a useful definition should align with the philosophical tenets of ABA, support the most effective current practice of ABA, and contribute to further improvement of ABA practice. A definition that is in conflict with the foundations of ABA or detracts from effective practice clearly would be counterproductive. Second, a useful definition of EBP of ABA should enhance social support for ABA practice by describing its empirical basis and decision-making processes in a way that is understandable to professions that already have well-established definitions of EBP. A definition that corresponds with the fundamental components of EBP in other fields would promote ABA practice by improving communication with external audiences. This improved communication is critical in the interdisciplinary contexts in which behavior analysts often practice and for legitimacy among those familiar with EBP who often control local contingencies (e.g., policy makers and funding agencies).

Based on these functions, we propose the following definition: Evidence-based practice of applied behavior analysis is a decision-making process that integrates (a) the best available evidence with (b) clinical expertise and (c) client values and context. This definition positions EBP as a pervasive feature of all professional decision-making by a behavior analyst with respect to client services; it is not limited to a narrowly restricted set of situations or decisions. The definition asserts that the best available evidence should be a primary influence on all decision-making related to services for clients (e.g., intervention selection, progress monitoring, etc.). It also recognizes that evidence cannot be the sole basis for a decision; effective decision-making in a discipline as complex as ABA requires clinical expertise in identifying, defining, and analyzing problems, determining what evidence is relevant, and deciding how it should be applied. In the absence of this decision-making framework, practitioners of ABA would be conceptualized as behavioral technicians rather than analysts. Further, the definition of EBP of ABA includes client values and context. Decision-making is necessarily based on a set of values that determine the goals that are to be pursued and the means that are appropriate to achieve them. Context is included in recognition of the fact that the effectiveness of an intervention is highly dependent upon the context in which it is implemented. The definition asserts that effective decision-making must be informed by important contextual factors. We elaborate on each component of the definition below, but first we contrast our definition with that offered by Smith ( 2013 ).

Although Smith ( 2013 ) made brief reference to the other critical components of EBP, he framed EBP as a list of multicomponent interventions that can claim a sufficient level of research support. We agree with his argument that such lists are valuable resources for practitioners and therefore developing them should be a goal of researchers. However, such lists are not, by themselves , a powerful means of improving the effectiveness of behavior analytic practice. The vast majority of decisions faced in the practice of behavior analysis cannot be made by implementing the kind of manualized, multicomponent treatment packages described by Smith.

There are a number of reasons a list of interventions is not an adequate basis for EBP of ABA. First, there are few interventions that qualify as “practices” under Smith’s definition. For example, when discussing the importance of manuals for operationalizing treatments, Smith stated that the requirement that a “practice” be based on a manual, “sharply reduces the number of ABA approaches that can be regarded as evidence based. Of the 11 interventions for ASD identified in the NAC ( 2009 ) report, only the three that have been standardized in manuals might be considered to be practices, and even these may be incomplete” (p. 18). Thus, although the example referenced the autism treatment literature, it seems apparent that even a loose interpretation of this particular criterion would leave all practitioners with a highly restricted number of intervention options.

Second, even if more “practices” were developed and validated, many consumers cannot be well served with existing multicomponent packages. In order to meet their clients’ needs, behavior analysts must be able to selectively implement focused interventions alone or in combination. This flexibility is necessary to meet the diverse needs of their clients and to minimize the response demands on direct care providers or staff, who are less likely to implement a complicated intervention with fidelity (Riley-Tillman and Chafouleas 2003 ).

Third, the strategy of assembling a list of treatments and describing these as “practices” severely limits the ways in which research findings are used by practitioners. With the list approach to defining EBP, research only impacts practice by placing an intervention on a list when a specific criteria has been met. Thus, any research on an intervention that is not sufficiently broad or manualized to qualify as a “practice” has no influence on EBP. Similarly, a research study that shows clear results but is not part of a sufficient body of support for an intervention would also have no influence. A study that provides suggestive results but is not methodologically strong enough to be definitive would have no influence, even if it were the only study that is relevant to a given problem.

The primary problem with a list approach is that it does not provide a strong framework that directs practitioners to include the best available evidence in all of their professional decision-making. Too often, practitioners who consult such lists find that no interventions relevant to their specific case have been validated as “evidence-based” and therefore EBP is irrelevant. In contrast, definitions of EBP as a decision-making process can provide a robust framework for including research evidence along with clinical expertise and client values and context in the practice of behavior analysis. In the next sections, we explore the components of this definition in more detail.

Best Available Evidence

The term “best available evidence” occupies a critical and central place in the definition and concept of EBP; this aligns with the fundamental reliance on scientific research that is one of the core tenets of ABA. The Behavior Analyst Certification Board ( 2010 ) Guidelines for Responsible Conduct for Behavior Analysts repeatedly affirm ways in which behavior analysts should base their professional conduct on the best available evidence. For example:

Behavior analysts rely on scientifically and professionally derived knowledge when making scientific or professional judgments in human service provision, or when engaging in scholarly or professional endeavors.

  • The behavior analyst always has the responsibility to recommend scientifically supported most effective treatment procedures. Effective treatment procedures have been validated as having both long-term and short-term benefits to clients and society.
  • Clients have a right to effective treatment (i.e., based on the research literature and adapted to the individual client).

A Continuum of Evidence Quality

The term best implies that evidence can be of varying quality, and that better quality evidence is preferred over lower quality evidence. Quality of evidence for informing a specific practical question involves two dimensions: (a) relevance of the evidence and (b) certainty of the evidence.

The dimension of relevance recognizes that some evidence is more germane to a particular decision than is other evidence. This idea is similar to the concept of external validity. External validity refers to the degree to which research results apply to a range of applied situations whereas relevance refers to the degree to which research results apply to a specific applied situation. In general, evidence is more relevant when it matches the particular situation in terms of (a) important characteristics of the clients, (b) specific treatments or interventions under consideration, (c) outcomes or target behaviors including their functions, and (d) contextual variables such as the physical and social environment, staff skills, and the capacity of the organization. Unless all conditions match perfectly, behavior analysts are necessarily required to use their expertise to determine the applicability of the scientific evidence to each unique clinical situation. Evidence based on functionally similar situations is preferred over evidence based on situations that share fewer important characteristics with the specific practice situation. However, functional similarity between a study or set of studies and a particular applied problem is not always obvious.

The dimension of certainty of evidence recognizes that some evidence provides stronger support for claims that a particular intervention produced a specific result. Any instance of evidence can be evaluated for its methodological rigor or internal validity (i.e., the degree to which it provides strong support for the claim of effectiveness and rules out alternative explanations). Anecdotes are clearly weaker than more systematic observations, and well-controlled experiments provide the strongest evidence. Methodological rigor extends to the quality of the dependent measure, treatment fidelity, and other variables of interest (e.g., maintenance of skill acquisition), all of which influence the certainty of evidence. But the internal validity of any particular study is not the only variable influencing the certainty of evidence; the quantity of evidence supporting a claim is also critical to its certainty. Both systematic and direct replication are vital for strengthening claims of effectiveness (Johnston and Pennypacker 1993 ; Sidman 1960 ). Certainty of evidence is based on both the rigor of each bit of evidence and the degree to which the findings have been consistently replicated. Although these issues are simple in principle, operationalizing and measuring rigor of research is extremely complex. Numerous quality appraisal systems for both group and single-subject research have been proposed and used in systematic reviews (see below for more detail).

Under ideal circumstances, consistently high-quality evidence that closely matches the specifics of the practice situation is available; unfortunately, this is not always the case, and evidence-based practitioners of ABA must proceed despite an imperfect evidence base. The mandate to use the best available evidence specifies that the practitioner make decisions based on the best evidence that is available. Although this statement may seem rather obvious, the point is worth underscoring because the implications are highly relevant to behavior analysts. In an area with considerable high-quality relevant research, the standards for evidence should be quite high. But in an area with more limited research, the practitioner should take advantage of the best evidence that is available. This may require tentative reliance on research that is somewhat weaker or is only indirectly relevant to the specific situation at hand. For example, ideally, evidence-based practitioners of ABA would rely on well-controlled experimental results that have been replicated with the precise population with whom they are working. However, if this kind of evidence is not available, they might have to make decisions based on a single study that involves a similar but not identical population.

This idea of using the best of the available evidence is very different from one of using only extremely high-quality evidence (i.e., empirically supported treatments). If we limit EBP to considering only the highest quality evidence, we leave the practitioner with no guidance in the numerous situations in which high-quality and directly relevant evidence (i.e., precise matching of setting, function, behavior, motivating operations and precise procedures) simply does not exist. This approach would lead to a form of EBP that is irrelevant to the majority of decisions that a behavior analyst must make on a daily basis. Instead, our proposed definition of EBP asserts that the practitioner should be informed by the best evidence that is available.

Expanding Research on Utility of Treatments

Smith ( 2013 ) argued that the research methods used by behavior analysts to evaluate these treatments should be expanded to more comprehensively describe the utility of interventions. He suggested that too much ABA research is conducted in settings that do not approximate typical service settings, optimizing experimental control at the expense of external validity. Along this same line of reasoning, he noted that it is important to test the generality of effects across clients and identify variables that predict differential effectiveness. He suggested systematically reporting results from all research participants (e.g., the intent-to-treat model), and purposive selection of participants would provide a more complete account of the situations in which treatments are successful and those in which they are unsuccessful. Smith argued that researchers should include more distal and socially important outcomes because with a narrow target “behavior may change, but remain a problem for the individual or may be only a small component of a much larger cluster of problems such as addiction or delinquency.” He pointed out that in order to best support effective practice, research must demonstrate that an intervention produces or contributes to producing the socially important outcomes that would cause a consumer to say that the problem is solved.

Further, Smith argues that many of the questions most relevant to EBP—questions about the likely outcomes of a treatment when applied in a particular type of situation—are well suited to group research designs. He argued that RCTs are likely to be necessary within a program of research because:

most problems pose important actuarial questions (e.g., determining whether an intervention package is more effective than community treatment as usual; deciding whether to invest in one intervention package or another, both, or neither; and determining whether the long-term benefits justify the resources devoted to the intervention)…. A particularly important actuarial issue centers on the identification of the conditions under which the intervention is most likely to be effective. (p. 23)

We agree that selection of research methods should be driven by the kinds of questions being asked and that group research designs are the methods of choice for some types of questions that are central to EBP. Therefore, we support Smith’s call for increased use of group research designs within ABA. If practice decisions are to be informed by the best available evidence, we must take advantage of both group and single-subject designs. However, we disagree with Smith’s statement that EBP should be limited to treatments that are validated “usually with group designs” (Smith, p. 27). Practitioners should be supported by reviews of research that draw from all of the available evidence and provide the best recommendations possible given the state of knowledge on the particular question. In most areas of behavior analytic practice, single-subject research makes up a large portion of the best available evidence. The Institute for Education Science (IES) has recognized the contribution single case designs can make toward identifying effective practices and has recently established standards for evaluating the quality of single case design studies (Institute of Educational Sciences, n.d. ; Kratochwill et al. 2013 ).

Classes of Evidence

Identifying the best available evidence to inform specific practice decisions is extremely complex, and no single currently available source of evidence can adequately inform all aspects of practice. Therefore, we outline a number of strategies for identifying and summarizing evidence in ways that can support the EBP of ABA. We do not intend to cover all sources of evidence comprehensively, but merely outline some of the options available to behavior analysts.

Empirically Supported Treatment Reviews

Empirically supported treatments (EST) are identified through a particular form of systematic literature review. Systematic reviews bring a rigorous methodology to the process of reviewing research. The development and use of these methods are, in part, a response to the recognition that the process of reviewing the literature is subject to threats to validity. The systematic review process is characterized by explicitly stated and replicable methods for (a) searching for studies, (b) screening studies for relevance to the review question, (c) appraising the methodological quality of studies, (d) describing outcomes from each study, and (e) determining the degree to which the treatment (or treatments) is supported by the research. When the evidence in support of a treatment is plentiful and of high quality, the treatment generally earns the status of an EST. Many systematic reviews, however, find that no intervention for a particular problem has sufficient evidence to qualify as an EST.

Well-known organizations in medicine (e.g., Cochrane Collaboration), education (e.g., What Works Clearinghouse), and mental health (e.g., National Registry of Evidence-based Programs and Practices) conduct EST reviews. Until recently, systematic reviews have focused nearly exclusively on group research; however, systematic reviews of single-subject research are quickly becoming more common and more sophisticated (e.g., Carr 2009 ; NAC 2009 ; Maggin et al. 2012 ).

Systematic reviews for EST status is one important way to summarize the best available evidence because it can give a relatively objective evaluation of the strength of the research literature supporting a particular intervention. But systematic reviews are not infallible; as with all other research and evaluation methods, they require skillful application and are subject to threats to validity. The results of reviews can change dramatically based on seemingly minor changes in operational definitions and procedures for locating articles, screening for relevance, describing treatments, appraising methodological quality, describing outcomes, summarizing outcomes for the body of research as a whole, and rating the degree to which an intervention is sufficiently supported (Slocum et al. 2012a ; Wilczynski 2012 ). Systematic reviews and claims based upon them must be examined critically with full recognition of their limitations just as one examines primary research reports.

Behavior analysts encounter many situations in which no ESTs have been established for the particular combination of client characteristics, target behaviors, functions, contexts, and other parameters for decision-making. This dearth may exist because no systematic review has addressed the particular problem or because a systematic review has been conducted but failed to find any well-supported treatments for the particular problem. For example, in a recent review of all of the recommendations in the empirically supported practice guides published by the IES, 45 % of the recommendations had minimal support (Slocum et al. 2012b ). As Smith noted ( 2013 ), only 3 of the 11 interventions that the NAC identified as meeting quality standards might be considered practices in the sense that they are manualized. In these common situations, a behavior analyst cannot respond by simply selecting an intervention from a list of ESTs. A comprehensive EBP of ABA requires additional strategies for reviewing research evidence and drawing practice recommendations from existing evidence—strategies that can glean the best available evidence from an imperfect research base and formulate practice recommendations that are most likely to lead to favorable outcomes under conditions of uncertainty.

Other Methods for Reviewing Research Literature

The three strategies outlined below may complement systematic reviews in guiding behavior analysts toward effective decision-making.

Narrative Reviews of the Literature

There has been a long tradition across disciplines of relying on narrative reviews to summarize what is known with respect to treatments for a class of problems (e.g., aggression) or what is known about a particular treatment (e.g., token economy). The author of the review, presumably an expert, selects the theme and synthesizes the research literature that he or she considers most relevant. Narrative reviews allow the author to consider a wide range of research including studies that are indirectly relevant (e.g., those studying a given problem with a different population or demonstrating general principles) and studies that may not qualify for systematic reviews because of methodological limitations but which illustrate important points nonetheless. Narrative reviews can consider a broader array of evidence and have greater interpretive flexibility than most systematic reviews.

As with all sources of evidence, there are difficulties with narrative reviews. The selection of the literature is left up to the author’s discretion; there are no methodological guidelines and little transparency about how the author decided which literature to include and which to exclude. There is always the risk of confirmation bias that the author emphasized literature that is consistent with her preconceived opinions. Even with a peer-review process, it is always possible that the author neglected or misinterpreted research relevant to the discussion. These concerns not withstanding, narrative reviews may provide the best available evidence when no systematic reviews exist or when substantial generalizations from the systematic review to the practice context are needed. Many textbooks (e.g., Cooper et al. 2007 ) and handbooks (e.g., Fisher et al. 2011 ; Madden et al. 2013 ) provide excellent examples of narrative reviews that can provide important guidance for evidence-based practitioners of ABA.

Best Practice Guides

Best practice guides are another source of evidence that can inform decisions in the absence of available and relevant systematic reviews. Best practice guides provide recommendations that reflect the collective wisdom of an expert panel. It is presumed that the recommendations reflect what is known from the research literature, but the validity of recommendations is largely derived from the panel’s expertise rather than from the rigor of their methodology. Recommendations from best practice panels are usually much broader than the recommendations from systematic reviews. The recommendations from these guides can provide important information about how to implement a treatment, how to adapt the treatment for specific circumstances, and what is necessary for broad scale or system-wide implementation.

The limitations to best practice guides are similar to those for narrative reviews; specifically, potential bias and lack of transparency are significant concerns. Panel members are typically not selected using a specific set of operationalized criteria. Bias is possible if the panel is drawn too narrowly. If the panel is drawn too broadly; however, the panel may have difficulty reaching a consensus (Wilczynski 2012 ).

Empirically Supported Practice Guides

Empirically supported practice guides, a more recently developed strategy, integrate the strengths of systematic reviews and best practice panels. In this type of review, an expert panel is charged with developing recommendations on a topic. As part of the process, a systematic review of the literature is conducted. Following the systematic review, the panel generates a set of recommendations and objectively determines the strength of evidence for the recommendation and assigns an evidence rating. When there is little empirical evidence directly related to a specific issue, the panel’s recommendations may have weak research support but nonetheless may be based on the best evidence that is available. The obvious advantage of empirically supported practice guides is that there is greater transparency about the review process and certainty of recommendations. Practice recommendations are usually broader than those derived from systematic reviews and address issues related to implementation and acceptable variations to enhance the treatment’s contextual fit (Shanahan et al. 2010 ; Slocum et al. 2012b ). Although empirically supported practice guides offer the objectivity of a systematic review and the flexibility of best practice guidelines, they also face potential sources of error from both methods. Systematic and explicit criteria are used to review the research and rate the level of evidence for each recommendation; however, it is the panel that formulates recommendations. Thus, results of these reviews are influenced by the selection of panel members. When research evidence is incomplete or equivocal, panelists must exercise judgment in interpreting the evidence and drawing conclusions (Shanahan et al. 2010 ).

Other Units of Analysis

Smith ( 2013 ) weighed in on the critical issue of the unit of analysis when describing and evaluating treatments (Slocum and Wilczynski 2008 ). The unit of analysis refers to whether EBP should focus on (a) principles, such as reinforcement; (b) tactics, such as backward chaining; (c) multicomponent packages, such as Functional Communication Training; or (d) even more comprehensive systems, such as Early Intensive Behavioral Intervention. After reviewing the ongoing debate between those favoring a smaller unit of analysis that focuses on specific procedures and those favoring a larger unit of analysis that evaluates the effects of multicomponent packages, Smith made a case that the multicomponent treatment package is the key unit in EBP. Smith noted that practitioners rarely solve a client’s problem with a single procedure; instead, solutions typically involve combinations of procedures. He argued that the unit should be “a service aimed at solving people’s problems” and procedures that are merely components of such services are not sufficiently complete to be the proper unit of analysis for EBP. He further stated that these treatment packages should include strategies for implementation in typical service settings and an intervention manual.

We concur that the multicomponent treatment package is a particularly significant and strategic unit of treatment because it specifies a suite of procedures and exactly how they are to be used together to solve a problem. Validated treatment packages are far more than the sum of their parts. A well-developed treatment package can be revised and optimized over many iterations in a way that would be difficult or impossible for a practitioner to accomplish independently. In addition, research outcomes from implementation of treatment packages reflect the interaction of the components, and these interactions may not be evident in the research literature on the individual components. Further, research on the outcomes from multicomponent packages can evaluate broader and more socially important outcomes than is generally possible when evaluating more narrowly defined treatments. For example, in the case of teaching a child with autism to communicate, research on a focused procedure such as time delay may indicate that its use leads to more independent communicative responses; however, research on a comprehensive Early Intensive Behavioral Intervention can evaluate the impact of the program on children’s global development or intellectual functioning.

Having recognized our agreement with Smith ( 2013 ) on the special importance of multicomponent treatment packages for EBP, we hasten to add that this type of intervention is not enough to support a broad and robust EBP of ABA. EBP must also provide guidance to the practitioner in the frequently encountered situations in which well-established treatment packages are not available. In these situations, problems may be best addressed by building an intervention from a set of elemental components. These components, referred to as practice elements (Chorpita et al. 2005 , 2007 ) or kernels (Embry 2004 ; Embry and Biglan 2008 ), may be validated either directly or indirectly. The practitioner assembles a particular combination of components to solve a specific problem. Because this newly constructed package has not been evaluated as a whole, there is additional uncertainty about the effectiveness of the package, and the quality of evidence may be considered lower than a well-supported treatment package (Slocum et al. 2012b ; Smith 2013 ; however, see Chorpita ( 2003 ) for a differing view). Nonetheless, treatment components that are supported by strong evidence provide the practitioner with tools to solve practical problems when EST packages are not relevant.

In some cases, behavior analysts are presented with problems that cannot be addressed even by assembling established components. In these cases, the ABA practitioner must apply principles of behavior to construct an intervention and must depend on these principles to guide sensible modifications of interventions in response to client needs and to support sensible implementation of interventions. Principles of behavior are broadly generalized statements describing behavioral relations. Their empirical base is extremely large and diverse including both human and nonhuman participants across numerous contexts, behaviors, and consequences. Although principles of behavior are based on an extremely broad research literature, they are also stated at a broad level. As a result, the behavior analyst must use a great deal of judgment in applying principles to particular problems and a particular attempt to apply a principle to solve a problem may not be successful. Thus, although behavioral principles are supported by evidence, newly constructed interventions based on these principles have not yet been evaluated. These interventions must be considered less certain or validated than treatment packages or elements that have been demonstrated to be effective for specific problems, populations, and context (Slocum et al. 2012b ).

Evidence-based practitioners of ABA recognize that the process of selecting and implementing treatments always includes some level of uncertainty (Detrich et al. 2013 ). One of the fundamental tenets of ABA shared with many other professions is that the best evidence regarding the effectiveness of an intervention does not come from systematic literature reviews, best practice guides, or principles of behavior, but from close continual contact with the relevant outcomes (Bushell and Baer 1994 ). The BACB guidelines ( 2010 ) state that, “behavior analysts recognize limits to the certainty with which judgments or predictions can be made about individuals” (item 3.0 [c]). As a result, “the behavior analyst collects data…needed to assess progress within the program” (item 4.07) and “modifies the program on the basis of data” (item 4.08). Thus, an important feature of the EBP of ABA is that professional decision-making does not end with the selection of an initial intervention. The process continues with ongoing progress monitoring and adjustments to the treatment plan as needed to achieve the targeted outcomes. Progress monitoring and data-based decision-making are the ultimate hedge against the inherent uncertainties of imperfect knowledge derived from research. As the quality of the best available evidence decreases, the importance of frequent direct measurement of client progress increases.

Practice decisions are always accompanied by some degree of uncertainty; however, better decisions are likely when multiple of sources of evidence are integrated. For example, a multicomponent treatment package may be an EST for clients who differ slightly from those the practitioner currently serves. Confidence in the use of this treatment may be increased if there is evidence showing the central components are effective with clients belonging to the population of interest. The principles of behavior might further inform sensible variations appropriate for the specific context of practice. When considered together, numerous sources of evidence increase the confidence the behavior analyst can have in the intervention. And when the plan is implemented, progress monitoring may reveal the need for additional adjustments. Each of these different classes of evidence provides answers to different questions for the practitioner, resulting in a more fine-grained analysis of the clinical problem and solutions to it (Detrich et al. 2013 ).

Client Values and Context

In order to be compatible with the underlying tenets of ABA, parallel with other professions, and to promote effective practice, a definition of EBP of ABA must include client values and context among the primary contributors to professional decision-making. Baer et al. ( 1968 ) suggested that the word applied refers to an immediate and important change in behavior that has practical value and that this value is determined “by the interest which society shows in the problems” (p. 92)—that is, by social values. Wolf ( 1978 ) went on to specify that behavior analytic practice can only be termed successful if it addresses goals that are meaningful to our clients, uses procedures that are judged appropriate by our clients, and produces effects that are valued by our clients. These foundational tenets of ABA correspond with the centrality of client values in classic definitions of EBP (e.g., Institute of Medicine 2001 ). Like medical professionals and those in the many other fields that have adopted similar conceptualizations of EBP, behavior analysts have long recognized that client values are critical contributors to responsible decision-making.

Behavior analysts have defined the client as the individual who is the focus of the behavior change, other individuals who are critical to the behavior change process (Baer et al. 1968 ; Heward et al. 2005 ), as well as outside individuals or groups who may have a stake in the target behavior or improved outcomes (Baer et al. 1987 ; Wolf 1978 ). Wolf ( 1978 ) argued that only our clients can judge the social validity of our work and suggested that behavior analysts address three levels of social validity: (a) the social significance of the goals, (b) the social desirability of the procedures, and (c) the social importance of the outcomes. With respect to selection of interventions, Wolf noted, “not only is it important to determine the acceptability of treatment procedures to participants for ethical reasons, it may also be that the acceptability of the program is related to effectiveness, as well as to the likelihood that the program will be adopted and supported by others” (p. 210). He further maintained that clients are the ultimate arbiters of whether or not the effects of a program are sufficiently helpful to be termed successful.

The concept of social validity directs our attention to some of the important aspects of the context of intervention. Intervention always occurs in some context and features of that context can directly influence the fidelity with which the intervention is implemented and its effectiveness. Albin et al. ( 1996 ) expanded further on the contextual variables that might be critical for designing and implementing effective interventions. They described the concept of contextual fit or the congruence of a behavioral support plan and the context and indicate that this fit will determine its implementation, effectiveness, and maintenance.

Contextual fit includes the issues of social validity, but also explicitly encompasses issues associated with the individuals who implement treatments and manage other aspects of the environments within which treatments are implemented. Behavioral intervention plans prescribe the behavior of implementers. These implementers may include professionals, such as therapists and teachers, as well as nonprofessionals, such as family and community members. It is important to consider characteristics of these implementers when developing plans because the success of a plan may hinge on how it corresponds with the values, skills, goals, and stressors of the implementers. Effective plans must be within the skill repertoire of the implementers, or training to fidelity must occur to introduce the plan components into that repertoire. Values, goals, and stressors refer to motivating operations that determine the reinforcing or punishing value of implementing the plan. Plans that provide little reinforcement and substantial punishment in the process of implementation or outcomes are unlikely to be implemented with fidelity or maintained over time. The effectiveness of behavioral interventions is also influenced by their compatibility with other aspects of their context. Plans that are compatible with ongoing routines are more likely to be implemented than those that conflict (Riley-Tillman and Chafouleas 2003 ). Interventions require various kinds of resources to be implemented and sustained. For example, financial resources may be necessary to purchase curricula, equipment, or other goods. Interventions may require human resources such as direct service staff, training, supervision, administration, and consultation. Fixsen et al. ( 2005 ) have completed an extensive review of contextual variables that can potentially influence the quality of intervention implementation. Behavior analytic practice is unlikely to be effective if it does not consider the context in which interventions will be implemented.

Extensive behavior analytic research has documented the importance of social validity and other contextual factors in producing behavioral changes with practical value. This research tradition is as old as our field (e.g., Jones and Azrin 1969 ) and continues through the present day. For example, Strain et al. ( 2012 ) provided multiple examples of the impact of social validity considerations on relevant outcomes. They reported that integrating client values, preferences, and characteristics in the selection and implementation of an intervention can successfully inform decisions regarding (a) how to design service delivery systems, (b) how to support implementers with complex strategies, (c) when to fade support, (e) how to identify important and unanticipated effects, and (f) how to focus on future research efforts.

Benazzi et al. ( 2006 ) examined the effect of stakeholder participation in intervention planning on the acceptability and usability of behavior intervention plans (BIP) based on descriptive functional behavior assessments (FBA). Plans developed by behavior experts were rated as high in technical adequacy, but low in acceptability. Conversely, plans developed by key stakeholders were highly acceptable, but lacked technical adequacy. However, when the process included both behavior experts and key stakeholders, BIPs were considered both acceptable and technically adequate. Thus, the BIPs developed by behavior analysts may be marginalized and implementation may be less likely to occur in the absence of key stakeholder input. Thus, a practical commitment to effective interventions that are implemented and maintained with integrity over time requires that behavior analysts consider motivational variables such as the alignment of interventions with the values, reinforcers, and punishers of relevant stakeholders.

Clinical Expertise

All of the key components for expert behavior analytic practice (i.e., identification of important behavioral problems, recognition of underlying behavioral processes, weighing of evidence supporting various treatment options, selecting and implementing treatments in complex social contexts, engaging in ongoing data-based decision making, and being responsive to client values and context) require clinical expertise. Clinical expertise refers to the competence attained by practitioners through education, training, and experience that results in effective practice (American Psychological Association Presidential Task Force of Evidence-Based Practice 2006 ). Clinical expertise is the means by which the best available evidence is applied to individual cases in all their complexity. Based on the work of Goodheart ( 2006 ), we suggest that clinical expertise in EBP of ABA includes (a) knowledge of the research literature and its applicability to particular clients, (b) incorporation of the conceptual system of ABA, (c) breadth and depth of clinical and interpersonal skills, (d) integration of client values and context, (e) recognition of the need for outside consultation, (f) data-based decision making, and (g) ongoing professional development. In the sections that follow, we describe each component of clinical expertise in ABA.

Knowledge and Application of the Research Literature

ABA practitioners must be skilled in applying the best available evidence to unique cases in specific contexts. The role of the best available evidence in EBP of ABA was discussed above. Practitioners need to be knowledgeable about the scientific literature and able to appropriately apply the literature to behaviors, clients, and contexts that are rarely a perfect match to the behaviors, clients, and contexts in any particular study. This confluence of knowledge and skillful application requires that the behavior analyst respond to the functionally important features of cases. A great deal of training is necessary to build the expertise required to discriminate critical functional features from those that are incidental. These discriminations must be made with respect to the presenting problem (i.e., the behavioral patterns that have been identified as problematic, their antecedent stimuli, motivating operations, and consequences); client variables such as histories, skills, and preferences; and contextual variables that may impact the effectiveness of various treatment options as applied to the particular case. These skills are reflected in BACB Guidelines 1.01 and 2.10 cited above.

Incorporation of the Conceptual System

The critical features of a case must be identified and mapped onto the conceptual system of ABA. It is not enough to recognize that a particular feature of the environment is important; it must also be understood in terms of its likely behavioral function. This initial conceptualization is necessary in order to generate reasonable hypotheses that may be tested in more thorough analyses. Developing the skill of describing cases in terms of likely behavioral functions typically requires a great deal of formal and informal training as well as ongoing learning from experience. These repertoires are usually acquired through extensive training, supervised practice, and the ongoing feedback of client outcomes. This is recognized in BACB Guidelines; for example, 4.0 states that “the behavior analyst designs programs that are based on behavior analytic principles” (BACB 2010 ).

Breadth and Depth of Clinical and Interpersonal Skills

Evidence-based practitioners of behavior analysis must be able to implement various assessment and intervention procedures with fidelity, and often to train and supervise others to implement such procedures with fidelity. Further, clinical expertise in ABA requires that the practitioner have effective interpersonal skills. For example, he must be able to explain the behavioral philosophy and approach, in nonbehavioral terms, to various audiences who may have different theoretical orientations. BCBA Guidelines 1.05 specifies that behavior analysts “use language that is fully understandable to the recipient of those services” (BACB 2010 ).

Integration of Client Values and Context

In all aspects of their work, practitioners of evidence-based ABA must integrate the values and preferences of the client and other stakeholders as well as the features of the specific context that may impact the effectiveness of an intervention. These factors can be considered additional variables that the behavior analyst must attend to when planning and providing behavior-analytic services. For example, when assessment data suggest behavior serves a particular function, a range of intervention alternatives may be considered (see Geiger, Carr, and LeBlanc for an example of a model for selecting treatments for escape-maintained problem behavior). A caregiver’s statements might suggest that one type of intervention may not be viable due to limited resources while another treatment may be acceptable based on financial considerations, available resources, or other practical factors; the behavior analyst must have the training and expertise to evaluate and incorporate these factors into initial treatment selection and to re-evaluate these concerns as a part of progress monitoring for both treatment integrity and client improvement. BACB Guideline 4.0 states that the behavior analyst “involves the client … in the planning of … programs, [and] obtains the consent of the client” and 4.1 states that “if environmental conditions hamper implementation of the behavior analytic program, the behavior analyst seeks to eliminate the environmental constraints, or identifies in writing the obstacles to doing so” (BACB 2010 ).

Recognition of Need for Outside Consultation

Behavior analysts engaging in responsible evidence-based practice discriminate between behaviors and contexts that are within the scope of their training and those that are not, and respond differently based on this discrimination. For example, a behavior analyst who has been trained to provide assessment and intervention for severe problem behavior may not have the specific training to provide organizational behavior management services to a corporation; in this case, a behavior analyst with clinical expertise would make this discrimination and seek additional consultation or make appropriate referrals. This aspect of expertise is described in BACB ( 2010 ) Guidelines 1.02 and 2.02.

Data-Based Decision Making

Data-based decision making plays a central role in the practice of ABA and is an indispensable feature of clinical expertise. The process of data-based decision making includes identifying useful measurement pinpoints, constructing measurement systems, and graphing results, as well as identifying meaningful patterns in data, interpreting these patterns, and making appropriate responses to them (e.g., maintaining, modifying, replacing, or ending a program). The functional features of the case, the best available research evidence, and the new evidence obtained through progress monitoring must inform these judgments and are central to this model of EBP of ABA. BACB ( 2010 ) Guidelines 4.07 and 4.08 specify that behavior analysts collect data to assess progress and modify programs on the basis of data.

Ongoing Professional Development

Clinical expertise is not static; rather, it requires ongoing professional development. Clinical expertise in ABA requires ongoing contact with the research literature to ensure that practice reflects current knowledge about the most effective and efficient assessment and intervention procedures. The critical literature includes primary empirical research as well as reviews and syntheses such as those described in the section on “ Best Available Evidence ”. In addition, professional consensus on important topics for professional practice evolves over time. For example, in ABA, there has been increased emphasis recently on ethics and supervision competence. All of these dynamics point to the need for ongoing professional development. This is reflected in the requirement that certified behavior analysts “undertake ongoing efforts to maintain competence in the skills they use by reading the appropriate literature, attending conferences and conventions, participating in workshops, and/or obtaining Behavior Analyst Certification Board certification” (Guideline 1.03, BACB 2010 ).

Conclusions

We propose that EBP of ABA be understood as a professional decision-making framework that draws on the best available evidence, client values and context, and clinical expertise. We argue that this conception of EBP of ABA is more compatible with the basic tenets of ABA and more closely aligned with definitions of EBP in other fields than that provided by Smith ( 2013 ). It is noteworthy that this notion of EBP is not necessarily in conflict with many of the observations and arguments put forth by Smith ( 2013 ). His concerns were primarily about how to define and validate EST, which is an important way to inform practitioners about the best available evidence to integrate into their overall EBP.

Given the close alignment between the proposed framework of EBP of ABA and broadly accepted descriptions of behavior analytic practice, one might wonder whether EBP offers anything new. We believe that the EBP of ABA framework, offered here, has several important implications for our field. First, this framework draws together numerous elements of ABA practice into a single coherent system, which can help behavior analysts provide an explicit rationale for their decision-making to clients and other stakeholders. The EBP of ABA provides a decision-making framework that supports a cogent and transparent description of (a) the evidence considered, including direct and frequent measurement of the client’s behavior; (b) why this evidence was identified as the “best available” for the particular case; (c) how client values and contextual factors influenced the process; and (d) the ways in which clinical expertise was used to conceptualize the case and integrate the various considerations. This transparency and explicitness allows the behavior analyst to offer empirically based treatment recommendations while addressing the concerns raised by stakeholders. It also highlights the critical analysis required to be an effective behavior analyst. For example, if an EST is available and appropriate, the behavior analyst can describe the relevance and certainty of the evidence for this intervention. If no relevant EST is available, the behavior analyst can describe how the best available evidence supports the intervention and emphasize the importance of progress monitoring.

Second, the EBP framework prompts the behavior analyst to refer to the important client values that underlie the goals of intervention, the specific methods of intervention, and describe how the intervention is supported by features of the context. This requires the behavior analyst to explicitly recognize that the effectiveness of an intervention is always context dependent. By serving as a prompt, the EBP framework should increase behavior analysts’ adherence to this central tenet of ABA.

Third, by explicitly recognizing the role of clinical expertise, the framework gives the behavior analyst a way to talk about the complex skills required to make appropriate decisions about client needs. In addition, the fact that the proposed definition of EBP of ABA is so closely aligned with definitions in other professions such as medicine and psychology that it provides a common framework and language for communicating about a particular case that can enhance collaboration between behavior analysts and other professionals.

Fourth, this framework for EBP of ABA suggests further development of behavior analysis as well. Examination of the meaning of best available evidence encourages behavior analysts to continue to refine methods for systematically reviewing research literature and identifying ESTs. Further, behavior analysts could better support EBP if we developed methods for validating other units of intervention such as practice elements, kernels, and even the principles of behavior; when these are invoked to support interventions, they must be supported by a clearly specified research base.

Finally, the explicit recognition of the role of clinical expertise in the EBP of ABA has important implications for training behavior analysts. This framework suggests that decision-making is at the heart of EBP of ABA and could be an organizing theme for ABA training programs. Training programs could systematically teach students to articulate the chain of logic that is the basis for their treatment recommendations. The chain of logic would include statements about which research was considered and why, how the client’s values influenced decision-making, and how contextual factors influenced the selection and adaptation (if necessary) of the treatment. This type of training could be embedded in all instructional activities. Formally requiring students to articulate a rationale for the decisions and receiving feedback about their decisions would sharpen their clinical expertise.

In addition to influencing our behavior analytic practice, the EBP of ABA framework impacts our relationship with other members of the broader human service field as well as individuals and agencies that control contingencies relevant to practitioners and scientists. Methodologically rigorous reviews that identify ESTs and other treatments supported by the best available evidence are extremely important for working with organizations that control funding for behavior analytic research and practice. Federal funding for research and service provision is moving strongly towards EBP and ESTs. This trend is clear in education through the No Child Left Behind Act of 2001 , the Individuals with Disabilities Education Act of 2004 , the funding policies of IES, and the What Works Clearinghouse. The recent memorandum by the Director of the Office of Management and Budget (Zients 2012 ) makes it clear that the importance of EBP is not limited to a single discipline or to one political party. In addition, insurance companies are increasingly making reimbursement decisions based, in part, on whether or not credible scientific evidence supports the use of the treatment (Small 2004 ). The insurance companies have consistently adopted criteria for scientific evidence that are closely related to EST (Bogduk and Fraifeld 2010 ). As a result, reimbursement for ABA services may depend on the scientific credibility of EST reviews, a critical component of EBP. Methodologically rigorous reviews that identify ESTs within a broader framework of EBP appear to be critical for ABA to maintain and expand its access to federal funding and insurance reimbursement for services. Establishment of this literature base will require behavior analysts to develop appropriate methods for reviewing and summarizing research based on single-subject designs. IES has established such standards for reviewing studies, but to date, there are no accepted methods for calculating a measure of effect size as an objective basis for combining result across studies (Kratochwill et al. 2013 ). If behavior analysts develop such a measure, it would reflect a significant methodological advance as a field and it would increase the credibility of behavior analytic research with agencies that fund research and services.

EBP of ABA emphasizes the research-supported selection of treatments and data-driven decisions about treatment progress that have always been at the core of ABA. ABA’s long-standing recognition of the importance of social validity is reflected in the definition of EBP. This framework for EBP of ABA offers many positive professional consequences for scientists and practitioners while promoting the best of the behavior analytic tradition and making contact with developments in other disciplines and the larger context in which behavior analysts work.

  • Albin RW, Lucyshyn JM, Horner RH, Flannery KB. Contextual fit for behavior support plans. In: Koegel LK, Koegel RL, Dunlap G, editors. Positive behavioral support: Including people with difficult behaviors in the community. Baltimore: Brookes; 1996. pp. 81–92. [ Google Scholar ]
  • American Occupational Therapy Association Occupational therapy practice framework: domain and process (2nd ed.) American Journal of Occupational Therapy. 2008; 62 :625–683. doi: 10.5014/ajot.62.6.625. [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • American Psychological Association (2005). Policy statement on evidence-based practice in psychology. http://www.apa.org/practice/resources/evidence/evidence-based-statement.pdf .
  • American Psychological Association Presidential Task Force of Evidence-Based Practice Evidence-based practice in psychology. American Psychologist. 2006; 61 :271–285. doi: 10.1037/0003-066X.61.4.271. [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • American Speech-Language-Hearing Association (2005). Evidence-based practice in communication disorders [position statement]. www.asha.org/policy .
  • Baer DM, Wolf MM, Risley TR. Some current dimensions of applied behavior analysis. Journal of Applied Behavior Analysis. 1968; 1 :91–97. doi: 10.1901/jaba.1968.1-91. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Baer DM, Wolf MM, Risley TR. Some still-current dimensions of applied behavior analysis. Journal of Applied Behavior Analysis. 1987; 20 :313–327. doi: 10.1901/jaba.1987.20-313. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Behavior Analyst Certification Board (2010). Guidelines for responsible conduct for behavior analysts. http://www.bacb.com/index.php?page=57 .
  • Benazzi L, Horner RH, Good RH. Effects of behavior support team composition on the technical adequacy and contextual-fit of behavior support plans. The Journal of Special Education. 2006; 40 (3):160–170. doi: 10.1177/00224669060400030401. [ CrossRef ] [ Google Scholar ]
  • Bogduk N, Fraifeld EM. Proof or consequences: who shall pay for the evidence in pain medicine? Pain Medicine. 2010; 11 (1):1–2. doi: 10.1111/j.1526-4637.2009.00770.x. [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Bushell D, Jr, Baer DM. Measurably superior instruction means close, continual contact with the relevant outcome data. Revolutionary! In: Gardner R III, Sainato DM, Cooper JO, Heron TE, Heward WL, Eshleman J, Grossi TA, editors. Behavior analysis in education: Focus on measurably superior instruction. Pacific Grove: Brooks; 1994. pp. 3–10. [ Google Scholar ]
  • Carnine D. Expanding the notion of teachers’ rights: access to tools that work. Journal of Applied Behavior Analysis. 1992; 25 (1):13–19. doi: 10.1901/jaba.1992.25-13. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Carr JE, Severtson JM, Lepper TL. Noncontingent reinforcement is an empirically supported treatment for problem behavior exhibited by individuals with developmental disabilities. Research in Developmental Disabilities. 2009; 30 :44–57. doi: 10.1016/j.ridd.2008.03.002. [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Chorpita BF. The frontier of evidence-based practice. In: Kazdin AE, Weisz JR, editors. Evidence-based psychotherapies for children and adolescents. New York: Oxford; 2003. pp. 42–59. [ Google Scholar ]
  • Chorpita BF, Daleiden EL, Weisz JR. Identifying and selecting the common elements of evidence based interventions: a distillation and matching model. Mental Health Services Research. 2005; 7 :5–20. doi: 10.1007/s11020-005-1962-6. [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Chorpita BF, Becker KD, Daleiden EL. Understanding the common elements of evidence-based practice: misconceptions and clinical examples. Journal of the American Academy of Child and Adolescent Psychiatry. 2007; 46 :647–652. doi: 10.1097/chi.0b013e318033ff71. [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Cook BG, Cook SC. Unraveling evidence-based practices in special education. Journal of Special Education. 2013; 47 (2):71–82. doi: 10.1177/0022466911420877. [ CrossRef ] [ Google Scholar ]
  • Cooper JO, Heron TE, Heward WL. Applied behavior analysis. 2. Upper Saddle River: Pearson; 2007. [ Google Scholar ]
  • Detrich, R. (Chair) (2009). Evidence-based, empirically supported, best practice: What does it all mean? Symposium conducted at the annual meeting of the Association for Behavior Analysis International, Phoenix, AZ.
  • Detrich R, Slocum TA, Spencer TD. Evidence-based education and best available evidence: Decision-making under conditions of uncertainty. In: Cook BG, Tankersley M, Landrum TJ, editors. Advances in learning and behavioral disabilities, 26. Bingly, UK: Emerald; 2013. pp. 21–44. [ Google Scholar ]
  • Embry DD. Community-based prevention using simple, low-cost, evidence-based kernels and behavior vaccines. Journal of Community Psychology. 2004; 32 :575–591. doi: 10.1002/jcop.20020. [ CrossRef ] [ Google Scholar ]
  • Embry DD, Biglan A. Evidence-based kernels: fundamental units of behavioral influence. Clinical Child and Family Psychology Review. 2008; 11 :75–113. doi: 10.1007/s10567-008-0036-x. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Fisher WW, Piazza CC, Roane HS, editors. Handbook of applied behavior analysis. New York: Guilford Press; 2011. [ Google Scholar ]
  • Fixsen DL, Naoom SF, Blase KA, Friedman RM, Wallace F. Implementation research: A synthesis of the literature (FMHI publication #231) Tampa: University of South Florida, Louis de la Parte Florida Mental Health Institute, The National Implementation Research Network; 2005. [ Google Scholar ]
  • Goodheart CD. Evidence, endeavor, and expertise in psychology practice. In: Goodheart CD, Kazdin AE, Sternberg RJ, editors. Evidence-based psychotherapy: Where practice and research meet. Washington, D.C.: APA; 2006. pp. 37–61. [ Google Scholar ]
  • Goodman KW. Ethics and evidence-based education: Fallibility and responsibility in clinical science. New York: Cambridge University Press; 2003. [ Google Scholar ]
  • Heward WL, et al., editors. Focus on behavior analysis in education: Achievements, challenges, and opportunities. Upper Saddle River: Prentice Hall; 2005. [ Google Scholar ]
  • Horner RH, Carr EG, Halle J, McGee G, Odom S, Wolery M. The use of single-subject research to identify evidence-based practice in special education. Exceptional Children. 2005; 71 (2):165–179. doi: 10.1177/001440290507100203. [ CrossRef ] [ Google Scholar ]
  • Horner RH, Sugai G, Todd AW, Lewis-Palmer T. Schoolwide positive behavior support. In: Bambera LM, Kern L, editors. Individualized supports for students with problem behaviors: Designing positive behavior plans. New York: Guilford Press; 2005. pp. 359–390. [ Google Scholar ]
  • Individuals with Disabilities Education Improvement Act of 2004, 70, Fed. Reg., (2005).
  • Institute of Education Sciences, US. Department of Education. (n.d.). What Works Clearinghouse Procedures and Standards Handbook (No. Version 3.0). Washington DC. Retrieved from http://ies.ed.gov/ncee/wwc/pdf/reference_resources/wwc_procedures_v3_0_standards_handbook.pdf .
  • Institute of Medicine . Crossing the quality chasm: A new health system for the 21st century. Washington, DC: National Academies Press; 2001. [ PubMed ] [ Google Scholar ]
  • Johnston JM, Pennypacker HS. Strategies and tactics of behavioral research. 2. Hillsdale: Erlbaum; 1993. [ Google Scholar ]
  • Jones RJ, Azrin NH. Behavioral engineering: stuttering as a function of stimulus duration during speech synchronization. Journal of Applied Behavior Analysis. 1969; 2 :223–229. doi: 10.1901/jaba.1969.2-223. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Kazdin AE. Psychotherapy for children and adolescents: Directions for research and practice. New York: Oxford University Press; 2000. [ Google Scholar ]
  • 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. Retrieved from http://ies.ed.gov/ncee/wwc/pdf/wwc_scd.pdf .
  • Kratochwill TR, Hitchcock JH, Horner RH, Levin JR, Odom SL, Rindskopf DM, et al. Single-case intervention research design standards. Remedial & Special Education. 2013; 34 (1):26–38. doi: 10.1177/0741932512452794. [ CrossRef ] [ Google Scholar ]
  • Madden GJ, Dube WV, Hackenberg TD, Hanley GP, Lattal KA, editors. American Psychological Association handbook of behavior analysis. Washington, DC: American Psychological Association; 2013. [ Google Scholar ]
  • Maggin DM, O’Keeffe BV, Johnson AH. A quantitative synthesis of single-subject meta-analyses in special education, 1985–2009. Exceptionality. 2011; 19 :109–135. doi: 10.1080/09362835.2011.565725. [ CrossRef ] [ Google Scholar ]
  • Maggin DM, Johnson AH, Chafouleas SM, Ruberto LM, Berggren M. A systematic evidence review of school-based group contingency interventions for students with challenging behavior. Journal of School Psychology. 2012; 50 :625–654. doi: 10.1016/j.jsp.2012.06.001. [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • McIntosh K, Filter KJ, Bennett JL, Ryan C, Sugai G. Principles of sustainable prevention: designing scale–up of School–wide Positive Behavior Support to promote durable systems. Psychology in the Schools. 2010; 47 (1):5–21. [ Google Scholar ]
  • National Autism Center . National Standards Project: Findings and conclusions. Randolph: National Autism Center; 2009. [ Google Scholar ]
  • No Child Left Behind Act of 2001, Pub. L. No. 107-110. (2002).
  • Polsgrove L. Reflections on the past and future. Education and Treatment of Children. 2003; 26 :337–344. [ Google Scholar ]
  • Riley-Tillman TC, Chafouleas SM. Using interventions that exist in the natural environment to increase treatment integrity and social influence in consultation. Journal of Educational & Psychological Consultation. 2003; 14 (2):139–156. doi: 10.1207/s1532768xjepc1402_3. [ CrossRef ] [ Google Scholar ]
  • Sackett DL, Rosenberg WM, Gray JA, Haynes RB, Richardson WS. Evidence based medicine: what it is and what it isn’t. British Medical Journal. 1996; 312 (7023):71. doi: 10.1136/bmj.312.7023.71. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Sackett DL, Straus SE, Richardson WS, Rosenberg W, Haynes RB, editors. Evidence-based medicine: How to teach and practice EBM. Edinburgh: Livingstone; 2000. [ Google Scholar ]
  • Shanahan, T., Callison, K., Carriere, C., Duke, N.K., Pearson, P.D., Schatschneider, C., et al. (2010). Improving reading comprehension in kindergarten through 3rd grade: A practice guide (NCEE 2010-4038). Washington, DC: National Center for Education Evaluation and Regional Assistance, Institute of Education Sciences, U.S. Department of Education. http://ies.ed.gov/ncee/wwc/publications/practiceguides . Accessed 12 Sept 2013
  • Sidman M. Tactics of scientific research: Evaluating experimental data in psychology. New York: Basic Books; 1960. [ Google Scholar ]
  • Slocum, T. A., & Wilczynski, S. (2008). The unit of analysis in evidence-based practice . Invited paper presented at the meeting the Association for Behavior Analysis International, Chicago, Il.
  • Slocum TA, Detrich R, Spencer TD. Evaluating the validity of systematic reviews to identify empirically supported treatments. Education and Treatment of Children. 2012; 35 :201–234. doi: 10.1353/etc.2012.0009. [ CrossRef ] [ Google Scholar ]
  • Slocum TA, Spencer TD, Detrich R. Best available evidence: three complementary approaches. Education and Treatment of Children. 2012; 35 :27–55. [ Google Scholar ]
  • Small RH. Maximize the likelihood of reimbursement when appealing managed care medical necessity denials. Getting Paid in Behavioral Healthcare. 2004; 9 (12):1–3. [ Google Scholar ]
  • Smith T. What is evidence-based behavior analysis? The Behavior Analyst. 2013; 36 :7–33. [ PMC free article ] [ PubMed ] [ Google Scholar ]
  • Strain PS, Barton EE, Dunap G. Lessons learned about the utility of social validity. Education and Treatment of Children. 2012; 35 (2):183–200. doi: 10.1353/etc.2012.0007. [ CrossRef ] [ Google Scholar ]
  • Wilczynski SM. Risk and strategic decision-making in developing evidence-based practice guidelines. Education and Treatment of Children. 2012; 35 :291–311. doi: 10.1353/etc.2012.0012. [ CrossRef ] [ Google Scholar ]
  • Wolf M. Social validity: the case for subjective measurement, or how applied behavior analysis is finding its heart. Journal of Applied Behavior Analysis. 1978; 11 :203–214. doi: 10.1901/jaba.1978.11-203. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Zients, J. D. (2012). M-12-14. Memorandum to the heads of executive departs. From: Jeffrey D. Zients, Acting Director. Subject: use of evidence and evaluation in the 2014 Budget. www.whitehouse.gov/sites/default/files/omb/…/2012/m-12-14.pdf . Accessed 30 Sept 2012

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  3. Single-Subject Experimental Designs versus Case Studies

  4. Single case designs

  5. ABA Design

  6. Alternative methods: 2

COMMENTS

  1. Single-Subject Research Designs

    The most basic single-subject research design is the reversal design , also called the ABA design. During the first phase, A, a baseline is established for the dependent variable. This is the level of responding before any treatment is introduced, and therefore the baseline phase is a kind of control condition.

  2. Single-Case Designs

    Single-case designs are synonymous with ABA research but should also be integrated with routine clinical practice. Performing as a scientist-practitioner demands that we evaluate the effects of our practices with the most control and rigor as possible.

  3. Optimizing behavioral health interventions with single-case designs

    Over the past 70 years, single-case design (SCD) research has evolved to include a broad array of methodological and analytic advances. In this article, we describe some of these advances and discuss how SCDs can be used to optimize behavioral health interventions.

  4. Single-Case Design, Analysis, and Quality Assessment for Intervention

    Single-case studies can provide a viable alternative to large group studies such as randomized clinical trials. Single case studies involve repeated measures, and manipulation of and independent variable. They can be designed to have strong internal validity for assessing causal relationships between interventions and outcomes, and external ...

  5. Applied Behavior Analysis: Single Subject Research Design

    Single case design (SCD), often referred to as single subject design, is an evaluation method that can be used to rigorously test the success of an intervention or treatment on a particular case (i.e., a person, school, community) and to also provide evidence about the general effectiveness of an intervention using a relatively small sample size.

  6. Single-Subject Experimental Design for Evidence-Based Practice

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

  7. PDF Design Options for Home Visiting Evaluation SINGLE CASE DESIGN BRIEF

    Single case design (SCD), often referred to as single subject design, is an evaluation method that can be used to rigorously test the success of an intervention or treatment on a particular case (i.e., a person, school, community) and to also provide evidence about the general effectiveness of an intervention using a relatively small sample size.

  8. 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 CSD treatment research. These designs provide a framework for a quantitative, scientifically rigorous approach where each participant provides his or her own experimental control.

  9. Structured visual analysis of single‐case experimental design data

    Visual analysis is the primary method used to interpret single-case experimental design (SCED) data in applied behavior analysis. Research shows that agreement between visual analysts can be suboptimal at times. To address the inconsistent interpretations of SCED data, recent structured visual-analysis technological advancements have been ...

  10. Randomized single-case AB phase designs: Prospects and pitfalls

    Single-case experimental designs (SCEDs) are increasingly used in fields such as clinical psychology and educational psychology for the evaluation of treatments and interventions in individual participants. The AB phase design, also known as the interrupted time series design, is one of the most basic SCEDs used in practice.

  11. Single Subject Experimental Designs

    When choosing a single-subject experimental design, ABA researchers are looking for certain characteristics that fit their study. First, individuals serve as their own control in single subject research. In other words, the results of each condition are compared to the participant's own data.

  12. A Meta-Analysis of Single-Case Research on Applied Behavior ...

    This systematic review evaluates single-case research design studies investigating applied behavior analytic (ABA) interventions for people with Down syndrome (DS). One hundred twenty-five studies examining the efficacy of ABA interventions on increasing skills and/or decreasing challenging behaviors met inclusion criteria.

  13. Meta-analysis of single-case treatment effects on self-injurious

    In examination of the 679 articles, we used the following criteria to select studies or datasets for inclusion: (a) the experimental study used a single-case research design, beginning with a baseline phase that was followed by a treatment phase; (b) the dependent variable was a quantitative measure of SIB (e.g., frequency of head-hitting); (c ...

  14. Systematic Protocols for the Visual Analysis of Single-Case Research

    Single-case research (SCR) is the predominant methodology used to evaluate causal relations between interventions and target behaviors in applied behavior analysis and related fields such as special education and psychology (Horner et al., 2005; Kazdin, 2011 ). This methodology focuses on the individual case as the unit of analysis and is well ...

  15. 3 Dimensions of a Single-case Study Design

    ABA Terms 3 Dimensions of a Single-case Study Design Prediction, verification and replication. Prediction involves anticipating what you think will happen in the future. Verification is showing that dependent variables (DVs) would not change without intervention (independent variables: IVs).

  16. Find Single Subject Research Articles

    Applied Behavior Analysis Includes suggested databases, search techniques for finding single subject studies, and links to ABA journals. reversal design withdrawal design ABAB design A-B-A-B design ABC design A-B-C design ABA design A-B-A design multiple baseline alternating treatments design multi-element design changing criterion design

  17. Creating Single-Subject Research Design Graphs with Google ...

    Several technical articles for the development of single-subject research design graphs have been published to assist ABA practitioners and researchers in their usage. For example, Carr and Burkholder ( 1998 ), Dixon et al. ( 2009 ), and Pritchard ( 2008 ) published tutorials on generating graphs with Microsoft Excel whereas Berkman et al ...

  18. The withdrawal design

    The withdrawal study is an effective study design that further defines the relationship between clinical intervention and outcome. Also called the ABA design, a withdrawal study is defined as an experimental single-case design that compares outcomes after the presentation of an independent variable (clinical intervention) followed by subsequent ...

  19. 10.2 Single-Subject Research Designs

    The most basic single-subject research design is the reversal design, also called the ABA design. During the first phase, A, a baseline is established for the dependent variable. This is the level of responding before any treatment is introduced, and therefore the baseline phase is a kind of control condition.

  20. Single Case Designs in Psychology Practice

    The clinician in practice is apt to select the AB 1 B 2, changing criterion design. The single case approach provides a means of measuring the increased amount of an intervention. For example, in Kazdin, 2 increased expected levels of quiz performance are used across math objectives 1, 2, 3 and 4 as measured during daily school sessions. 3, 5.

  21. (PDF) Single Case Designs in Psychology Practice

    In this study, the ABA design was used for the single participants and included participant recruitment, baseline phase A, intervention phase B, baseline phase A ′ , and follow-up phase [20].The ...

  22. The Family of Single-Case Experimental Designs

    Figure 1. The main family of single-case experimental designs and nonexperimental designs. A = Baseline, B and C refer to different treatments. The most common approach to evaluating the effectiveness of interventions on outcomes is using randomized controlled trials (RCTs).

  23. The Evidence-Based Practice of Applied Behavior Analysis

    The Institute for Education Science (IES) has recognized the contribution single case designs can make toward identifying effective practices and has recently established standards for evaluating the quality of single case design studies (Institute of Educational Sciences, n.d.; Kratochwill et al. 2013).

  24. New Research Shows the 15-Minute City Can Work in the US

    New research proves there's a better way. As the first country to be built for the car, the US pioneered single-use neighborhoods that require long drives to travel between them.