Compare and contrast the differences between single case research designs (SCRD) and group research designs used to investigate the effectiveness of intervention

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This week, you have learned about the characteristics of SCRD’s, including how they differ from traditional group design research. You have also been introduced to the types of SCRD graphs, and what features of the design contribute to the determination of experimental control within evidence-based practices, along with the importance that internal and external validity have within SCRD.

Please respond to the following:· Provide a discussion that compares and contrasts the differences between single case research designs (SCRD) and group research designs used to investigate the effectiveness of interventions (e.g., studying a problem in an applied setting).o Make sure to include a detailed description of the characteristics of SCRD, including the considerations regarding internal and external validity.· Explain the primary purpose of a comparative, component, and parametric analysis, and provide an original example of how each might be used as part of a single case research design and incorporated purposefully into a practical intervention plan.

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Requirements: 300 words

This week, you have learned about the characteristics of SCRD’s, including how they differ from traditional group design research. You have also been introduced to the types of SCRD graphs, and what features of the design contribute to the determination of experimental control within evidence-based practices, along with the importance that internal and external validity have within SCRD.

Please respond to the following:

Provide a discussion that compares and contrasts the differences between single case research designs (SCRD) and group research designs used to investigate the effectiveness of interventions (e.g., studying a problem in an applied setting).

Make sure to include a detailed description of the characteristics of SCRD, including the considerations regarding internal and external validity.

Explain the primary purpose of a comparative, component, and parametric analysis, and provide an original example of how each might be used as part of a single case research design and incorporated purposefully into a practical intervention plan.

1 What Works ClearinghouseSINGLE‐CASE DESIGN TECHNICAL DOCUMENTATION     Developed for the What Works Clearinghouse by the following panel: Kratochwill, T. R. Hitchcock, J. Horner, R. H. Levin, J. R. Odom, S. L. Rindskopf, D. M Shadish, W. R. June 2010 Recommended citation: 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 What Works Clearinghouse website: http://ies.ed.gov/ncee/wwc/pdf/wwc_scd.pdf.

2 SINGLE-CASE DESIGNS TECHNICAL DOCUMENTATION In an effort to expand the pool of scientific evidence available for review, the What Works Clearinghouse (WWC) assembled a panel of national experts in single-case design (SCD) and analysis to draft SCD Standards. In this paper, the panel provides an overview of SCDs, specifies the types of questions that SCDs are designed to answer, and discusses the internal validity of SCDs. The panel then proposes SCD Standards to be implemented by the WWC. The Standards are bifurcated into Design and Evidence Standards (see Figure 1). The Design Standards evaluate the internal validity of the design. Reviewers assign the categories of Meets Standards, Meets Standards with Reservations and Does not Meet Standards to each study based on the Design Standards. Reviewers trained in visual analysis will then apply the Evidence Standards to studies that meet standards (with or without reservations), resulting in the categorization of each outcome variable as demonstrating Strong Evidence, Moderate Evidence, or No Evidence. A. OVERVIEW OF SINGLE-CASE DESIGNS SCDs are adaptations of interrupted time-series designs and can provide a rigorous experimental evaluation of intervention effects (Horner & Spaulding, in press; Kazdin, 1982, in press; Kratochwill, 1978; Kratochwill & Levin, 1992; Shadish, Cook, & Campbell, 2002). Although the basic SCD has many variations, these designs often involve repeated, systematic measurement of a dependent variable before, during, and after the active manipulation of an independent variable (e.g., applying an intervention). SCDs can provide a strong basis for establishing causal inference, and these designs are widely used in applied and clinical disciplines in psychology and education, such as school psychology and the field of special education. SCDs are identified by the following features: • An individual “case” is the unit of intervention and unit of data analysis (Kratochwill & Levin, in press). A case may be a single participant or a cluster of participants (e.g., a classroom or a community). • Within the design, 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. These different conditions are referred to as phases (e.g., baseline phase, intervention phase). As experimental designs, a central goal of SCDs is to determine whether a causal relation (i.e., functional relation) exists between the introduction of a researcher-manipulated independent variable (i.e., an intervention) and change in a dependent (i.e., outcome) variable (Horner & Spaulding, in press; Levin, O’Donnell, & Kratochwill, 2003). Experimental control

3 involves replication of the intervention in the experiment and this replication is addressed with one of the following methods (Horner, et al., 2005): • Introduction and withdrawal (i.e., reversal) of the independent variable (e.g., ABAB design) • Iterative manipulation of the independent variable across different observational phases (e.g., alternating treatments design) • Staggered introduction of the independent variable across different points in time (e.g., multiple baseline design) SCDs have many variants. Although flexible and adaptive, a SCD is shaped by its research question(s) and objective(s) which must be defined with precision, taking into consideration the specifics of the independent variable tailored to the case(s), setting(s), and the desired outcome(s) (i.e., a primary dependent variable). For example, if the dependent variable is unlikely to be reversed after responding to the initial intervention, then an ABAB reversal design would not be appropriate, whereas a multiple baseline design across cases would be appropriate. Therefore, the research question generally drives the selection of an appropriate SCD. B. CAUSAL QUESTIONS THAT SCDS ARE DESIGNED TO ANSWER The goal of a SCD is usually to answer “Is this intervention more effective than the current “baseline” or “business-as-usual” condition?” SCDs are particularly appropriate for understanding the responses of one or more cases to an intervention under specific conditions (Horner & Spaulding, in press). SCDs are implemented when pursuing the following research objectives (Horner et al., 2005): • Determining whether a causal relation exists between the introduction of an independent variable and a change in the dependent variable. For example, a research question might be “Does Intervention B reduce a problem behavior for this case (or these cases)?” • Evaluating the effect of altering a component of a multi-component independent variable on a dependent variable. For example, a research question might be “Does adding Intervention C to Intervention B further reduce a problem behavior for this case (or these cases)?” • Evaluating the relative effects of two or more independent variables (e.g., alternating treatments) on a dependent variable. For example, a research question might be “Is Intervention B or Intervention C more effective in reducing a problem behavior for this case (or these cases)?”

4 SCDs are especially appropriate for pursuing research questions in applied and clinical fields. This application is largely because disorders with low prevalence may be difficult to study with traditional group designs that require a large number of participants for adequate statistical power (Odom, et al., 2005). Further, in group designs, the particulars of who responded to an intervention under which conditions might be obscured when reporting only group means and associated effect sizes (Horner et al. 2005). SCDs afford the researcher an opportunity to provide detailed documentation of the characteristics of those cases that did respond to an intervention and those that did not (i.e., nonresponders). For this reason, the panel recommends that What Works Clearinghouse (WWC) reviewers systematically specify the conditions under which an intervention is and is not effective for cases being considered, if this information is available in the research report. Because the underlying goal of SCDs is most often to determine “Which intervention is effective for this case (or these cases)?” the designs are intentionally flexible and adaptive. For example, if a participant is not responding to an intervention, then the independent variables can be manipulated while continuing to assess the dependent variable (Horner et al., 2005). Because of the adaptive nature of SCD designs, nonresponders might ultimately be considered “responders” under particular conditions.1 In this regard, SCDs provide a window into the process of participant change. SCDs can also be flexible in terms of lengthening the number of data points collected during a phase to promote a stable set of observations, and this feature may provide additional insight into participant change. C. THREATS TO INTERNAL VALIDITY IN SINGLE-CASE DESIGN2 Similar to group randomized controlled trial designs, SCDs are structured to address major threats to internal validity in the experiment. Internal validity in SCDs can be improved through replication and/or randomization (Kratochwill & Levin, in press). Although it is possible to use randomization in structuring experimental SCDs, these applications are still rare. Unlike most randomized controlled trial group intervention designs, most single-case researchers have addressed internal validity concerns through the structure of the design and systematic replication of the effect within the course of the experiment (e.g., Hersen & Barlow, 1976; Horner et al., 2005; Kazdin, 1982; Kratochwill, 1978; Kratochwill & Levin, 1992). The former (design structure, discussed in the Standards as “Criteria for Designs…”) can be referred to as “methodological soundness” and the latter (effect replication, discussed in the Standards as “Criteria for Demonstrating Evidence…”) is a part of what can be called “evidence credibility” (see, for example, Kratochwill & Levin, in press). 1 WWC Principal Investigators (PIs) will need to consider whether variants of interventions constitute distinct interventions. Distinct interventions will be evaluated individually with the SCD Standards. For example, if the independent variable is changed during the course of the study, then the researcher must begin the replication series again to meet the design standards. 2 Prepared by Thomas Kratochwill with input from Joel Levin, Robert Horner, and William Shadish.

5 In SCD research, effect replication is an important mechanism for controlling threats to internal validity and its role is central for each of the various threats discussed below. In fact, the replication criterion discussed by Horner et al. (2005, p. 168) represents a fundamental characteristic of SCDs: “In most [instances] experimental control is demonstrated when the design documents three demonstrations of the experimental effect at three different points in time with a single case (within-case replication), or across different cases (inter-case replication) (emphasis added).” As these authors note, an experimental effect is demonstrated when the predicted changes in the dependent measures covary with manipulation of the independent variable. This criterion of three replications has been included in the Standards for designs to “meet evidence” standards. Currently, there is no formal basis for the “three demonstrations” recommendation; rather, it represents a conceptual norm in published articles, research, and textbooks that recommend methodological standards for single-case experimental designs (Kratochwill & Levin, in press). Important to note are the terms level, trend and variability. “Level” refers to the mean score for the data within a phase. “Trend” refers to the slope of the best-fitting straight line for the data within a phase, and “variability” refers to the fluctuation of the data (as reflected by the data’s range or standard deviation) around the mean. See pages 17-20 for greater detail. Table 1, adapted from Hayes (1981) but without including the original “design type” designations, presents the three major types of SCDs and their variations. In AB designs, a case’s performance is measured within each condition of the investigation and compared between or among conditions. In the most basic two-phase AB design, the A condition is a baseline or preintervention series/phase and the B condition is an intervention series/phase. It is difficult to draw valid causal inferences from traditional two-phase AB designs because the lack of replication in such designs makes it more difficult to rule out alternative explanations for the observed effect (Kratochwill & Levin, in press). Furthermore, repeating an AB design across several cases in separate or independent studies would typically not allow for drawing valid inferences from the data (Note: this differs from multiple baseline designs, described below, which introduce the intervention at different points in time). The Standards require a minimum of four A and B phases, such as the ABAB design. There are three major classes of SCD that incorporate phase repetition, each of which can accommodate some form of randomization to strengthen the researcher’s ability to draw valid causal inferences (see Kratochwill & Levin, in press, for discussion of such randomization applications). These design types include the ABAB design (as well as the changing criterion design, which is considered a variant of the ABAB design), the multiple baseline design, and the alternating treatments design. Valid inferences associated with the ABAB design are tied to the design’s structured repetition. The phase repetition occurs initially during the first B phase, again in the second A phase, and finally in the return to the second B phase (Horner et al., 2005). This design and its effect replication standard can be extended to multiple repetitions of the treatment (e.g., ABABABAB) and might include multiple treatments in combination that are introduced in a repetition sequence as, for example, A/(B+C)/A/(B+C)/A (see Table 1). In the case of the changing criterion design, the researcher begins with a baseline phase and then schedules a series of criterion changes or shifts that set a standard for participant performance over time. The criteria are typically pre-selected and change is documented by outcome measures changing with the criterion shifts over the course of the experiment.

6 TABLE 1 EXAMPLE SINGLE-CASE DESIGNS AND ASSOCIATED CHARACTERISTICS Representative Example Designs Characteristics Simple phase change designs [e.g., ABAB; BCBC and the changing criterion design].* (In the literature, ABAB designs are sometimes referred to as withdrawal designs, intrasubject replication designs, or reversal designs) Complex phase change [e.g., interaction element: B(B+C)B; C(B+C)C] Changing criterion design In these designs, estimates of level, trend, and variability within a data series are assessed under similar conditions; the manipulated variable is introduced and concomitant changes in the outcome measure(s) are assessed in the level, trend, and variability between phases of the series, with special attention to the degree of overlap, immediacy of effect, and similarity of data patterns in similar phases (e.g., all baseline phases). In these designs, estimates of level, trend, and variability in a data series are assessed on measures within specific conditions and across time. In this design the researcher examines the outcome measure to determine if it covaries with changing criteria that are scheduled in a series of predetermined steps within the experiment. An A phase is followed by a series of B phases (e.g., B1, B2, B3…BT), with the Bs implemented with criterion levels set for specified changes. Changes/ differences in the outcome measure(s) are assessed by comparing the series associated with the changing criteria. Alternating treatments (In the literature, alternating treatment designs are sometimes referred to as part of a class of multi-element designs) Simultaneous treatments (in the literature simultaneous treatment designs are sometimes referred to as concurrent schedule designs). In these designs, estimates of level, trend, and variability in a data series are assessed on measures within specific conditions and across time. Changes/differences in the outcome measure(s) are assessed by comparing the series associated with different conditions. In these designs, estimates of level, trend, and variability in a data series are assessed on measures within specific conditions and across time. Changes/differences in the outcome measure(s) are assessed by comparing the series across conditions. Multiple baseline (e.g., across cases, across behaviors, across situations) In these designs, multiple AB data series are compared and introduction of the intervention is staggered across time. Comparisons are made both between and within a data series. Repetitions of a single simple phase change are scheduled, each with a new series and in which both the length and timing of the phase change differ across replications. Source: Adapted from Hayes (1981) and Kratochwill & Levin (in press). To be reproduced with permission. * A represents a baseline series; “B” and “C” represent two different intervention series.

7 Another variation of SCD methodology is the alternating treatments design, which relative to the ABAB and multiple baseline designs potentially allows for more rapid comparison of two or more conditions (Barlow & Hayes, 1979; Hayes, Barlow, & Nelson-Gray, 1999). In the typical application of the design, two separate interventions are alternated following the baseline phase. The alternating feature of the design occurs when, subsequent to a baseline phase, the interventions are alternated in rapid succession for some specified number of sessions or trials. As an example, Intervention B could be implemented on one day and Intervention C on the next, with alternating interventions implemented over multiple days. In addition to a direct comparison of two interventions, the baseline (A) condition could be continued and compared with each intervention condition in the alternating phases. The order of this alternation of interventions across days may be based on either counterbalancing or a random schedule. Another variation, called the simultaneous treatment design (sometimes called the concurrent schedule design), involves exposing individual participants to the interventions simultaneously, with the participant’s differential preference for the two interventions being the focus of the investigation. This latter design is used relatively infrequently in educational and psychological research, however. The multiple baseline design involves an effect replication option across participants, settings, or behaviors. Multiple AB data series are compared and introduction of the intervention is staggered across time. In this design, more valid causal inferences are possible by staggering the intervention across one of the aforementioned units (i.e., sequential introduction of the intervention across time). The minimum number of phase repetitions needed to meet the standard advanced by Horner et al. (2005) is three, but four or more is recognized as more desirable (and statistically advantageous in cases in which, for example, the researcher is applying a randomization statistical test). Adding phase repetitions increases the power of the statistical test, similar to adding participants in a traditional group design (Kratochwill & Levin, in press). The number and timing of the repetitions can vary, depending on the outcomes of the intervention. For example, if change in the dependent variable is slow to occur, more time might be needed to demonstrate experimental control. Such a circumstance might also reduce the number of phase repetitions that can be scheduled due to cost and logistical factors. Among the characteristics of this design, effect replication across series is regarded as the characteristic with the greatest potential for enhancing internal and statistical-conclusion validity (see, for example, Levin, 1992). Well-structured SCD research that embraces phase repetition and effect replication can rule out major threats to internal validity. The possible threats to internal validity in single-case research include the following (see also Shadish et al., 2002, p. 55): 1. Ambiguous Temporal Precedence: Lack of clarity about which variable occurred first may yield confusion about which variable is the cause and which is the effect.

8 Embedded in the SCD Standards is a criterion that the independent variable is actively manipulated by the researcher, with measurement of the dependent variable occurring after that manipulation. This sequencing ensures the presumed cause precedes the presumed effect. A SCD cannot meet Standards unless there is active manipulation of the independent variable.3 Replication of this manipulation-measurement sequence in the experiment further contributes to an argument of unidirectional causation (Shadish et al., 2002). Effect replication, as specified in the Standards, can occur either through within-case replication or multiple-case replication in a single experiment, or by conducting two or more experiments with the same or highly similar intervention conditions included. The Standards specify that the study must show a minimum of three demonstrations of the effect through the use of the same design and procedures. Overall, studies that can meet standards are designed to mitigate the threat of ambiguous temporal precedence. 2. Selection: Systematic differences between/among conditions in participant characteristics could cause the observed effect. In most single-case research, selection is generally not a concern because one participant is exposed to both (or all) of the conditions of the experiment (i.e., each case serves as its own control, as noted in features for identifying a SCD in the Standards). However, there are some conditions under which selection might affect the design’s internal validity. First, in SCDs that involve two or more between-case intervention conditions comprised of intact “units” (e.g., pairs, small groups, and classrooms), differential selection might occur. The problem is that the selected units might differ in various respects before the study begins. Because in most single-case research the units are not randomly assigned to the experiment’s different intervention conditions, selection might then be a problem. This threat can further interact with other invalidating influences so as to confound variables (a methodological soundness problem) and compromise the results (an evidence credibility problem). Second, the composition of intact units (i.e., groups) can change (generally decrease in size, as a result of participant attrition) over time in a way that could compromise interpretations of a treatment effect. This is a particular concern when within-group individual participants drop out of a research study in a treatment-related (nonrandom) fashion (see also No. 6 below). The SCD Standards address traditional SCDs and do not address between-case group design features (for Standards for group designs, see the WWC Handbook). Third, in the multiple baseline design across cases, selection might be an issue when different cases sequentially begin the intervention based on “need” rather than on a randomly determined basis (e.g., a child with the most serious behavior problem among several candidate participants might be selected to receive the treatment first, thereby weakening the study’s external validity). 3 Manipulation of the independent variable is usually either described explicitly in the Method section of the text of the study or inferred from the discussion of the results. Reviewers will be trained to identify cases in which the independent variable is not actively manipulated and in that case, a study Does Not Meet Standards.

9 3. History: Events occurring concurrently with the intervention could cause the observed effect. History is typically the most important threat to any time series, including SCDs. This is especially the case in ex post facto single-case research because the researcher has so little ability to investigate what other events might have occurred in the past and affected the outcome, and in simple (e.g., ABA) designs, because one need find only a single plausible alternative event about the same time as treatment. The most problematic studies, for example, typically involve examination of existing databases or archived measures in some system or institution (such as a school, prison, or hospital). Nevertheless, the study might not always be historically confounded in such circumstances; the researcher can investigate the conditions surrounding the treatment and build a case implicating the intervention as being more plausibly responsible for the observed outcomes relative to competing factors. Even in prospective studies, however, the researcher might not be the only person trying to improve the outcome. For instance, the patient might make other outcome-related changes in his or her own life, or a teacher or parent might make extra-treatment changes to improve the behavior of a child. SCD researchers should be diligent in exploring such possibilities. However, history threats are lessened in single-case research that involves one of the types of phase repetition necessary to meet standards (e.g., the ABAB design discussed above). Such designs reduce the plausibility that extraneous events account for changes in the dependent variable(s) because they require that the extraneous events occur at about the same time as the multiple introductions of the intervention over time, which is less likely to be true than is the case when only a single intervention is done. 4. Maturation: Naturally occurring changes over time could be confused with an intervention effect. In single-case experiments, because data are gathered across time periods (for example, sessions, days, weeks, months, or years), participants in the experiment might change in some way due to the passage of time (e.g., participants get older, learn new skills). It is possible that the observed change in a dependent variable is due to these natural sources of maturation rather than to the independent variable. This threat to internal validity is accounted for in the Standards by requiring not only that the design document three replications/demonstrations of the effect, but that these effects must be demonstrated at a minimum of three different points in time. As required in the Standards, selection of an appropriate design with repeated assessment over time can reduce the probability that maturation is a confounding factor. In addition, adding a control series (i.e., an A phase or control unit such as a comparison group) to the experiment can help diagnose or reduce the plausibility of maturation and related threats (e.g., history, statistical regression). For example, see Shadish and Cook (2009). 5. Statistical Regression (Regression toward the Mean): When cases (e.g., single participants, classrooms, schools) are selected on the basis of their extreme scores, their scores on other measured variables (including re-measured initial variables) typically will be less extreme, a psychometric occurrence that can be confused with an intervention effect.

10 In single-case research, cases are often selected because their pre-experimental or baseline measures suggest high need or priority for intervention (e.g., immediate treatment for some problem is necessary). If only pretest and posttest scores were used to evaluate outcomes, statistical regression would be a major concern. However, the repeated assessment identified as a distinguishing feature of SCDs in the Standards (wherein performance is monitored to evaluate level, trend, and variability, coupled with phase repetition in the design) makes regression easy to diagnose as an internal validity threat. As noted in the Standards, data are repeatedly collected during baseline and intervention phases and this repeated measurement enables the researcher to examine characteristics of the data for the possibility of regression effects under various conditions. 6. Attrition: Loss of respondents during a single-case time-series intervention study can produce artifactual effects if that loss is systematically related to the experimental conditions. Attrition (participant dropout) can occur in single-case research and is especially a concern under at least three conditions. First, premature departure of participants

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