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Patrick Onghena - One of the best experts on this subject based on the ideXlab platform.
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A systematic review of applied single-Case Research published between 2016 and 2018: Study designs, randomization, data aspects, and data analysis
Behavior Research Methods, 2020Co-Authors: René Tanious, Patrick OnghenaAbstract:Single-Case experimental designs (SCEDs) have become a popular Research methodology in educational science, psychology, and beyond. The growing popularity has been accompanied by the development of specific guidelines for the conduct and analysis of SCEDs. In this paper, we examine recent practices in the conduct and analysis of SCEDs by systematically reviewing applied SCEDs published over a period of three years (2016–2018). Specifically, we were interested in which designs are most frequently used and how common randomization in the study design is, which data aspects applied single-Case Researchers analyze, and which analytical methods are used. The systematic review of 423 studies suggests that the multiple baseline design continues to be the most widely used design and that the difference in central tendency level is by far most popular in SCED effect evaluation. Visual analysis paired with descriptive statistics is the most frequently used method of data analysis. However, inferential statistical methods and the inclusion of randomization in the study design are not uncommon. We discuss these results in light of the findings of earlier systematic reviews and suggest future directions for the development of SCED methodology.
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A randomization test wrapper for synthesizing single-Case experiments using multilevel models: A Monte Carlo simulation study
Behavior Research Methods, 2019Co-Authors: Bart Michiels, René Tanious, Tamal Kumar De, Patrick OnghenaAbstract:Multilevel models (MLMs) have been proposed in single-Case Research, to synthesize data from a group of Cases in a multiple-baseline design (MBD). A limitation of this approach is that MLMs require several statistical assumptions that are often violated in single-Case Research. In this article we propose a solution to this limitation by presenting a randomization test (RT) wrapper for MLMs that offers a nonparametric way to evaluate treatment effects, without making distributional assumptions or an assumption of random sampling. We present the rationale underlying the proposed technique and validate its performance (with respect to Type I error rate and power) as compared to parametric statistical inference in MLMs, in the context of evaluating the average treatment effect across Cases in an MBD. We performed a simulation study that manipulated the numbers of Cases and of observations per Case in a dataset, the data variability between Cases, the distributional characteristics of the data, the level of autocorrelation, and the size of the treatment effect in the data. The results showed that the power of the RT wrapper is superior to the power of parametric tests based on F distributions for MBDs with fewer than five Cases, and that the Type I error rate of the RT wrapper is controlled for bimodal data, whereas this is not the Case for traditional MLMs.
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Nonparametric meta-analysis for single-Case Research: Confidence intervals for combined effect sizes
Behavior Research Methods, 2019Co-Authors: Bart Michiels, Patrick OnghenaAbstract:In this article we present a nonparametric technique for meta-analyzing randomized single-Case experiments by using inverted randomization tests to calculate nonparametric confidence intervals for combined effect sizes (CICES). Over the years, several proposals for single-Case meta-analysis have been made, but most of these proposals assume either specific population characteristics (e.g., heterogeneity of variances or normality) or independent observations. However, such assumptions are seldom plausible in single-Case Research. The CICES technique does not require such assumptions, but only assumes that the combined effect size of multiple randomized single-Case experiments can be modeled as a constant difference in the phase means. CICES can be used to synthesize the results from various single-Case alternation designs, single-Case phase designs, or a combination of the two. Furthermore, the technique can be used with different standardized or unstandardized effect size measures. In this article, we explain the rationale behind the CICES technique and provide illustrations with empirical as well as hypothetical datasets. In addition, we discuss the strengths and weaknesses of this technique and offer some possibilities for future Research. We have implemented the CICES technique for single-Case meta-analysis in a freely available R function.
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Sequential meta-analysis of single-Case experimental data
Behavior Research Methods, 2011Co-Authors: Sofie Kuppens, Mieke Heyvaert, Wim Van Den Noortgate, Patrick OnghenaAbstract:In this article, sequential meta-analysis is presented as a method for determining the sufficiency of cumulative knowledge in single-Case Research synthesis. Sufficiency addresses the question of whether there is enough cumulative knowledge on a topic to yield convincing statistical evidence. The method combines cumulative meta-analysis of single-Case experimental data with formal sequential testing. After describing the underlying statistical techniques, a strategy for conducting a sequential single-Case meta-analysis is illustrated using a real meta-analytic database. The sequential methodology may serve as a valuable tool for behavioral Researchers to guide them in making optimal use of limited resources.
Kimberly J. Vannest - One of the best experts on this subject based on the ideXlab platform.
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Caregiver Involvement in Communication Skills for Individuals with ASD and IDD: a Meta-analytic Review of Single-Case Research on the English, Chinese, and Japanese Literature
Review Journal of Autism and Developmental Disorders, 2020Co-Authors: Ching-yi Liao, Kimberly J. Vannest, J. B. Ganz, Sanikan Wattanawongwan, Lauren M. Pierson, Valeria YlladesAbstract:The current meta-analytic review analyzed 43 studies published in English, Chinese, and Japanese to determine the effects of caregiver involvement for promoting communication skills of individuals with ASD and IDD. Tau-U effect sizes, the Kruskal-Wallis H test, and the Dunn post hoc test were employed to calculate for moderator analyses: child age, settings, service delivery formats, and dosages of services provided to caregivers. The overall effect size for family involvement had a moderate effect on child communicative outcomes, as well as on child ages, settings, delivery formats, and dosages of services provided to caregivers of individuals with ASD and IDD. A statistically significant difference was found in children’s communication outcomes between the four dosage groups of services provided to caregivers.
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academic benefits of peer tutoring a meta analytic review of single Case Research
School Psychology Review, 2019Co-Authors: Lisa Bowmanperrott, Kimberly J. Vannest, Heather Davis, Lauren Williams, Charles R Greenwood, Richard I ParkerAbstract:Peer tutoring is an instructional strategy that involves students helping each other learn content through repetition of key concepts. This meta-analysis examined effects of peer tutoring across 26...
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A Visual Aid and Objective Rule Encompassing the Data Features of Visual Analysis.
Behavior Modification, 2019Co-Authors: Rumen Manolov, Kimberly J. VannestAbstract:Visual analysis of single-Case Research is commonly described as a gold standard, but it is often unreliable. Thus, an objective tool for applying visual analysis is necessary, as an alternative to...
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effect size for token economy use in contemporary classroom settings a meta analysis of single Case Research
School Psychology Review, 2016Co-Authors: Denise A Soares, Kimberly J. Vannest, Judith R Harrison, Susan S McclellandAbstract:Abstract Recent meta-analyses of the effectiveness of token economies (TEs) report insufficient quality in the Research or mixed effects in the results. This study examines the contemporary (post-Public Law 94-142) peer-reviewed published single-Case Research evaluating the effectiveness of TEs. The results are stratified across quality of demonstrated functional relationship using a nonparametric effect size (ES) that controls for undesirable baseline trends in the analysis. In addition, moderators (i.e., classroom setting, age of participant, outcomes, use of response cost, and use of verbal cueing) were analyzed. Eighty-eight AB phase contrasts were calculated from 28 studies (1980–2014) representing 90 participants and produced a weighted mean ES of 0.82 (SE = 0.03, 95% CI [0.77, 0.88]). Strong quality produced a combined weighted mean ES of 0.85 (SE = 0.642, 95% CI [0.74, 0.97]). Moderator analyses revealed that a TE was slightly more effective for youth between the ages of 6 and 15 years than for ch...
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evaluating intervention effects in single Case Research designs
Journal of Counseling and Development, 2015Co-Authors: Kimberly J. Vannest, Jennifer NinciAbstract:Single-Case Research design (SCRD) is a rigorous method for evaluating treatment effects (Horner et al., 2005; Shadish, Cook, & Campbell, 2002) that is promoted by counselors across settings (Lenz, 2015; Lundervold & Belwood, 2000). Although the use of SCRDs is not new, interest in the analysis and interpretation of SCRD results has increased (Gast, 2010; Parker, Vannest, & Davis, 2011; Smith, 2012). This momentum is due, in part, to the emphasis on higher-quality Research and evidence-based practice (Lenz, 2013; Parker & Vannest, 2009; Parker, Vannest, & Davis, 2014; Sharpley, 2007), with the increased use of effect size (ES) calculations for quantifying results receiving notable attention (Parker & Brossart, 2003; Parker, Vannest, & Davis, 2011). In this article, we describe the application and interpretation of ESs within SCRDs for increasing rigor in Research, informing practice, and demonstrating counseling outcomes. An ES is a quantitative index of practical significance that estimates the meaningfulness of change associated with an intervention. Although visual analysis remains the most widely used analysis for SCRD (Smith, 2012), visual analysis can be supplemented with an ES to provide standardized and reliable results that contribute to evidence-based practices. Therefore, when counselors provide credible ES estimations in addition to visually analyzed data, they improve the credibility, reliability, and defensibility of their findings. This article illustrates general guidelines for four techniques that counselors can use when evaluating the outcomes of SCRDs. The article is organized by the steps involved in using ES: determining functional relationships, internal validity, and experimental control; calculating ES; and interpreting ES. Steps Involved in Using ES Step 1: Determining Functional Relationships, Internal Validity, and Experimental Control American Statesman Henry Clay (1777-1852) stated, "Statistics are no substitute for judgment." This statement is particularly appreciated in SCRD analysis, in which the first step is not to calculate, but to evaluate. The clinical judgment of the counselor is required to evaluate the design and the data to decide if a functional relationship is demonstrated. A functional relationship is considered present when a believable and consistent pattern of change is demonstrated in target outcomes by a systematic manipulation of an independent variable (Kennedy, 2005). Some have suggested that three or more replications are required to establish this functional relationship (Horner et al., 2005; Kratochwill et al., 2013). However, this determination is ultimately based on a synthesis decision about changes in the mean or level between phases, the immediacy of the change, the nature of the change, the presence and direction of trend in the baseline and intervention, and the degree of variability. Determining that a functional relationship exists between the counseling treatment and the outcome variable is recommended prior to calculating an ES. Experimental control is critical in empirical SCRD Research, but there are no set rules for replicating effects, especially in clinical practice. If replication of effect is not established, the ES may indicate the size of a change but not the reason for the change. For example, if a counselor investigates the effect of cognitive behavior therapy on reported client anxiety, the counselor may begin by taking repeated measures during a baseline period. The counselor will then introduce the treatment and continue data collection. These time-series data permit the calculation of an ES on the data collected. However, the resulting number cannot tell the counselor if the change was a result of the cognitive behavior therapy or something else, such as an undisclosed change in medication. The "something else" is any alternative explanation for the behavior change, and these alternative explanations are typically related to an uncontrolled threat to the internal validity of the study. …
Richard I Parker - One of the best experts on this subject based on the ideXlab platform.
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academic benefits of peer tutoring a meta analytic review of single Case Research
School Psychology Review, 2019Co-Authors: Lisa Bowmanperrott, Kimberly J. Vannest, Heather Davis, Lauren Williams, Charles R Greenwood, Richard I ParkerAbstract:Peer tutoring is an instructional strategy that involves students helping each other learn content through repetition of key concepts. This meta-analysis examined effects of peer tutoring across 26...
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a meta analysis of single Case Research studies on aided augmentative and alternative communication systems with individuals with autism spectrum disorders
Journal of Autism and Developmental Disorders, 2012Co-Authors: Jennifer B Ganz, Richard I Parker, Theresa L Earlesvollrath, Mandy Rispoli, Amy K Heath, Jaime B DuranAbstract:Many individuals with autism cannot speak or cannot speak intelligibly. A variety of aided augmentative and alternative communication (AAC) approaches have been investigated. Most of the Research on these approaches has been single-Case Research, with small numbers of participants. The purpose of this investigation was to meta-analyze the single Case Research on the use of aided AAC with individuals with autism spectrum disorders (ASD). Twenty-four single-Case studies were analyzed via an effect size measure, the Improvement Rate Difference (IRD). Three Research questions were investigated concerning the overall impact of AAC interventions on targeted behavioral outcomes, effects of AAC interventions on individual targeted behavioral outcomes, and effects of three types of AAC interventions. Results indicated that, overall, aided AAC interventions had large effects on targeted behavioral outcomes in individuals with ASD. AAC interventions had positive effects on all of the targeted behavioral outcome; however, effects were greater for communication skills than other categories of skills. Effects of the Picture Exchange Communication System and speech-generating devices were larger than those for other picture-based systems, though picture-based systems did have small effects.
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an aggregate study of single Case Research involving aided aac participant characteristics of individuals with autism spectrum disorders
Research in Autism Spectrum Disorders, 2011Co-Authors: Jennifer B Ganz, Theresa L Earlesvollrath, Rose A Mason, Mandy Rispoli, Amy K Heath, Richard I ParkerAbstract:Abstract Individuals with autism spectrum disorders (ASD) who cannot speak at all or not intelligibly are frequently taught to use aided augmentative and alternative communication (AAC). The majority of the Research on the use of AAC with individuals with ASD has been single-Case Research studies. This investigation involved a meta-analysis of the single-Case Research on the use of aided AAC with individuals with autism spectrum disorders (ASD), investigating the differential impacts of AAC by participant characteristics. An effect size measure, the Improvement Rate Difference (IRD) was used to analyze 24 single-Case studies. Two Research questions were investigated concerning (a) the impact of AAC interventions on individuals diagnosed with subcategories of ASD and co-morbid conditions, and (b) the effects of AAC interventions on individuals in different age groups. Results indicated that participants with ASD and no additional diagnoses had better outcomes than others and that participants with ASD and developmental disabilities outperformed participants with ASD and multiple disabilities. Further, preschool-aged participants had better outcomes than elementary-aged and secondary-aged participants. Participants in all diagnostic categories and age ranges had moderate or better effects.
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combining nonoverlap and trend for single Case Research tau u
Behavior Therapy, 2011Co-Authors: Richard I Parker, Kimberly J. Vannest, John L Davis, Stephanie B SauberAbstract:A new index for analysis of single-Case Research data was proposed, Tau-U, which combines nonoverlap between phases with trend from within the intervention phase. In addition, it provides the option of controlling undesirable Phase A trend. The derivation of Tau-U from Kendall's Rank Correlation and the Mann-Whitney U test between groups is demonstrated. The equivalence of trend and nonoverlap is also shown, with supportive citations from field leaders. Tau-U calculations are demonstrated for simple AB and ABA designs. Tau-U is then field tested on a sample of 382 published data series. Controlling undesirable Phase A trend caused only a modest change from nonoverlap. The inclusion of Phase B trend yielded more modest results than simple nonoverlap. The Tau-U score distribution did not show the artificial ceiling shown by all other nonoverlap techniques. It performed reasonably well with autocorrelated data. Tau-U shows promise for single-Case applications, but further study is desirable.
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effect size in single Case Research a review of nine nonoverlap techniques
Behavior Modification, 2011Co-Authors: Richard I Parker, Kimberly J. Vannest, John L DavisAbstract:With rapid advances in the analysis of data from single-Case Research designs, the various behavior-change indices, that is, effect sizes, can be confusing. To reduce this confusion, nine effect-size indices are described and compared. Each of these indices examines data nonoverlap between phases. Similarities and differences, both conceptual and computational, are highlighted. Seven of the nine indices are applied to a sample of 200 published time series data sets, to examine their distributions. A generic meta-analytic method is presented for combining nonoverlap indices across multiple data series within complex designs.
Hedda Meadan - One of the best experts on this subject based on the ideXlab platform.
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A Systematic and Quality Review of Parent-Implemented Language and Communication Interventions Conducted via Telepractice
Journal of Behavioral Education, 2019Co-Authors: Yusuf Akemoglu, Reem Muharib, Hedda MeadanAbstract:The purpose of this study was to systematically review the literature Researching telepractice and parent-implemented language and communication interventions. A total of 12 studies met inclusion criteria and comprise the final study sample. A majority of the included articles were single-Case Research studies, and two were randomized controlled trials. We analyzed participant characteristics, intervention types, outcomes, and Research quality in all 12 studies. All telepractice-based parent-implemented interventions reported improvements in parent and/or child outcomes. We evaluated the rigor of the studies against single-Case Research and group design quality indicators as well as What Works Clearinghouse standards. Of the 10 single-Case Research studies, one met single-Case Research design standards and six met standards with reservations, and of the two group design studies, only one met all quality indicators and standards. Results are discussed and future directions are provided.
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Systematic Protocols for the Visual Analysis of Single-Case Research Data.
Behavior Analysis in Practice, 2019Co-Authors: Katie Wolfe, Erin E. Barton, Hedda MeadanAbstract:Researchers in applied behavior analysis and related fields such as special education and school psychology use single-Case designs to evaluate causal relations between variables and to evaluate the effectiveness of interventions. Visual analysis is the primary method by which single-Case Research data are analyzed; however, Research suggests that visual analysis may be unreliable. In the absence of specific guidelines to operationalize the process of visual analysis, it is likely to be influenced by idiosyncratic factors and individual variability. To address this gap, we developed systematic, responsive protocols for the visual analysis of A-B-A-B and multiple-baseline designs. The protocols guide the analyst through the process of visual analysis and synthesize responses into a numeric score. In this paper, we describe the content of the protocols, illustrate their application to 2 graphs, and describe a small-scale evaluation study. We also describe considerations and future directions for the development and evaluation of the protocols.
Katie Wolfe - One of the best experts on this subject based on the ideXlab platform.
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The Analysis of Single-Case Research Data: Current Instructional Practices
Journal of Behavioral Education, 2020Co-Authors: Katie Wolfe, Meka N. MccammonAbstract:Visual analysis is the predominant method of analysis in single-Case Research (SCR). However, most Research suggests that agreement between visual analysts is poor, which may be due to a lack of clear guidelines and criteria for visual analysis, as well as variability in how individuals are trained. We developed a survey containing questions about the content and methods used to teach visual and statistical analysis of SCR data in verified course sequences (VCS) and distributed it via the VCS Coordinator Listserv. Thirty-seven instructors completed the survey. Results suggest that there is variability across instructors in some fundamental aspects of data analysis (e.g., number of effects required for a functional relation) but a great deal of consistency in others (e.g., emphasizing visual over statistical analysis). We discuss our results along with their implications both for teaching students to analyze SCR data and for conducting additional Research on behavior-analytic training programs.
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Systematic Protocols for the Visual Analysis of Single-Case Research Data.
Behavior Analysis in Practice, 2019Co-Authors: Katie Wolfe, Erin E. Barton, Hedda MeadanAbstract:Researchers in applied behavior analysis and related fields such as special education and school psychology use single-Case designs to evaluate causal relations between variables and to evaluate the effectiveness of interventions. Visual analysis is the primary method by which single-Case Research data are analyzed; however, Research suggests that visual analysis may be unreliable. In the absence of specific guidelines to operationalize the process of visual analysis, it is likely to be influenced by idiosyncratic factors and individual variability. To address this gap, we developed systematic, responsive protocols for the visual analysis of A-B-A-B and multiple-baseline designs. The protocols guide the analyst through the process of visual analysis and synthesize responses into a numeric score. In this paper, we describe the content of the protocols, illustrate their application to 2 graphs, and describe a small-scale evaluation study. We also describe considerations and future directions for the development and evaluation of the protocols.
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A Meta-Analysis of Functional Communication Training Effects on Problem Behavior and Alternative Communicative Responses:
Focus on Autism and Other Developmental Disabilities, 2017Co-Authors: Laura C. Chezan, Katie Wolfe, Erik DrasgowAbstract:We conducted a meta-analysis of single-Case Research design (SCRD) studies on functional communication training (FCT). First, we used the What Works Clearinghouse (WWC) Standards to evaluate each s...