Pustejovsky (2017) - Using response ratios
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Pustejovsky (2017) - Using response ratios
USING RESPONSE RATIOSUsing response ratios for meta-analyzing single-case designs with behavioral outcomes James E. PustejovskyThe University of Texas Pustejovsky (2017) - Using response ratioss at AustinFeburary 23. 2018Forthcoming in Journal of School PsychologyThis manuscript is not the copy of record and may not exactly replicate the final, authoritativeversion. The version of record is available at https: doi.org 10.1016 j.isp.2018.02.003Author noteJames E. Pustejovsky, Ph D. Univers Pustejovsky (2017) - Using response ratiosity of Texas at Austin. Austin, TX. USA.A previous version of this paper w as presented at the annual convention of the AmericanEducational Research APustejovsky (2017) - Using response ratios
ssociation. April 28. 2017 in San Antonio, Texas. Supplementary materials are available at https: osf.io c3fe9 ■Correspondence concerning this articleUSING RESPONSE RATIOSUsing response ratios for meta-analyzing single-case designs with behavioral outcomes James E. PustejovskyThe University of Texas Pustejovsky (2017) - Using response ratiosTX 78712-1289. Phone: 512-471-0683. Email: pustoíậ austin.utexas.edu.USING RESPONSE RATIOS2AbstractMethods for meta-analyzing single-case designs (SCDs) are needed to inform evidencebased practice in clinical and school settings and to draw broader and more defensible generalizations in areas where Pustejovsky (2017) - Using response ratiosSCDs comprise a large part of the research base. The most widely used outcomes in single-case research are measures of behavior collected using systemPustejovsky (2017) - Using response ratios
atic direct observation, which typically take the form of rates or proportions. For studies that use such measures, one simple and intuitive way to quUSING RESPONSE RATIOSUsing response ratios for meta-analyzing single-case designs with behavioral outcomes James E. PustejovskyThe University of Texas Pustejovsky (2017) - Using response ratiosods for estimating log response ratios and combining the estimates using meta-analysis. The methods are based on a simple model for comparing two phases, where the level of the outcome is stable within each phase and the repeated outcome measurements are independent. Although auto-correlation will l Pustejovsky (2017) - Using response ratiosead to biased estimates of the sampling variance of the effect size, metaanalysis of response ratios can be conducted with robust variance estimationPustejovsky (2017) - Using response ratios
procedures that remain valid even when sampling variance estimates are biased. The methods are demonstrated using data from a recent meta-analysis on USING RESPONSE RATIOSUsing response ratios for meta-analyzing single-case designs with behavioral outcomes James E. PustejovskyThe University of Texas Pustejovsky (2017) - Using response ratiosESPONSE RATIOS3Using Log Response Ratios for Meta-Analyzing Single-Case Designs with Behavioral OutcomesStudies that use single-case designs (SCDs) comprise a large and important part of the research base in certain areas of psychological and educational research. 1 or instance. SCDs feature promine Pustejovsky (2017) - Using response ratiosntly in research on interventions for students with emotional or behavioral disorders (c.g.. Lane. Kalbcrg. & Shcpcaro. 2009). for children with autisPustejovsky (2017) - Using response ratios
m (c.g.. Wong cl al.. 2015). and for individuals with other low-incidence disabilities. SCDs are relatively feasible in these sellings because they reUSING RESPONSE RATIOSUsing response ratios for meta-analyzing single-case designs with behavioral outcomes James E. PustejovskyThe University of Texas Pustejovsky (2017) - Using response ratiosnd so can be applied even when cases exhibit highly heterogeneous or idiosyncratic problems.A well-designed SCD makes it possible to draw inferences about the effects of an intervention for the participating individual(s). However, the growing focus on evidence-based practices in psychology and educ Pustejovsky (2017) - Using response ratiosation has led to the need to address further, broader questions— not only about what works for individual research participants, but under what conditPustejovsky (2017) - Using response ratios
ions and for what types of individuals an intervention is generally effective (Hitchcock. Kratochwill, & Chezan, 2015; Maggin. 2015). Such questions aUSING RESPONSE RATIOSUsing response ratios for meta-analyzing single-case designs with behavioral outcomes James E. PustejovskyThe University of Texas Pustejovsky (2017) - Using response ratiosion procedures, and of course most include only a few participants.Tn light of the limitations of individual SCDs. there has long been interest in using metaanalysis methods to draw broader generalizations by synthesizing results across multiple SCDs (Gingerich. 1984; White. Rusch. Kazdin. & Hartman Pustejovsky (2017) - Using response ratiosn. 1989). There have recently been many-new developments in the methodology for analyzing and synthesizing data from SCDs (Manolov & Moeyaert. 2017; SPustejovsky (2017) - Using response ratios
hadish. 2014a). as well as increased production of systematic reviews andUSING RESPONSE RATIOS4meta-analyses of SCDs (Maggin. O’Keeffe. & Joluison, 20USING RESPONSE RATIOSUsing response ratios for meta-analyzing single-case designs with behavioral outcomes James E. PustejovskyThe University of Texas Pustejovsky (2017) - Using response ratiosWorks Clearinghouse (Kratochwill et al., 2013). Council for Exceptional Children (Council for Exceptional Children Working Group. 2014), and the Single-Case Reporting Guidelines in Behavioral Interventions (Tate et al.. 2016).A critical methodological decision in any meta-analysis is what effect siz Pustejovsky (2017) - Using response ratiose measure to use to quantify study results. In the context of SCDs. an effect size is a numerical index that quantifies the direction and magnitude ofPustejovsky (2017) - Using response ratios
the functional relationship between an intervention and an outcome. A wide array of effect size indices have been proposed for summarizing SCD resultUSING RESPONSE RATIOSUsing response ratios for meta-analyzing single-case designs with behavioral outcomes James E. PustejovskyThe University of Texas Pustejovsky (2017) - Using response ratiosof non-overlapping data (PND; Scruggs, Mastropieri, & Casto, 1987). and the non-overlap of all pairs (NAP: Parker & Vannest. 2009). to more complex estimators based on linear regressions or hierarchical linear models (Maggin, Swaminathan et al.. 2011; Van den Noortgate & Onghena. 2008), as well as b Pustejovsky (2017) - Using response ratiosetween-case standardized mean difference (BC-SMD) estimators that are designed to be comparable to effect sizes from between-groups designs (Shadish.Pustejovsky (2017) - Using response ratios
Hedges. & Pustejovsky. 2014). However, there remains a lack of consensus about which effect size indices are most useful for meta-analyzing SCDs (KratUSING RESPONSE RATIOSUsing response ratios for meta-analyzing single-case designs with behavioral outcomes James E. PustejovskyThe University of Texas Pustejovsky (2017) - Using response ratiosges. Higgins. & Rothstein. 2009: Hedges. 2008). In meta-analysis of between-case experimental designs, a key consideration in selecting an effect size metric is how the study outcomes are measured. For example, standardized mean differences are often used to summarize results for outcome constructs Pustejovsky (2017) - Using response ratiosassessed using continuous, intervalUSING RESPONSE RATIOS5scale measures such as psychological scales or academic achievement test scores, whereas oddsPustejovsky (2017) - Using response ratios
ratios or relative risk ratios are typically used to summarize dichotomous outcomes, such as school dropout or mortality (Borenstein et al., 2009. ChUSING RESPONSE RATIOSUsing response ratios for meta-analyzing single-case designs with behavioral outcomes James E. PustejovskyThe University of Texas Pustejovsky (2017) - Using response ratiosmith & Wilson. 2013). In contrast, existing effect size measures for SCDs are typically conceived as generic indices and are often applied with little consideration for how study outcomes are measured.By analogy to effect sizes for between-case research, it is possible that usefill effect size indic Pustejovsky (2017) - Using response ratioses for SCDs can be identified by focusing not on single-case research in its entirety, but rather on studies that use a common class of outcome measurPustejovsky (2017) - Using response ratios
es. There are at least two reasons for doing so. First, universally applicable effect size metrics are seldom needed because effect sizes are typicallUSING RESPONSE RATIOSUsing response ratios for meta-analyzing single-case designs with behavioral outcomes James E. PustejovskyThe University of Texas Pustejovsky (2017) - Using response ratios(e.g.. how should one interpret an average effect size that combines academic performance and disruptive behavior measures?). Second, all effect sizes are based on modeling assumptions, and outcome measurement properties are an important consideration in developing and validating such assumptions. J Pustejovsky (2017) - Using response ratiosust as different modeling assumptions may be required for different classes of outcome measurements, different types of effect size measures may- be nPustejovsky (2017) - Using response ratios
eeded as well.The most widely used outcomes in single-case research are behavioral measures collected through systematic direct observation (Ayres & GUSING RESPONSE RATIOSUsing response ratios for meta-analyzing single-case designs with behavioral outcomes James E. PustejovskyThe University of Texas Pustejovsky (2017) - Using response ratiosting, and interval recording methods. The measurements resulting from these procedures are typically summarized in the form of counts, rates, or percentages. ResearchersUSING RESPONSE RATIOS6may also choose to record behavior for longer or shorter observation sessions, which will influence the varia Pustejovsky (2017) - Using response ratiosbility of the resulting scores (i.e., longer observation sessions will produce less variable outcome measurements). Recent evidence indicates that behPustejovsky (2017) - Using response ratios
avioral observation data have features that are not well-described by regression models with normally distributed errors (Solomon. 2014: Solomon. HowaGọi ngay
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