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Session Type: Structured Poster Session
This session dissiminates recent methodological innovations and complexities for the quantitative analysis and meta-analysis of SCEDs to applied researchers and methodologists. There is a sustained and increased interest in using single-case experimental designs (SCEDs) to make (causal) inferences about the effectiveness of an intervention. To date, methodologists continue the development of single-case analysis techniques and research on the underlying statistical properties of these techniques. Quantitative analysis, complementary to visual inspection of the SCED data, is intended to provide objective and parsimonious summary measures that can be used for documenting results, communication within the broader research field, and integrating the results in quantitative meta-analyses. These synthesis results can be used to identify evidence-based practices.
Applicability of an Effect Size for Single-Case Studies That Indexes Progress Toward a Goal - John M. Ferron, University of South Florida; Howard Goldstein, University of South Florida; Arnold Olszewski, Miami University; Lodi Lipien, University of South Florida
How to Detect Change Points in Single-Case Designs: Further Results - David M. Rindskopf, City University of New York - CUNY
The Impact of Response-Guided Designs on Within-Case Effect Size Estimates From Count-Outcome Treatment Reversal Designs - Daniel Swan, University of Oregon; James Eric Pustejovsky, University of Wisconsin - Madison; Tasha Beretvas, The University of Texas at Austin
Impact of Within-Case Variability on Tau Indices and the Hierarchical Linear Modeling Approach for Multiple-Baseline Design Data: A Monte Carlo Simulation Study - Joelle Fingerhut, Rutgers University; Xinyun Xu, University at Albany-State University of New York; Mariola Moeyaert, University at Albany
Power to Estimate Moderator Effects in Two-Level Hierarchical Linear Modeling of Single-Case Data - Panpan Yang, University at Albany - SUNY; Xinyun Xu, University at Albany-State University of New York; Esther Kyu Yon Kim, University at Albany - SUNY; Mariola Moeyaert, University at Albany
Creating an Excel Tool to Help Researchers Pick and Rationalize Single-Case Experimental Design Metric - Joelle Fingerhut, Rutgers University; Mariola Moeyaert, University at Albany
On the Multivariate Distribution of Effect Size Estimates From Single-Case Experimental Designs - James Eric Pustejovsky, University of Wisconsin - Madison
Meta-Analysis of Single-Case Experimental Designs Using Robust Variance Estimation - Man Chen, University of Wisconsin - Madison; James Eric Pustejovsky, University of Wisconsin - Madison
Single-Case Multilevel Meta-Analysis: Handling Multiple Regression Coefficients - Laleh Jamshidi, University of Regina; Wim Van den Noortgate, KU Leuven
Multilevel Modeling in Single Case Studies with Count and Proportion Data - Haoran Li, Texas A&M University - College Station; Wen Luo, Texas A&M University - College Station; Kwok Hap Lam, Texas A&M University - College Station; Eunkyeng Baek, Texas A&M University - College Station; Christopher G. Thompson, Texas A&M University - College Station
Quality Appraisal of Single-Case Experimental Designs: Current State of the Art - Oliver Wendt, University of Potsdam; Bridget T. Miller, University of South Carolina - Columbia
Evaluating Methodological Quality of Single-Case Experimental Design Studies Using the What Works Clearinghouse Design and Evidence Standards - Marzieh Dehghan-Chaleshtori, University at Albany - SUNY; Mariola Moeyaert, University at Albany; Daniel Swan, University of Oregon
A Systematic Review of Applied Single-Case Research Published Between 2016 and 2018: Study Designs, Randomization, Data Aspects, and Analytical Methods - Rene Tanious, KU Leuven; Patrick Onghena, KU Leuven