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51.029 - Handling Methodological Complexities in Effect Size Calculation and Meta-Analysis of Single-Case Experimental Design Data
Sun, April 7, 3:40 to 5:10pm, Metro Toronto Convention Centre, Floor: 800 Level, Room 801ASession Type: Structured Poster Session
Abstract
For decades, single-case experimental design (SCED) researchers have relied on visual analysis to make inferences about the effectiveness of treatments. Recently, methodologists have focused on developing effect size estimates and meta-analytic techniques to complement these inferences. There remains methodological dilemmas and complexities to be handled when analyzing raw and meta-analytic SCED data and this session offers exposure to eleven research studies introducing recent innovations.
Several studies use real SCED data to demonstrate their relevant methodological advances intended to facilitate applied researchers’ use of statistical developments, while other studies entail simulation studies evaluating the innovations. Two studies present a systematic review giving an overview of methodological innovations. Each author and the discussant will give a short presentation before the session begins.
Sub Unit
Chair
Papers
1. Synthesizing Single-Case Studies: Model Selection Considering Data Complexity - Ke Cheng, University of South Florida - Tampa; ZHIYAO YI, University of South Florida; John M. Ferron, University of South Florida
2. Recognizing Model Uncertainty Using Bayesian Model Averaging of Effect Sizes for Multiple-Baseline Design Data - Bethany Hamilton, The University of Texas at Austin; Tasha Beretvas, The University of Texas at Austin; Mariola Moeyaert, University at Albany
3. Credible Data-Generating Models for Single-Case Designs - Daniel Swan, University of Oregon; James Eric Pustejovsky, The University of Texas at Austin; Tasha Beretvas, The University of Texas at Austin
4. Bayesian Rate Ratio Effect Size for Count Data in Single-Case Experimental Designs - Prathiba Natesan, University of North Texas
5. Applications of a Simple Effect Size Estimator for Single-Case Designs - David M. Rindskopf, City College of New York - CUNY
6. Multilevel Meta-Analysis of Standardized Single-Case Experimental Data: Bias Corrections in Estimating Variance Components - Laleh Jamshidi, KU Leuven; Wim Van den Noortgate, KU Leuven
7. Multilevel Analysis of Multiple Single-Case Regression Coefficients - Lies Declercq, KU Leuven; Wim Van den Noortgate, KU Leuven
8. Meta-Analysis of Single-Case Design Using Multilevel Modeling: Modeling Between-Outcomes Variation in Autocorrelation - Eunkyeng Baek, Texas A&M University - College Station; Wen Luo, Texas A&M University - College Station; Maria Antoun Henri, Texas A&M University - College Station
9. Consistency in Single-Case ABAB Phase Designs: A Systematic Review - Rene Tanious, University of Leuven; Patrick Onghena, KU Leuven
10. A Systematic Review of Meta-Analyzing Single-Case Data Using Multilevel Modeling - Eunkyeng Baek, Texas A&M University - College Station; Brandie Semma, Texas A&M University - College Station; Fatma Altinsoy, Texas A&M University - College Station; Yuhong (Melissa) Ji, Texas A&M University - College Station; Maria Antoun Henri, Texas A&M University - College Station; Amy Lam, Texas A&M University - College Station; Wen Luo, Texas A&M University - College Station; Christopher Glen Thompson, Texas A&M University - College Station
11. An Examination of Measurement Procedures and Baseline Behavioral Outcomes in Single-Case Research - James Eric Pustejovsky, The University of Texas at Austin; Daniel Swan, University of Oregon; Kyle Warren English, The University of Texas at Austin