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Mariola Moeyaert; Panpan Yang; Yukang Xue – Journal of Experimental Education, 2024
We have entered an era in which scientific evidence increasingly informs research practice and policy. As there is an exponential increase in the use of single-case experimental designs (SCEDs) to evaluate intervention effectiveness, there is accumulating evidence available for quantitative synthesis. Consequently, there is a growing interest in…
Descriptors: Meta Analysis, Research Design, Synthesis, Patients
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Vembye, Mikkel Helding; Pustejovsky, James Eric; Pigott, Therese Deocampo – Journal of Educational and Behavioral Statistics, 2023
Meta-analytic models for dependent effect sizes have grown increasingly sophisticated over the last few decades, which has created challenges for a priori power calculations. We introduce power approximations for tests of average effect sizes based upon several common approaches for handling dependent effect sizes. In a Monte Carlo simulation, we…
Descriptors: Meta Analysis, Robustness (Statistics), Statistical Analysis, Models
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Poom, Leo; af Wåhlberg, Anders – Research Synthesis Methods, 2022
In meta-analysis, effect sizes often need to be converted into a common metric. For this purpose conversion formulas have been constructed; some are exact, others are approximations whose accuracy has not yet been systematically tested. We performed Monte Carlo simulations where samples with pre-specified population correlations between the…
Descriptors: Meta Analysis, Effect Size, Mathematical Formulas, Monte Carlo Methods
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Huang, Hening – Research Synthesis Methods, 2023
Many statistical methods (estimators) are available for estimating the consensus value (or average effect) and heterogeneity variance in interlaboratory studies or meta-analyses. These estimators are all valid because they are developed from or supported by certain statistical principles. However, no estimator can be perfect and must have error or…
Descriptors: Statistical Analysis, Computation, Measurement Techniques, Meta Analysis
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Moeyaert, Mariola; Ugille, Maaike; Ferron, John M.; Beretvas, S. Natasha; Van den Noortgate, Wim – Journal of Experimental Education, 2016
The impact of misspecifying covariance matrices at the second and third levels of the three-level model is evaluated. Results indicate that ignoring existing covariance has no effect on the treatment effect estimate. In addition, the between-case variance estimates are unbiased when covariance is either modeled or ignored. If the research interest…
Descriptors: Hierarchical Linear Modeling, Monte Carlo Methods, Computation, Statistical Bias
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López-López, José Antonio; Botella, Juan; Sánchez-Meca, Julio; Marín-Martínez, Fulgencio – Journal of Educational and Behavioral Statistics, 2013
Since heterogeneity between reliability coefficients is usually found in reliability generalization studies, moderator analyses constitute a crucial step for that meta-analytic approach. In this study, different procedures for conducting mixed-effects meta-regression analyses were compared. Specifically, four transformation methods for the…
Descriptors: Reliability, Generalization, Meta Analysis, Regression (Statistics)
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Society for Research on Educational Effectiveness, 2013
One of the vexing problems in the analysis of SSD is in the assessment of the effect of intervention. Serial dependence notwithstanding, the linear model approach that has been advanced involves, in general, the fitting of regression lines (or curves) to the set of observations within each phase of the design and comparing the parameters of these…
Descriptors: Research Design, Effect Size, Intervention, Statistical Analysis
Swaminathan, Hariharan; Horner, Robert H.; Rogers, H. Jane; Sugai, George – Society for Research on Educational Effectiveness, 2012
This study is aimed at addressing the criticisms that have been leveled at the currently available statistical procedures for analyzing single subject designs (SSD). One of the vexing problems in the analysis of SSD is in the assessment of the effect of intervention. Serial dependence notwithstanding, the linear model approach that has been…
Descriptors: Evidence, Effect Size, Research Methodology, Intervention
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Lee, Ji Hee; Nam, Suk Kyung; Kim, A-Reum; Kim, Boram; Lee, Min Young; Lee, Sang Min – Journal of Counseling & Development, 2013
This study investigated the relationship between psychological resilience and its relevant variables by using a meta-analytic method. The results indicated that the largest effect on resilience was found to stem from the protective factors, a medium effect from risk factors, and the smallest effect from demographic factors. (Contains 4 tables.)
Descriptors: Meta Analysis, Resilience (Psychology), Risk, Correlation
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Hafdahl, Adam R.; Williams, Michelle A. – Psychological Methods, 2009
In 2 Monte Carlo studies of fixed- and random-effects meta-analysis for correlations, A. P. Field (2001) ostensibly evaluated Hedges-Olkin-Vevea Fisher-[zeta] and Schmidt-Hunter Pearson-r estimators and tests in 120 conditions. Some authors have cited those results as evidence not to meta-analyze Fisher-[zeta] correlations, especially with…
Descriptors: Monte Carlo Methods, Computer Software, Statistical Analysis, Correlation
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Jenson, William R.; Clark, Elaine; Kircher, John C.; Kristjansson, Sean D. – Psychology in the Schools, 2007
Evidence-based practice approaches to interventions has come of age and promises to provide a new standard of excellence for school psychologists. This article describes several definitions of evidence-based practice and the problems associated with traditional statistical analyses that rely on rejection of the null hypothesis for the…
Descriptors: School Psychologists, Statistical Analysis, Hypothesis Testing, Intervention