Heterogeneity Estimators in Random-Effects Meta-Analysis in Education

Autor/in	Wang, Qian
Titel	Heterogeneity Estimators in Random-Effects Meta-Analysis in Education
Quelle	(2022), (130 Seiten) PDF als Volltext Verfügbarkeit Ph.D. Dissertation, Western Michigan University
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
ISBN	979-8-3684-4852-7
Schlagwörter	Hochschulschrift; Dissertation; Meta Analysis; Educational Research; Maximum Likelihood Statistics; Statistical Bias; Monte Carlo Methods; Sample Size; Multivariate Analysis; Regression (Statistics) + Suchen Sie Ihr Suchwort? Thesis; Dissertations; Academic thesis; Meta-analysis; Metaanalyse; Bildungsforschung; Pädagogische Forschung; Monte-Carlo-Methode; Multivariate Analyse; Regression; Regressionsanalyse
Abstract	Over the last four decades, meta-analysis has proven to be a vital analysis strategy in educational research for synthesizing research findings from different studies. When synthesizing studies in a meta-analysis, it is common to assume that the true underlying effect varies from study to study, as studies will differ in design, participants, interventions, or sample size that can lead to heterogeneity in their underlying effects. The magnitude of this heterogeneity between studies can be quantified as T[superscript 2] in a random-effects meta-analysis. Estimating the between-study heterogeneity (T[superscript 2]) becomes an important part of random effects meta-analysis reporting, since this quantity plays a vital role in understanding how the effect sizes in the studies are dispersed around the mean effect size. Unfortunately for practitioners, there are a multitude of T[superscript 2] estimators that have been derived. Moreover, there are few studies that have compared the different forms of T[superscript 2] estimators, thus understanding their differences and similarities is needed and the conditions under which observed T[superscript 2] values might vary among the different estimators. The purpose of this quantitative study is to investigate the performance of five T[superscript 2] estimators commonly used in education-related random-effects meta-analysis. These five estimators are DerSimonian and Laird (DL), Two-step DerSimonian and Laird (DL2), Maximum Likelihood (ML), Restricted Maximum Likelihood (REML), and Sidik and Jonkman (SJ). Estimator performance was operationalized in this dissertation as: (a) the magnitude of T[superscript 2], (b) bias, (c) mean squared error (MSE), and (d) the coverage of 95% confidence interval (CI). A Monte Carlo simulation study compared the performance of the five heterogeneity estimators varying the following experimental conditions: (1) number of studies included in a meta-analysis, (2) study sample size, and (3) level of heterogeneity among the enrolled studies. The data analysis was conducted using multivariate analysis of variance (MANOVA) and multivariate logistic regression, followed by post-hoc analyses controlling for a family-wise type I error of 0.05.Findings in this study suggest that estimates of heterogeneity magnitude, bias, MSE and coverage derived from different estimators can be notably different as a function of experimental conditions. These findings have important implications for educational researchers who wish to report the between-study heterogeneity among studies via a T[superscript 2] estimator in random-effects meta-analysis. Recommendations for researchers on the selection of heterogeneity estimators and areas for future research about between-study heterogeneity estimation are discussed. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.] (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01