Multilevel Meta-Analysis of Multiple Regression Coefficients from Single-Case Experimental Studies

Autor/inn/en	Jamshidi, Laleh; Declercq, Lies; Fernández-Castilla, Belén; Ferron, John M.; Moeyaert, Mariola; Beretvas, S. Natasha; Van den Noortgate, Wim
Titel	Multilevel Meta-Analysis of Multiple Regression Coefficients from Single-Case Experimental Studies
Quelle	(2020), (14 Seiten) PDF als Volltext (1); PDF als Volltext kostenfreie Datei (2) Verfügbarkeit
Zusatzinformation	Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
DOI	10.3758/s13428-020-01380-w
Schlagwörter	Multivariate Analysis; Hierarchical Linear Modeling; Meta Analysis; Regression (Statistics); Case Studies; Research Design; Robustness (Statistics); Effect Size; Statistical Bias; Error of Measurement + Suchen Sie Ihr Suchwort? Multivariate Analyse; Meta-analysis; Metaanalyse; Regression; Regressionsanalyse; Case study; Fallstudie; Case Study; Forschungsdesign; Widerstandsfähigkeit; Messfehler
Abstract	The focus of the current study is on handling the dependence among multiple regression coefficients representing the treatment effects when meta-analyzing data from single-case experimental studies. We compare the results when applying three different multilevel meta-analytic models (i.e., a univariate multilevel model avoiding the dependence, a multivariate multilevel model ignoring covariance at higher levels, and a multivariate multilevel model modeling the existing covariance) to deal with the dependent effect sizes. The results indicate better estimates of the overall treatment effects and variance components when a multivariate multilevel model is applied, independent of modeling or ignoring the existing covariance. These findings confirm the robustness of multilevel modeling to misspecifying the existing covariance at the case and study level in terms of estimating the overall treatment effects and variance components. The results also show that the overall treatment effect estimates are unbiased regardless of the underlying model, but the between-case and between-study variance components are biased in certain conditions. In addition, the between-study variance estimates are particularly biased when the number of studies is smaller than 40 (i.e., 10 or 20) and the true value of the between-case variance is relatively large (i.e., 8). The observed bias is larger for the between-case variance estimates compared to the between-study variance estimates when the true between-case variance is relatively small (i.e., 0.5). [This paper is the online first version of an article published in "Behavior Research Methods" (e-ISSN 1554-3528).] (As Provided).
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2022/1/01