Multilevel meta-analysis of multiple regression coefficients from single-case experimental studies
| Autor: | John M. Ferron, Laleh Jamshidi, Wim Van Den Noortgate, Belén Fernández-Castilla, S. Natasha Beretvas, Lies Declercq, Mariola Moeyaert |
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| Rok vydání: | 2020 |
| Předmět: |
DESIGNS
Multivariate statistics MODELS Social Sciences Experimental and Cognitive Psychology 050105 experimental psychology Psychology Mathematical Multivariate multilevel model SUBJECT RESEARCH 03 medical and health sciences 0302 clinical medicine MONTE-CARLO Arts and Humanities (miscellaneous) Robustness (computer science) Linear regression Statistics Developmental and Educational Psychology Psychology 0501 psychology and cognitive sciences General Psychology Mathematics EFFECT SIZES Psychology Experimental 05 social sciences Multilevel model Univariate Variance (accounting) Covariance Meta-analysis Single-case experimental design Robust variance estimator Multilevel meta-analysis Psychology (miscellaneous) 030217 neurology & neurosurgery |
| Zdroj: | Behavior Research Methods. 52:2008-2019 |
| ISSN: | 1554-3528 |
| DOI: | 10.3758/s13428-020-01380-w |
| Popis: | 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). ispartof: BEHAVIOR RESEARCH METHODS vol:52 issue:5 pages:2008-2019 ispartof: location:United States status: published |
| Databáze: | OpenAIRE |
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