OBJECTIVE: Findings from multilevel and latent growth modeling analysis (GMA) need to be included in literature reviews, and this article explicates 4 rarely discussed approaches for using GMA studies in meta-analysis. METHOD: Extant and new equations are presented for calculating the effect size (d) and its variance (v) from reported statistics from GMA studies with each method, and a fixed effects meta-analysis of results from 5 randomized clinical trials was conducted to demonstrate their applications. RESULTS: Two common problematic practices--one that introduces bias in effect sizes because of attrition, measurement errors, and probable violations of assumptions for classical analysis, and the other that confounds the treatment effect with the intraclass correlation--were both found to yield smaller effect sizes from retrieved studies than were obtained with a newer model-based framework and its associated GMA d statistic. CONCLUSIONS: The optimal strategy for including a GMA study in a meta-analysis is to use GMA d and its v calculated with the standard error of the unstandardized coefficient for the treatment effect. When that standard error is unknown, the use of GMA d and its v estimated with an alternative equation that requires only GMA d and sample size is recommended. (PsycINFO Database Record (c) 2017 APA, all rights reserved).
OBJECTIVE: Findings from multilevel and latent growth modeling analysis (GMA) need to be included in literature reviews, and this article explicates 4 rarely discussed approaches for using GMA studies in meta-analysis. METHOD: Extant and new equations are presented for calculating the effect size (d) and its variance (v) from reported statistics from GMA studies with each method, and a fixed effects meta-analysis of results from 5 randomized clinical trials was conducted to demonstrate their applications. RESULTS: Two common problematic practices--one that introduces bias in effect sizes because of attrition, measurement errors, and probable violations of assumptions for classical analysis, and the other that confounds the treatment effect with the intraclass correlation--were both found to yield smaller effect sizes from retrieved studies than were obtained with a newer model-based framework and its associated GMA d statistic. CONCLUSIONS: The optimal strategy for including a GMA study in a meta-analysis is to use GMA d and its v calculated with the standard error of the unstandardized coefficient for the treatment effect. When that standard error is unknown, the use of GMA d and its v estimated with an alternative equation that requires only GMA d and sample size is recommended. (PsycINFO Database Record (c) 2017 APA, all rights reserved).
Authors: Steven A Safren; Conall M O'Cleirigh; Jacqueline R Bullis; Michael W Otto; Michael D Stein; Mark H Pollack Journal: J Consult Clin Psychol Date: 2012-04-30
Authors: R D Gibbons; D Hedeker; I Elkin; C Waternaux; H C Kraemer; J B Greenhouse; M T Shea; S D Imber; S M Sotsky; J T Watkins Journal: Arch Gen Psychiatry Date: 1993-09