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This study introduces four methods for incorporating growth modeling analysis (GMA) findings into meta-analyses. A model-based framework using GMA d statistics provides more accurate effect sizes than traditional approaches.

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Area of Science:

  • Psychometrics
  • Statistical Modeling
  • Meta-Analysis

Background:

  • Growth modeling analysis (GMA) findings are crucial for literature reviews.
  • Existing methods for integrating GMA into meta-analysis are limited.
  • Rarely discussed approaches are needed to improve the inclusion of GMA studies.

Purpose of the Study:

  • To explicate four rarely discussed approaches for using GMA studies in meta-analysis.
  • To present equations for calculating effect size (d) and its variance (v) from GMA studies.
  • To demonstrate the application of these methods using a fixed effects meta-analysis.

Main Methods:

  • Development of new and extant equations for calculating effect size (d) and variance (v) from GMA.
  • Application of four distinct methods for integrating GMA into meta-analysis.
  • Conducting a fixed effects meta-analysis of 5 randomized clinical trials.

Main Results:

  • Common practices can bias effect sizes due to attrition, measurement errors, and assumption violations.
  • A newer model-based framework and its GMA d statistic yield larger effect sizes.
  • The proposed methods offer a more accurate estimation of treatment effects from GMA studies.

Conclusions:

  • The optimal strategy involves using GMA d and its variance (v) calculated with the standard error of the unstandardized coefficient.
  • When the standard error is unknown, GMA d and its v can be estimated using an alternative equation.
  • These methods enhance the accurate inclusion of GMA studies in meta-analyses.