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Maximum likelihood estimation in meta-analytic structural equation modeling.

Frans J Oort1, Suzanne Jak2,3

  • 1Research Institute of Child Development and Education, Faculty of Social and Behavioural Sciences, University of Amsterdam, Amsterdam, The Netherlands.

Research Synthesis Methods
|June 12, 2016
PubMed
Summary
This summary is machine-generated.

Meta-analytic structural equation modeling (MASEM) integrates findings from multiple studies. A new method, ML MASEM, offers slightly less biased parameter estimates than the traditional two-stage approach.

Keywords:
maximum likelihoodmeta-analysissimulation studystructural equation modelingweighted least squares

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Area of Science:

  • Psychometrics
  • Statistical Modeling
  • Quantitative Psychology

Background:

  • Meta-analytic structural equation modeling (MASEM) synthesizes correlation coefficients from independent studies to estimate a common population correlation matrix.
  • The established two-stage structural equation modeling approach uses maximum likelihood (ML) for initial matrix estimation and weighted least squares (WLS) for model fitting.
  • This method is widely used but may have limitations in statistical properties.

Purpose of the Study:

  • To introduce and evaluate an alternative MASEM method, termed ML MASEM, which employs ML estimation throughout the entire modeling process.
  • To compare the performance of ML MASEM against the traditional two-stage MASEM using a simulation study.
  • To assess key statistical properties including chi-square distributions, parameter estimate bias, and true/false positive rates.

Main Methods:

  • A simulation study was conducted to compare two MASEM approaches: the standard two-stage method and the proposed ML MASEM.
  • The comparison focused on statistical properties such as chi-square distributions, bias in parameter estimates, false positive rates, and true positive rates.
  • Both methods involved estimating a common correlation matrix and fitting structural equation models.

Main Results:

  • Both ML MASEM and the two-stage method produced unbiased parameter estimates with false and true positive rates approximating expected values.
  • ML MASEM demonstrated marginally less bias in parameter estimates compared to the two-stage structural equation modeling.
  • The observed differences in bias between the two methods were very small, suggesting comparable practical utility.

Conclusions:

  • ML MASEM is a viable alternative to the traditional two-stage MASEM, offering slightly improved parameter estimation.
  • The choice between ML MASEM and the two-stage method may hinge on fundamental statistical considerations or practical implementation factors.
  • Further research could explore the implications of these subtle differences in various research contexts.