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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

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Fixed- and random-effects meta-analytic structural equation modeling: examples and analyses in R.

Mike W-L Cheung¹

¹Department of Psychology, Faculty of Arts and Social Sciences, National University of Singapore, Block AS4, Level 2, 9 Arts Link, Singapore, 117570, Singapore, mikewlcheung@nus.edu.sg.

Behavior Research Methods

|June 29, 2013

Summary

This summary is machine-generated.

This study extends meta-analytic structural equation modeling (MASEM) by introducing a random-effects model. This approach accounts for study-specific variations in synthesizing correlation matrices for more robust analysis.

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Area of Science:

Psychometrics
Quantitative Psychology
Statistical Modeling

Background:

Meta-analytic structural equation modeling (MASEM) synthesizes correlation or covariance matrices.
Existing two-stage structural equation modeling (TSSEM) for MASEM relies on a fixed-effects model, assuming identical population matrices across studies.

Purpose of the Study:

To extend the two-stage structural equation modeling (TSSEM) approach for meta-analytic structural equation modeling (MASEM) to a random-effects model.
To incorporate study-specific random effects into MASEM.
To demonstrate the application of the extended MASEM approach using the metaSEM package in R.

Main Methods:

The study extends the two-stage structural equation modeling (TSSEM) framework.
A random-effects model is introduced by including study-specific random effects.
Procedures are demonstrated using two examples within the metaSEM package in the R statistical environment.

Main Results:

The article presents an extension of the TSSEM approach for MASEM to a random-effects model.
The methodology allows for the incorporation of heterogeneity across studies.
Practical implementation is illustrated via R code examples.

Conclusions:

The developed random-effects MASEM approach provides a more flexible framework for synthesizing research findings.
This extension addresses limitations of fixed-effects models in capturing inter-study variability.
Future research directions and related issues in MASEM are discussed.