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September 2, 2026Psychological MethodsOpen Access

Fitting two-level structural equation models to summary statistics: Leveling up meta-analytic structural equation modeling.

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Authors

SJSuzanne JakUniversity of AmsterdamMCMike W.‐L. CheungUniversity of Akron

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Overview

Methodological study demonstrates that meta-analytic structural equation modeling fits two-level models to summary statistics, suggesting robust parameter estimation under covariance heterogeneity.

Key Points

  • To introduce and evaluate a meta-analytic structural equation modeling (MASEM) method for fitting two-level structural equation models directly from summary statistics without requiring raw data.
  • Developed a MASEM approach capable of fitting two-level models to summary statistics and accommodating heterogeneity in within-cluster covariances.
  • Evaluated model performance by comparing results against individual-level raw data from the Programme for International Student Assessment (PISA) and simulated datasets with heterogeneous conditions.
  • Application to PISA data showed that fitting two-level models using summary statistics yielded virtually identical parameter estimates to models fitted on raw individual-level data.
  • Simulation analyses demonstrated that MASEM maintained robust performance under heterogeneous covariance conditions, whereas standard two-level SEM produced inflated Type I error rates and significantly underestimated standard errors.
  • Empirical demonstration confirmed the method can evaluate measurement invariance across clusters and incorporate study-level covariates to explain cross-study parameter variation.

Cite This Study

Jak et al. (2026) studied this question.

synapsesocial.com/papers/6a97e20ec562ede874ec61e2https://doi.org/10.1037/met0000864
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