Croon’s Bias-Corrected Estimation of Latent Interactions

ABSTRACT

Estimation of structural equation model (SEM) parameters is frequently complicated by the inclusion of latent interactions. A computationally simple sequential estimation approach using corrections for measurement uncertainty has shown promise across several types of SEMs but has yet to be assessed within the context of latent interactions. We develop Croon-based corrections for two-way latent interactions and use simulation studies to provide an initial assessment of correction performance. In terms of convergence, bias, and efficiency, Croon’s approach was comparable to or exceeded alternative estimators including maximum likelihood and Bayesian approaches. The totality of these results suggests Croon’s approach is a viable alternative or supplementary estimator for models incorporating latent interactions and comparative results improve guidance for study-specific estimator selection.

KEYWORDS:

• Latent variable interaction
• Croon
• Bayesian estimation
• factor score

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Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

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Funding

This article is based on work funded by the National Science Foundation [#1552535 and # 1760884]. The opinions expressed herein are those of the authors and not the funding agency.