Multilevel structural equation models for assessing moderation within and across levels of analysis

Kristopher J. Preacher, Zhen Zhang, Michael J. Zyphur

Research output: Contribution to journalArticle

72 Scopus citations

Abstract

Social scientists are increasingly interested in multilevel hypotheses, data, and statistical models as well as moderation or interactions among predictors. The result is a focus on hypotheses and tests of multilevel moderation within and across levels of analysis. Unfortunately, existing approaches to multilevel moderation have a variety of shortcomings, including conflated effects across levels of analysis and bias due to using observed cluster averages instead of latent variables (i.e., "random intercepts") to represent higher-level constructs. To overcome these problems and elucidate the nature of multilevel moderation effects, we introduce a multilevel structural equation modeling (MSEM) logic that clarifies the nature of the problems with existing practices and remedies them with latent variable interactions. This remedy uses random coefficients and/or latent moderated structural equations (LMS) for unbiased tests of multilevel moderation. We describe our approach and provide an example using the publicly available High School and Beyond data with Mplus syntax in Appendix. Our MSEM method eliminates problems of conflated multilevel effects and reduces bias in parameter estimates while offering a coherent framework for conceptualizing and testing multilevel moderation effects.

Original languageEnglish (US)
Pages (from-to)189-205
Number of pages17
JournalPsychological Methods
Volume21
Issue number2
DOIs
StatePublished - Jan 1 2016

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Keywords

  • Interactions
  • Latent variables
  • Moderation
  • Multilevel modeling
  • Random slopes

ASJC Scopus subject areas

  • Psychology (miscellaneous)

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