Testing Model Fit in Path Models with Dependent Errors Given Non-Normality, Non-Linearity and Hierarchical Data

Jacob C. Douma*, Bill Shipley

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Scopus)

Abstract

We provide a generic method of testing path models that include dependent errors, nonlinear functional relationships and using nonnormal, hierarchically structured data. First, we provide a decomposition of the causal model into smaller, independent sets. These sets can be modeled independently of each other with methods that respect the type of data in these sets. Second, we introduce copulas to model the dependent errors between non-normally distributed variables. Our method yields identical results as classical covariance-based path modelling when meeting its assumptions of linearity and normality, outperforms classical SEM given nonlinear functional relationships, and can easily accommodate any parametric probability function and nonlinear functional relationships.

Original languageEnglish
Pages (from-to)222-233
JournalStructural Equation Modeling
Volume30
Issue number2
Early online date4 Oct 2022
DOIs
Publication statusPublished - 4 Mar 2023

Keywords

  • Copulas
  • covariance modelling
  • non-normal errors
  • path analysis
  • structural causal modelling

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