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Show Me Some ID: A Universal Identification Program for Structural Equation Models

Research output: Contribution to journalArticlepeer-review

Abstract

With models and research designs ever increasing in complexity, the foundational question of model identification is more important than ever. The determination of whether or not a model can be fit at all or fit to some particular data set is the essence of model identification. In this article, we pull from previously published work on data-independent model identification applicable to a broad set of structural equation models, and extend it further to include extremely flexible exogenous covariate effects and also to include data-dependent empirical model identification. For illustrative purposes, we apply this model identification solution to several small examples for which the answer is already known, including a real data example from the National Longitudinal Survey of Youth; however, the method applies similarly to models that are far from simple to comprehend. The solution is implemented in the open-source OpenMx package in R.

Original languageEnglish (US)
Pages (from-to)418-441
Number of pages24
JournalPsychometrika
Volume90
Issue number2
DOIs
StatePublished - Apr 1 2025

All Science Journal Classification (ASJC) codes

  • General Psychology
  • Applied Mathematics

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