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 language | English (US) |
|---|---|
| Pages (from-to) | 418-441 |
| Number of pages | 24 |
| Journal | Psychometrika |
| Volume | 90 |
| Issue number | 2 |
| DOIs | |
| State | Published - Apr 1 2025 |
All Science Journal Classification (ASJC) codes
- General Psychology
- Applied Mathematics
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