Many-Level Multilevel Structural Equation Modeling: An Efficient Evaluation Strategy

Joshua N. Pritikin, Michael D. Hunter, Timo von Oertzen, Timothy R. Brick, Steven M. Boker

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


Structural equation models are increasingly used for clustered or multilevel data in cases where mixed regression is too inflexible. However, when there are many levels of nesting, these models can become difficult to estimate. We introduce a novel evaluation strategy, Rampart, that applies an orthogonal rotation to the parts of a model that conform to commonly met requirements. This rotation dramatically simplifies fit evaluation in a way that becomes more potent as the size of the data set increases. We validate and evaluate the implementation using a 3-level latent regression simulation study. Then we analyze data from a statewide child behavioral health measure administered by the Oklahoma Department of Human Services. We demonstrate the efficiency of Rampart compared to other similar software using a latent factor model with a 5-level decomposition of latent variance. Rampart is implemented in OpenMx, a free and open source software package.

Original languageEnglish (US)
Pages (from-to)684-698
Number of pages15
JournalStructural Equation Modeling
Issue number5
StatePublished - Sep 3 2017

All Science Journal Classification (ASJC) codes

  • General Decision Sciences
  • Modeling and Simulation
  • Sociology and Political Science
  • Economics, Econometrics and Finance(all)


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