Many-Level Multilevel Structural Equation Modeling: An Efficient Evaluation Strategy. Issue 5 (3rd September 2017)

• Record Type: Journal Article
• Authors: Pritikin, Joshua N.; Hunter, Michael D.; von Oertzen, Timo; Brick, Timothy R.; Boker, Steven M.
• Is Part Of: Structural equation modeling. Volume 24:Issue 5(2017)
• Page Start: 684
• Page End: 698
• Publication Date: 2017-09-03
• Subjects: big data -- hierarchical linear models -- multilevel models -- open-source software -- relational database theory; Multivariate analysis -- Periodicals; Social sciences -- Statistical methods -- Periodicals; 519.535
• DOI: 10.1080/10705511.2017.1293542
• Languages: English
• ISSNs: 1070-5511

Abstract

Abstract : 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, a free and open source software package.