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Cited by 831

The DOI matches its Crossref record; 72 of the 72 references it cites were found in a registry or an index.

Generalized Multilevel Structural Equation Modeling

Open access
Rabe-Hesketh, Sophia, Skrondal, Anders, Pickles, Andrew·2004·Psychometrika
DOI 10.1007/bf02295939
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Abstract

A unifying framework for generalized multilevel structural equation modeling is introduced. The models in the framework, called generalized linear latent and mixed models (GLLAMM), combine features of generalized linear mixed models (GLMM) and structural equation models (SEM) and consist of a response model and a structural model for the latent variables. The response model generalizes GLMMs to incorporate factor structures in addition to random intercepts and coefficients. As in GLMMs, the data can have an arbitrary number of levels and can be highly unbalanced with different numbers of lower-level units in the higher-level units and missing data. A wide range of response processes can be modeled including ordered and unordered categorical responses, counts, and responses of mixed types. The structural model is similar to the structural part of a SEM except that it may include latent and observed variables varying at different levels. For example, unit-level latent variables (factors or random coefficients) can be regressed on cluster-level latent variables. Special cases of this framework are explored and data from the British Social Attitudes Survey are used for illustration. Maximum likelihood estimation and empirical Bayes latent score prediction within the GLLAMM framework can be performed using adaptive quadrature in gllamm, a freely available program running in Stata.

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Metadata

Authors
Rabe-Hesketh, S., Skrondal, A., & Pickles, A.
Venue
Psychometrika
Publisher
Cambridge University Press (CUP)
Published
2004-06
Volume
69
Issue
2
Pages
167-190
ISSN
  • 0033-3123 (ISSN-L)
  • 1860-0980
DOI
10.1007/bf02295939
OpenAlex
W2015536561
Cited by
831
References
72
FWCI
17.39
Type
journal article
Language
en
Open access
green

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