Theory And Methods


, Volume 69, Issue 2, pp 167-190

First online:

Generalized multilevel structural equation modeling

  • Sophia Rabe-HeskethAffiliated withGraduate School of Education, University of California Email author 
  • , Anders SkrondalAffiliated withNorwegian Institute of Public Health
  • , Andrew PicklesAffiliated withThe University of Manchester

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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.

Key words

multilevel structural equation models generalized linear mixed models latent variables random effects hierarchical models item response theory factor models adaptive quadrature empirical Bayes GLLAMM