Generalized multilevel structural equation modeling Export

@article{rabe:hesketh:skrondal:pickles:2004, 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.}, author = {Rabe-Hesketh, Sophia and Skrondal, Anders and Pickles, Andrew}, citeulike-article-id = {983564}, citeulike-linkout-0 = {http://dx.doi.org/10.1007/BF02295939}, day = {27}, doi = {10.1007/BF02295939}, journal = {Psychometrika}, keywords = {gllamm, glm, latent\_variables, sem}, month = {June}, number = {2}, pages = {167--190}, posted-at = {2006-12-07 16:50:54}, priority = {2}, title = {Generalized multilevel structural equation modeling}, url = {http://dx.doi.org/10.1007/BF02295939}, volume = {69}, year = {2004} }

TY - JOUR ID - rabe:hesketh:skrondal:pickles:2004 L3 - citeulike-article-id:983564 N2 - 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. IS - 2 JF - Psychometrika EP - 190 TI - Generalized multilevel structural equation modeling VL - 69 SP - 167 KW - gllamm KW - glm KW - latent_variables KW - sem AU - Rabe-Hesketh, Sophia AU - Skrondal, Anders AU - Pickles, Andrew PY - 2004/06/27/ UR - http://dx.doi.org/10.1007/BF02295939 ER -