Generalized Multilevel Structural Equation Modeling
نویسندگان
چکیده
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.
منابع مشابه
Some applications of generalized linear latent and mixed models in epidemiology: Repeated measures, measurement error and multilevel modeling
We describe generalized linear latent and mixed models (GLLAMMs) and illustrate their potential in epidemiology. GLLAMMs include many types of multilevel random effect, factor and structural equation models. A wide range of response types are accommodated including continuous, dichotomous, ordinal and nominal responses as well as counts and survival times. Multivariate responses can furthermore...
متن کاملUnderlying Predictors of Tobacco Smoking among Iranian Teenagers: Generalized Structural Equation Modeling
Background: To define underlying predictors of tobacco smoking among Iranian Teenagers in a generalized structural equation model. Materials and Methods: In this cross-sectional study, a Generalized Structural Equation Model based on planned behavioral theory was used to explain the relationship among different factors such as demographic factors, subjective norms, and the intention to tobacco ...
متن کاملMultilevel Structural Equation Modeling
In conventional structural equation models, all latent variables and indicators vary between units (typically subjects) and are assumed to be independent across units. The latter assumption is violated in multilevel settings where units are nested in clusters, leading to within-cluster dependence. Different approaches to extending structural equation models for such multilevel settings are exam...
متن کاملExamining Differences in Within- and Between-Person Simple Structures of an Engineering Qualification Test Using Multilevel MIMIC Structural Equation Modeling
Citation: Tsaousis I, Sideridis GD and Al-Harbi K (2018) Examining Differences in Withinand Between-Person Simple Structures of an Engineering Qualification Test Using Multilevel MIMIC Structural Equation Modeling. Front. Appl. Math. Stat. 4:3. doi: 10.3389/fams.2018.00003 Examining Differences in Withinand Between-Person Simple Structures of an Engineering Qualification Test Using Multilevel M...
متن کاملMultilevel Regression and Multilevel Structural Equation Modeling
Multilevel modeling in general concerns models for relationships between variables defined at different levels of a hierarchical data set, which is often viewed as a multistage sample from a hierarchically structured population. Common applications are individuals within groups, repeated measures within individuals, longitudinal modeling, and cluster randomized trials. This chapter treats the m...
متن کامل