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Psychometrika, 2006
Multilevel models are proven tools in social research for modeling complex, hierarchical systems. In multilevel modeling, statistical inference is based largely on quantification of random variables. This paper distinguishes among three types of random variables in multilevel modeling—model disturbances, random coefficients, and future response ...
Frees, Edward W., Kim, Jee-Seon
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Multilevel models are proven tools in social research for modeling complex, hierarchical systems. In multilevel modeling, statistical inference is based largely on quantification of random variables. This paper distinguishes among three types of random variables in multilevel modeling—model disturbances, random coefficients, and future response ...
Frees, Edward W., Kim, Jee-Seon
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Multilevel Mixture Factor Models
Multivariate Behavioral Research, 2012Factor analysis is a statistical method for describing the associations among sets of observed variables in terms of a small number of underlying continuous latent variables. Various authors have proposed multilevel extensions of the factor model for the analysis of data sets with a hierarchical structure.
Varriale R., Vermunt J. K.
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2009
Abstract This article addresses multilevel models in which units are nested within one another. The focus is primarily two-level models. It also describes cross-unit heterogeneity. Moreover, it assesses the fixed and random effects from the multilevel model.
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Abstract This article addresses multilevel models in which units are nested within one another. The focus is primarily two-level models. It also describes cross-unit heterogeneity. Moreover, it assesses the fixed and random effects from the multilevel model.
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2018
This chapter introduces a statistical approach for analyzing nested data structures that both accounts for the dependence of observations due to hierarchical arrangements and allows for testing hypotheses at multiple levels. The most common application of multilevel models is for analyses of objects (e.g., people) nested within groups or clusters of ...
Peter Miksza, Kenneth Elpus
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This chapter introduces a statistical approach for analyzing nested data structures that both accounts for the dependence of observations due to hierarchical arrangements and allows for testing hypotheses at multiple levels. The most common application of multilevel models is for analyses of objects (e.g., people) nested within groups or clusters of ...
Peter Miksza, Kenneth Elpus
openaire +1 more source