Latent class analysis is an analytic technique often used in educational and psychological
research to identify meaningful groups of individuals within a larger heterogeneous …

A multilevel latent class model (MLCM) is a useful tool for analyzing data arising from
hierarchically nested structures. One important issue for MLCMs is determining the minimum …

The latent class (LC) models that have been developed so far assume that observations are
independent. Parametric and nonparametric random–coefficient LC models are proposed …

Latent class analysis (LCA) is a method for testing theories about unobserved
(hypothesized) categorical variables that are measured (imperfectly) by observed …

In this chapter, we consider latent class models that include concomitant variables. As in an
analysis of variance, concomitant variables may be classified as blocking variables or as …

Common applications of latent variable analysis fail to recognize that data may be obtained
from several populations with different sets of parameter values. This article describes the …

The multilevel latent class model (MLCM) is a multilevel extension of a latent class model
(LCM) that is used to analyze nested structure data structure. The nonparametric version of …

Filled with well-chosen empirical examples to teach the technique, this book provides a
thorough guide to latent class scaling models for binary response variables. Substantive …

Notes that models based on latent variables play important roles in educational research.
Summarizes the theory underlying latent class models and presents two real-world …

Often quantities of interest in psychology cannot be observed directly. These unobservable
quantities, known as latent variables, tend to be complex, often multidimensional, constructs …