Latent class analysis (LCA) offers a powerful analytical approach for categorizing groups (or
“classes”) within a heterogenous population. LCA identifies these hidden classes by a set of�…

Latent class analysis (LCA) for categorical data is a model-based clustering and
classification technique applied in a wide range of fields including the social sciences�…

Latent class analysis (LCA) is a statistical method used to identify unobserved subgroups in
a population with a chosen set of indicators. Given the increasing popularity of LCA, our aim�…

Latent class analysis (LCA) is a statistical method for identifying unobserved groups based
on patterns of categorical data. LCA is related to cluster analysis (see Chapter 4, this�…

In the introduction we give a brief characterization of the usual measures for indicating the
quality of diagnostic procedures (sensitivity, specificity and predictive value) and we refer to�…

Latent class (LC) analysis is a widely used method for extracting meaningful groups (LCs)
from data. The basic concept was introduced by Paul Lazarsfeld in 1950 for building�…

Latent class analysis (LCA) is a widely used statistical technique for identifying subgroups in
the population based upon multiple indicator variables. It has a number of advantages over�…

Kaufman and Rousseeuw (1990) define cluster analysis as the classification of similar
objects into groups, in which the number of groups as well as their forms are unknown. The�…

Latent class analysis (LCA) is a statistical method used to identify a set of discrete, mutually
exclusive latent classes of individuals based on their responses to a set of observed�…

Latent class analysis is a probabilistic modeling algorithm that allows clustering of data and
statistical inference. There has been a recent upsurge in the application of latent class�…