Researchers using latent class (LC) analysis often proceed using the following three
steps:(1) an LC model is built for a set of response variables,(2) subjects are assigned to …

Latent class analysis (LCA) provides a means of identifying a mixture of subgroups in a
population measured by multiple categorical indicators. Latent transition analysis (LTA) is a …

Selecting the number of different classes that will be assumed to exist in the population is an
important step in latent class analysis (LCA). The bootstrap likelihood ratio test (BLRT) …

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 …

The literature on latent class/profile analysis (LCA) dates back to the seminal works of
Lazarsfeld and Henry (1968) and Goodman (1974); it reflects an extraordinarily flexible …

Latent class analysis (LCA) is a statistical procedure used to identify qualitatively different
subgroups within populations who often share certain outward characteristics. The …

The basic idea underlying latent class (LC) analysis is a very simple one: some of the
parameters of a postulated statistical model differ across unobserved subgroups. These …

Over the past several years more significant books have been published on latent class (LC)
and finite mixture models than any other class of statistical models. The recent increase in …

The authors illustrate how to perform maximum-likelihood estimation in latent class (LC)
analysis when there are sampling weights. The methods are natural extensions of the …

Latent class analysis (LCA) is an increasingly popular tool that researchers can use to
identify latent groups in the population underlying a sample of responses to categorical …