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 (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 statistical method used to identify a set of discrete, mutually
exclusive latent classes of individuals based on their responses to a set of observed�…

Despite recent methodological advances in latent class analysis (LCA) and a rapid increase
in its application in behavioral research, complex research questions that include latent�…

Latent class analysis is frequently used when the researcher has a set of categorically
scored observed measures that are highly interrelated. The latent class model (LCM)–which�…

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�…

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

The purpose of this study was to examine in which way adding more indicators or a
covariate influences the performance of latent class analysis (LCA). We varied the sample�…

Latent class (LC) modeling was initially introduced by Lazarsfeld and Henry (1968) as a way
of formulating latent attitudinal variables from dichotomous survey items. In contrast to factor�…