Part I: a friendly introduction to latent class analysis
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
predefined features, known as “indicators”. Unlike many other grouping analytical
approaches, LCA derives classes using a probabilistic approach. In this first paper, we
describe the common applications of LCA, and outline its advantages over other analytical
subgrouping methods.
“classes”) within a heterogenous population. LCA identifies these hidden classes by a set of
predefined features, known as “indicators”. Unlike many other grouping analytical
approaches, LCA derives classes using a probabilistic approach. In this first paper, we
describe the common applications of LCA, and outline its advantages over other analytical
subgrouping methods.
Abstract
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 predefined features, known as “indicators”. Unlike many other grouping analytical approaches, LCA derives classes using a probabilistic approach. In this first paper, we describe the common applications of LCA, and outline its advantages over other analytical subgrouping methods.
Elsevier