What is latent class analysis? If you asked that question thirty or
forty years ago you would have gotten a different answer than you would
today. Closer to its time of inception, latent class analysis was viewed
primarily as a categorical data analysis technique, often framed as a
factor analysis model where both the measured variable indicators and
underlying latent variables are categorical. Today, however, it rests
within much broader mixture and diagnostic modeling framework,
integrating measured and latent variables that may be categorical and/or
continuous, and where latent classes serve to define the subpopulations
for whom many aspects of the focal measured and latent variable model
may differ.
For latent class analysis to take these developmental leaps required
contributions that were methodological, certainly, as well as didactic.
Among the leaders on both fronts was C. Mitchell "Chan" Dayton, at the
University of Maryland, whose work in latent class analysis spanning
several decades helped the method to expand and reach its current
potential. The current volume in the Center for Integrated Latent
Variable Research (CILVR) series reflects the diversity that is latent
class analysis today, celebrating work related to, made possible by, and
inspired by Chan's noted contributions, and signaling the even more
exciting future yet to come.
