Commentary on latent class, latent profile, and latent transition analysis for characterizing individual differences in learning

Abstract

The collection of articles in this special issue focus on latent variable mixture models including latent class analysis (LCA), latent profile analysis (LPA), and latent transition analysis (LTA). These are all methods for summarizing observed variables by postulating an underlying categorical latent variable representing a type or status; in the case of LTA, the status of an individual may change over time and the pathways of change are of interest. As the introductory article by Hickendorff, Edelsbrunner, McMullen, Schneider, and Trezise points out, these methods are useful when theory suggests that a learning or problem-solving process can occur in distinct modes or phases. They can also be useful when it is desirable to give qualitative descriptions of individuals' approaches to a task based on their responses across several variables rather than just simple numerical scores. The articles in this special issue use latent variable mixture models in creative and insightful ways, demonstrating their versatility and practicality. However, some challenges remain for researchers using these methods. A number of exciting future directions remain for quantitative methodologists and applied researchers to work together to address new questions in learning and individual differences research. Latent variable mixture modeling will continue to be a powerful tool learning researchers can use to address the critical, sophisticated, theoretically based research questions facing the field.