Informative tools for characterizing individual differences in learning: Latent class, latent profile, and latent transition analysis

• Published in: Learning and individual differences Vol. 66; pp. 4 - 15
• Main Authors: Hickendorff, Marian, Edelsbrunner, Peter A., McMullen, Jake, Schneider, Michael, Trezise, Kelly
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.08.2018
• Subjects: Developmental patterns; Individual differences; Latent variable mixture models; Learning pathways; Person-centered methods; Latent variable mixture models; Developmental patterns; Learning pathways; Person-centered methods; Individual differences
• ISSN: 1041-6080; 1873-3425
• DOI: 10.1016/j.lindif.2017.11.001

This article gives an introduction to latent class, latent profile, and latent transition models for researchers interested in investigating individual differences in learning and development. The models allow analyzing how the observed heterogeneity in a group (e.g., individual differences in conceptual knowledge) can be traced back to underlying homogeneous subgroups (e.g., learners differing systematically in their developmental phases). The estimated parameters include a characteristic response pattern for each subgroup, and, in the case of longitudinal data, the probabilities of transitioning from one subgroup to another over time. This article describes the steps involved in using the models, gives practical examples, and discusses limitations and extensions. Overall, the models help to characterize heterogeneous learner populations, multidimensional learning outcomes, non-linear learning pathways, and changing relations between learning processes. The application of these models can therefore make a substantial contribution to our understanding of learning and individual differences. •Learning is often multidimensional, heterogeneous, and discontinuous.•Traditional statistical analyses are limited in capturing this complexity.•Latent class and latent profile models identify subgroups of learners.•Latent transition models characterize discontinuous, non-linear, learning paths.•These models contribute to our understanding of learning and individual differences.