Hickendorff_inpress_LatentClassProfileTransitionAnalysis_preprint_20171112.pdf (921.32 kB)
Informative tools for characterizing individual differences in learning: latent class, latent profile, and latent transition analysis
journal contributionposted on 2021-03-31, 10:28 authored by Marian Hickendorff, Peter A. Edelsbrunner, Jake McMullen, Michael Schneider, Kelly Trezise
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.
- Mathematics Education Centre
Published inLearning and Individual Differences
Pages4 - 15
- AM (Accepted Manuscript)
Rights holder© Elsevier
Publisher statementThis paper was accepted for publication in the journal Learning and Individual Differences and the definitive published version is available at https://doi.org/10.1016/j.lindif.2017.11.001.