A1 Refereed original research article in a scientific journal
Informative tools for characterizing individual differences in learning: Latent class, latent profile, and latent transition analysis
Authors: Marian Hickendorff, Peter A. Edelsbrunner, Jake McMullen, Michael Schneider, Kelly Trezise
Publisher: Elsevier
Publication year: 2018
Journal: Learning and Individual Differences
Volume: 66
First page : 4
Last page: 15
Number of pages: 12
ISSN: 1041-6080
eISSN: 1873-3425
DOI: https://doi.org/10.1016/j.lindif.2017.11.001
Web address : http://www.sciencedirect.com/science/article/pii/S1041608017301863
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/27049564
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.
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