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 contribution
posted on 2021-03-31, 10:28 authored by Marian Hickendorff, Peter A. Edelsbrunner, Jake McMullen, Michael Schneider, Kelly TreziseThis 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.
History
School
- Science
Department
- Mathematics Education Centre
Published in
Learning and Individual DifferencesVolume
66Pages
4 - 15Publisher
ElsevierVersion
- AM (Accepted Manuscript)
Rights holder
© ElsevierPublisher statement
This 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.Acceptance date
2017-11-02Publication date
2017-11-09Copyright date
2017ISSN
1041-6080eISSN
1873-3425Publisher version
Language
- en