A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Informative tools for characterizing individual differences in learning: Latent class, latent profile, and latent transition analysis
Tekijät: Marian Hickendorff, Peter A. Edelsbrunner, Jake McMullen, Michael Schneider, Kelly Trezise
Kustantaja: Elsevier
Julkaisuvuosi: 2018
Journal: Learning and Individual Differences
Vuosikerta: 66
Aloitussivu: 4
Lopetussivu: 15
Sivujen määrä: 12
ISSN: 1041-6080
eISSN: 1873-3425
DOI: https://doi.org/10.1016/j.lindif.2017.11.001
Verkko-osoite: http://www.sciencedirect.com/science/article/pii/S1041608017301863
Rinnakkaistallenteen osoite: 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.
Ladattava julkaisu This is an electronic reprint of the original article. |