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




Marian Hickendorff, Peter A. Edelsbrunner, Jake McMullen, Michael Schneider, Kelly Trezise

PublisherElsevier

2018

Learning and Individual Differences

66

4

15

12

1041-6080

1873-3425

DOIhttps://doi.org/10.1016/j.lindif.2017.11.001

http://www.sciencedirect.com/science/article/pii/S1041608017301863

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.


Last updated on 2024-26-11 at 12:50