Gene

Classical conditioning

Conditioning

Transition (genetics)

Cognitive psychology

Statistics

Probabilistic latent semantic analysis

Latent growth modeling

Latent variable

Developmental psychology

Biochemistry

Data mining

Computer science

Homogeneous

Psychology

Mathematics

Latent inhibition

Artificial intelligence

Longitudinal data

Combinatorics

Chemistry

Class (philosophy)

Latent class model

Local independence

Machine learning

Latent variable model

Learning and Individual Differences

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.

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