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

Abstract

Highlights• learning is often multidimensional, heterogeneous, and discontinuous• traditional statistical analyses are limited in capturing this complexity• latent class and latent profile models identify subgroups of learners• latent transition models characterize discontinuous, non-linear, learning paths• these models contribute to our understanding of learning and individual differences 3 Informative Tools for Characterizing Individual Differences in Learning: Latent Class, Latent Profile, and Latent Transition AnalysisThis 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, nonlinear 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.

Symptom profiles of late‐life anxiety and depression: The influence of migration, religion and loneliness

“…Solutions for six classes were estimated and interpreted, with log-likelihoods, information criteria (IC) and classification accuracy reported. To identify the best solution, we followed the recommended approach and considered a combination of statistical criteria (ICs and LRT), model parsimony, interpretability and meaningfulness of the model (Collins & Lanza, 2010), and the need for theory and judgement when deciding the class structure (Hickendorff, Edelsbrunner, McMullen, Schneider, & Trezise, 2018). The three-class solution provided a more parsimonious and theoretically meaningful model (particularly compared to the four-class solution).…”

Grade‐related differences in strategy use in multidigit division in two instructional settings

“…We formulated three hypotheses. First, as strategy use is characterized by variability both between and within individuals (e.g., Siegler, 2007), and this variability can likely be captured by a limited number of distinctive patterns or profiles of strategy use across a set of problems (e.g., Hickendorff, Edelsbrunner, McMullen, Schneider, & Trezise, 2017;Hickendorff et al, 2009), we expected to find different classes of children with similar strategy use profiles across the problems. We used a model-based clustering technique, specifically latent class analysis (e.g., Collins & Lanza, 2010) to identify these profiles.…”

Naïve and informed views on the nature of scientific inquiry in large‐scale assessments: Two sides of the same coin or different currencies?

“…Based on the chosen models, we carried out LCA using MPLUS 8 and fitted mixture models (Hickendorff, Edelsbrunner, McMullen, Schneider, & Trezise, 2018) containing one to eight classes. In order to converge these models, we used 10000 random sets of starting values for the initial stage and 20 iterations for the final stage.…”

“…This means that for each item, we analyzed whether the agreement or disagreement on the Likert-scale was in line with the reasoning processes or the justifications made by the students. In order to perform the analysis, we used the software ELAN that is freely available and supplied by the Max Planck Institute for Psycholinguistics (Hellwig, 2018). Using a simple dichotomous two-step classification system, we classified whether a student agreed or disagreed on a certain item and whether the agreement or disagreement involved reasoning that was associated with the view the item is supposed to assess (process validity given) or whether the reasoning showed aspects that had nothing to do with the view, such us misinterpretation of wording, for example (process validity not given).…”