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.

In this guide, we present a reading list to serve as a concise introduction to Bayesian data analysis. The introduction is geared toward reviewers, editors, and interested researchers who are new to Bayesian statistics. We provide commentary for eight recommended sources, which together cover the theoretical and practical cornerstones of Bayesian statistics in psychology and related sciences. The resources are presented in an incremental order, starting with theoretical foundations and moving on to applied issues. In addition, we outline an additional 32 articles and books that can be consulted to gain background knowledge about various theoretical specifics and Bayesian approaches to frequently used models. Our goal is to offer researchers a starting point for understanding the core tenets of Bayesian analysis, while requiring a low level of time commitment. After consulting our guide, the reader should understand how and why Bayesian methods work, and feel able to evaluate their use in the behavioral and social sciences.

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.

Hypothesis tests for which the null hypothesis cannot be rejected ("null findings") are often seen as negative outcomes in psychology. Null findings can, however, bear important insights about the validity of theories and hypotheses. In addition, the tendency to publish mainly significant findings is considered a key reason for failures to replicate previous studies in various fields, including psychology. In this editorial, we discuss the relevance of non-significant results in psychological research and ways to render these results more informative. We discuss the possibility to test whether null findings provide evidence for the absence or negligible size of an effect, based both on frequentist and Bayesian statistical methods. We further discuss the role of adequate power analysis in obtaining informative evidence for null findings, with a special emphasis on student research. Lastly, we encourage researchers at all career stages to submit null findings for publication.

In a quasi-experimental classroom study, we longitudinally investigated whether inquiry-based, content-focused physics instruction improves students' ability to apply the control-of-variables strategy, a domain-general experimentation skill. Twelve third grade elementary school classes (Mdnage = 9 years, N = 189) were randomly assigned to receive either four different physics curriculum units (intervention) or traditional instruction (control). Experiments were frequent elements in the physics units; however, there was no explicit instruction of the control-ofvariables strategy or other experimentation skills. As intended, students in the intervention classes strongly increased their conceptual physics knowledge. More importantly, students in the intervention classes also showed stronger gains in their ability to apply the control-of-variables strategy correctly in novel situations compared to students in the control classes. Thus, a high dose of experimentation had the collateral benefit of improving the transfer of the control-ofvariables strategy. The study complements lab-based studies with convergent findings obtained in real classrooms.