Detecting Strategies in Developmental Psychology

• Published in: Computational brain & behavior 2019-06, Vol.2 (2), p.128-140
• Main Authors: Steingroever, Helen, Jepma, Marieke, Lee, Michael D., Jansen, Brenda R. J., Huizenga, Hilde M.
• Format: Article
• Language: English
• Subjects: Behavioral Science and Psychology; Cognitive Psychology; Mathematical Models of Cognitive Processes and Neural Networks; Psychological Methods/Evaluation; Psychology
• ISSN: 2522-0861; 2522-087X
• DOI: 10.1007/s42113-019-0024-x

Differential strategy use is a topic of intense investigation in developmental psychology. Questions under study are as follows: How do strategies change with age, how can individual differences in strategy use be explained, and which interventions promote shifts from suboptimal to optimal strategies? In order to detect such differential strategy use, developmental psychology currently relies on two approaches—the rule assessment methodology and latent class analysis—each having their own strengths and weaknesses. In order to optimize strategy detection, a new approach that combines the strengths of both existing methods and avoids their weaknesses was recently developed: model-based latent-mixture analysis using Bayesian inference. We performed a simulation study to test the ability of this new approach to detect differential strategy use. Next, we illustrate the benefits of this approach by a re-analysis of decision making data from 210 children and adolescents. We conclude that the new approach yields highly informative results, and provides an adequate account of the observed data. To facilitate the application of this new approach in other studies, we provide open access documented code, and a step-by-step tutorial of its usage.