Implementation of the multiple-measure maximum likelihood strategy classification method in R: Addendum to Glöckner (2009) and practical guide for application


One major challenge to behavioral decision research is to identify the cognitive processes underlying judgment and decision making. Glöckner (2009) has argued that, compared to previous methods, process models can be more efficiently tested by simultaneously analyzing choices, decision times, and confidence judgments. The Multiple-Measure Maximum Likelihood (MM-ML) strategy classification method was developed for this purpose and implemented as a ready-to-use routine in STATA, a commercial package for statistical data analysis. In the present article, we describe the implementation of MM-ML in R, a free package for data analysis under the GNU general public license, and we provide a practical guide to application. We also provide MM-ML as an easy-to-use R function. Thus, prior knowledge of R programming is not necessary for those interested in using MM-ML.

Judgment and Decision Making, 5, 54–63
Marc Jekel
Marc Jekel
Post-Doctoral Researcher (Akademischer Rat)

My research interests include coherence-based reasoning, artificial neural networks, information distortion, and methods & statistics.