Hecht, Martin

Researchers working with intensive longitudinal designs often encounter the challenge of determining whether to relax the assumption of stationarity in their models. Given that these designs typically involve data from a large number of subjects (N >> 1), visual screening all time series can quickly become tedious. Even when conducted by experts, such screenings can lack accuracy. In this article, we propose a nonvisual procedure that enables fast and accurate screening. This procedure has potential to become a widely adopted approach for detecting nonstationarity and guiding model building in psychology and related fields, where intensive longitudinal designs are used and time series data are collected.

The assessment of individual students is not only crucial in the school setting but also at the core of educational research. Although classical test theory focuses on maximizing insights from student responses, the Bayesian perspective incorporates the assessor’s prior belief, thereby enriching assessment with knowledge gained from previous interactions with the student or with similar students. We propose and illustrate a formal Bayesian approach that not only allows to form a stronger belief about a student’s competency but also offers a more accurate assessment than classical test theory. In addition, we propose a straightforward method for gauging prior beliefs using two specific items and point to the possibility to integrate additional information.

We point out potential drawbacks of some of Leising et al.’s (2022a) proposed ways how personality science can be improved. We argue that it is ill-advised to use only one measure for a concept. Also, we argue that researchers should not refrain from conducting a study when a high level of statistical power is precluded. Then, we go one step further and formulate additional ideas of how to improve research. Specifically, we argue that it is a good thing to use different methods rather than only one when attempting to generalize across these methods. Moreover, we argue for a more theory-driven strategy for specifying factor analytic models, and we emphasize that high-quality research is often interdisciplinary. Finally, we point to a particular risk associated with any formal reward system.