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Theories are among the most important tools of science. Lewin (1943) already noted "There is nothing as practical as a good theory." Although psychologists discussed problems of theory in their discipline for a long time, weak theories are still widespread in most subfields. One possible reason for this is that psychologists lack the tools to systematically assess the quality of their theories. Thagard (1989) developed a computational model for formal theory evaluation based on the concept of explanatory coherence. However, there are possible improvements to Thagard's (1989) model and it is not available in software that psychologists typically use. Therefore, we developed a new implementation of explanatory coherence based on the Ising model. We demonstrate the capabilities of this new Ising model of Explanatory Coherence (IMEC) on several examples from psychology and other sciences. In addition, we implemented it in the R-package IMEC to assist scientists in evaluating the quality of their theories in practice. Theories are among the most important tools of science. Lewin (1943) already noted "There is nothing as practical as a good theory." However, although there has been a lot of discussion about improving theories, most theories in psychology are still of low quality. One reason for this is that it is difficult to assess the quality of a theory in practice. For example, when is it worthwhile to add more assumptions to explain more patterns in data? To give researchers the ability to answer questions like this, we developed a computational model for theory comparison. We also make the model available in a package for the statistical software R, so researchers can use it with ease. We hope that the availability of a tool to assess theory quality will improve the state of theory in psychology and beyond. [ABSTRACT FROM AUTHOR]

Meta-regression is an essential meta-analytic tool for investigating sources of heterogeneity and assessing the impact of moderators. However, existing methods for meta-regression have limitations, such as inadequate consideration of model uncertainty and poor performance under publication bias. To overcome these limitations, we extend robust Bayesian meta-analysis (RoBMA) to meta-regression (RoBMA-regression). RoBMA-regression allows for moderator analyses while simultaneously taking into account the uncertainty about the presence and impact of other factors (i.e., the main effect, heterogeneity, publication bias, and other potential moderators). The methodology presents a coherent way of assessing the evidence for and against the presence of both continuous and categorical moderators. We further employ a Savage-Dickey density ratio test to quantify the evidence for and against the presence of the effect at different levels of categorical moderators. We illustrate RoBMA-regression in an empirical example and demonstrate its performance in a simulation study. We implemented the methodology in the RoBMA R package. Overall, RoBMA-regression presents researchers with a powerful and flexible tool for conducting robust and informative meta-regression analyses. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

We demonstrate that all conventional meta-analyses of correlation coefficients are biased, explain why, and offer solutions. Because the standard errors of the correlation coefficients depend on the size of the coefficient, inverse-variance weighted averages will be biased even under ideal meta-analytical conditions (i.e., absence of publication bias, p -hacking, or other biases). Transformation to Fisher's z often greatly reduces these biases but still does not mitigate them entirely. Although all are small-sample biases ( n < 200), they will often have practical consequences in psychology where the typical sample size of correlational studies is 86. We offer two solutions: the well-known Fisher's z-transformation and new small-sample adjustment of Fisher's that renders any remaining bias scientifically trivial. (PsycInfo Database Record (c) 2024 APA, all rights reserved).