Enabling analytical power calculations for multilevel models with autocorrelated errors through deriving and approximating the precision matrix.

To unravel how within-person psychological processes fluctuate in daily life, and how these processes differ between persons, intensive longitudinal (IL) designs in which participants are repeatedly measured, have become popular. Commonly used statistical models for those designs are multilevel models with autocorrelated errors. Substantive hypotheses of interest are then typically investigated via statistical hypotheses tests for model parameters of interest.

To which world regions does the valence-dominance model of social perception apply?

Over the past 10 years, Oosterhof and Todorov's valence-dominance model has emerged as the most prominent account of how people evaluate faces on social dimensions. In this model, two dimensions (valence and dominance) underpin social judgements of faces. Because this model has primarily been developed and tested in Western regions, it is unclear whether these findings apply to other regions.

The reproducibility of statistical results in psychological research: An investigation using unpublished raw data.

We investigated the reproducibility of the major statistical conclusions drawn in 46 articles published in 2012 in three APA journals. After having identified 232 key statistical claims, we tried to reproduce, for each claim, the test statistic, its degrees of freedom, and the corresponding p value, starting from the raw data that were provided by the authors and closely following the Method section in the article. Out of the 232 claims, we were able to successfully reproduce 163 (70%), 18 of which only by deviating from the article's analytical description.

Heteroscedasticity as a Basis of Direction Dependence in Reversible Linear Regression Models.

Heteroscedasticity is a well-known issue in linear regression modeling. When heteroscedasticity is observed, researchers are advised to remedy possible model misspecification of the explanatory part of the model (e.g.