Structural equation modeling (SEM) is a widespread and commonly used approach to test substantive hypotheses in the social and behavioral sciences. When performing hypothesis tests, it is vital to rely on a sufficiently large sample size to achieve an adequate degree of statistical power to detect the hypothesized effect. However, applications of SEM rarely consider statistical power in informing sample size considerations or determine the statistical power for the focal hypothesis tests performed. One reason is the difficulty in translating substantive hypotheses into specific effect size values required to perform power analyses, as well as the lack of user-friendly software to automate this process. The present paper presents the second version of the R package semPower which includes comprehensive functionality for various types of power analyses in SEM. Specifically, semPower 2 allows one to perform both analytical and simulated a priori, post hoc, and compromise power analysis for structural equation models with or without latent variables, and also supports multigroup settings and provides user-friendly convenience functions for many common model types (e.g., standard confirmatory factor analysis [CFA] models, regression models, autoregressive moving average [ARMA] models, cross-lagged panel models) to simplify power analyses when a model-based definition of the effect in terms of model parameters is desired.

Multinomial processing tree (MPT) models have become popular in cognitive psychology in the past two decades. In contrast to general-purpose data analysis techniques, such as log-linear models or other generalized linear models, MPT models are substantively motivated stochastic models for categorical data. They are best described as tools (a) for measuring the cognitive processes that underlie human behavior in various tasks and (b) for testing the psychological assumptions on which these models are based. The present article provides a review of MPT models and their applications in psychology, focusing on recent trends and developments in the past 10 years. Our review is nontechnical in nature and primarily aims at informing readers about the scope and utility of MPT models in different branches of cognitive psychology.

Based on lexical studies, the HEXACO (honesty-humility, emotionality, extraversion, agreeableness, conscientiousness, and openness to experience) model of personality has been proposed as a model of basic personality structure that summarizes individual differences in six broad trait dimensions. Although research across various fields relies on the HEXACO model increasingly, a comprehensive investigation of the nomological net of the HEXACO dimensions is missing entirely. Thus, it remains unclear whether each HEXACO dimension accounts for individual variation across theoretically relevant outcome criteria. We close this gap through a large-scale meta-analytic investigation, testing whether each HEXACO dimension is uniquely linked to one broad and theoretically relevant outcome domain. Results from 426 individual meta-analyses, 436 independent samples, and 3,893 effect-size estimates corroborate this unique mapping. Specifically, honesty-humility maps onto the outcome domain of exploitation, emotionality onto insecurity, extraversion onto sociality, agreeableness versus anger onto obstruction, conscientiousness onto duty, and openness to experience onto exploration. Overall, the current investigation provides a comprehensive empirical test of the (breadth of) content captured by the HEXACO dimensions and allows for a broad specification of the nomological net of the HEXACO model overall.