Ordinal variables, although extremely common in psychology, are almost exclusively analyzed with statistical models that falsely assume them to be metric. This practice can lead to distorted effect-size estimates, inflated error rates, and other problems. We argue for the application of ordinal models that make appropriate assumptions about the variables under study. In this Tutorial, we first explain the three major classes of ordinal models: the cumulative, sequential, and adjacent-category models. We then show how to fit ordinal models in a fully Bayesian framework with the R package brms, using data sets on opinions about stem-cell research and time courses of marriage. The appendices provide detailed mathematical derivations of the models and a discussion of censored ordinal models. Compared with metric models, ordinal models provide better theoretical interpretation and numerical inference from ordinal data, and we recommend their widespread adoption in psychology.

Forced-choice questionnaires have been proposed to avoid common response biases typically associated with rating scale questionnaires. To overcome ipsativity issues of trait scores obtained from classical scoring approaches of forced-choice items, advanced methods from item response theory (IRT) such as the Thurstonian IRT model have been proposed. For convenient model specification, we introduce the thurstonianIRT R package, which uses Mplus, lavaan, and Stan for model estimation. Based on practical considerations, we establish that items within one block need to be equally keyed to achieve similar social desirability, which is essential for creating forced-choice questionnaires that have the potential to resist faking intentions. According to extensive simulations, measuring up to five traits using blocks of only equally keyed items does not yield sufficiently accurate trait scores and inter-trait correlation estimates, neither for frequentist nor for Bayesian estimation methods. As a result, persons’ trait scores remain partially ipsative and, thus, do not allow for valid comparisons between persons. However, we demonstrate that trait scores based on only equally keyed blocks can be improved substantially by measuring a sizable number of traits. More specifically, in our simulations of 30 traits, scores based on only equally keyed blocks were non-ipsative and highly accurate. We conclude that in high-stakes situations where persons are motivated to give fake answers, Thurstonian IRT models should only be applied to tests measuring a sizable number of traits.

This meta-analysis addresses the question of whether expressive writing shows an effect on reducing depressive symptoms. It focuses on samples of physically healthy adults with varying degrees of stress but without posttraumatic stress disorder. A total of 39 randomized controlled trials with 64 intervention-control group comparisons were obtained through keyword search in databases and backward search. Expressive writing did not yield significant long-term effects on depressive symptoms. However, effects were larger when the number of sessions was higher and when the writing topic was more specific. The results of this meta-analysis did not support the effectiveness of brief, self-directed expressive writing as an intervention that decreases depressive symptoms in physically healthy adults with varying degrees of psychological stress. Future research should examine whether longer, more directed writing interventions with additional therapeutic support would lead to different results. K E Y W O R D Sdepressive symptoms, emotional disclosure, expressive writing, meta-analysis, scriptotherapy

Ordinal predictors are commonly used in regression models. They are often incorrectly treated as either nominal or metric, thus under‐ or overestimating the information contained. Such practices may lead to worse inference and predictions compared to methods which are specifically designed for this purpose. We propose a new method for modelling ordinal predictors that applies in situations in which it is reasonable to assume their effects to be monotonic. The parameterization of such monotonic effects is realized in terms of a scale parameter b representing the direction and size of the effect and a simplex parameter bold-italicς modelling the normalized differences between categories. This ensures that predictions increase or decrease monotonically, while changes between adjacent categories may vary across categories. This formulation generalizes to interaction terms as well as multilevel structures. Monotonic effects may be applied not only to ordinal predictors, but also to other discrete variables for which a monotonic relationship is plausible. In simulation studies we show that the model is well calibrated and, if there is monotonicity present, exhibits predictive performance similar to or even better than other approaches designed to handle ordinal predictors. Using Stan, we developed a Bayesian estimation method for monotonic effects which allows us to incorporate prior information and to check the assumption of monotonicity. We have implemented this method in the R package brms, so that fitting monotonic effects in a fully Bayesian framework is now straightforward.

Individuals who experience recurrent negative thoughts are at elevated risk for mood and anxiety disorders. It is thus essential to understand why some individuals get stuck in recurrent negative thinking (RNT), whereas others are able to disengage eventually. Theoretical models propose that individuals high in recurrent negative thinking suffer from deficits in controlling the contents of working memory. Empirical findings, however, are inconclusive. In this meta-analysis, we synthesize findings from 94 studies to examine the proposed association between RNT and deficits in cognitive control. We included numerous effect sizes not reported in the primary publications. Moderator analyses tested the influence of variables, such as stimuli valence, cognitive control function (e.g., shifting, discarding), or type of RNT (i.e., rumination or worry). Results demonstrated an association between repetitive negative thinking and deficits in only one specific cognitive control function, namely difficulty discarding no longer relevant material from working memory (r = -0.20). This association remained significant after controlling for level of psychopathology. There was no substantial association between RNT and deficits in any other cognitive control function. All other moderators were not significant. We discuss limitations (e.g., primary sample sizes, reliability of paradigms) and highlight implications for future research and clinical interventions.