Although the statistical tools most often used by researchers in the field of psychology over the last 25 years are based on frequentist statistics, it is often claimed that the alternative Bayesian approach to statistics is gaining in popularity. In the current article, we investigated this claim by performing the very first systematic review of Bayesian psychological articles published between 1990 and 2015 (n = 1,579). We aim to provide a thorough presentation of the role Bayesian statistics plays in psychology. This historical assessment allows us to identify trends and see how Bayesian methods have been integrated into psychological research in the context of different statistical frameworks (e.g., hypothesis testing, cognitive models, IRT, SEM, etc.). We also describe take-home messages and provide "big-picture" recommendations to the field as Bayesian statistics becomes more popular. Our review indicated that Bayesian statistics is used in a variety of contexts across subfields of psychology and related disciplines. There are many different reasons why one might choose to use Bayes (e.g., the use of priors, estimating otherwise intractable models, modeling uncertainty, etc.). We found in this review that the use of Bayes has increased and broadened in the sense that this methodology can be used in a flexible manner to tackle many different forms of questions. We hope this presentation opens the door for a larger discussion regarding the current state of Bayesian statistics, as well as future trends. (PsycINFO Database Record

Nostalgia is a frequently-experienced complex emotion, understood by laypersons in the United Kingdom and United States of America to (1) refer prototypically to fond, selfrelevant, social memories and (2) be more pleasant (e.g., happy, warm) than unpleasant (e.g., sad, regretful). This research examined whether people across cultures conceive of nostalgia in the same way. Students in 18 countries across 5 continents (N = 1704) rated the prototypicality of 35 features of nostalgia. The samples showed high levels of agreement on the rank-order of features. In all countries, participants rated previously-identified central (vs. peripheral) features as more prototypical of nostalgia, and showed greater inter-individual agreement regarding central (vs. peripheral) features. Cluster analyses revealed subtle variation among groups of countries with respect to the strength of these pancultural patterns.All except African countries manifested the same factor structure of nostalgia features. In Japan, a woman drives past her childhood school and exclaims how natsukashii it is. In Ethiopia, a musician sings a Tizita ballad reliving memories of a lost lover. In the USA, a man smiles nostalgically as he listens to an old record that reminds him of his carefree teenage years. And in ancient Greece, the mythical hero Odysseus is galvanized by memories of his family as he struggles to make his way home from war (Homer, trans. 1921). To what extent are these four characters experiencing the same emotion? Is nostalgia universal?Growing evidence indicates that nostalgia is a self-relevant emotion associated with fond memories (Hepper, Ritchie, Sedikides, & Wildschut, 2012; and that it serves psychological functions (Routledge, Wildschut, Sedikides, & Juhl, 2013;. If nostalgia qualifies as an emotion and an adaptive psychological resource, it may be pancultural. The present article begins to address this issue by examining the equivalence of prototypical conceptions of nostalgia across a range of cultures. The Universality of EmotionThe universality of emotion concepts has long attracted scholarly attention. Darwin (1872/1965) proposed that emotions evolved as adaptive responses to social living, and thus some emotions should be universal. In contrast, Harré (1986) argued that emotions are primarily cultural constructions and thus should vary according to the meanings and practices of different cultural settings. Although the issues are textured, two major lines of research have supported the universality view. The first line of research has identified universally recognized facial expressions, focusing on a core set of "basic" emotions (e.g., anger, joy, sadness; Ekman, 1992;Ekman & Friesen, 1971;Russell, 1991a). The second line of research has examined conceptions of emotion words (Fontaine, Scherer, Roesch, & Ellsworth, 2007; Kuppens, Ceulemans, Timmerman, Diener, & Kim-Prieto, 2006; Páez & Vegara, 1995). This lexical literature has established that, across cultures, emotion (and specific emotions) is a fuzzy c...

In recent years, a growing chorus of researchers has argued that psychological theory is in a state of crisis: Theories are rarely developed in a way that indicates an accumulation of knowledge. Paul Meehl raised this very concern more than 40 years ago. Yet in the ensuing decades, little has improved. We aim to chart a better path forward for psychological theory by revisiting Meehl’s criticisms, his proposed solution, and the reasons his solution failed to meaningfully change the status of psychological theory. We argue that Meehl identified serious shortcomings in our evaluation of psychological theories and that his proposed solution would substantially strengthen theory testing. However, we also argue that Meehl failed to provide researchers with the tools necessary to construct the kinds of rigorous theories his approach required. To advance psychological theory, we must equip researchers with tools that allow them to better generate, evaluate, and develop their theories. We argue that formal theories provide this much-needed set of tools, equipping researchers with tools for thinking, evaluating explanation, enhancing measurement, informing theory development, and promoting the collaborative construction of psychological theories.

Over the past decade there has been a surge of empirical research investigating mental disorders as complex systems. In this paper, we investigate how to best make use of this growing body of empirical research and move the field toward its fundamental aims of explaining, predicting, and controlling psychopathology. We first review the contemporary philosophy of science literature on scientific theories and argue that fully achieving the aims of explanation, prediction, and control requires that we construct formal theories of mental disorders: theories expressed in the language of mathematics or a computational programming language. We then investigate three routes by which one can use empirical findings (i.e., data models) to construct formal theories: (a) using data models themselves as formal theories, (b) using data models to infer formal theories, and (c) comparing empirical data models to theory-implied data models in order to evaluate and refine an existing formal theory. We argue that the third approach is the most promising path forward. We conclude by introducing the Abductive Formal Theory Construction (AFTC) framework, informed by both our review of philosophy of science and our methodological investigation. We argue that this approach provides a clear and promising way forward for using empirical research to inform the generation, development, and testing of formal theories both in the domain of psychopathology and in the broader field of psychological science.

The cross-lagged panel model (CLPM), a discrete-time (DT) SEM model, is frequently used to gather evidence for (reciprocal) Granger-causal relationships when lacking an experimental design. However, it is well known that CLPMs can lead to different parameter estimates depending on the time-interval of observation. Consequently, this can lead to researchers drawing conflicting conclusions regarding the sign and/or dominance of relationships. Multiple authors have suggested the use of continuous-time models to address this issue.In this article, we demonstrate the exact circumstances under which such conflicting conclusions occur. Specifically, we show that such conflicts are only avoided in general in the case of bivariate, stable, nonoscillating, first-order systems, when comparing models with uniform time-intervals between observations. In addition, we provide a range of tools, proofs, and guidelines regarding the comparison of discrete-and continuous-time parameter estimates.