Organizations are increasingly turning toward personnel selection tools that rely on artificial intelligence (AI) technologies and machine learning algorithms that, together, intend to predict the future success of employees better than traditional tools. These new forms of assessment include online games, video-based interviews, and big data pulled from many sources, including test responses, test-taking behavior, applications, resumes, and social media. Speedy processing, lower costs, convenient access, and applicant engagement are often and rightfully cited as the practical advantages for using these selection tools. At the same time, however, these tools raise serious concerns about their effectiveness in terms their conceptual relevance to the job, their basis in a job analysis to ensure job relevancy, their measurement characteristics (reliability and stability), their validity in predicting employee-relevant outcomes, their evidence and normative information being updated appropriately, and the associated ethical concerns around what information is being represented to employers and told to job candidates. This paper explores these concerns, concluding with an urgent call to industrial and organizational psychologists to extend existing professional standards for employment testing to these new AI and machine learning based forms of testing, including standards and requirements for their documentation.

Recent years have seen dramatic changes in research practices in psychological science. In particular, preregistration of study plans before conducting a study has been identified as an important tool to help increase the transparency of science and to improve the robustness of psychological research findings. This article presents the Psychological Research Preregistration-Quantitative (PRP-QUANT) Template produced by a Joint Psychological Societies Preregistration Task Force consisting of the American Psychological Association (APA), the British Psychological Society (BPS), and the German Psychological Society (DGPs), supported by the Center for Open Science (COS) and the Leibniz Institute for Psychology (ZPID). The goal of the Task Force was to provide the psychological community with a consensus template for the preregistration of quantitative research in psychology, one with wide coverage and the ability, if necessary, to adapt to specific journals, disciplines, and researcher needs. This article covers the structure and use of the PRP-QUANT template, while outlining and discussing the benefits of its use for researchers, authors, funders, and other relevant stakeholders. We hope that by introducing this template and by demonstrating the support of preregistration by major academic psychological societies, we

Using game-based assessments (GBAs) to assess and select job applicants presents the dual challenges of measuring intended job-relevant constructs while analyzing GBA data that contain more predictors than observations. Exploring those challenges, we analyzed two GBAs that were designed to measure conscientiousness facets (i.e., achievement striving, self-discipline, and cautiousness). Scores on traditional measures of personality and cognitive ability were modeled using either a restricted set of GBA predictors using cross-validated ordinary least squares (OLS) regression or by the fuller set (p = 248) using random forests regression. Overall, the prediction of personality was near-zero; but the latter approach explained 14%-30% of the variance in predicting cognitive ability. Our findings warn of GBAs potentially measuring unintended constructs rather than their intended constructs.

Although the research literature has established that Conscientiousness predicts task performanceacross a variety of achievement contexts (e.g., Barrick & Mount, 1991; O’Connor & Paunonen, 2007), comparatively less is known about the processes that underlie these relations. To the latter end, the current research examines effortful strategies and achievement goals as mediating factors that might explain why people with higher levels of Conscientiousness are predicted to reach higher levels of academic performance. In a longitudinal study, 347 college students completed measures of personality and achievement goals at the beginning of the class, followed by measures of effortful strategies multiple times throughout the semester. Results support the hypothesis that effortful strategies mediate the association between Conscientiousness and academic performance. Moreover, the statistical effects of Conscientiousness were generally independent of achievement goals, but a small portion of the effect was mediated through approach, but not avoidance, achievement goals. These results highlight the importance of examining mediating processes between personality and outcomes, and in the case of Conscientiousness, our results suggest that effortful strategies might serve as a useful target for performance-enhancing interventions.

Leadership traits and behaviors are observed early in human development, and although an improved understanding of youth leadership would usefully inform many real-world contexts (e.g., education, parenting, policy), most empirical work on leadership has been limited to adult populations. The purpose of the current article is to add a developmental perspective to leadership research that has so far been absent. Here, we (a) highlight adolescence as a critical developmental period for leadership emergence and development, (b) argue that leadership among youths is poorly understood and critically understudied, (c) provide exemplars of synergy between research on leadership and adolescent development that are ripe for focused inquiry, and (d) underscore some of the positive consequences of accelerating empirical research on leadership in adolescence, including implications for a deeper understanding of leadership in adult working populations.