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This study provides a comprehensive assessment of the associations of personality and intelligence. It presents a meta-analysis (N = 162,636, k = 272) of domain, facet, and item-level correlations between personality and intelligence (general, fluid, and crystallized) for the major Big Five and HEXACO hierarchical frameworks of personality: NEO PI-R, Big Five Aspect Scales (BFAS), BFI-2, and HEXACO PI R. It provides the first meta-analysis of personality and intelligence to comprehensively examine (a) facet-level correlations for these hierarchical frameworks of personality, (b) item-level correlations, (c) domain- and facet-level predictive models. Age and sex differences in personality and intelligence, and study-level moderators, are also examined. The study was complemented by four of our own unpublished datasets (N = 26,813) which were used to assess the ability of item-level models to provide generalizable prediction. Results showed that openness (ρ = .20) and neuroticism (ρ = -.09) were the strongest Big Five correlates of intelligence and that openness correlated more with crystallized than fluid intelligence. At the facet-level, traits related to intellectual engagement and unconventionality were more strongly related to intelligence than other openness facets, and sociability and orderliness were negatively correlated with intelligence. Facets of gregariousness and excitement seeking had stronger negative correlations, and openness to aesthetics, feelings, and values had stronger positive correlations with crystallized than fluid intelligence. Facets explained more than twice the variance of domains. Overall, the results provide the most nuanced and robust evidence to date of the relationship between personality and intelligence.

With an archival data set from an Australian-based asynchronous video interview (AVI) vendor, we examined how employers implemented AVI features as a step toward understanding how AVIs can be more optimally designed. The multilevel data contained information about 2,550,105 responses from 627,999 candidates to 52,623 questions, nested within 12,105 interview templates. We found that AVIs were often used for small applicant pools (Mdn = 10 candidates per AVI) and that a majority of the AVIs comprised four to five questions, with candidates typically given 30 seconds to prepare a response, and 2 minutes to record it. Only rarely were candidates permitted to preview questions or re-record responses. Finally, we noted that the settings typically applied were highly consistent with platform's default settings.

Objective This study aimed to examine operator state variables (workload, fatigue, and trust in automation) that may predict return-to-manual (RTM) performance when automation fails in simulated air traffic control. Background Prior research has largely focused on triggering adaptive automation based on reactive indicators of performance degradation or operator strain. A more direct and effective approach may be to proactively engage/disengage automation based on predicted operator RTM performance (conflict detection accuracy and response time), which requires analyses of within-person effects. Method Participants accepted and handed-off aircraft from their sector and were assisted by imperfect conflict detection/resolution automation. To avoid aircraft conflicts, participants were required to intervene when automation failed to detect a conflict. Participants periodically rated their workload, fatigue and trust in automation. Results For participants with the same or higher average trust than the sample average, an increase in their trust (relative to their own average) slowed their subsequent RTM response time. For participants with lower average fatigue than the sample average, an increase in their fatigue (relative to own average) improved their subsequent RTM response time. There was no effect of workload on RTM performance. Conclusions RTM performance degraded as trust in automation increased relative to participants’ own average, but only for individuals with average or high levels of trust. Applications Study outcomes indicate a potential for future adaptive automation systems to detect vulnerable operator states in order to predict subsequent RTM performance decrements.

The COVID-19 pandemic was a key event forcing an increase in virtual work. Drawing on event system theory, we examined whether virtual teams showed enhanced processes in later stages of the pandemic compared to the early stages of the pandemic. We collected data from 54 virtual teams ( N = 152 individuals) who worked on a 30-minute task. We measured team processes and performance. Virtual teams during the post-transition phase (June–August 2020) showed better levels of team action processes and conflict management compared to teams working in the immediate transition phase (March–May 2020), indicative of an adaptation effect.

We investigated how age‐based worker stereotypes correspond with attributes of expected work performance. Participants (N = 220) rated 86 stereotypical descriptors of older (e.g., ‘resistant to change’) and younger workers (e.g., ‘savvy with technology’). Each descriptor was rated on both the extent that it was a common stereotype about younger (vs. older) workers, and characteristic of good (vs. poor) expected work performance. Ratings revealed stereotypes that were strongly associated with both a worker age group and with work performance quality, offering an explanatory mechanism for how candidates’ ages might influence judgments around personnel selection. A table of the paired age/performance ratings is provided to encourage the development of less age‐biased recruitment and selection materials.

Forced choice (FC) personality measures are increasingly popular in research and applied contexts. To date however, no method for detecting faking behavior on this format has been both proposed and empirically tested. We introduce a new methodology for faking detection on FC measures, based on the assumption that individuals engaging in faking try to approximate the ideal response on each block of items. Individuals’ responses are scored relative to the ideal using a model for rank-order data not previously applied to FC measures (Generalized Mallows Model). Scores are then used as predictors of faking in a regularized logistic regression. In Study 1, we test our approach using cross-validation, and contrast generic and job-specific ideal responses. Study 2 replicates our methodology on two measures matched and mismatched on item desirability. We achieved between 80 – 92% balanced accuracy in detecting instructed faking, and predicted probabilities of faking correlated with self-reported faking behavior. We discuss how this approach, driven by trying to capture the faking process, differs methodologically and theoretically to existing faking detection paradigms, and measure and context-specific factors impacting accuracy.

The recently proposed Cognitive Experiential Leadership Model (CELM) states that leaders’ preference for rational thinking and behavioral coping will be related to their level of transformational leadership. The CELM was based on research that principally used cross-sectional self-report methods. Study 1 compared both self-ratings and follower-ratings of leadership styles with leaders’ self-rated thinking styles in 160 leader-follower dyads. Study 2 compared both self-ratings and coworker-ratings of leadership styles with leaders’ self-rated thinking styles for 74 leaders rated by 607 coworkers. In both Studies, leaders’ rational thinking, imaginative thinking, and behavioral coping correlated positively with their self-rated transformational leadership. However, only behavioral coping, but not rational thinking, was correlated with follower-rated (FR) transformational leadership in Study 1, and thinking styles were unrelated to other-rated transformational leadership in Study 2. These results partly support and partly challenge the CELM. Practically, this study suggests that leadership may be improved by leaders developing their capacity for behavioral coping.

Abstract. Forced choice (FC) personality questionnaires attempt to constrain job applicants from presenting idealized responses (or “faking”). FC questionnaires are designed by identifying items equally desirable in applicants, matching these into “blocks,” and instructing respondents to rank the items “most like” themselves. Nonetheless, how closely items should be matched remains unclear, and desirability seems dependent on the job. We investigated how strongly respondents ( N = 436) agreed regarding the “ideal” applicant response, while varying (a) how closely items were matched into blocks and (b) the job context. While the most closely matched blocks elicited slight agreement on an ideal response, agreement increased noticeably with poorer matching. Nonetheless, differences in item desirability between different job conditions were evident even in closely matched blocks.

Model generalizability describes how well the findings from a sample are applicable to other samples in the population. In this Tutorial, we explain model generalizability through the statistical concept of model overfitting and its outcome (i.e., validity shrinkage in new samples), and we use a Shiny app to simulate and visualize how model generalizability is influenced by three factors: model complexity, sample size, and effect size. We then discuss cross-validation as an approach for evaluating model generalizability and provide guidelines for implementing this approach. To help researchers understand how to apply cross-validation to their own research, we walk through an example, accompanied by step-by-step illustrations in R. This Tutorial is expected to help readers develop the basic knowledge and skills to use cross-validation to evaluate model generalizability in their research and practice.

Ree, Carretta, and Teachout (2015) outlined a compelling argument for the pervasiveness of dominant general factors (DGFs) in psychological measurement. We agree that DGFs are important and that they are found for various constructs (e.g., cognitive abilities, work withdrawal), especially when an “unrotated principal components” analysis is conducted (Ree et al., p. 8). When studying hierarchical constructs, however, a narrow emphasis on uncovering DGFs would be incomplete at best and detrimental at worst. This commentary largely echoes the arguments made by Wee, Newman, and Joseph (2014), and Schneider and Newman (2015), who provided reasons for considering second-stratum cognitive abilities. We believe these same arguments in favor of second-stratum factors in the ability domain can be applied to hierarchical constructs more generally.

Extensive research has examined the effectiveness of admissions tests for use in higher education. What has gone unexamined is the extent to which tests are similarly effective for predicting performance at both the master’s and doctoral levels. This study empirically synthesizes previous studies to investigate whether or not the Graduate Record Examination (GRE) predicts the performance of students in master’s programs as well as the performance of doctoral students. Across nearly 100 studies and 10,000 students, this study found that GRE scores predict first year grade point average (GPA), graduate GPA, and faculty ratings well for both master’s and doctoral students, with differences that ranged from small to zero.

Organizational researchers increasingly recognize the need to consider the Dark Triad traits (i.e., psychopathy, Machiavellianism, and narcissism) when explaining undesirable work outcomes (e.g., counterproductive behaviors). However, little research has focused on the motivations of those who actually hold the traits. In this study (N = 361) we examined how the Dark Triad traits predispose individuals to perceive situations as competitive, prestigious, and comprised of restrictions (i.e., autonomy) which differentially predict job satisfaction. Individuals high on psychopathy and Machiavellianism perceived their workplaces as competitive, whereas individuals high on narcissism perceived their workplaces as prestigious and with fewer restrictions. Sex differences in perceptions were fully mediated by psychopathy and Machiavellianism. We discuss our results from an Evolutionary Industrial/Organization Psychology framework.

Our research focused on the vocational interests correlated with the Dark Triad traits (i.e., narcissism, psychopathy, and Machiavellianism). By understanding how these traits facilitate the structuring of one’s environment, we hypothesized that psychopaths will be more interested in realistic and practical careers, narcissists will be more interested in artistic, enterprising, and social careers, and Machiavellians will be more interested in avoiding careers that involve caring for others. In two cross-sectional studies ( N = 424; N = 274), we provide general support for these hypotheses. Overall, our study showed those high on the Dark Triad traits may structure their social environment through idealized career preferences. We also show that sex differences in career preferences might be a function, in part of, individual differences in the Dark Triad traits.

We describe the summative assessment of role-play scenarios that we previously developed to teach central topics in the responsible conduct of research (RCR) to graduate students in science and engineering. Interviews with role-play participants, with participants in a case discussion training session, and with untrained students suggested that role-playing might promote a deeper appreciation of RCR by shifting the focus away from wanting to simply "know the rules." We also present the results of a think-aloud case analysis study and describe the development of a behaviorally-anchored rating scale (BARS) to assess participants' case analysis performance.

This study examined how undergraduate engineering students form career goals. We hypothesized that a student's development of an engineering identity might be correlated with that student's persistence in engineering. We focused on four areas: (a) motivations for studying engineering, (b) engineering-related experiences, (c) fit perceptions, and (d) engineering-related self-efficacy. We employed a mixed-methods approach: we surveyed all engineering freshmen, and we conducted eight single-sex focus group interviews (one male and one female group at each of four college levels). A total of 1351 (19% female, 81% male) engineering freshmen and 42 (60% female, 40% male) focus group members participated in the study. Statistical analysis of the survey data showed that freshman female engineering students tended to have significantly lower self-efficacy than freshman male engineering students, although they tended to have higher outcome expectations than their male counterparts. Preliminary analysis of the qualitative data provided evidence that for both men and women, out-of class experiences such as internships, design projects, and participation in student organizations tended to increase engagement and commitment to engineering as a profession.

This study introduced an individual difference construct of willingness to compromise and examined its implications for understanding and predicting career-related decisions in work settings. In Study 1 ( N = 53), critical incidents of career decisions were analyzed to identify commonalities across different types of career-related compromises. In Study 2 ( N = 171), an initial 17-item scale was developed and revised. In Study 3 ( N = 201), the convergent and criterion-related validity of the scale was examined in relation to specific personality traits, regret, dealing with uncertainty, career adaptability, and a situational dilemma task. Willingness to compromise was negatively related to neuroticism, and positively related to dealing with uncertainty, openness to experience, and career adaptability; it also predicted responses to the situational dilemma task. Results provided support for the reliability and validity of the scale.