Bright or dark, or virtues and vices? A reexamination of the big five and job performance

Personality research in industrial/organizational psychology has been dominated by the description of personality traits and outcomes as either bright or dark. Unfortunately, research has shown that bright traits have dark outcomes and vice versa, suggesting that a paradox is plaguing the literature. To resolve this paradox, I propose that a different heuristic stemming from positive psychology be utilized: virtues and vices. Virtues refer to exercises of human excellence while vices refer to actions of human failure. Drawing on the virtue ethics concept of the Aristotelian mean, dark traits are viewed as extreme or elevated levels of bright personality traits, allowing both to be described by a common set of dimensions. Further, I posit that under certain circumstances, even extreme trait standings might result in acts of human excellence. Importantly, this resolution implies that nonlinear relationships may accurately describe the functional form of relationships linking personality dimensions to valued outcomes. To test this model, I applied the virtues and vices heuristic to five basic personality dimensions (the Big Five) described by the Five-Factor Model (FFM) of personality in order to construct a measurement model that views extreme levels of these dimensions as dark. To develop this measurement model, trained item writers generated nine hundred fifty-eight items according to these specifications (approximately 30 items per each of the 30 narrow traits of the FFM). Two subject-matter experts rated these items on extremity for the purposes of reducing this initial item pool to a smaller set of usable resulted in a set of three hundred items that were administered to a sample of 728 working employees obtained through Amazon\u27s Mechanical Turk along with self-descriptions of task performance, organizational citizenship, and deviant work behavior (outcomes widely accepted as either virtuous or vice-like). Ideal-point item-response theory was used to estimate person parameters for the five personality dimensions. Small nonlinear effects were detected linking several traits and outcomes. Small-n employee selection scenarios were simulated to demonstrate the practical importance of these small effects. Implications for theory and practice are discussed

The Incremental Value of Controlling for Covert Insufficient Effort Responding

Insufficient effort responding (IER) is a common concern of survey researchers especially those who collect data through crowdsourcing. Methods of controlling for IER may be overt (identifiable by respondents) or covert. This study examines the relative impact of controlling for covert IER when overt-IER methods are in the survey design. Using data from an experiment on performance feedback reactions where overt IER controls were in place, we examine the scale reliabilities and convergent and discriminant validity, both of which change negligibly by controlling for covert IER. Findings suggest controlling for covert IER lacks incremental value beyond controlling for overt IER

The Psychological Science Accelerator: Advancing Psychology through a Distributed Collaborative Network

Concerns have been growing about the veracity of psychological research. Many findings in psychological science are based on studies with insufficient statistical power and nonrepresentative samples, or may otherwise be limited to specific, ungeneralizable settings or populations. Crowdsourced research, a type of large-scale collaboration in which one or more research projects are conducted across multiple lab sites, offers a pragmatic solution to these and other current methodological challenges. The Psychological Science Accelerator (PSA) is a distributed network of laboratories designed to enable and support crowdsourced research projects. These projects can focus on novel research questions, or attempt to replicate prior research, in large, diverse samples. The PSA\u27s mission is to accelerate the accumulation of reliable and generalizable evidence in psychological science. Here, we describe the background, structure, principles, procedures, benefits, and challenges of the PSA. In contrast to other crowdsourced research networks, the PSA is ongoing (as opposed to time-limited), efficient (in terms of re-using structures and principles for different projects), decentralized, diverse (in terms of participants and researchers), and inclusive (of proposals, contributions, and other relevant input from anyone inside or outside of the network). The PSA and other approaches to crowdsourced psychological science will advance our understanding of mental processes and behaviors by enabling rigorous research and systematically examining its generalizability

The Psychological Science Accelerator: Advancing Psychology Through a Distributed Collaborative Network

Source at https://doi.org/10.1177/2515245918797607.Concerns about the veracity of psychological research have been growing. Many findings in psychological science are based on studies with insufficient statistical power and nonrepresentative samples, or may otherwise be limited to specific, ungeneralizable settings or populations. Crowdsourced research, a type of large-scale collaboration in which one or more research projects are conducted across multiple lab sites, offers a pragmatic solution to these and other current methodological challenges. The Psychological Science Accelerator (PSA) is a distributed network of laboratories designed to enable and support crowdsourced research projects. These projects can focus on novel research questions or replicate prior research in large, diverse samples. The PSA’s mission is to accelerate the accumulation of reliable and generalizable evidence in psychological science. Here, we describe the background, structure, principles, procedures, benefits, and challenges of the PSA. In contrast to other crowdsourced research networks, the PSA is ongoing (as opposed to time limited), efficient (in that structures and principles are reused for different projects), decentralized, diverse (in both subjects and researchers), and inclusive (of proposals, contributions, and other relevant input from anyone inside or outside the network). The PSA and other approaches to crowdsourced psychological science will advance understanding of mental processes and behaviors by enabling rigorous research and systematic examination of its generalizability

Prevailing To The Peers\u27 Detriment: Organizational Constraints Motivate Machiavellians To Undermine Their Peers

Since Christie and Geis\u27s (1970) seminal work suggested that Machiavellians win more and are persuaded less, researchers have debated the merits and faults of Machiavellianism. Recent findings suggest competition over resources lead Machiavellians to secure their superior\u27s approval, promoting their career advancement. However, the strategies Machiavellians use in such contexts have yet to be identified. Social undermining research suggests that undermining one\u27s coworkers might make it difficult for targets of undermining to maintain effective working relationships while promoting a perpetrator\u27s relative status (Duffy, Shaw, Scott, & Tepper, 2006). Thus, drawing on trait activation theory, we argue that resource constraints motivate Machiavellians to undermine their coworkers, which might help them achieve higher relative status. Additionally, with increased effort devoted toward undermining one\u27s peers, Machiavellians should be distracted from performing core duties resulting in increased production deviance. Data collected from 170 employees supported our arguments. Our study addresses a gap in the literature by suggesting that Machiavellians successfully navigate competitive work environments by undermining their coworkers. We conclude with theoretical and practical implications for both understanding and mitigating the extended detrimental influence of workplace Machiavellianism

Big team science initiatives: A catalyst for trustworthy advancements in IO psychology

Keener et al. (2023) raise concerns about the trustworthiness of Industrial/Organizational (IO) Psychology research and related fields due to the low reproducibility and replicability of research findings. The authors provide various solutions to resolve this crisis, such as improving training, realigning incentives, and adopting open science practices. Our commentary elaborates on one solution to which they briefly allude: Big Team Science Initiatives (BTSIs). BTSIs allow scholars to address the trustworthiness of our science by facilitating large sample theory testing, sharing and allocating resources, and selecting appropriate research strategies, all of which support the reproducibility and replication of research. Further, we propose that BTSIs may facilitate researcher training, encourage data sharing and materials, and realign incentives in our field. We discuss how BTSIs could be implemented in IO psychology and related fields, identifying and drawing upon similar BTSIs in related disciplines. Thus, our commentary is an extension of the focal article, encouraging scholars to collaboratively address the “crisis of confidence” facing our field using a big team science approach

The psychological science accelerator: advancing psychology through a distributed collaborative network

Concerns about the veracity of psychological research have been growing. Many findings in psychological science are based on studies with insufficient statistical power and nonrepresentative samples, or may otherwise be limited to specific, ungeneralizable settings or populations. Crowdsourced research, a type of large-scale collaboration in which one or more research projects are conducted across multiple lab sites, offers a pragmatic solution to these and other current methodological challenges. The Psychological Science Accelerator (PSA) is a distributed network of laboratories designed to enable and support crowdsourced research projects. These projects can focus on novel research questions or replicate prior research in large, diverse samples. The PSA’s mission is to accelerate the accumulation of reliable and generalizable evidence in psychological science. Here, we describe the background, structure, principles, procedures, benefits, and challenges of the PSA. In contrast to other crowdsourced research networks, the PSA is ongoing (as opposed to time limited), efficient (in that structures and principles are reused for different projects), decentralized, diverse (in both subjects and researchers), and inclusive (of proposals, contributions, and other relevant input from anyone inside or outside the network). The PSA and other approaches to crowdsourced psychological science will advance understanding of mental processes and behaviors by enabling rigorous research and systematic examination of its generalizability