New strategies for addressing the diversity-validity dilemma with big data.

The diversity-validity dilemma is one of the enduring challenges in personnel selection. Technological advances and new techniques for analyzing data within the fields of machine learning and industrial organizational psychology, however, are opening up innovative ways of addressing this dilemma. Given these rapid advances, we first present a framework unifying analytical methods commonly used in these two fields to reduce group differences. We then propose and demonstrate the effectiveness of two approaches for reducing group differences while maintaining validity, which are highly applicable to numerous big data scenarios: iterative predictor removal and multipenalty optimization. Iterative predictor removal is a technique where predictors are removed from the data set if they simultaneously contribute to higher group differences and lower predictive validity. Multipenalty optimization is a new analytical technique that models the diversity-validity trade-off by adding a group difference penalty to the model optimization. Both techniques were tested on a field sample of asynchronous video interviews. Although both techniques effectively decreased group differences while maintaining predictive validity, multipenalty optimization outperformed iterative predictor removal. Strengths and weaknesses of these two analytical techniques are also discussed along with future research directions. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Distressed and distracted by COVID-19 during high-stakes virtual interviews: The role of job interview anxiety on performance and reactions.

Employers have increasingly turned to virtual interviews to facilitate online, socially distanced selection processes in the face of the COVID-19 pandemic. However, there is little understanding about the experience of job candidates in these virtual interview contexts. We draw from Event System Theory (Morgeson et al., 2015) to advance and test a conceptual model that focuses on a high-stress, high-stakes setting and integrates literatures on workplace stress with literatures on applicant reactions. We predict that when applicants ruminate about COVID-19 during an interview and have higher levels of COVID-19 exhaustion, they will have higher levels of anxiety during virtual interviews, which in turn relates to reduced interview performance, lower perceptions of fairness, and reduced intentions to recommend the organization. Further, we predict that three factors capturing COVID-19 as an enduring and impactful event (COVID-19 duration, COVID-19 cases, COVID-19 deaths) will be positively related to COVID-19 exhaustion. We tested our propositions with 8,343 job applicants across 373 companies and 93 countries/regions. Consistent with predictions, we found a positive relationship between COVID-19 rumination and interview anxiety, and this relationship was stronger for applicants who experienced higher (vs. lower) levels of COVID-19 exhaustion. In turn, interview anxiety was negatively related to interview performance, fairness perceptions, and recommendation intentions. Moreover, using a relevant subset of the data (n = 6,136), we found that COVID-19 duration and deaths were positively related to COVID-19 exhaustion. This research offers several insights for understanding the virtual interview experience embedded in the pandemic and advances the literature on applicant reactions. (PsycInfo Database Record (c) 2021 APA, all rights reserved).