Browsing by Author "Prasad, Josh, advisor"

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Open Access
Predicting the paycheck: using machine learning to understand determinants of income
(Colorado State University. Libraries, 2024) Benson, Annika, author; Prasad, Josh, advisor; Gardner, Danielle, committee member; Prince, Mark, committee member; Conroy , Samantha, committee member
Income is a variable of interest in industrial/organizational psychology due to its relationship with outcomes like turnover, motivation, and psychological well-being. However, current research on income has generally assumed a linear relationship between predictors and income, not accounting for potential curvilinear effects or variable interactions. Further, studies on income indicate that large amounts of variance are unaccounted for, suggesting there are predictors yet to be identified. This study addresses those gaps in the research by using machine learning techniques and a large archival data set to investigate the strength and nature of how variables contribute to predicting income. Results demonstrate the effectiveness of machine learning techniques over traditional OLS regression and identifies variables not found currently in the literature. Findings from this research can be used both to create more effective organizational compensation systems as well as indicate targets for interventions to address income inequality.
Open Access
Reducing hiring bias in asynchronous video interviews
(Colorado State University. Libraries, 2022) Benson, Annika, author; Prasad, Josh, advisor; Gardner, Danielle, committee member; Chavez, Ernest, committee member; Trzebiatowski, Tiffany, committee member
Due to COVID 19, many organizations have made the switch to asynchronous video interviews. Current research on video interviewing does not adequately address the potential bias that may arise from using a video platform rather than a face-to-face interview. Online, candidates may inadvertently give off signals that are interpreted as indicators of competence, potentially leading to lower hiring rates of minority interviewees. The current study aims to determine how a hiring manager's perception of warmth and competence of an interviewee, coupled with their Social Dominance Orientation, affects hiring decisions. Experimental stimuli include fictitious Hispanic, Black, and White job applicants who provide video interview responses with manipulations made to impact video quality. Hiring manager perceptions of warmth and competence, along with overall perceptions of hirability, were assessed considering the impacts of candidate race, video quality, and manager Social Dominance Orientation. This work may highlight considerations that should be made to ensure equity in online video interviews.