GENERATING BIODATA FROM SCRATCH: CREATING AND VALIDATING A COMPREHENSIVE COLLECTION OF BIODATA ITEMS AND SCALES

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Valid pre-employment tests facilitate hiring qualified employees who perform better at work. Biodata inventories are a type of pre-hire assessment that uses questions about life history and past experiences to effectively evaluate job applicants across a wide range of constructs. Research consistently supports their efficacy in hiring, and yet biodata implementation is hindered by the proprietary nature and restricted availability of biodata questions, alongside insufficient data on individual item and scale properties across work settings. To overcome these challenges, this project leverages natural language processing (NLP) to build the most comprehensive, publicly accessible biodata repository. Paired with a linked work analysis, the repository houses thousands of items across dozens of constructs, including their psychometric properties. This project, therefore, helps democratize biodata and empower users to create tailored biodata scales within organizations, as well as provide rigorous tests of the efficacy of different types of biodata inventories. Moreover, this project will explore biodata’s potential for reducing adverse impact and test bias, addressing a concern in personnel selection.This project begins by crafting prototypical items to assess dozens of work-related constructs. This initial item pool is being expanded by using automated item generation via NLP, paired with researcher refinement and content validity judgments. Data are being gathered for the biodata content, along with work-related dependent variables. Empirical, rational, and hybrid scoring keys are being developed, and a job analysis tool as well to help users identify optimal biodata content and to estimate validity based on the job analysis. Finally, all content are examined for potential group differences to determine susceptibility to adverse impact or statistical test bias. In sum, this research establishes a comprehensive biodata repository paired with an empirical database of reliability, validity, and adverse impact information for biodata scales and items, thus leading to improved hiring methods and expanding biodata knowledge and expertise.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.