Meta-Analysis of Biodata in Employment Settings: Providing Clarity to Criterion and Construct-Related Validity Estimates

• 成果类型: Article
• 署名作者: Speer, Andrew B.; Tenbrink, Andrew P.; Wegmeyer, Lauren J.; Sendra, Caitlynn C.; Shihadeh, Mike; Kaur, Sugandhjot
• 署名单位: Wayne State University
• 刊物名称: JOURNAL OF APPLIED PSYCHOLOGY
• ISSN/ISSBN: 0021-9010
• DOI: 10.1037/apl0000964
• 发表日期: 2022
• 页码: 1678-1705
• 关键词: biodata; application blanks; biographical information; employee selection; Meta-analysis

摘要

Although biodata inventories have long been used to hire job applicants, there are limitations to current biodata knowledge and little in the way of contemporary biodata meta-analytic reviews. This study establishes a precise understanding of biodata validity by conducting an updated meta-analysis that differentiates biodata validity in terms of two important defining features: construct domain and scoring method (rational, hybrid, empirical). Evidence was established in terms of criterion-related validity with job performance and additional work outcomes, as well as convergent validity with common external hiring measures. In total, 180 independent samples of criterion correlations were examined, and 63 samples were analyzed containing correlations with convergent measures. Findings across the meta-analyses revealed that biodata inventories are one of the most predictive assessment methods available, but that the relationship with work outcomes differs by construct domain and scoring method. Empirically scored overall composite scales had superior criterion-related validity (rho = .44) to rationally scored composite scales (rho = .24). Scales developed to measure conscientiousness and leadership were generally the most predictive of the job performance of the narrow construct domains, and particularly when empirically keyed. However, when biodata scores were correlated with theoretically aligned performance ratings, rational scoring resulted in similar validity coefficients as empirical scoring. Finally, biodata scales exhibited expected patterns of correlations with external measures and were only modestly correlated with cognitive ability and five-factor model personality scores. Taken together, biodata inventories are highly predictive assessment methods and are likely to provide unique variance over other common predictors.