USING A META‐ANALYTIC PERSPECTIVE TO ENHANCE JOB COMPONENT VALIDATION
基于元分析加权最小二乘法开发合成效度估计，用于工作成分效度验证，发现该方法比传统方法更稳定，且工作特征数据能调节效度系数。
This paper develops synthetic validity estimates based on a meta‐analytic‐weighted least squares (WLS) approach to job component validity (JCV), using position analysis questionnaire (PAQ) estimates of job characteristics, and the Data, People, & Things ratings from the Dictionary of Occupational Titles as indices of job complexity. For the general aptitude test battery database of 40,487 employees, nine validity coefficients were estimated for 192 positions. The predicted validities from the WLS approach had lower estimated variability than would be obtained from either the classic JCV approach or local criterion‐related validity studies. Data, People, & Things summary ratings did not consistently moderate validity coefficients, whereas the PAQ data did moderate validity coefficients. In sum, these results suggest that synthetic validity procedures should incorporate a WLS regression approach. Moreover, researchers should consider a comprehensive set of job characteristics when considering job complexity rather than a single aggregated index.