The Impact of Job Complexity and Performance Measurement on the Temporal Consistency, Stability, and Test–Retest Reliability of Employee Job Performance Ratings

School of Hotel Administration, Cornell University, Ithaca, NY 14853, USA.
Journal of Applied Psychology (Impact Factor: 4.31). 04/2005; 90(2):269-83. DOI: 10.1037/0021-9010.90.2.269
Source: PubMed


Although research has shown that individual job performance changes over time, the extent of such changes is unknown. In this article, the authors define and distinguish between the concepts of temporal consistency, stability, and test-retest reliability when considering individual job performance ratings over time. Furthermore, the authors examine measurement type (i.e., subjective and objective measures) and job complexity in relation to temporal consistency, stability, and test-retest reliability. On the basis of meta-analytic results, the authors found that the test-retest reliability of these ratings ranged from .83 for subjective measures in low-complexity jobs to .50 for objective measures in high-complexity jobs. The stability of these ratings over a 1-year time lag ranged from .85 to .67. The analyses also reveal that correlations between performance measures decreased as the time interval between performance measurements increased, but the estimates approached values greater than zero.

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    • "Thus the independent variables temporally preceded the ratings. The correlation between a person's rating in year t and in t – 1 was above .60 in both firms, which is consistent with prior work on performance ratings in high-complexity jobs (Sturman, Cheramie, and Cashen, 2005). Thus, consistent with prior studies, the ratings were relatively stable over time but also showed some non-trivial temporal variation, making it possible to estimate fixed-effects models. "
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    Administrative Science Quarterly 11/2014; 59(4):639-668. DOI:10.1177/0001839214549042 · 4.21 Impact Factor
    • "Subjective measures often correct for determinants of performance outside the control of the individual (Campbell et al., 1993) and can encompass a wide variety of behaviors and outputs relevant to the job (Medoff and Abraham, 1981). Meta-analyses have shown high test-retest reliability in performance evaluations (Sturman, Cheramie, and Cashen, 2005), and supervisory ratings are among the most common dependent variables in studies of performance (Sturman, 2003). Compensation. "
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    Administrative Science Quarterly 02/2011; 56(3):369-407. DOI:10.1177/0001839211433562 · 4.21 Impact Factor
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    • "Similarly, interview methodologies tend to give lower validities for more complex jobs, despite the fact that interview results typically have moderate to high correlations with general mental ability (Berry, Sackett, & Landers, 2007; Huffcutt, Conway, Roth, & Klehe, 2004). These findings raise doubts regarding the moderating effect of Data, People, & Things job complexity on general mental ability validity coefficients (e.g., Bertua et al., 2005; Wilk & Sackett, 1996; Sturman, Cheramie, & Cashen, 2005). One of the most problematic issues is that complexity is an aggregated estimate of a number of job characteristics. "
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    ABSTRACT: 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.
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