J. N. Cleveland and K. R. Murphy (1992) suggested that phenomena such as rater errors and interrater disagreements could be understood in terms of differences in the goals pursued by various raters. We measured 19 rating goals of students at the beginning of a semester, grouped them into scales, and correlated these with teacher evaluations collected at the end of the semester. We found significant multiple correlations, both within classes and in an analysis of the pooled sample (adjusting for instructor mean differences, incremental R2 =.08). Measures of rating goals obtained after raters had observed a significant proportion of ratee performance accounted for variance (incremental R2 =.07) not accounted for by measures of goals obtained at the beginning of the semester.

This study examined the negative effect of likely applicant distortion on mean scores and validity of personality measures. The personality test scores and performance ratings of applicants were directly compared to those of incumbents with the same occupation in four different samples. The results showed that applicant mean scores were higher and validity coefficients were lower than for incumbents.

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Although it is commonly assumed that implementing unproctored internet testing (UIT) in employee selection systems can result in increased applicant pool diversity, this assumption has not been explicitly tested. Thus, we analyzed the applicant pool composition of a major U.S.-based manufacturing organization across the span of over 8 years (N = 24,963) using an interrupted time series analytic approach. This allowed us to evaluate changes before and after the implementation of unproctored testing as well as changes following subsequent mobile device blocking. The results of this analysis suggested that although adding a UIT option appeared to increase the size of the applicant pool, the magnitude of this effect did not appear to differ between Black and White applicants. Furthermore, removing the option to apply on a mobile device dampened this general effect, but similarly, there were no differences in the magnitude applicant pool reduction between Black and White applicants. This evidence contradicts the common notion that UITs result in more diverse applicant pools, suggesting UIT's primary value in this regard is increasing access to the application process across groups. Additionally, this study demonstrates the use of interrupted time series analysis as a powerful framework to understand longitudinal effects in real-world employee selection data.