Leniency of Raters across Time-Points

Hung and Wang (2012) report that 238 workers from were assessed on four occasions by five managers according to five job criteria (thoroughness, creativity, complexity, structure, and accuracy) along a 5-point rating scale.

WinBUGS was used to model the changes in rater leniency/severity. In the Paper's Figure 2, reproduced here, we see that the raters did not follow a predictable pattern across time. Always the most severe was Rater 2. If we assume that the true distribution of the workers are the same in each Department, then workers rated by Rater 2 are disadvantaged. Their ratings would always be lower than workers in the other Departments. This Paper supports the proposition that performance ratings must be adjusted for the severity of the raters.

Hung, L-F, Wang, W-C (2012). The Generalized Multilevel Facets Model for Longitudinal Data. Journal of Educational and Behavioral Statistics, 37, 2, 231-255.

Figure 2. Mean severity for the five raters across time points in Hung & Wang (2012).

Adjusting for Rater Leniency

"Figure 3, which plots the average number of stars awarded (Y-axis) as function of the Rasch product parameters (X-axis). It can be seen that the relation between these two variables is decidedly non-linear because raw differences near the top underestimate the true differences, thereby again calling into question the use of raw scores (presently, the number of stars) as quantitative indices."

Lange R., Lange X. (2012) Quality Control in Crowdsourcing. AAAI Spring Symposium Series, 2012

Leniency of Raters across Time-Points, Hung, L-F, Wang, W-C, Rasch Measurement Transactions, 2012, 26:1, 1358

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