Abstrakt
In this work we address disparities in ratings of internal and external applicants. We develop model-based inter-rater reliability (IRR) estimate to account for various sources of measurement error, their hierarchical structure and the presence of covariates, such as assessed status, that have the potential to moderate IRR.
Using dataset of ratings of applicants to teaching positions in Spokane district in Washington, USA, we first test for bias in ratings of applicants external to the district, which is shown to be significant even after including various measures of teacher quality in the model. Moreover, with model-based IRR, we show that consistency between raters is significantly lower when rating external applicants.
We further address how IRR affects the predictive power of measurement in different scenarios and conclude the work by discussing policy implications and applications of our model-based IRR estimate for teacher hiring practices.