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Student evaluations of teaching (SETs) are widely used to measure teaching quality in higher education and compare it across different courses, teachers, departments and institutions. Indeed, SETs are of increasing importance for teacher promotion decisions, student course selection, as well as for auditing practices demonstrating institutional performance. However, survey response is typically low, rendering these uses unwarranted if students who respond to the evaluation are not randomly selected along observed and unobserved dimensions. This paper is the first to fully quantify this problem by analyzing the direction and size of selection bias resulting from both observed and unobserved characteristics for over 3000 courses taught in a large European university. We find that course evaluations are upward biased, and that correcting for selection bias has nonnegligible effects on the average evaluation score and on the evaluation-based ranking of courses. Moreover, this bias mostly derives from selection on unobserved characteristics, implying that correcting evaluation scores for observed factors such as student grades does not solve the problem. However, we find that adjusting for selection only has small impacts on the measured effects of observables on SETs, validating a large related literature which considers the observable determinants of evaluation scores without correcting for selection bias.
Goos et al. (Fri,) studied this question.