Non-Response Bias

Berg, Nathan

doi:10.1016/b0-12-369398-5/00038-4

Cited by 103 publications

(67 citation statements)

References 8 publications

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“…In the statistics literature on missing data, this is known as the problem of not-at-random missing data or non-ignorable non-response (Allison 2001;Rubin and Little 1987): see Berg (2005) for an overview of non-response bias. For SETs this particular problem arises when we observe evaluations only for a subset of students who are not randomly selected on observables such as grade, gender, and course size; and/or conditional on these observables, indicating selection on unobservable factors such as intrinsic motivation or opinions on the anonymity of SETs.…”

Section: A Selection Model For Setsmentioning

confidence: 99%

Measuring teaching quality in higher education: assessing selection bias in course evaluations

2016

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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.

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Section: A Selection Model For Setsmentioning

confidence: 99%

Measuring teaching quality in higher education: assessing selection bias in course evaluations

2016

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“…Previous work emphasizes the importance of jointly accounting for misreporting and non-response when using data with self-reported sexual orientation (Berg 2005;Berg and Lien 2006). This appendix provides additional detail on how the data were cleaned and how item non-response correlates with important demographic characteristics.…”

Section: Appendix Bmentioning

confidence: 99%

Sexual orientation and self-reported lying

Berg

Lien

2008

Rev Econ Household

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“…Whether or not the non-response is problematic depends on the extent to which the overall results would have substantially changed had the non-respondents actually responded, which is labeled as 'non-response bias' (Creswell, 2009). When non-response bias is severe, it is not possible to generalize the results of the study to the non-respondents and the population (Berg, 2005). The next question is: 'how to determine the extent to which non-response bias is severe?'…”

Section: Non-response Biasmentioning

confidence: 99%

Cost-benefit analysis in practice

2018

Cost-Benefit Analysis and the Environment

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Non-Response Bias

Cited by 103 publications

References 8 publications

Measuring teaching quality in higher education: assessing selection bias in course evaluations

Measuring teaching quality in higher education: assessing selection bias in course evaluations

Sexual orientation and self-reported lying

Cost-benefit analysis in practice

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