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    Student evaluations of teaching (mostly) do not measure teaching effectiveness

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        Abstract

        Student evaluations of teaching (SET) are widely used in academic personnel decisions as a measure of teaching effectiveness. We show: SET are biased against female instructors by an amount that is large and statistically significant.The bias affects how students rate even putatively objective aspects of teaching, such as how promptly assignments are graded.The bias varies by discipline and by student gender, among other things.It is not possible to adjust for the bias, because it depends on so many factors.SET are more sensitive to students’ gender bias and grade expectations than they are to teaching effectiveness.Gender biases can be large enough to cause more effective instructors to get lower SET than less effective instructors.These findings are based on nonparametric statistical tests applied to two datasets: 23,001 SET of 379 instructors by 4,423 students in six mandatory first-year courses in a five-year natural experiment at a French university, and 43 SET for four sections of an online course in a randomized, controlled, blind experiment at a US university.

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        On the Application of Probability Theory to Agricultural Experiments. Essay on Principles. Section 9

         T Speed,  D. Dabrowska,  Jerzy Splawa-Neyman (1990)
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          Does Professor Quality Matter? Evidence from Random Assignment of Students to Professors

           Scott E. Carrell,  James West (2010)
          Article 0 comments Cited 27 times – based on 0 reviews     Review now
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            Beauty in the classroom: instructors’ pulchritude and putative pedagogical productivity

             Daniel Hamermesh,  Amy L. Parker (2005)
            Article 0 comments Cited 27 times – based on 0 reviews     Review now
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              Author and article information

              Affiliations
              [1 ]OFCE, SciencesPo, Paris, France
              [2 ]PSL, Université Paris-Dauphine, LEDa, UMR DIAL, Paris, France
              [3 ]Department of Statistics, University of California, Berkeley, CA, USA
              Author notes
              [* ]Corresponding author's e-mail address: pbstark@ 123456berkeley.edu
              Contributors
              Anne Boring: (View ORCID Profile)
              Kellie Ottoboni: (View ORCID Profile)
              Philip B. Stark: (View ORCID Profile)
              Journal
              Journal ID (publisher-id): SOR-EDU
              Title: ScienceOpen Research
              Publisher: ScienceOpen
              ISSN (Electronic): 2199-1006
              Publication date 07 January 2016
              Volume: 0 (ID: 818d8ec0-5908-47d8-86b4-5dc38f04b23e )
              Issue: 0
              Pages: 1-11
              Publisher ID: 3507:XE
              DOI: 10.14293/S2199-1006.1.SOR-EDU.AETBZC.v1
              Copyright statement: © 2016 Boring et al.

              License:

              This work has been published open access under Creative Commons Attribution License CC BY 4.0 , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at www.scienceopen.com .

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              Figures: 0, Tables: 11, References: 23, Pages: 11
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              Subject: Original article

              Link to data:

              ScienceOpen disciplines: Labor law, Assessment, Evaluation & Research methods

              Keywords: disparate impact, nonparametric statistics, gender bias, permutation tests

              Comments

              wrote:

              I have some comments:

              1. Use confidence intervals, not p values (cf. 'New Statistics'). These can be obtained by bootstrapping. If you really want p values, then report both.
              2. Put the effect sizes with confidence intervals in the abstract.
              3. Alter the empahsis to be more about the lack of correlation between SET and teacher ability. The focus on gender bias is not warranted with the small effect. It is clearly not "large" as claimed. mean r = 0.09 ≈ d 0.20, which by Cohen's standards is small. There is too much social justice warrior about this article.
              4. It is possible to adjust for the gender bias by simply adding a little to the females SET multiplied by the proportion of male students.
              5. Note which gender was the biased one in the abstract (male against females, not female against males, cf. Table 5).
              6. A simpler recommendation is that SETs should not be used at all since they apparently do not correlate with actual performance. Why have them? This side-steps the entire gender bias issue.
              7. Add to the abstract that the gender effect could not be explained by male instructors being better (i.e. findings from Table 4).
              8. The technical aspect of the analyses seemed fine to me.

              Hope these comments are useful. Overall, I liked the article.

              2016-03-22 12:29 UTC
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