scholarly journals Review of "Small samples, unreasonable generalizations, and outliers: Gender bias in student evaluation of teaching or three unhappy students?"

2020 ◽  
Author(s):  
Philip Stark
Keyword(s):  
Gender Bias ◽  
Student Evaluation ◽  
Small Samples ◽  
Student Evaluation Of Teaching ◽  
Evaluation Of Teaching

scholarly journals Small samples, unreasonable generalizations, and outliers: Gender bias in student evaluation of teaching or three unhappy students?

2021 ◽  
Author(s):  
Bob Uttl ◽  
Victoria C. Violo
Keyword(s):  
Gender Bias ◽  
Student Evaluation ◽  
Real Life ◽  
Small Sample ◽  
Small Samples ◽  
Online Course ◽  
Evaluation Of Teaching ◽  
Opposite Gender ◽  
Male Instructor ◽  
Section Sample

In a widely cited and widely talked about study, MacNell et al. (2015) [1] examined SET ratings of one female and one male instructor, each teaching two sections of the same online course, one section under their true gender and the other section under false/opposite gender. MacNell et al. concluded that students rated perceived female instructors more harshly than perceived male instructors, demonstrating gender bias against perceived female instructors. Boring, Ottoboni, and Stark (2016) [2] re-analyzed MacNell et al.’s data and confirmed their conclusions. However, the design of MacNell et al. study is fundamentally flawed. First, MacNell et al.’ section sample sizes were extremely small, ranging from 8 to 12 students. Second, MacNell et al. included only one female and one male instructor. Third, MacNell et al.’s findings depend on three outliers – three unhappy students (all in perceived female conditions) who gave their instructors the lowest possible ratings on all or nearly all SET items. We re-analyzed MacNell et al.’s data with and without the three outliers. Our analyses showed that the gender bias against perceived female instructors disappeared. Instead, students rated the actual female vs. male instructor higher, regardless of perceived gender. MacNell et al.’s study is a real-life demonstration that conclusions based on extremely small sample-sized studies are unwarranted and uninterpretable.

scholarly journals The validity of student evaluation of teaching: Is there gender bias?

2019 ◽  
Vol 21 (3) ◽  
Author(s):  
Milica Maričić ◽  
Aleksandar Đoković ◽  
Veljko Jeremić
Keyword(s):  
Gender Bias ◽  
Regression Models ◽  
Assessment Tool ◽  
Student Evaluation ◽  
Great Success ◽  
Linear Regression Models ◽  
Student Evaluation Of Teaching ◽  
Independent Variables ◽  
Level Of Satisfaction ◽  
Evaluation Of Teaching

Student evaluation of teaching (SET) has steadily, but surely, become an important assessment tool in higher education. Although SET provides feedback on students level of satisfaction with the course and the lecturer, the validity of its results has been questioned. After extensive studies, the factor which is believed to distort the SET results is gender of the lecturer. In this paper, Potthoff analysis is employed to additionally explore whether there is gender bias in SET. Namely, this analysis has been used with great success to compare linear regression models between groups. Herein, we aimed to model the overall lecturer impression with independent variables related to teaching, communication skills, and grading and compare the models between genders. The obtained results reveal that gender bias exists in certain cases in the observed SET. We believe that our research might provide additional insights on the interesting topic of gender bias in SET.

scholarly journals Small samples, unreasonable generalizations, and outliers: Gender bias in student evaluation of teaching or three unhappy students?

2020 ◽  
Author(s):  
Bob Uttl ◽  
Victoria Violo ◽  
Bob Uttl ◽  
Bob Uttl ◽  
Bob Uttl
Keyword(s):  
Gender Bias ◽  
Student Evaluation ◽  
Real Life ◽  
Small Sample ◽  
Small Samples ◽  
Online Course ◽  
Evaluation Of Teaching ◽  
Opposite Gender ◽  
Male Instructor ◽  
Section Sample

In a widely cited and widely talked about study, MacNell et al. (2015) examined SET ratings of one female and one male instructor, each teaching two sections of the same online course, one section under their true gender and the other section under false/opposite gender. MacNell et al. concluded that students rated perceived female instructors more harshly than perceived male instructors, demonstrating gender bias against perceived female instructors. Boring, Ottoboni, and Stark (2016) re-analyzed MacNell et al.s data and confirmed their conclusions. However, the design of MacNell et al. study is fundamentally flawed. First, MacNell et al. section sample sizes were extremely small, ranging from 8 to 12 students. Second, MacNell et al. included only one female and one male instructor. Third, MacNell et al.s findings depend on three outliers -- three unhappy students (all in perceived female conditions) who gave their instructors the lowest possible ratings on all or nearly all SET items. We re-analyzed MacNell et al.s data with and without the three outliers. Our analyses showed that the gender bias against perceived female instructors disappeared. Instead, students rated the actual female vs. male instructor higher, regardless of perceived gender. MacNell et al.s study is a real-life demonstration that conclusions based on extremely small sample-sized studies are unwarranted and uninterpretable.

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