Gender biases in student evaluations of teaching
TL;DR: This article used data from a French university to analyze gender biases in student evaluations of teaching (SETs) and found that male students express a bias in favor of male professors, despite the fact that students appear to learn as much from women as from men.
About: This article is published in Journal of Public Economics.The article was published on 2017-01-01. It has received 296 citations till now.
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TL;DR: This paper showed that student evaluations of teaching (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.
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.
316 citations
Cites background or methods or result from "Gender biases in student evaluation..."
...Boring [5] finds that SET are affected by...
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...We used permutation tests to examine data collected by Boring [5] and MacNell et al....
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...Here, we apply nonparametric permutation tests to data from Boring [5] and MacNell et al....
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...These permutation tests confirm the results found by Boring [5]....
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TL;DR: It is found that women-led work tends to be undercited relative to expectations and this imbalance is driven largely by the citation practices of men and is increasing over time as the field diversifies.
Abstract: Similarly to many scientific disciplines, neuroscience has increasingly attempted to confront pervasive gender imbalances. Although publishing and conference participation are often highlighted, recent research has called attention to the prevalence of gender imbalance in citations. Because of the downstream effects of citations on visibility and career advancement, understanding the role of gender in citation practices is vital for addressing scientific inequity. Here, we investigate whether gendered patterns are present in neuroscience citations. Using data from five top neuroscience journals, we find that reference lists tend to include more papers with men as first and last author than would be expected if gender were unrelated to referencing. Importantly, we show that this imbalance is driven largely by the citation practices of men and is increasing over time as the field diversifies. We assess and discuss possible mechanisms and consider how researchers might approach these issues in their own work.
299 citations
TL;DR: This paper explored the relationship between gender and teaching evaluations by using both content analysis in student-evaluation comments and quantitative analysis of students' ordinal scoring of their instructors, finding that the language students use in evaluations regarding male professors is significantly different than language used in evaluating female professors.
Abstract: Many universities use student evaluations of teachers (SETs) as part of consideration for tenure, compensation, and other employment decisions. However, in doing so, they may be engaging in discriminatory practices against female academics. This study further explores the relationship between gender and SETs described by MacNell, Driscoll, and Hunt (2015) by using both content analysis in student-evaluation comments and quantitative analysis of students’ ordinal scoring of their instructors. The authors show that the language students use in evaluations regarding male professors is significantly different than language used in evaluating female professors. They also show that a male instructor administering an identical online course as a female instructor receives higher ordinal scores in teaching evaluations, even when questions are not instructor-specific. Findings suggest that the relationship between gender and teaching evaluations may indicate that the use of evaluations in employment decisions is discriminatory against women.
211 citations
TL;DR: It is found that reference lists tend to include more papers with men as first and last author than would be expected if gender were not a factor in referencing, and this overcitation of men and undercitation of women is driven largely by the citation practices of men, and is increasing over time as the field becomes more diverse.
Abstract: Like many scientific disciplines, neuroscience has increasingly attempted to confront pervasive gender imbalances within the field. While much of the conversation has centered around publishing and conference participation, recent research in other fields has called attention to the prevalence of gender bias in citation practices. Because of the downstream effects that citations can have on visibility and career advancement, understanding and eliminating gender bias in citation practices is vital for addressing inequity in a scientific community. In this study, we sought to determine whether there is evidence of gender bias in the citation practices of neuroscientists. Using data from five top neuroscience journals, we find that reference lists tend to include more papers with men as first and last author than would be expected if gender were not a factor in referencing. Importantly, we show that this overcitation of men and undercitation of women is driven largely by the citation practices of men, and is increasing over time as the field becomes more diverse. We develop a co-authorship network to assess homophily in researchers9 social networks, and we find that men tend to overcite men even when their social networks are representative. We discuss possible mechanisms and consider how individual researchers might address these findings in their own practices.
177 citations
Cites background from "Gender biases in student evaluation..."
...JAMA, 284(9):1085–1092, September 2000....
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...Proceedings of the National Academy of Sciences, 117(9):4609–4616, March 2020....
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TL;DR: This paper found that women receive systematically lower teaching evaluations than their male colleagues, and that the bias is driven by male students' evaluations, is larger for mathematical courses and particularly pronounced for junior women.
Abstract: This paper provides new evidence on gender bias in teaching evaluations. We exploit a quasi-experimental dataset of 19,952 student evaluations of university faculty in a context where students are randomly allocated to female or male instructors. Despite the fact that neither students’ grades nor self-study hours are affected by the instructor’s gender, we find that women receive systematically lower teaching evaluations than their male colleagues. This bias is driven by male students’ evaluations, is larger for mathematical courses and particularly pronounced for junior women. The gender bias in teaching evaluations we document may have direct as well as indirect effects on the career progression of women by affecting junior women’s confidence and through the reallocation of instructor resources away from research and towards teaching.
169 citations
References
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TL;DR: Evidence from varied research paradigms substantiates that consequences of perceived incongruity between the female gender role and leadership roles are more difficult for women to become leaders and to achieve success in leadership roles.
Abstract: A role congruity theory of prejudice toward female leaders proposes that perceived incongruity between the female gender role and leadership roles leads to 2 forms of prejudice: (a) perceiving women less favorably than men as potential occupants of leadership roles and (b) evaluating behavior that fulfills the prescriptions of a leader role less favorably when it is enacted by a woman. One consequence is that attitudes are less positive toward female than male leaders and potential leaders. Other consequences are that it is more difficult for women to become leaders and to achieve success in leadership roles. Evidence from varied research paradigms substantiates that these consequences occur, especially in situations that heighten perceptions of incongruity between the female gender role and leadership roles.
4,947 citations
"Gender biases in student evaluation..." refers result in this paper
...The results are consistent with role congruity theory (Eagly and Karau, 2002): students may expect women to behave according to female gender stereotypes and men according to male gender stereotypes, while also evaluating overall teaching competence according to the characteristics of the…...
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TL;DR: In this paper, the authors consider how identity, a person's sense of self, affects economic outcomes and incorporate the psychology and sociology of identity into an economic model of behavior, and construct a simple game-theoretic model showing how identity can affect individual interactions.
Abstract: This paper considers how identity, a person's sense of self, affects economic outcomes. We incorporate the psychology and sociology of identity into an economic model of behavior. In the utility function we propose, identity is associated with different social categories and how people in these categories should behave. We then construct a simple game-theoretic model showing how identity can affect individual interactions. The paper adapts these models to gender discrimination in the workplace, the economics of poverty and social exclusion, and the household division of labor. In each case, the inclusion of identity substantively changes conclusions of previous economic analysis.
4,825 citations
Additional excerpts
...First, in line with the identity economics literature [Akerlof and Kranton, 2000], a “role model” effect (e....
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...However, an experiment by Arbuckle and Williams [2003] suggests that students spontaneously rate (young) male teachers higher than female teachers controlling for a same level of teacher enthusiasm....
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...First, in line with the identity economics literature [Akerlof and Kranton, 2000], a “role model” effect (e.g. Canes and Rosen [1995]; Bettinger and Long [2005]; Dee [2005]; Hoffmann and Oreopoulos [2009]; Carrell and West [2010]) can partly explain how students evaluate their teachers....
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Book•
10 Sep 2014
TL;DR: In this article, the authors present a brief tutorial for estimating, testing, fit, and interpretation of ordinal and binary outcomes using Stata. But they do not discuss how to apply these models to other estimation commands, such as post-estimation analysis.
Abstract: Preface PART I GENERAL INFORMATION Introduction What is this book about? Which models are considered? Whom is this book for? How is the book organized? What software do you need? Where can I learn more about the models? Introduction to Stata The Stata interface Abbreviations How to get help The working directory Stata file types Saving output to log files Using and saving datasets Size limitations on datasets Do-files Using Stata for serious data analysis Syntax of Stata commands Managing data Creating new variables Labeling variables and values Global and local macros Graphics A brief tutorial Estimation, Testing, Fit, and Interpretation Estimation Postestimation analysis Testing estat command Measures of fit Interpretation Confidence intervals for prediction Next steps PART II MODELS FOR SPECIFIC KINDS OF OUTCOMES Models for Binary Outcomes The statistical model Estimation using logit and probit Hypothesis testing with test and lrtest Residuals and influence using predict Measuring fit Interpretation using predicted values Interpretation using odds ratios with listcoef Other commands for binary outcomes Models for Ordinal Outcomes The statistical model Estimation using ologit and oprobit Hypothesis testing with test and lrtest Scalar measures of fit using fitstat Converting to a different parameterization The parallel regression assumption Residuals and outliers using predict Interpretation Less common models for ordinal outcomes Models for Nominal Outcomes with Case-Specific Data The multinomial logit model Estimation using mlogit Hypothesis testing of coefficients Independence of irrelevant alternatives Measures of fit Interpretation Multinomial probit model with IIA Stereotype logistic regression Models for Nominal Outcomes with Alternative-Specific Data Alternative-specific data organization The conditional logit model Alternative-specific multinomial probit The sturctural covariance matrix Rank-ordered logistic regression Conclusions Models for Count Outcomes The Poisson distribution The Poisson regression model The negative binomial regression model Models for truncated counts The hurdle regression model Zero-inflated count models Comparisons among count models Using countfit to compare count models More Topics Ordinal and nominal independent variables Interactions Nonlinear models Using praccum and forvalues to plot predictions Extending SPost to other estimation commands Using Stata more efficiently Conclusions Appendix A Syntax for SPost Commands Appendix B Description of Datasets References Author Index Subject Index
4,703 citations
Book•
15 Nov 2005
TL;DR: This book discusses models for ordinal and nominal independent variables, and describes the development of models for Nominal Outcomes with Case-Specific Data and its use in Stata.
Abstract: Regression Models for Categorical Dependent Variables Using Stata, 2nd Edition, by J. Scott Long and Jeremy Freese, shows how to fit and interpret regression models for categorical data with Stata. Nearly 50% longer than the previous edition, the book covers new topics for fitting and interpretating models included in Stata 9, such as multinomial probit models, the stereotype logistic model, and zero-truncated count models. Many of the interpretation techniques have been updated to include interval as well as point estimates.
4,002 citations
Posted Content•
TL;DR: The theory of racial and sexual discrimination in the labor market was first introduced by Arrow as mentioned in this paper, who introduced the Inflation Policy and Unemployment Theory (INPT) and introduced the first formalization of the theory in terms of exact statistical models.
Abstract: My recent book, Inflation Policy and Unemployment Theory, introduces what is called the statistical theory of racial (and sexual) discrimination in the labor market.' The theory fell naturally out of the non-Walrasian treatment there of the labor "market" as operating imperfectly because of the scarcity of information about the existence and characteristics of workers and jobs. A paradigm for the theory is the traveller in a strange town faced with choosing between dinner at the hotel and dinner somewhere in the town. If he makes it a rule to dine outside the hotel without any prior investigation, he is said to be discriminating against the hotel. Though there will be instances where the hotel cuisine would have been preferable, the rule represents rational behavior it maximizes expected utilityif the cost of acquiring evaluations of restaurants is sufficiently high and if the hotel restaurant is believed to be inferior at least half the time. In the same way, the employer who seeks to maximize expected profit will discriminate against blacks or women if he believes them to be less qualified, reliable, long-term, etc. on the average than whites and men, respectively, and if the cost of gaining information about the individual applicants is excessive. Skin color or sex is taken as a proxy for relevant data not sampled. The a priori belief in the probable preferability of a white or a male over a black or female candidate who is not known to differ in other respects might stem from the employer's previous statistical experience with the two groups (members from the less favored groups might have been, and continue to be, hired at less favorable terms); or it might stem from prevailing sociological beliefs that blacks and women grow up disadvantaged due to racial hostility or at least prejudices toward them in the society (in which latter case the discrimination is self-perpetuating). The theory is applicable to the class of "liberal" employers and workers who have no distaste for hiring and working alongside black or female workers. By contrast, the theory of discrimination originated by Gary Becker is based on the factor of racial taste. The pioneering work of Gunnar Myrdal et al. also appears to center on racial (and, in an appendix, sexual) antagonism. Some indications of interest in the new theory, and the independent discovery of the same statistical theoryby Kenneth Arrow, convince me that it is time for a formalization of the theory in terms of an exact statistical model. Though what follows is very simple, it may be useful to those who like exact models and it may stimulate others to develop the theory further. An employer samples from a population of job applicants. The employer is able to measure the performance of each applicant in some kind of test, yi, which, after suitable scaling, may be said to measure the applicant's promise or degree of qualification, qi, plus an error term, ps.
3,203 citations
"Gender biases in student evaluation..." refers result in this paper
...…in experimental settings, in which the researchers were able to control for teaching styles (Arbuckle and Williams, 2003, and MacNell et al., 2014).1 The fact that gender stereotypes may be driving students’ ratings is consistent with statistical discrimination theory (Arrow, 1973; Phelps, 1972)....
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