There is no electronic link in the Inside Higher Education story on the study purporting to refute grading bias against conservative students. Short of fully examining its methodology, it’s difficult to assess how much it actually contributes to the debate about discrimination. Offhand, however, a few points might be worth making.
From what Inside Higher Education tells us, the study doesn’t seem to take the professors’ politics into account, only those of the students. To test for grading bias, it would be interesting to isolate those cases in which the politics of professors and students were especially at variance. (Given the politics of the professorate, these cases are likely to involve professors on the left and students on the right). Moreover, while student grades were tracked throughout their attendance at the University of Nevada, it’s not clear that changes in student political attitudes were also tracked. A conservative freshman going on to major in sociology or women’s studies might no longer be a conservative once he got into the class of a biased grader. It would also be valuable to isolate those classes in which the subject matter was politically salient — even in sociology, after all, there are courses in statistics and research design. Findings are generally reported only where they are statistically significant. If the most “at-risk-for-bias” cases, so to speak, are lumped together with those that are less so, their impact might wash out and the statistical significance of the correlations be lost.
As has already been pointed out by George Leef, students are generally savvy enough to give professors what they want once they know how badly the professor wants it. Moreover, they’re also often savvy enough to avoid sections and instructors from whom they anticipate trouble. Finally, conservatives majoring in sociology, African-American studies, cultural anthropology, and women’s studies (all fields mentioned in the story) who actually stay conservative, may be too few in number to allow for a statistically meaningful analysis of data.