An Elephant in the Room: Bias in Evaluating a Required Quantitative Methods Course
Bibliographic record
Abstract
Undergraduate Political Science programs often require students to take a quantitative research methods course. Such courses are typically among the most poorly rated. This can be due, in part, to the way in which courses are evaluated. Students are generally asked to provide an overall rating, which, in turn, is widely used by students, faculty, and administrators to assess a course. Unfortunately, even questions composed with the best of intentions have the potential to bias the results. In this article, we evaluate the global rating question used at our university and show that it introduces bias into the measure by cuing extraneous considerations. It artificially inflates the number of negative reactions to the course by leading students to think about its required status and their initial level of enthusiasm rather than their level of accomplishment and its value as a learning experience. By locating our results in the course evaluation and framing literature, we suggest an approach to evaluating overall rating questions that can be adapted for use at other institutions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.384 | 0.644 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".