<b>Research Commentary</b>—Generalizability of Information Systems Research Using Student Subjects—A Reflection on Our Practices and Recommendations for Future Research
Bibliographic record
Abstract
Information systems researchers, like those in many other disciplines in the social sciences, have debated the value and appropriateness of using students as research subjects. This debate appears in several articles that have been published on the subject as well as in the review process. In this latter arena, however, the debate has become increasingly like a script—the actors (authors and reviewers) simply read their parts of the script; some avoid the underlying issues whereas others cursorily address generalizability without real consideration of those issues. As a result, despite the extent of debate, we seem no closer to a resolution. Authors who use student subjects rely on their scripted arguments to justify the use of student subjects and do not always consider whether those arguments are valid. But reviewers who oppose the use of student subjects are equally culpable. They, too, rely on scripted arguments to criticize work using student subjects, and do not always consider whether those arguments are salient to the particular study. By presenting and reviewing one version of this script in the context of theoretical discussions of generalizability, we hope to demonstrate its limitations so that we can move beyond these scripted arguments into a more meaningful discussion. To do this, we review empirical studies from the period 1990–2010 to examine the extent to which student subjects are being used in the field and to critically assess the discussions within the field about the use of student samples. We conclude by presenting recommendations for authors and reviewers, for determining whether the use of students is appropriate in a particular context, and for presenting and discussing work that uses student subjects.
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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 | 0.299 | 0.653 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.012 | 0.037 |
| Scholarly communication | 0.025 | 0.034 |
| Open science | 0.014 | 0.008 |
| Research integrity | 0.051 | 0.050 |
| Insufficient payload (model declined to judge) | 0.016 | 0.011 |
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".