Looking Our Limitations in the Eye: A Call for More Thorough and Honest Reporting of Study Limitations
Bibliographic record
Abstract
The replication crisis and subsequent credibility revolution in psychology have highlighted many suboptimal research practices such as p-hacking, overgeneralizing, and a lack of transparency. These practices may have been employed reflexively but upon reflection, they are hard to defend. We suggest that current practices for reporting and discussing study limitations are another example of an area where there is much room for improvement. In this article, we call for more rigorous reporting of study limitations in social and personality psychology articles, and we offer advice for how to do this. We recommend that authors consider what the best argument is against their conclusions (which we call the “steel-person principle”). We consider limitations as threats to construct, internal, external, and statistical conclusion validity (Shadish et al., 2002), and offer some examples for better practice reporting of common study limitations. Our advice has its own limitations — both our representation of current practices and our recommendations are largely based on our own metaresearch and opinions. Nevertheless, we hope that we can prompt researchers to write more deeply and clearly about the limitations of their research, and to hold each other to higher standards when reviewing each other's work.
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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 | 0.831 | 0.946 |
| Meta-epidemiology (narrow) | 0.005 | 0.008 |
| Meta-epidemiology (broad) | 0.015 | 0.013 |
| Bibliometrics | 0.018 | 0.014 |
| Science and technology studies | 0.011 | 0.041 |
| Scholarly communication | 0.035 | 0.061 |
| Open science | 0.016 | 0.018 |
| Research integrity | 0.035 | 0.074 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".