Careless Responding: Why Many Findings Are Spurious or Spuriously Inflated
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Contrary to long-standing conventional wisdom, failing to exclude data from carelessly responding participants on questionnaires or behavioral tasks will frequently result in false-positive or spuriously inflated findings. Despite prior publications demonstrating this disturbing statistical confound, it continues to be widely underappreciated by most psychologists, including highly experienced journal editors. In this article, we aim to comprehensively explain and demonstrate the severity and widespread prevalence of careless responding’s (CR) inflationary effects in psychological research. We first describe when and why one can expect to observe the inflationary effect of unremoved CR data in a manner accessible to early graduate or advanced undergraduate students. To this end, we provide an online simulator tool and instructional videos for use in classrooms. We then illustrate realistic magnitudes of the severity of unremoved CR data by presenting novel reanalyses of data sets from three high-profile articles: We found that many of their published effects would have been meaningfully, sometimes dramatically, inflated if they had not rigorously screened out CR data. To demonstrate the frequency with which researchers fail to adequately screen for CR, we then conduct a systematic review of CR screening procedures in studies using paid online samples (e.g., MTurk) published across two prominent psychological-science journals. These findings suggest that most researchers either did not conduct any kind of CR screening or conducted only bare minimal screening. To help researchers avoid publishing spuriously inflated findings, we summarize best practices to help mitigate the threats of CR data.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it