Careless Responding: Why Many Findings Are Spurious or Spuriously Inflated
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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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 | 0.467 | 0.864 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.003 | 0.019 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.006 |
| 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".