Are Attention Check Questions a Threat to Scale Validity?
Bibliographic record
Abstract
Attention checks have become increasingly popular in survey research as a means to filter out careless respondents. Despite their widespread use, little research has empirically tested the impact of attention checks on scale validity. In fact, because attention checks can induce a more deliberative mindset in survey respondents, they may change the way respondents answer survey questions, posing a threat to scale validity. In two studies, we tested this hypothesis ( N = 816). We examined whether common attention checks—instructed‐response items (Study 1) and an instructional manipulation check (Study 2)—impact responses to a well‐validated management scale. Results showed no evidence that they affect scale validity, both in reported scale means and tests of measurement invariance. These findings allow researchers to justify the use of attention checks without compromising scale validity and encourage future research to examine other survey characteristic‐respondent dynamics to advance our use of survey methods.
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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 | 0.561 | 0.834 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| 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".