Are Attention Check Questions a Threat to Scale Validity?
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it