Straightlining in Web survey panels over time
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
Straightlining, an indicator of satisficing, refers to giving the same answer in a series of questions arranged on a grid. We investigated whether straightlining changes with respondents’ panel experience in two open-access Internet panels in the Netherlands: the LISS and Dutch Immigrant panels. Specifically, we considered straightlining on 10 grid questions in LISS core modules (7 waves) and on a grid of evaluation questions in both the LISS panel (150+ waves) and the Dutch immigrant panel (50+ waves). For both core modules and evaluation questions we found that straightlining increases with respondents’ panel experience for at least three years. Straightlining is also associated with younger age and non-western 1st generation immigrants. Where straightlining was a plausible set of answers, prevalence of straightlining was much larger (15-40%) than where straightlining was implausible (
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 | 0.099 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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