Do Strength‐Related Attitude Properties Determine Susceptibility to Response Effects? New Evidence From Response Latency, Attitude Extremity, and Aggregate Indices
Bibliographic record
Abstract
A great deal of research has shown that small changes in question wording, format, orordering can sometimes substantially alter people's reports of their attitudes. Althoughmany scholars have presumed that these so‐called response effects are likely to be morepronounced when the attitudes being measured are weak, a number of studies have disconfirmedthis notion. This paper presents several new tests of this hypothesis using a variety of measuresand analytic techniques. The findings largely replicated previously documented effects andnon‐effects but also uncovered new effects not previously tested. No single strength‐relatedattitude attribute emerged as a consistent moderator of all response effects. Rather, differentindividual attributes moderated different effects, and a conglomeration of strength‐relateddimensions did not emerge as a reliable moderator. Taken together, these results support theconclusions that different response effects occur as the result of different cognitive processes,and that various strength‐related attitude attributes reflect distinct latent constructs rather than asingle one.
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.
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 | 0.030 | 0.137 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".