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Record W1986752580 · doi:10.1207/s15327752jpa7803_06

Response Styles in Affect Ratings: Making a Mountain Out of a Molehill

2002· article· en· W1986752580 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Personality Assessment · 2002
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsAurora CollegeUniversity of Toronto
Fundersnot available
KeywordsAffect (linguistics)PsychologyDiscriminant validityTest validityResponse biasDiscriminantSocial psychologyLinear discriminant analysisPsychometricsCognitive psychologyDevelopmental psychologyStatisticsCommunicationArtificial intelligenceMathematics

Abstract

fetched live from OpenAlex

Ratings of affect words are the most commonly used method to assess pleasant affect (PA) and unpleasant affect (UA). The reliance on self-reports would be problematic if affect ratings were heavily influenced by response styles. Several recent publications have indeed suggested (a) that the influence of response styles on affect ratings is pervasive, (b) that this influence can be controlled by variations of the response format using multitrait-multimethod models, and (c) the discriminant validity of PA and UA is spurious. In this article, we examined the evidence for these claims. We demonstrate that (a) response styles have a negligible effect on affect ratings, (b) multiple response formats produce the same results as a single response format, and (c) the discriminant validity of PA and UA is not a method artifact. Rather, evidence against discriminant validity is due to the use of inappropriate response formats that respondents interpreted as bipolar scales.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.181
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.137
GPT teacher head0.484
Teacher spread0.347 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it