The jingle and jangle of emotion assessment: Imprecise measurement, casual scale usage, and conceptual fuzziness in emotion research.
Bibliographic record
Abstract
Although affective science has seen an explosion of interest in measuring subjectively experienced distinct emotional states, most existing self-report measures tap broad affect dimensions and dispositional emotional tendencies, rather than momentary distinct emotions. This raises the question of how emotion researchers are measuring momentary distinct emotions in their studies. To address this question, we reviewed the self-report measurement practices regularly used for the purpose of assessing momentary distinct emotions, by coding these practices as observed in a representative sample of articles published in Emotion from 2001-2011 (n = 467 articles; 751 studies; 356 measurement instances). This quantitative review produced several noteworthy findings. First, researchers assess many purportedly distinct emotions (n = 65), a number that differs substantially from previously developed emotion taxonomies. Second, researchers frequently use scales that were not systematically developed, and that include items also used to measure at least 1 other emotion on a separate scale in a separate study. Third, the majority of scales used include only a single item, and had unknown reliability. Together, these tactics may create ambiguity regarding which emotions are being measured in empirical studies, and conceptual inconsistency among measures of purportedly identical emotions across studies. We discuss the implications of these problematic practices, and conclude with recommendations for how the field might improve the way it measures emotions. (PsycINFO Database Record
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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 | 0.378 | 0.632 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.003 | 0.027 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.001 | 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".