Examining the Origins and Outcomes of Research-Related Emotions in Faculty: Developing the Research Emotions Questionnaire (REQ)
Bibliographic record
Abstract
University faculty experience many emotions that have implications for their research success; however, previous studies on research-related emotions in faculty have consistently employed self-report measures with limited validity, reliability, and scope. The current study aimed to validate the Research Emotions Questionnaire (REQ) among STEM faculty, examine potential differences in emotions by demographic and job-related factors, and test a hypothesized model of emotions as predictors of faculty research success based on Pekrun’s control-value theory (CVT). An online survey was completed by 611 STEM faculty from 10 research-intensive US universities, with the data showing the REQ to be valid and reliable. Women reported more anxiety and disappointment, underrepresented minorities reported more anxiety, and full professors reported more enjoyment and pride, as well as less anxiety and disappointment, compared to junior colleagues. Structural equation modeling results showed perceived control and value appraisals significantly predicted research emotions and, in turn, self-reported research success. Negative binomial regressions revealed enjoyment, boredom, disappointment, and frustration as significant predictors of bibliometric counts of publications and citations. The REQ is an improved tool for understanding faculty research emotions, with implications for developing targeted emotional regulation programs to enhance faculty well-being, success, and job satisfaction, particularly for underrepresented groups.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".