Scale Abbreviation with Recursive Feature Elimination and Genetic Algorithms: An Illustration with the Test Emotions Questionnaire
Bibliographic record
Abstract
Psychological scales play a key role in the assessment, screening, and diagnosis of latent variables, such as emotions, mental health, and well-being. In practice, researchers need shorter scales of psychological traits to save administration time and cost. Thus, a variety of optimization algorithms have been proposed to abbreviate lengthy psychological scales into shorter instruments efficiently. The main goal of this application is to form an abbreviated scale with fewer items while maintaining reliability, relationships among the subscales, and model fit for the full scale. In this study, we use an optimization algorithm (genetic algorithm) and a feature selection algorithm (recursive feature elimination) to abbreviate a psychological scale automatically. Although both algorithms search for an optimal subset of features within a large pool of features, the search mechanism underlying each algorithm is quite different. The genetic algorithm employs a systematic but computationally-expensive sampling process to find the optimal features, whereas recursive feature elimination removes the least important features iteratively until a desired number of features are retained. In this study, we use a 77-item measure of test emotions (Test Emotions Questionnaire) to demonstrate how these algorithms can be used for scale abbreviation. We generate a 40-item short form using each algorithm and compare the quality of the selected items against the full-length scale. The results indicate that both methods can provide researchers and practitioners with a systematic procedure for creating psychometrically sound, shorter versions of lengthy psychological instruments.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".