Exploring the Validity of the Work Preferences Questionnaire
Bibliographic record
Abstract
Abstract: This study aimed was to develop and validate a new instrument called the Work Preferences Questionnaire (WPQ) to measure individuals’ preferences for work characteristics that are relevant to today’s work environment. A multi-step approach and two samples were used to develop and validate the WPQ across various industries. In the first study, a group of experts developed a bank of items that were then administered to 984 workers. Exploratory factor analysis revealed a nine-factor structure with good internal consistency. In the second study, an independent sample of 687 workers was used to confirm the factorial structure and highlight the distinctness of the work preference dimensions. The study found small mean differences in interindividual preference scores based on age and confirmed data equivalence between gender and education level. The WPQ addresses limitations of existing measures by focusing on a narrow set of work preferences that are highly relevant in the current work environment and includes modern aspects such as teleworking and work-life balance. The WPQ promises to be an effective tool for research and human resources practices, enabling individuals and organizations to better understand work preferences and make informed decisions about work design and personnel selection.
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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 | 0.014 | 0.036 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".