Factorial structure validation of the Entrepreneurship Qualities Questionnaire 2.0
Bibliographic record
Abstract
Entrepreneurs are more than ever very important economic actors in all societies.In many countries such as Canada, governments are mobilizing school curricula to include activities aimed to develop entrepreneurship competencies in high school students.However, there is no clear data on the actual level of these competencies among high school students and adults as well (Yergeau & Gingras, 2023).Accordingly, there is also few instruments intended to evaluate dimensions of entrepreneurship.This study examines the factorial solution of a modified version of an open online instrument aiming at measuring entrepreneurship qualities.The original Entrepreneurship Qualities Questionnaire (EQQ, L'Heureux et al., 2000) contains 59 items grouped in 6 continuous scales (Commitment, Motivation, Result-oriented, Creativity, Self-competition, Leadership) and a Total score.The EQQ 2.0 is an updated version based on previous work showing some items factor loadings were problematic in the original factorial solution (Yergeau, Busque-Carrier, Gingras & Lépine, 2023).A sample of 5527 high school students and n=5309 adults from the province of Québec have answered the EQQ between 2013 and 2023.An exploratory structural equation modeling within confirmatory factor analysis (EwC) was used to assess the second-order factor structure used by the EQQ 2.0.EwC mostly replicated the novel four first-order and one second-order factor structure.Results were validated with a subsample.Overall, these findings support the utility of the EQQ 2.0 to assess entrepreneurship qualities.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".