Establishing the validity and reliability of the Project Talent Personality Inventory
Bibliographic record
Abstract
Project Talent is a national longitudinal study that started in 1960. The original sample included over 440,000 students, which amounted to a 5% representative sample of high school students across the United States. Previous research has not yet established the validity and reliability of the personality measure used in this study, that is, the Project Talent Personality Inventory (PTPI). Given the potential interest and use of the PTPI in forthcoming research, the goals of the present paper were to establish (a) the construct and predictive validity and (b) the internal consistency and test-retest reliability of the PTPI. This information will be valuable to researchers who might be interested in using the PTPI to predict life course outcomes, such as mortality, occupational success, relationship success, and health. Study 1 found that the 10 sub-scales of the PTPI showed good internal consistency reliability, as well as good construct and predictive validity. With the use of several modern personality measures, we showed how the 10 PTPI scales can be mapped onto the Big Five personality traits, and we examined their relations with health, well-being, and life satisfaction outcomes. Study 2 found that the 10 PTPI scales showed good test-retest reliability. Together, these findings allow researchers to better understand and use the PTPI scales, as they are available in Project Talent.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".