Development and Validation of a Short-Form Inventory to Identify Personality Types: The Personality Identity Estimator (PIE)
Bibliographic record
Abstract
The use of personality inventories has been limited because of their cost and the length. To overcome these limitations, this study created the Personality Identity Estimator (PIE), an easy-to-use inventory to estimate personality types that can be used at no cost. PIE is a categorical inventory containing 12 items with 3 items for each of the 4 personality-type dimensions in Jungian theory. A sample of 1,104 was used to create PIE. Validity was established through multivariate analyses using data from the administration of the Myers-Briggs Type Indicator to 553 of the sample. Reliability was established by test-retest. Consequently, PIE is a new short-form instrument to estimate personality type following Jung’s concept of personality types. PIE can be completed quickly, is easy to score and interpret, and can be used for self-assessment of personality types. PIE can be used to address individual differences, to increase self-awareness, and as an interactive instructional tool. Thus, PIE is a new valid and reliable tool that can be used in both instructional and research settings confidently and at no charge. Permission is granted to use the Personality Identity Estimator in practice and research. A printable copy of PIE is appended.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.001 |
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".