Internal Consistency of the Object Relations and Social Cognition Scales for the Thematic Apperception Test
Bibliographic record
Abstract
We examined 8 data sets to determine whether it is possible to attain acceptable levels of internal consistency (coefficient alpha) reliability for the 4 Object Relations and Social Cognition scales (ORSC; Westen, Lohr, Silk, Kerber, & Goodrich, 1989) for the Thematic Apperception Test (TAT; Murray, 1943) when cards are considered as items in a scale. Number of cards used in the data sets ranged from 4 to 10, and the Spearman-Brown prophecy formula was applied to estimate the number of cards that would be required to attain alpha levels of different magnitudes. The two more structural subscales (Complexity of Representations and Understanding of Social Causality) have somewhat higher consistencies than the two more affective ones (Affect Tone and Capacity for Emotional Investment and Moral Standards). The results suggest that the use of 10 to 12 cards provides internal consistencies of alpha > or = .70 across each of the 4 ORSC scales.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".