A method to aid in the interpretation of EFA results: An application of Pratt’s measures
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Bibliographic record
Abstract
This article describes a method based on Pratt’s measures and demonstrates its use in exploratory factor analyses. The article discusses the interpretational complexities due to factor correlations and how Pratt’s measures resolve these interpretational problems. Two real data examples demonstrate the calculation of what we call the “D matrix,” of which the elements are Pratt’s measures. Focusing on the rows of the D matrix allows one to compare the importance of the factors to the communality of each observed indicator ( horizontal interpretation); whereas a focus on the columns of the D matrix allows one to compare the contribution of the indicators to the common variance extracted by each factor ( vertical interpretation). The application showed that the method based on Pratt’s measures is a very simple but useful technique for EFA, in particular, for behavioral and developmental constructs, which are often multidimensional and mutually correlated.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it