Why do judgments on different person-descriptive attributes correlate with one another? A conceptual analysis with relevance for most psychometric research
Bibliographic record
Abstract
Patterns of correlations among judgments of targets on different items are the basis for common psychometric procedures such as factor analysis and network modeling. The outcomes of such analyses may shape the images (i.e., theories) that we as scientists have of the phenomena that we study. However, key conceptual issues tend to be overlooked in these analyses, which is especially problematic when the items are person descriptions espressed in the natural language. A correlation between judgments on two such items may reflect the influences of (a) a common substantive cause, (b) substantive target characteristics on another, (c) semantic redundancy, (d) the perceivers’ attitudes toward the targets, (e) the perceivers’ formal response styles, or (f) any mixture of these. We present a conceptual framework integrating all of these mechanisms and use it to connect formerly unrelated strands of theorizing with one another. A lack of awareness regarding the complexity involved may compromise the validity of interpretations of psychometric analyses. We also review the effectiveness of a broad range of solutions that have been proposed for dealing with the various influences, and provide recommendations for future research.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".