The Extra-Factor Phenomenon Revisited: Unidimensional Unfolding as Quadratic Factor Analysis
Bibliographic record
Abstract
The application of linear factor analysis to a set of unfoldable (unidimensional) items produces a two-dimensional solution, called the extra-factor phenomenon, which potentially results in incorrect conclusions about the nature of a set of items (van Schuur& Kiers, 1994). Many explanations have been offered for this phenomenon. This study attempted further clarification within the general theory of factor analysis. Specifically, it was demonstrated that the extra-factor phenomenon arises because: (1) the metric unidimensional unfolding model is equivalent to the unidimensional quadratic factor model; and (2) at the level of covariance structure, the unidimensional quadratic factor model is not distinguishable from the two-dimensional linear factor model (McDonald, 1967). Also discussed are a number of theoretical linkages and bases of distinguishability that exist between unidimensional unfolding and linear factor analysis.
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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 | 0.031 | 0.114 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.006 |
| 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 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".