Point Estimation, Hypothesis Testing, and Interval Estimation Using the RMSEA: Some Comments and a Reply to Hayduk and Glaser
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Bibliographic record
Abstract
Abstract Hayduk and Glaser (2000) asserted that the most commonly used point estimate of the Root Mean Square Error of Approximation index of fit (Steiger & Lind, 1980) has two significant problems: (a) The frequently cited target value of. 05 is not a stable target, but a "sample size adjustment"; and (b) the truncated point estimate Rt = max(R, 0) effectively throws away a substantial part of the sampling distribution of the test statistic with "proper models," rendering it useless a substantial portion of the time. In this article, I demonstrate that both issues discussed by Hayduk and Glaser are actually not problems at all. The first "problem" derives from a false premise by Hayduk and Glaser that Steiger (1995) specifically warned about in an earlier publication. The second so-called problem results from the point estimate satisfying a fundamental property of a good estimator and can be shown to have virtually no negative implications for statistical practice.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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