WITHDRAWN
Bibliographic record
Abstract
Confirmatory factor analysis (CFA) enables researchers to evaluate how well the hypothesized structure of a measure fits the data.Fit indices, which quantify the degree of fit or misspecification, are used to evaluate the factorial model.Traditionally, researchers have used fixed index cutoff values to judge the appropriateness of their factorial models.However, many have discussed the limitations of using fixed cutoffs, as fixed values don't generalize to all kinds of models.Recently, McNeish & Wolf (2023) have developed the Dynamic Fit Index approach (DFI) which enables the generation of fit index values that are tailored to the characteristics of the model being tested.In the following tutorial, we conduct a CFA on the Attainment of School Achievement Goal Scale (A-SAGS) using the lavaan package in R. We then generate fit indices using the dynamic package.When using both fixed index cutoffs and fit indices generated by dynamic, the fit of the A-SAGS is mixed.We conclude that the DFI approach provides valuable insight when evaluating factorial models and that it's very promising.We encourage psychology researchers to use it to evaluate their own models.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.564 | 0.421 |
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".