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 misfit, 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 do not 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 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 is very promising.We encourage psychology researchers to use it when using fit indices to evaluate their own models.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".