Investigating Structural Model Fit Evaluation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A full structural equation model (SEM) typically consists of both a measurement model (describing relationships between latent variables and observed scale items) and a structural model (describing relationships among latent variables). However, often researchers are primarily interested in testing hypotheses related to the structural model while treating the measurement model as a necessary but not primary focus of the overall model. In this case, researchers often wish to isolate and just evaluate the fit of the structural model. In our research, we examine a two-stage approach that can compute the chi-square statistic and fit indices for evaluating only the fit of the structural model in a full SEM. We call these the structural chi-square statistic and structural fit indices. For structural fit indices, we focused on the root mean square error of approximation (RMSEA), comparative fit index (CFI), and standardized root mean square residual (SRMR). We developed several new versions of the structural chi-square statistic, structural fit indices, and confidence intervals (CIs) of the structural fit indices. Through a simulation study, we demonstrated that several versions of our newly developed structural chi-square statistic yielded the nominal Type-I error rate; and the same versions of the structural fit indices exhibited low bias and their corresponding CIs had high coverage rates. Therefore, we recommend researchers use these versions of the structural chi-square test of fit alongside the structural fit indices when evaluating the fit of the structural model.
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.
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 | 0.010 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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