Comparison of Latent Variable and Psychological Network Models in PROMIS Data: Output Metrics and Factor Structure
Bibliographic record
Abstract
Purpose: Much research is still needed to compare traditional latent variable models such as confirmatory factor analysis (CFA) to emerging psychometric models such as the Gaussian graphical model (GGM). Previous comparisons of GGM centrality indices with factor loadings from CFA have discovered redundancies, and investigations into how well a GGM-based alternative to exploratory factor analysis (i.e., exploratory graph analysis, or EGA) is able to recover the hypothesized factor structure show mixed results. Importantly, such comparisons have not typically been examined in real mental and physical health symptom data, despite such data being an excellent candidate for the GGM. Our goal was to extend previous work by comparing the GGM and CFA using data from Wave 1 of the Patient Reported Outcomes Measurement Information System (PROMIS). Methods: Models were fit to PROMIS data based on 16 test forms designed to measure 9 mental and physical health domains. Our analyses borrowed a two-stage approach for handling missing data from the structural equation modeling literature. Results: We found weaker correspondence between centrality indices and factor loadings than found by previous research, but in a similar pattern of correspondence. EGA recommended a factor structure discrepant with PROMIS domains in most cases yet may be taken to provide substantive insight into the dimensionality of PROMIS domains. Conclusion: In real mental and physical health data, GGM and EGA may provide complementary information to traditional CFA metrics.
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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 | 0.060 | 0.243 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".