Building a Better Model: An Introduction to Structural Equation Modelling
Bibliographic record
Abstract
Confirmatory factor analysis (CFA) and structural equation modelling (SEM) are powerful extensions of path analysis, which was described in a previous article in this series. CFA differs from the more traditional exploratory factor analysis in that the relations among the variables are specified a priori, which permits more powerful tests of construct validity for scales. It can also be used to compare different versions of a scale (for example, English and French) and to determine whether the scale performs equivalently in different groups (for example, men and women). SEM expands on path analysis by allowing paths to be drawn between latent variables (which, in other techniques, are called factors or hypothetical constructs), that is, variables that are not seen directly but, rather, through their effect on observable variables, such as questionnaires and behavioural measures. Each latent variable and its associated measured variables form small CFAs, with the added advantage that the correlations among the variables can be corrected for the unreliability of the measures.
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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 | 0.018 | 0.046 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".