Improving measurement-invariance assessments: correcting entrenched testing deficiencies
Bibliographic record
Abstract
BACKGROUND: Factor analysis historically focused on measurement while path analysis employed observed variables as though they were error-free. When factor- and path-analysis merged as structural equation modeling, factor analytic notions dominated measurement discussions - including assessments of measurement invariance across groups. The factor analytic tradition fostered disregard of model testing and consequently entrenched this deficiency in measurement invariance assessments. DISCUSSION: Applying contemporary model testing requirements to the so-called configural model initiating invariance assessments will improve future assessments but a substantial backlog of deficient assessments remain to be overcome. This article summarizes the issues, demonstrates the problem using a recent example, illustrates a superior model assessment strategy, and documents disciplinary entrenchment of inadequate testing as exemplified by the journal Organizational Research Methods. Employing the few methodologically and theoretically best, rather than precariously-multiple, indicators of latent variables increases the likelihood of achieving properly causally specified structural equation models capable of displaying measurement invariance. Just as evidence of invalidity trumps reliability, evidence of configural model misspecification trumps invariant estimates of misspecified coefficients.
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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 | 0.073 | 0.300 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.014 | 0.027 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".