Dual-Faceted Multidimensional IRT Models with Hierarchical Structure
Bibliographic record
Abstract
In marketing, there are many important multidimensional constructs, such as service quality, market orientation, and consumer-based brand equity (CBBE). Survey methods and multidimensional scales are used to measure these constructs. But the survey responses collected for these constructs are also dual-faceted. Except for consumers, the other facet (such as services, firms or brands) is involved too. Assessment of multidimensional marketing scales (such as SERVQUAL and MARKOR) has relied on confirmatory factor analysis (CFA) and structural equation modeling (SEM), which may not give accurate and unbiased evidence about the estimates and dimensionality across both facets. The authors introduce a hierarchical extension of many facet item response theory (MFIRT) to empirically investigate the causal relationships between a construct and its dimensions for scales that generate dual-faceted data. CBBE data for soft drink brands are used in an empirical study, which demonstrates the method and finds CBBE best modeled as formative from its dimensions along the consumer facet.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".