A Psychometric Theory that Measures up to Marketing Reality: An Adapted Many Faceted IRT Model
Bibliographic record
Abstract
The marketplace has been defined by the interaction between consumers and brands, which has been recognized by the majority of marketing literatures with the exception of the measurement literature. Measurement researchers in marketing have been continuously working on improving the quality of measurement of marketing constructs by applying psychometric theories from the early Classical Test Theory to later generations such as Generalizability Theory and Item Response Theory. But only main effects (normally consumers, sometimes brands) have been focused on, and interactions between them are either ignored or treated as measurement error. This is surprising, given the voluminous literature in other areas of marketing (e.g., marketing segmentation, customer lifetime value, and customer relationship management) that build their entire frameworks on the interpretation and usage of this interaction. In the current research, we propose a new Many Faceted Item Response Theory model to fill this gap in measurement literature. Two sets of indexes describe consumers (and brands); individual main effects (and brand main effects) and brand-specific individual effects (or individual-specific brand effects). Soft drink brand equity data were used for the empirical examination.
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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 | 0.021 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".