The Role of Brand Equity in Reputational Rankings of Specialty Graduate Programs in Colleges of Education: Variables Considered by College of Education Deans and Associate Deans Ranking the Programs
Bibliographic record
Abstract
Seeking to identify and further understand the variables considered when ranking specialty programs in colleges of education, this research study surveyed all deans, and associate deans responsible for graduate education, at United States institutions that offer the terminal degree in at least one of the ten education specialty areas. The study utilized a three-dimension model of brand equity from the marketing literature, which included the elaboration likelihood model of persuasion. Descriptive statistics determined that research by the faculty of the specialty program is the variable most widely considered by deans and associate deans when determining reputation. In order to determine what predicts a person's motivation to correctly rank programs, a principal components analysis was utilized as a data reduction technique, with parallel analysis determining component retention. The model identified five components which explained 66.224% of total variance. A multiple regression analysis determined that characteristics of a specialty program was the only statistically significant predictor component of motivation to correctly rank programs (β = .317, p = .008, rs2 = .865); however, a large squared structure coefficient was observed on perceived quality (rs2 = .623). Using descriptive discriminant analyses, the study found there is little evidence that marketing efforts have differing effects on groups. Further, a canonical correlation analysis that examined the overall picture of advertising on different groups was not statistically significant at F (15, 271) = .907, p = .557, and had a relatively small effect size (Rc2 = .099).
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".