Assessment Model for Determinant Factor Constructs in Edu-Tourism Using Confirmatory Factor Analysis (CFA)
Bibliographic record
Abstract
The purpose of this study is to evaluate the determinant factors model construct using Confirmatory Factor Analysis (CFA).The determinant factor constructs consist of: i) tour operators, ii) event management, iii) local communities, iv) investments, v) educational institutions, and vi) tourism organizations.The data for the study was collected by the researcher from 384 respondents who are tourists who arrived and visit the Edu-tourism destinations in Terengganu.CFA is carried out for measurement models of the latent constructs which verify the fitness for determinant factors constructs.A total of 36 items of overall variables were studied.Data were analyzed using IBM-SPSS-AMOS (SEM) program version 21.0 which consists of two main models: the measurement model and the Structural model.The result for Cronbach alpha is greater than 0.6 and AVE is above 0.5 which demonstrated reliability.Meanwhile, the study resolves that the discriminant validity for all constructs is achieved.The study produces new knowledge which resulted in practical contributions that can be practiced by tourism organizations, event managers, tourism operators, academicians and government sectors.
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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 | 0.019 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".