Factor Analysis to Explore the Indicators of Quality Assurance Mechanism on Higher Educational Institutions in Pakistan
Bibliographic record
Abstract
The study aims to gauge the effectiveness of Quality Assurance Mechanism (QAM) and its impact on quality of education in Higher Education Institutions in Pakistan. In this study, a comprehensive survey was conducted, through a well-structured questionnaire, to collect essential data from the respondents. A total of 300 teachers of 5 private and 5 public sector universities were participated in the study. The Cronbach’s alpha reliability value is found to be almost 0.80 for all construct of the instrument in this study. An independent t- test was carried out to reveal the significant difference among private and public sector universities in terms of quality of education. The p-value (significance value) of this test in each construct indicates that there is significant difference among private and public sector universities regarding quality assurance practices. Furthermore, the multivariate statistical tool “Exploratory Factor Analysis (EFA)” was used to explore the underlying pattern of the public and private sector universities data. Finally, four factors emerged in the data whose eigen value are greater than one. Factors emerges in public sector universities data represents teacher’s satisfaction and combination of budget allocation and funding while factors that emerges on private sector universities are the combination of globalization, ranking and adequate funding.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".