Construction of Attitude Scale: Attitude of Schoolteachers Towards Teaching Profession
Bibliographic record
Abstract
The progress of the nation is based on the competencies of the citizens, and the overall development of the person depends on the effectiveness of the educational system implemented in the country. The teacher is a key factor in the implementation of educational programs. Without a favorable attitude toward the teaching profession, teachers cannot do well in the teaching profession. Regarding this context, this study was conducted to construct an attitude scale to measure the attitude of schoolteachers towards the teaching profession. Steps of standardization of the test were adopted to construct a reliable and valid tool. Piloting was made in the Indo-Nepalese context. A quantitative research design was used in this study. A draft of 50 statements was designed, and 40 were selected on the basis of expert judgment. A sample of 374 schoolteachers from five districts of Nepal and India was selected using a random sampling technique. Eight statements were rejected based on item analysis and respondents' suggestions. A standardized attitude scale was developed comprising 32 statements, which were again categorized into eight factors on the basis of factor analysis. Various reliability coefficients ranging from .80 to .87 were found. For interpretation of the results, z-score and percentile norms were developed. This test will be beneficial to researchers who are interested in identifying the attitudes of schoolteachers in the Indo-Nepalese context.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".