Reliability and Validity of the Disloyalty in the Workplace Scale
Bibliographic record
Abstract
The aim of this study is to develop a valid and reliable scale, which measures disloyalty in the workplace. The population of the study consisted of teachers working in the central area of Samsun in Turkey during 2017-2018 academic year. 742 teachers, who volunteered to take part in the study, constituted the sample of the study, and they were selected randomly. In data analysis process, exploratory factor analysis, confirmatory factor analysis and reliability analysis were used. According to the results of explanatory factor analysis, the scale was composed of 21 items and 2 sub-scales called manager disloyalty and colleague disloyalty and these two sub-dimensions have been explaining .86.7 of total variance. According to the results of confirmatory factor analysis, all items constituted a meaningful structure under related factors and factor loadings of all items were above .30. As for the reliability analysis, Cronbach Alfa, Guttman and Spearman Brown analyses were carried out and Cronbach Alfa coefficient of total points of the scale was found as .99; the Guttman coefficient was found as .89 and Spearman Brown coefficient was found as .89. This scale of which validity and reliability analysis were completed in this study can be used by researchers studying on disloyalty in the workplace as a data collection instrument.
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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".