Detecting DIF (Differential Item Functional ) Across Gender Groups in the Item of Alexithymia Scale Based on Indigenous Psychology Approach
Bibliographic record
Abstract
The aim of this study was to detect the presence of DIF (Differential Item Functional) in a quantitative survey data using alexithymia scale based on gender. Alexithymia scale in this study use the Toronto Alexi scale adapted by Bagby RM, Parker JDA, Taylor GJ in 1994, which has been modified and adapted to indigenous psychology approach. This study uses analysis of DIF (Mantel-Haenszel) as a means of processing the data. The research involves a number of 102 subjects (N = 102) consisted of 66 women and 36 men who registered in a senior high school students in Jogjakarta, Indonesia. Results show that 15 items infected by the DIF from 34 existing items. DIF value is varied, some of items are favorable for men, the rest favorable for women. Items that infected by DIF measure difficulty to find emotion term, physical sensation, tends to analyze than describe. The results will be discussed further.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".