CONSTRUCT VALIDITY OF FRABONI SCALE OF AGEISM IN A CHINESE-MAJORITY SAMPLE FROM SINGAPORE
Bibliographic record
Abstract
Abstract The 29-item Fraboni Scale of Ageism (FSA; Fraboni et al., 1990) was constructed to measure three dimensions of ageism (Antilocution, Discrimination, and Avoidance) using samples from Canada. The factor structure of the FSA has been challenged in recent studies using US samples (Rupp et al., 2000) and Chinese samples (Fan et al., 2020), resulting in alternative factor structures. The current study aimed to test the different factor structures proposed in these past studies with a sample from Singapore, a Chinese-majority multicultural country. Data from 311 individuals, aged between 21- and 55-year-old were collected. They completed the 29-item FSA using a six-point agreement scale. Confirmatory factor analysis was used to test the goodness-of-fit of the aforementioned factor structures. The results indicated that none of three models provided a good fit to the data. A follow-up exploratory factor analysis with parallel analysis suggested a 3-factor structure. Thirteen out of the 29 items were found to have at least one salient cross-loading after Geomin rotation. These findings suggest that the samples likely interpreted and responded to the items differently resulting in different item-factor configurations, implying the impact of regional culture on the construct validity of FSA. Researchers aiming to quantify ageism in their population using FSA are advised to examine the FSA factor structure prior to using it.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".