Measurement of Social Support Across Women from Four Ethnic Groups: Evidence of Factorial Invariance
Bibliographic record
Abstract
To examine whether a multidimensional social support instrument can be used for comparative research in four diverse ethnic groups of women (African American, Latina, Chinese, non-Latina White). The social support instrument was administered as part of a larger survey to 1,137 women. We tested the reliability and validity of this instrument. A confirmatory factor analytic (CFA) framework was used to test for the invariance of the instrument's psychometric properties across ethnic groups. We used multitrait scaling to eliminate items that did not meet the item-convergence criterion (r > 0.30) and where items were non-convergent items in at least three groups. A series of nested CFA models assessed the level of factorial invariance. One thousand seventy-four women completed the survey; Their mean age was 61 years with Chinese and Latinas reporting lower education compared to non-Latino Whites (p <. 001). A four-factor model (Tangible, Informational, Financial, Emotional/Companionship) fit within each ethnic group separately, suggested good fit. Multi-group CFA supported configural and metric invariance across all ethnic groups. Only partial scalar invariance was supported. This 8-item instrument is a reliable and valid tool that can be used as a multidimensional measure of social support. It can used to examine social support within one ethnic group and for comparative research across diverse ethnic groups of women.
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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 | 0.012 | 0.031 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".