Rasch model of the bridging social capital questionnaire
Bibliographic record
Abstract
We previously identified an important gap in the literature related to its measurement. We developed and validated a scale to measure bridging social capital to be used in Latinx immigrant populations living in the U.S using Classical Test Theory. The structure of the questionnaire comprised the following sub-scales: Socializing in the work place (5 items); Participation in community activities (16 items); Socializing in community activities (5 items); Contact with similar/different people (7 items); Assistance (17 items); Trust of institutions, corporations and other people (14 items); and Trust of intimate people (3 items). Although basic psychometric validation was performed on our original instrument (e.g., content and construct validity, internal consistency reliability), modern testing theory recommends a more comprehensive set of evaluations, including assessment of data quality, scaling assumptions, targeting, reliability, validity and responsiveness. Rasch measurement theory (RMT) is one of the Modern Test Theory methods that assesses the extent to which rigorous measurement is achieved. In the present work, our objective was to further evaluate the instrument using CTT and to use modern psychometric techniques to further validate the questionnaire and create version 2 (v2) using a new sample (N = 224). We developed a Rasch model of the questionnaire to evaluate item fit statistics, item category thresholds, person separation index (PSI), local dependency, differential item functioning (DIF), unidimensionality and targeting and item locations. Assistance was the most problematic sub-scale of all, as item-to-total correlations ranged from 0.27 to 0.66. There were no disordered thresholds on any item, either examined as part of the overall score or as part of sub-scales. However, the analysis provided evidence of the need to modify some of the sub-scales as there was lack of support for unidimensionality or fit to the Rasch model. The Bridging Social Capital Questionnaire v2 has 61 items (compared to 67 in version 1). Our questionnaire may be suitable for adaptation to other immigrant groups in different countries.
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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 | 0.017 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".