POSTER: Factor Structure, Wording Effects, and Reliability Estimates in Health and Housing Specific Self-efficacy Scales with Individuals who are Homeless or Vulnerably Housed
Bibliographic record
Abstract
Introduction Among individuals who are homeless or vulnerably housed (HVH), most research has focused on efforts to make health and housing resources more available and accessible, with only recent interest in the role of self-efficacy (one’s sense of personal competence) for this group. The 8-item Perceived Health Competence Scale (PHCS) and 7-item Housing SE Scale (HSES) measure health and housing specific SE, respectively. Each scale uses a total score, implying the scales are at least essentially unidimensional. The factor structures of these scales, however, have yet to be verified with a HVH sample. Objectives The study purpose was to examine the factor structure and reliability of scores of the PHCS and HSES with a sample of 333 HVH men and women. Design/Methodology Data were obtained from the Health and Housing in Transition (HHiT) study. Because the scales use 5-point Likert-type response formats, a one-factor confirmatory factor analysis (CFA) model was tested with each scale using a robust ordinal estimator and fit was determined using CFI and RMSEA (with 90% confidence intervals). With a lack of fit, exploratory factor analysis (EFA) and parallel analysis, based on the polychoric matrix, were employed to verify the number of factors. Internal consistency was examined using ordinal alpha. Results Results showed that a one-factor CFA model for strict unidimensionality did not fit the data for either scale. For each scale, EFA showed extra covariance that suggested a possible minor (negative) wording factor, but parallel analysis clearly supported an essentially unidimensional factor structure. Ordinal alpha was .87 for the PHCS and .85 for the HSES. Conclusions This study found, that despite some minor effects of (negative) wording, both the PHCS and HSES were essentially unidimensional. This supports using a total score for each scale. Moreover, reliability estimates for both scales were satisfactory for this HVH sample.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.020 | 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".