Rasch Analysis of the Fullerton Advanced Balance (FAB) Scale
Bibliographic record
Abstract
PURPOSE: This cross-sectional study explores the psychometric properties and dimensionality of the Fullerton Advanced Balance (FAB) Scale, a multi-item balance test for higher-functioning older adults. METHODS: Participants (n=480) were community-dwelling adults able to ambulate independently. Data gathering consisted of survey and balance performance assessment. Psychometric properties were assessed using Rasch analysis. RESULTS: Mean age of participants was 76.4 (SD=7.1) years. Mean FAB Scale scores were 24.7/40 (SD=7.5). Analyses for scale dimensionality showed that 9 of the 10 items fit a unidimensional measure of balance. Item 10 (Reactive Postural Control) did not fit the model. The reliability of the scale to separate persons was 0.81 out of 1.00; the reliability of the scale to separate items in terms of their difficulty was 0.99 out of 1.00. Cronbach's alpha for a 10-item model was 0.805. Items of differing difficulties formed a useful ordinal hierarchy for scaling patterns of expected balance ability scoring for a normative population. CONCLUSION: The FAB Scale appears to be a reliable and valid tool to assess balance function in higher-functioning older adults. The test was found to discriminate among participants of varying balance abilities. Further exploration of concurrent validity of Rasch-generated expected item scoring patterns should be undertaken to determine the test's diagnostic and prescriptive utility.
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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 | 0.006 | 0.029 |
| 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".