Race/Ethnicity and the Measurement of Cognition in the National Social Life, Health, and Aging Project: Recommendations for Robustness
Bibliographic record
Abstract
OBJECTIVES: In this study, we examine the measurement of cognition in different racial/ethnic groups to move toward a less biased and more inclusive set of measures for capturing cognitive change and decline in older adulthood. METHODS: We use data from Round 2 (N = 3,377) and Round 3 (N = 4,777) of the National Social Life, Health, and Aging Project (NSHAP) and examine the study's Survey Adjusted version of the Montreal Cognitive Assessment (MoCA-SA). We employ exploratory factor analyses to explore configural invariance by racial/ethnic group. Using modification indexes, 2-parameter item response theory models, and split-sample testing, we identify items that seem robust to bias by race. We test the predictive validity of the full (18-item) and short (4-item) MoCA-SAs using self-reported dementia diagnosis, instrumental activities of daily living, proxy reports of dementia, proxy reports of dementia-related death, and National Death Index reports of dementia-related death. RESULTS: We found that 4 measures out of the 18 used in NSHAP's MoCA-SA formed a scale that was more robust to racial bias. The shortened form predicted consequential outcomes as well as NSHAP's full MoCA-SA. The short form was also moderately correlated with the full form. DISCUSSION: Although sophisticated structural equation modeling techniques would be preferable for assuaging measurement invariance by race in NSHAP, the shortened form of the MoCA-SA provides a quick way for researchers to carry out robustness checks and to see if the disparities and associations by race they document are "real" or the product of artifactual bias.
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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 | 0.547 | 0.746 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.006 | 0.022 |
| Bibliometrics | 0.009 | 0.016 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.009 | 0.010 |
| Research integrity | 0.006 | 0.011 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".