Normative comparison standards for measures of cognition in the Canadian Longitudinal Study on Aging (CLSA): Does applying sample weights make a difference?
Bibliographic record
Abstract
Large-scale studies present the opportunity to create normative comparison standards relevant to populations. Sampling weights applied to the sample data facilitate extrapolation to the population of origin, but normative scores are often developed without the use of these sampling weights because the values derived from large samples are presumed to be precise estimates of the population parameter. The present article examines whether applying sample weights in the context of deriving normative comparison standards for measures of cognition would affect the distributions of regression-based normative data when using data from a large population-based study. To address these questions, we examined 3 cognitive measures from the Canadian Longitudinal Study on Aging tracking cohort (N = 14,110, Age 45-84 years at recruitment): Rey Auditory Verbal Learning Test - Immediate Recall, Animal Fluency, and the Mental Alternation Test. The use of sampling weights resulted in similar model parameter estimates to unweighted regression analyses and similar cumulative frequency distributions to the unweighted analyses. We randomly sampled progressively smaller subsets from the full database to test the hypothesis that sampling weights would help maintain the estimates from the full sample, but discovered that the weighted and unweighted estimates were similar and were less precise with smaller samples. These findings suggest that although use of sampling weights can help mitigate biases in data from sampling procedures, the application of weights to adjust for sampling biases do not appreciably impact the normative data, which lends support to the current practice in creation of normative data. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".