Unweighted versus weighted regression methods may be sufficient to analyze complex survey data in the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
Abstract Introduction Complex surveys use stratified or cluster sampling to recruit participants. Researchers analyzing these surveys often wish to make inferences about the source populations from which the participants are drawn. In such cases, methodologists recommend employing sample weights in regression analyses; however, the utilization of weights in studies of associations are not without dispute. Materials and methods To help guide analyses of complex surveys, we utilized baseline data from the Comprehensive Cohort of the Canadian Longitudinal Study on Aging (CLSA) (n = 30,097) and compared unweighted and weighted regression analyses of the association between social support availability (SSA) and cognitive function. We also conducted simulation studies to validate our findings in the CLSA. Results The regression coefficients for SSA were similar across the unweighted and weighted regression models; the standard errors of the were lower in the unweighted models. The simulation studies mimicking CLSA confirmed these findings. Overall, our findings demonstrated a small advantage for the unweighted analysis of CLSA data due to the smaller standard errors. Discussion Although we cannot guarantee that this would be the case for all association analyses with CLSA data, the current study showed the use of analytical weights was not necessary for our associations of interest.
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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 | 0.082 | 0.268 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".