Confounding in Non-Experimental Studies Linking Caregiver Warmth to Youth Adjustment: The Problem and Potential Solutions
Bibliographic record
Abstract
Objective: The literature linking more caregiver warmth to better youth adjustment is almost entirely non-experimental, leaving estimates vulnerable to confounding bias. We evaluated the extent and potential impact of confounding in these non-experimental designs.Method: Reviewing the literature, we identified potential confounders of the link between caregiver warmth and three indices of youth adjustment: externalizing, internalizing, and prosocial behavior. We then examined the associations between these confounders, warmth, and adjustment in longitudinal data from the ABCD Study: 11,880 youth ages 9- to 15-years-old (48% female, 20% Hispanic, 64% white, 16% Black, 2% Asian).Results: Across analyses, 64-70 of the 80 potentially confounding variables we examined held significant associations with both caregiver warmth and youth adjustment, always in directions that exaggerated the apparent beneficial effect of warmth. When the full set of potential confounding variables were covaried, the apparent effect of caregiver warmth on youth adjustment shrunk by 36%-100% (median estimate = 72%). In follow-up analyses, we could successfully eliminate most of the bias introduced by the full set of 80 confounding variables by controlling for a carefully chosen subset of 2-9 “top confounders” (e.g., prior measurements of warmth and adjustment.Conclusions: There are many confounders of caregiver warmth and youth adjustment in non-experimental data. The magnitude of the resulting bias is quite large and would plausibly change scientific conclusions in a given study. Regularly measuring and adjusting for “top confounders” would help, as would incorporating more quasi-experimental and experimental designs.
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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 | 0.496 | 0.629 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".