Understanding and Managing Confounders, Mediators and Colliders in Research
Bibliographic record
Abstract
RATIONALE: Researchers often make causal inferences about relationships among variables and constructs. However, third-variable effects may obscure the relationship among studied variables. Third-variable effects generally include confounders and mediators, but recently there has been an emerging discussion on colliders. AIM: To provide a concise introduction of confounders, colliders, and mediators for health researchers and outline strategies for minimising the impact of confounders, colliders and mediators in quantitative research. METHODS: Methodological literature from biostatistics textbooks, methodology papers, and methodological reviews published in nursing, health, psychological and behavioural sciences. CONCLUSIONS: Understanding third-variable effects is crucial to conducting rigorous research and drawing valid causal inferences from research data. Health researchers should embrace both theory and model-based thinking as a foundational element of their methodology. This involves explicitly theorising the underlying causal structures before data collection and analysis using Directed Acyclic Graphs which are useful for visually representing hypothesised causal pathways and their relationships with potential third variables.
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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 | 0.327 | 0.543 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.005 | 0.026 |
| Scholarly communication | 0.015 | 0.034 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.008 | 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".