Quantitative and Qualitative Strategies to Strengthen Internal Validity in Randomized Trials
Bibliographic record
Abstract
Although the randomized controlled trial (RCT) is the most reliable design to infer causality, evidence suggests that it is vulnerable to biases that weaken internal validity. In this paper, we review factors that introduce biases in RCTs and we propose quantitative and qualitative strategies for colleting relevant data to strengthen internal validity. The factors are related to participants' reactions to randomization, attrition, treatment perceptions, and implementation of the intervention. The way in which these factors operate is explained and pertinent empirical evidence is synthesized. Quantitative and qualitative strategies are described. Researchers can plan to assess these factors and examine their influence, providing evidence of what actually contributed to the interventions' causal impact.
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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 | 0.812 | 0.893 |
| Meta-epidemiology (narrow) | 0.007 | 0.006 |
| Meta-epidemiology (broad) | 0.012 | 0.014 |
| Bibliometrics | 0.031 | 0.023 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.008 | 0.016 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".