Establishing a causal chain: Why experiments are often more effective than mediational analyses in examining psychological processes.
Bibliographic record
Abstract
The authors propose that experiments that utilize mediational analyses as suggested by R. M. Baron and D. A. Kenny (1986) are overused and sometimes improperly held up as necessary for a good social psychological paper. The authors argue that when it is easy to manipulate and measure a proposed psychological process that a series of experiments that demonstrates the proposed causal chain is superior. They further argue that when it is easy to manipulate a proposed psychological process but difficult to measure it that designs that examine underlying process by utilizing moderation can be effective. It is only when measurement of a proposed psychological process is easy and manipulation of it is difficult that designs that rely on mediational analyses should be preferred, and even in these situations careful consideration should be given to the limiting factors of such 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 | 0.364 | 0.644 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.039 |
| Scholarly communication | 0.011 | 0.028 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.014 | 0.015 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".