An Explanation of Path Analysis and Recommendations for Best Practice
Bibliographic record
Abstract
ABSTRACT Path analysis has become increasingly popular, but many studies do not show a deep understanding of how path analysis works or the assumptions on which it relies. In this paper, we explain that path analysis is statistically equivalent to either OLS when the researcher assumes uncorrelated errors, or instrumental variable (IV) estimation when the researcher allows correlated errors and obtains identification using exclusion restrictions. We then identify two problems with the way path analysis is used. First, studies claim that they use path analysis to provide evidence on the causal process, but they assume away endogeneity by imposing the unrealistic assumption of uncorrelated errors. Second, many studies do not explicitly disclose their key assumptions, including the assumptions of uncorrelated errors or exclusion restrictions. This nondisclosure makes it difficult for a reader to determine whether endogeneity is assumed away or whether the study is attempting to address endogeneity. We conclude with detailed guidance for researchers who are considering whether to use path analysis in their research.
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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 | 0.070 | 0.217 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.011 | 0.017 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.011 | 0.018 |
| Open science | 0.009 | 0.006 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.035 | 0.008 |
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".