Three Threats to Validity of Choice‐based and Matched‐Sample Studies in Accounting Research*
Bibliographic record
Abstract
We consider three technical errors in the statistical analysis of choice-based and matched sample studies in accounting research. These problems constitute threats to both internal and external validity of the research. First, we note that researchers have often failed to control for the effects of matching variables used in sample selection. Commonly, researchers believe that the selection of a matched sample already controls for the matching variables, and hence controlling for them in analyses would not be necessary, but in fact it is. Typically an unconditional analysis is performed, rather than the conditional one that is justified. Thus, failure to account for industry, size, and other matching variables may have driven incorrect findings in many research studies, or may have suppressed results waiting to be revealed. Second, where matching is by closest size or other continuous measure, the matching is imperfect, and there remains the possibility that case vs. control differences in this matching variable could be the cause of differences in outcome, so researchers must evaluate that possibility and perhaps control for it. Third, the disproportionate sampling for different population strata that is implicit in the choice-based and matched sample selection would usually necessitate weighting data in statistical analyses by the sampling rates in each strata, but reweighting or other appropriate adjustment to the analysis is often not implemented. A logit exemption to the need for reweighting has been noted in the literature, but has been used in settings where it does not apply. We provide a simulation example to demonstrate problems, and provide suggestions for more precise ways to analyze choice-based and matched samples.
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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 | 0.512 | 0.801 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.007 | 0.018 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.010 | 0.012 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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".