Simulating power of economic experiments: the powerBBK package
Bibliographic record
Abstract
Abstract In this article, we highlight how simulation methods can be used to analyze power of economic experiments. We provide the powerBBK package programmed for experimental economists, that can be used to perform simulations in STATA. Power can be simulated using a single command line for various statistical tests (nonparametric and parametric), estimation methods (linear, binary, and censored regression models), treatment variables (binary, continuous, time-invariant or time varying), sample sizes, experimental periods, and other design features (within or between-subjects design). The package can be used to predict minimum sample sizes required to reach a user-specific level of power, to maximize power of a design given the researcher supplied a budget constraint, or to compute power to detect a user-specified treatment order effect in within-subjects designs. The package can also be used to compute the probability of sign errors—the probability of rejecting the null hypothesis in the wrong direction as well as the share of rejections pointing in the wrong direction. The powerBBK package is provided as an .ado file along with a help file, both of which can be downloaded here ( http://www.bbktools.org ).
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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 | 0.023 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.071 | 0.011 |
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".