A dataset containing a subset of results from a simulation study examining the performance of methods to correct for publication bias in meta-analyses that involve dependent effect sizes.
Format
A tibble with 10,368 rows and 22 variables:
- k
Parameter setting for the number of studies included in each meta-analysis.
- mu
Parameter setting for the average effect size across studies.
- tau
Parameter setting for the between-study standard deviation of the effect size distribution.
- cor_mu
Parameter setting for the correlation between outcomes measured within the same study.
- wts
Parameter setting for the selection weight, which controls the probability that a non-affirmative result is reported.
- iterations
Number of simulation iterations per condition.
- method
Estimation method applied to each simulated dataset.
- n_converged
Number of simulation iterations in which the estimation method converged.
- bias
Bias of the estimator for the average effect size (mu)
.
- bias_mcse
Monte Carlo standard error of bias.
- var
Variance of the estimator method for the average effect size (mu)
.
- var_mcse
Monte Carlo standard error of variance.
- mse
Mean squared error of the estimator for the average effect size (mu)
.
- mse_mcse
Monte Carlo standard error of mean squared error.
- rmse
Root mean squared error of the estimator for the average effect size (mu)
.
- rmse_mcse
Monte Carlo standard error of root mean squared error.
- coverage
Coverage level of the 95% confidence interval for the average effect size (mu).
.
- coverage_mcse
Monte Carlo standard error of the coverage level.
- width
Average width of the 95% confidence interval for the average effect size (mu)
.
- width_mcse
Monte Carlo standard error of the average width.
- rej_rate
Rejection rate of a hypothesis test that the average effect size (mu) is equal to zero.
.
- rej_rate_mcse
Monte Carlo standard error of the rejection rate.
Source
Chen M, Pustejovsky JE (2025). “Adapting Methods for Correcting Selective Reporting Bias in Meta-Analysis of Dependent Effect Sizes.” Psychological Methods, Advance online publication. doi:10.1037/met0000773 .
Details
This dataset contains only a subset of the results from the
simulation study reported in Chen and Pustejovsky (2025). The simulation
followed a full factorial design involving 4 levels for k, 4 levels
for mu, 4 levels for tau, 3 levels for cor_mu, and 6
levels for wts, for a total of 1152 unique conditions. For each
condition, the dataset includes performance measures for each of 9
estimation methods:
"3PSM": a three-parameter step function selection model, with a step at \(\alpha = .025\), ignoring the presence of dependent effect sizes"4PSM": a four-parameter step function selection model, with steps at \(\alpha = .025, .500\), ignoring the presence of dependent effect sizes"CHE-ISCW": a summary meta-analysis using the correlated-and-heirarchical effects working model with inverse sampling-covariance weighting"EK": a multivariate version of the endogenous kink meta-regression"PET-PEESE": a multivariate version of PET-PEESE meta-regression (i.e., a limit meta-regression)"TF": Trim-and-Fill, ignoring the presence of dependent effect sizes"WAAP": a multivariate version of the weighted average of adequately powered studies"WILS": a multivariate version of the weighted-and-iterated least squares method, stopping at a minimum of \(k = 5\) studies"p-uniform*": the p-uniform* estimator, ignoring the presence of dependent effect sizes