pcurve (P-Curve) Method

Implements the p-Curve method which analyzes the distribution of p-values from significant studies to assess whether the significant findings reflect true effects or QRP/publication bias. The method also provides tests for the evidential value, lack of evidential value, and p-hacking. See Simonsohn et al. (2014) for details.

The current implementation does not provide a test against the null hypothsis of no effect and does not produce confidence intervals of the estimate.

Usage

# S3 method for class 'pcurve'
method(method_name, data, settings)

Arguments

method_name

Method name (automatically passed)

data

Data frame with yi (effect sizes), sei (standard errors), and ni (sample sizes wherever available, otherwise set to Inf)

settings

List of method settings (see Details)

Value

Data frame with P-Curve results

Details

The following settings are implemented

"default"

no options

References

Simonsohn U, Nelson LD, Simmons JP (2014). “p-curve and effect size: Correcting for publication bias using only significant results.” Perspectives on Psychological Science, 9(6), 666–681. doi:10.1177/1745691614553988 .

Examples

# Generate some example data
data <- data.frame(
  yi = c(0.2, 0.3, 0.1, 0.4, 0.25),
  sei = c(0.1, 0.15, 0.08, 0.12, 0.09)
)

# Apply pcurve method
result <- run_method("pcurve", data)
print(result)
#>   method estimate standard_error ci_lower ci_upper p_value BF convergence note
#> 1 pcurve  3.99994             NA       NA       NA      NA NA        TRUE   NA
#>   p_value_evidence p_value_lack p_value_hack method_setting
#> 1        0.3699365    0.6300635    0.1962818        default