Compute or extract the p-values for effects in (robust) mediation analysis.
p_value(object, ...)# S3 method for boot_test_mediation
p_value(object, parm = NULL, type = c("boot", "data"), digits = 4L, ...)
# S3 method for sobel_test_mediation
p_value(object, parm = NULL, ...)
A numeric vector containing the requested p-values.
an object inheriting from class
"test_mediation" containing results
from (robust) mediation analysis.
for the generic function, additional arguments to be passed down to methods. For the methods, additional arguments are currently ignored.
an integer, character or logical vector specifying the paths
for which to extract or compute p-values, or NULL to extract or
compute p-values for all coefficients. In case of a character vector,
possible values are "a", "b", "d" (only serial
multiple mediator models), "total", "direct", and
"indirect".
a character string specifying how to compute the p-values of
the effects other than the indirect effect(s). Possible values are
"boot" (the default) to compute bootstrap p-values using the normal
approximation (i.e., to assume a normal distribution of the corresponding
effect with the standard deviation computed from the bootstrap replicates),
or "data" to compute p-values via statistical theory based on the
original data (e.g., based on a t-distribution if the coefficients are
estimated via regression). Note that this is only relevant for mediation
analysis via a bootstrap test, where the p-value of the indirect effect is
always computed as described in ‘Details’.
an integer determining how many digits to compute for the p-values of the indirect effects (see ‘Details’). The default is to compute 4 digits after the comma.
Andreas Alfons
For bootstrap tests, the p-value of the indirect effect is computed as the smallest significance level \(\alpha\) for which the \((1 - \alpha) * 100\%\) confidence interval obtained from the bootstrapped distribution does not contain 0.
This is a simple implementation, where each digit after the comma is
determined via a grid search. Hence computation time can be long if
confidence intervals are computed via the bias-corrected and accelerated
method ("bca").
For Sobel tests, the p-value of the indirect effect is already stored in the
object returned by test_mediation() and is simply extracted.
data("BSG2014")
boot <- test_mediation(BSG2014,
x = "ValueDiversity",
y = "TeamCommitment",
m = "TaskConflict",
level = 0.9)
p_value(boot)
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