neg.esm: Test for Evaluating Substantial Mediation

In negligible: A Collection of Functions for Negligible Effect/Equivalence Testing

Test for Evaluating Substantial Mediation

Description

Function computes the equivalence testing method (total effect) for evaluating substantial mediation and Kenny method for full mediation.

Usage

neg.esm(
  X,
  Y,
  M,
  alpha = 0.05,
  minc = 0.15,
  eil = -0.15,
  eiu = 0.15,
  nboot = 1000L,
  data = NULL,
  plot = TRUE,
  saveplot = FALSE,
  seed = NA
)

## S3 method for class 'neg.esm'
print(x, ...)

Arguments

X	predictor variable
Y	outcome variable
M	mediator variable
alpha	alpha level (default = .05)
minc	minimum correlation between x and Y (default is .15)
eil	lower bound of equivalence interval in standardized units(default is -.15)
eiu	upper bound of equivalence interval in standardized units (default is .15)
nboot	number of bootstraps (default = 500L)
data	optional data argument
plot	logical, plotting the results (default = TRUE)
saveplot	saving plots (default = FALSE)
seed	optional argument to set seed
x	object of class neg.esm
...	extra arguments

Details

This function evaluates whether a negligible direct effect of X on Y exists after controlling for the mediator. Another way to word this is that the indirect effect accounts for a substantial proportion of the variability in X-Y relationship. See Beribisky, Mara, and Cribbie (https://doi.org/10.20982/tqmp.16.4.p424)

The user specifies the IV (X), DV (Y) and mediator (M). The user can also specify the alpha level, the lower/upper bound of the negligible effect interval (eiL, eiU), the number of bootstrap samples (nboot), as well as the minimum correlation between X and Y that is permitted for a valid test of substantial mediation.

The variables X, Y and M can be specified as stand-alone, or a data argument can be used if the data reside in an R dataset.

For the Kenny method see: https://davidakenny.net/cm/mediate.htm

The proportional distance quantifies the proportional distance from 0 to the nearest negligible effect (equivalence) interval (eiL, eiU). As values get farther from 0 the relationship becomes more substantial, with values greater than 1 indicating that the effect falls outside of the negligible effect (equivalence) interval.

Note that the number of bootstrap samples (nboot) are low for the example since the example has a time limit of 5 seconds to pass CRAN testing; we recommend running a much higher number of bootstrap samples for analyses.

Value

A list including the following:

• minc Minimum correlation between X and Y for a valid negligible effect (equivalence) test

• corxy Sample correlation between the IV (X) and DV (Y)

• dir_eff Sample standardized direct effect between the IV (X) and DV (Y) after controlling for the mediator (M)

• eiL Lower bound of the negligible effect (equivalence) interval

• eiU Upper bound of the negligible effect (equivalence) interval

• cil Lower bound of the 1-2*alpha CI for the standardized direct effect of X on Y

• ciu Upper bound of the 1-2*alpha CI for the standardized direct effect of X on Y

• PD Proportional distance (PD)

• cilpd Lower bound of the 1-alpha CI for the PD

• ciupd Upper bound of the 1-alpha CI for the PD

• ab_par Standardized indirect effect

• abdivc_k Proportion mediated: Standardized indirect effect divided by the standardized total effect

• alpha Nominal Type I error rate

Author(s)

Rob Cribbie [e-mail] and Nataly Beribisky [e-mail]

Examples

#equivalence test for substantial mediation
#with an equivalence interval of -.15 to .15
X<-rnorm(100,sd=2)
M<-.5*X + rnorm(100)
Y<-.5*M + rnorm(100)
neg.esm(X,Y,M, eil = -.15, eiu = .15, nboot = 5)

negligible documentation built on Sept. 11, 2024, 9:24 p.m.