Prints the mediation analysis results to the console.
Usage
# S3 method for class 'rmedsem'
summary(object, ...)Arguments
- object
the
rmedsemobject- ...
additional arguments passed to
print.rmedsem()
Examples
mod.txt <- "
read ~ math
science ~ read + math
"
mod <- lavaan::sem(mod.txt, data=rmedsem::hsbdemo)
out <- rmedsem(mod, indep="math", med="read", dep="science")
summary(out)
#> Significance testing of indirect effect (standardized)
#> Model estimated with package 'lavaan'
#> Mediation effect: 'math' -> 'read' -> 'science'
#>
#> Sobel Delta Monte-Carlo
#> Indirect effect 0.2506 0.251 0.2506
#> Std. Err. 0.0456 0.046 0.0417
#> z-value 5.5006 5.446 6.0984
#> p-value 3.79e-08 5.15e-08 1.07e-09
#> CI [0.161, 0.34] [0.16, 0.341] [0.169, 0.327]
#>
#> Baron and Kenny approach to testing mediation
#> STEP 1 - 'math:read' (X -> M) with B=0.662 and p=0.000
#> STEP 2 - 'read:science' (M -> Y) with B=0.378 and p=0.000
#> STEP 3 - 'math:science' (X -> Y) with B=0.380 and p=0.000
#> As STEP 1, STEP 2 and STEP 3 as well as the Sobel's test above
#> are significant the mediation is partial.
#>
#> Zhao, Lynch & Chen's approach to testing mediation
#> Based on p-value estimated using Monte-Carlo
#> STEP 1 - 'math:science' (X -> Y) with B=0.380 and p=0.000
#> As the Monte-Carlo test above is significant, STEP 1 is
#> significant and their coefficients point in same direction,
#> there is complementary mediation (partial mediation).
#>
#> Effect sizes
#> RIT = (Indirect effect / Total effect)
#> (0.251/0.631) = 0.397
#> Meaning that about 40% of the effect of 'math'
#> on 'science' is mediated by 'read'
#> RID = (Indirect effect / Direct effect)
#> (0.251/0.380) = 0.659
#> That is, the mediated effect is about 0.7 times as
#> large as the direct effect of 'math' on 'science'
#> Upsilon (v) = Variance in 'science' explained indirectly by 'math' through 'read'
#> v(unadj) = 0.063, v(adj) = 0.061
#>