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Mediation Analysis for Structural Equation Models

Source: R/rmedsem.R
rmedsem.Rd

Conducts mediation analysis on a fitted SEM model using the Baron and Kenny (1986) and/or Zhao, Lynch & Chen (2010) approaches.

Usage

rmedsem(
  mod,
  indep,
  med,
  dep,
  approach = c("bk", "zlc"),
  p.threshold = 0.05,
  effect.size = c("RIT", "RID", "upsilon"),
  ...
)

Arguments

mod

a fitted SEM model object (from lavaan, blavaan, cSEM, or modsem)

indep

a string indicating the name of the independent variable

med

a string indicating the name of the mediator variable

dep

a string indicating the name of the dependent variable

approach

either "bk" or "zlc" or both c("bk", "zlc") (default)

p.threshold

a numeric giving the p-value threshold for significance

effect.size

character vector; one or more of "RIT", "RID", "upsilon"

...

additional arguments passed to methods

Value

an object of class 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")
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         0.0468
#> z-value                5.5006         5.446         5.3134
#> p-value              3.79e-08      5.15e-08       1.08e-07
#> CI              [0.161, 0.34] [0.16, 0.341] [0.165, 0.335]
#> 
#> 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
#>