Sample Dataset: Simple Mediation with Both Paths Moderated (Two Moderators)
Source:R/dat_2_and_3.R
data_med_mod_ab.RdA simple mediation model with a-path and b-path each moderated by a moderator.
Format
A data frame with 100 rows and 7 variables:
- x
Predictor. Numeric.
- w1
Moderator 1. Numeric.
- w2
Moderator 2. Numeric.
- m
Mediator. Numeric.
- y
Outcome variable. Numeric.
- c1
Control variable. Numeric.
- c2
Control variable. Numeric.
Examples
library(lavaan)
data(data_med_mod_ab)
data_med_mod_ab$xw1 <-
data_med_mod_ab$x *
data_med_mod_ab$w1
data_med_mod_ab$mw2 <-
data_med_mod_ab$m *
data_med_mod_ab$w2
mod <-
"
m ~ a * x + w1 + d1 * xw1 + c1 + c2
y ~ b * m + x + w1 + w2 + d2 * mw2 + c1 + c2
w1 ~~ v_w1 * w1
w1 ~ m_w1 * 1
w2 ~~ v_w2 * w2
w2 ~ m_w2 * 1
ab := a * b
ab_lolo := (a + d1 * (m_w1 - sqrt(v_w1))) * (b + d2 * (m_w2 - sqrt(v_w2)))
ab_lohi := (a + d1 * (m_w1 - sqrt(v_w1))) * (b + d2 * (m_w2 + sqrt(v_w2)))
ab_hilo := (a + d1 * (m_w1 + sqrt(v_w1))) * (b + d2 * (m_w2 - sqrt(v_w2)))
ab_hihi := (a + d1 * (m_w1 + sqrt(v_w1))) * (b + d2 * (m_w2 + sqrt(v_w2)))
"
fit <- sem(mod, data_med_mod_ab,
meanstructure = TRUE, fixed.x = FALSE)
parameterEstimates(fit)[c(1, 3, 6, 10, 41:45), ]
#> lhs op rhs label est
#> 1 m ~ x a -0.663
#> 3 m ~ xw1 d1 0.204
#> 6 y ~ m b -0.150
#> 10 y ~ mw2 d2 0.203
#> 41 ab := a*b ab 0.099
#> 42 ab_lolo := (a+d1*(m_w1-sqrt(v_w1)))*(b+d2*(m_w2-sqrt(v_w2))) ab_lolo 0.043
#> 43 ab_lohi := (a+d1*(m_w1-sqrt(v_w1)))*(b+d2*(m_w2+sqrt(v_w2))) ab_lohi 0.108
#> 44 ab_hilo := (a+d1*(m_w1+sqrt(v_w1)))*(b+d2*(m_w2-sqrt(v_w2))) ab_hilo 0.159
#> 45 ab_hihi := (a+d1*(m_w1+sqrt(v_w1)))*(b+d2*(m_w2+sqrt(v_w2))) ab_hihi 0.397
#> se z pvalue ci.lower ci.upper
#> 1 0.179 -3.700 0.000 -1.014 -0.312
#> 3 0.012 16.774 0.000 0.180 0.228
#> 6 0.048 -3.115 0.002 -0.244 -0.056
#> 10 0.012 16.258 0.000 0.178 0.227
#> 41 0.042 2.383 0.017 0.018 0.181
#> 42 0.039 1.100 0.271 -0.034 0.120
#> 43 0.095 1.129 0.259 -0.079 0.295
#> 44 0.050 3.173 0.002 0.061 0.257
#> 45 0.095 4.175 0.000 0.211 0.584