Sample Dataset: Parallel Mediation with Two Moderators
Source:R/dat_2_and_3.R
data_med_mod_parallel.RdA parallel mediation model with a1-path and b2-path moderated.
Format
A data frame with 100 rows and 8 variables:
- x
Predictor. Numeric.
- w1
Moderator 1. Numeric.
- w2
Moderator 2. Numeric.
- m1
Mediator 1. Numeric.
- m2
Mediator 2. Numeric.
- y
Outcome variable. Numeric.
- c1
Control variable. Numeric.
- c2
Control variable. Numeric.
Examples
library(lavaan)
data(data_med_mod_parallel)
data_med_mod_parallel$xw1 <-
data_med_mod_parallel$x *
data_med_mod_parallel$w1
data_med_mod_parallel$m2w2 <-
data_med_mod_parallel$m2 *
data_med_mod_parallel$w2
mod <-
"
m1 ~ a1 * x + w1 + da1 * xw1 + c1 + c2
m2 ~ a2 * x + w1 + c1 + c2
y ~ b1 * m1 + b2 * m2 + x + w1 + w2 + db2 * m2w2 + c1 + c2
w1 ~~ v_w1 * w1
w1 ~ m_w1 * 1
w2 ~~ v_w2 * w2
w2 ~ m_w2 * 1
a1b1 := a1 * b1
a2b2 := a2 * b2
a1b1_w1lo := (a1 + da1 * (m_w1 - sqrt(v_w1))) * b1
a1b1_w1hi := (a1 + da1 * (m_w1 + sqrt(v_w1))) * b2
a2b2_w2lo := a2 * (b2 + db2 * (m_w2 - sqrt(v_w2)))
a2b2_w2hi := a2 * (b2 + db2 * (m_w2 + sqrt(v_w2)))
"
fit <- sem(mod, data_med_mod_parallel,
meanstructure = TRUE, fixed.x = FALSE)
parameterEstimates(fit)[c(1, 3, 6, 10, 11, 15, 48:53), ]
#> lhs op rhs label est se z
#> 1 m1 ~ x a1 -1.328 0.136 -9.741
#> 3 m1 ~ xw1 da1 0.289 0.010 28.738
#> 6 m2 ~ x a2 0.184 0.068 2.704
#> 10 y ~ m1 b1 0.368 0.030 12.437
#> 11 y ~ m2 b2 -0.363 0.091 -3.966
#> 15 y ~ m2w2 db2 0.296 0.007 39.907
#> 48 a1b1 := a1*b1 a1b1 -0.489 0.064 -7.669
#> 49 a2b2 := a2*b2 a2b2 -0.067 0.030 -2.234
#> 50 a1b1_w1lo := (a1+da1*(m_w1-sqrt(v_w1)))*b1 a1b1_w1lo -0.075 0.042 -1.815
#> 51 a1b1_w1hi := (a1+da1*(m_w1+sqrt(v_w1)))*b2 a1b1_w1hi -0.155 0.053 -2.943
#> 52 a2b2_w2lo := a2*(b2+db2*(m_w2-sqrt(v_w2))) a2b2_w2lo 0.046 0.025 1.831
#> 53 a2b2_w2hi := a2*(b2+db2*(m_w2+sqrt(v_w2))) a2b2_w2hi 0.158 0.061 2.571
#> pvalue ci.lower ci.upper
#> 1 0.000 -1.595 -1.061
#> 3 0.000 0.270 0.309
#> 6 0.007 0.051 0.318
#> 10 0.000 0.310 0.426
#> 11 0.000 -0.542 -0.183
#> 15 0.000 0.282 0.311
#> 48 0.000 -0.614 -0.364
#> 49 0.025 -0.126 -0.008
#> 50 0.069 -0.157 0.006
#> 51 0.003 -0.258 -0.052
#> 52 0.067 -0.003 0.095
#> 53 0.010 0.038 0.278