data_med_mod_parallel {manymome}

Sample Dataset: Parallel Mediation with Two Moderators

Description

A parallel mediation model with a1-path and b2-path moderated.

Usage

data_med_mod_parallel

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), ]