EVB strength DGM

evb_strength.R

N <- 5000
sim <- tibble(
  group =  rep(0:1, each = N/2),
  evb_strength =  0.1 + group * 0.9,
  # four orthogonal traits
  trait1 = rnorm(N),
  trait2 = rnorm(N),
  trait3 = rnorm(N),
  trait4 = rnorm(N),
  # evaluative bias
  evb    = rnorm(N),
  # indicators/facets are caused by each trait and by evb (pos or neg)
  # strength of evb loading varies as a function of group
  t1m1 = trait1 + evb_strength * -0.3 * evb + rnorm(N),
  t1m2 = trait1 + evb_strength * 0.3 * evb + rnorm(N),
  t1m3 = trait1 + evb_strength * -0.3 * evb + rnorm(N),
  t1m4 = trait1 + evb_strength * 0.3 * evb + rnorm(N),
  t2m1 = trait2 + evb_strength * -0.3 * evb + rnorm(N),
  t2m2 = trait2 + evb_strength * 0.3 * evb + rnorm(N),
  t2m3 = trait2 + evb_strength * -0.3 * evb + rnorm(N),
  t2m4 = trait2 + evb_strength * 0.3 * evb + rnorm(N),
  t3m1 = trait3 + evb_strength * -0.3 * evb + rnorm(N),
  t3m2 = trait3 + evb_strength * 0.3 * evb + rnorm(N),
  t3m3 = trait3 + evb_strength * -0.3 * evb + rnorm(N),
  t3m4 = trait3 + evb_strength * -0.3 * evb + rnorm(N),
  t4m1 = trait4 + evb_strength * 0.3 * evb + rnorm(N),
  t4m2 = trait4 + evb_strength * -0.3 * evb + rnorm(N),
  t4m3 = trait4 + evb_strength * 0.3 * evb + rnorm(N),
  t4m4 = trait4 + evb_strength * -0.3 * evb + rnorm(N)
)