Compare model fit of nested dyadic invariance models in order from most parsimonious (residual) to least parsimonious (configural)

Usage

getInvarCompTable(mods)

Arguments

mods: a list of neted lavaan dyadic invariance models, in the order of residual, intercept, loading, configural

Value

a data frame of model fit statistics for each model, as well as the difference in fit statistics between each model and the previous model

Examples


dvn <- scrapeVarCross(dat = commitmentQ, x_order = "spi",
x_stem = "sat.g", x_delim1 = ".", x_delim2="_", distinguish_1="1", distinguish_2="2")

sat.residual.script <- scriptCFA(dvn, lvname = "Sat",
constr_dy_meas = c("loadings", "intercepts", "residuals"), constr_dy_struct = "none")

sat.intercept.script <- scriptCFA(dvn, lvname = "Sat",
constr_dy_meas = c("loadings", "intercepts"), constr_dy_struct = "none")

sat.loading.script <- scriptCFA(dvn, lvname = "Sat",
constr_dy_meas = c("loadings"), constr_dy_struct = "none")

sat.config.script <- scriptCFA(dvn, lvname = "Sat",
constr_dy_meas = "none", constr_dy_struct = "none")

sat.residual.fit <- lavaan::cfa(sat.residual.script, data = commitmentQ,
std.lv = FALSE, auto.fix.first= FALSE, meanstructure = TRUE)

sat.intercept.fit <- lavaan::cfa(sat.intercept.script, data = commitmentQ,
std.lv = FALSE, auto.fix.first= FALSE, meanstructure = TRUE)

sat.loading.fit <- lavaan::cfa(sat.loading.script, data = commitmentQ,
std.lv = FALSE, auto.fix.first= FALSE, meanstructure = TRUE)

sat.config.fit <- lavaan::cfa(sat.config.script, data = commitmentQ,
std.lv = FALSE, auto.fix.first= FALSE, meanstructure = TRUE)

mods <- list(sat.residual.fit, sat.intercept.fit, sat.loading.fit, sat.config.fit)

getInvarCompTable(mods)
#>          mod  chisq df pvalue      aic      bic rmsea   cfi chisq_diff df_diff
#> 1   residual 78.842 42  0.000 3855.175 3918.308 0.087 0.970         NA      NA
#> 2  intercept 60.321 37  0.009 3846.654 3923.512 0.074 0.981    -18.521      -5
#> 3    loading 58.810 33  0.004 3853.143 3940.981 0.082 0.979     -1.511      -4
#> 4 configural 53.026 29  0.004 3855.359 3954.176 0.085 0.981     -5.784      -4
#>   p_diff aic_diff bic_diff rmsea_diff cfi_diff
#> 1     NA       NA       NA         NA       NA
#> 2  0.002   -8.521    5.204     -0.013    0.011
#> 3  0.825    6.489   17.469      0.008   -0.002
#> 4  0.216    2.216   13.196      0.002    0.001