pop_mod {FCO}

Helper function to obtain population model for simulation based on data and model

Description

Helper function to obtain population model for simulation based on data and model

Usage

pop_mod(mod, x, type = "NM", standardized = TRUE, afl = 0.7, aco = 0.3)

Arguments

mod

A lavaan model (only CFA supported so far)

x

A dataset for the model of nrow observations (minimum: 50) and ncol indicators (minimum: 4)

type

Type of population model. NM (the default): Uses the factor loadings and covariances from Niemand & Mai's (2018) simulation study. HB: Uses the factor loadings and covariances from Hu & Bentler's (1999) simulation study. EM: Empirical, uses the given factor loadings and covariances. EM is not recommended for confirmative use as it leads to the least generalizable cutoffs.

standardized

Are factor loadings assumed to be standardized and covariances to be correlations (default: TRUE)?

afl

Average factor loading of indicators per factor, only relevant for type = "NM" (default: .7).

aco

Average correlation between factors, only relevant for type = "NM" (default: .3).

Value

List of population model type, standardized, average factor loading and average correlation. All values are round to three decimals.

Examples

mod <- "
F1 =~ Q5 + Q7 + Q8
F2 =~ Q2 + Q4
F3 =~ Q10 + Q11 + Q12 + Q13 + Q18 + Q19 + Q20 + Q21 + Q22
F4 =~ Q1 + Q17
F5 =~ Q6 + Q14 + Q15 + Q16
"
pop_mod(mod, x = bb1992, type = "NM")$pop.mod
pop_mod(mod, x = bb1992, type = "HB")$pop.mod
pop_mod(mod, x = bb1992, type = "EM")$pop.mod
pop_mod(mod, x = bb1992, type = "NM", afl = .9)$pop.mod
pop_mod(mod, x = bb1992, type = "NM", aco = .5)$pop.mod
pop_mod(mod, x = bb1992, type = "EM", standardized = FALSE)$pop.mod