cfa_meas_inv: Estimation of a Unidimensional Factor Model under Full and...

In alexanderrobitzsch/sirt: Supplementary Item Response Theory Models

Estimation of a Unidimensional Factor Model under Full and Partial Measurement Invariance

Description

Estimates a unidimensional factor model based on the normal distribution fitting function under full and partial measurement invariance. Item loadings and item intercepts are successively freed based on the largest modification index and a chosen significance level alpha.

Usage

cfa_meas_inv(dat, group, weights=NULL, alpha=0.01, verbose=FALSE, op=c("~1","=~"))

Arguments

dat	Data frame containing items
group	Vector of group identifiers
weights	Optional vector of sampling weights
alpha	Significance level
verbose	Logical indicating whether progress should be shown
op	Operators (intercepts or loadings) for which estimation should be freed

Value

List with several entries

pars_mi	Model parameters under full invariance
pars_pi	Model parameters under partial invariance
mod_mi	Fitted model under full invariance
mod_pi	Fitted model under partial invariance
...	More output

See Also

See also sirt::invariance.alignment

Examples

## Not run: 
#############################################################################
# EXAMPLE 1: Factor model under full and partial invariance
#############################################################################

#--- data simulation

set.seed(65)
G <- 3  # number of groups
I <- 5  # number of items
# define lambda and nu parameters
lambda <- matrix(1, nrow=G, ncol=I)
nu <- matrix(0, nrow=G, ncol=I)
err_var <- matrix(1, nrow=G, ncol=I)

# define size of noninvariance
dif <- 1
#- 1st group: N(0,1)
lambda[1,3] <- 1+dif*.4; nu[1,5] <- dif*.5
#- 2nd group: N(0.3,1.5)
gg <- 2 ;
lambda[gg,5] <- 1-.5*dif; nu[gg,1] <- -.5*dif
#- 3nd group: N(.8,1.2)
gg <- 3
lambda[gg,4] <- 1-.7*dif; nu[gg,2] <- -.5*dif
#- define distributions of groups
mu <- c(0,.3,.8)
sigma <- sqrt(c(1,1.5,1.2))
N <- rep(1000,3) # sample sizes per group

#* use simulation function
dat <- sirt::invariance_alignment_simulate(nu, lambda, err_var, mu, sigma, N,
                exact=TRUE)

#--- estimate CFA
mod <- sirt::cfa_meas_inv(dat=dat[,-1], group=dat$group, verbose=TRUE, alpha=0.05)
mod$pars_mi
mod$pars_pi

## End(Not run)

alexanderrobitzsch/sirt documentation built on Dec. 1, 2024, 2:18 a.m.