TAM: IRT.linearCFA – R documentation

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IRT.linearCFA

Linear Approximation of a Confirmatory Factor Analysis

Description

This function approximates a fitted item response model by a linear confirmatory factor analysis. I.e., given item response functions, the expectation E(X_i | θ_1, …, θ_D) is linearly approximated by a_{i1} θ _1 + … + a_{iD} θ_D. See Vermunt and Magidson (2005) for details.

Usage

IRT.linearCFA( object, group=1)

## S3 method for class 'IRT.linearCFA'
summary(object,  ...)

Arguments

`object`	Fitted item response model for which the `IRT.expectedCounts` method is defined.
`group`	Group identifier which defines the selected group.
`...`	Further arguments to be passed.

Value

A list with following entries

`loadings`	Data frame with factor loadings. `Mlat` and `SDlat` denote the model-implied item mean and standard deviation. The values `ResidVar` and `h2` denote residual variances and item communality.
`stand.loadings`	Data frame with standardized factor loadings.
`M.trait`	Mean of factors
`SD.trait`	Standard deviations of factors

References

Vermunt, J. K., & Magidson, J. (2005). Factor Analysis with categorical indicators: A comparison between traditional and latent class approaches. In A. Van der Ark, M.A. Croon & K. Sijtsma (Eds.), New Developments in Categorical Data Analysis for the Social and Behavioral Sciences (pp. 41-62). Mahwah: Erlbaum

Examples

## Not run: 
library(lavaan)

#############################################################################
# EXAMPLE 1: Two-dimensional confirmatory factor analysis data.Students
#############################################################################

data(data.Students, package="CDM")
# select variables
vars <- scan(nlines=1, what="character")
    sc1 sc2 sc3 sc4 mj1 mj2 mj3 mj4
dat <- data.Students[, vars]

# define Q-matrix
Q <- matrix( 0, nrow=8, ncol=2 )
Q[1:4,1] <- Q[5:8,2] <- 1

#*** Model 1: Two-dimensional 2PL model
mod1 <- TAM::tam.mml.2pl( dat, Q=Q, control=list( nodes=seq(-4,4,len=12) ) )
summary(mod1)

# linear approximation CFA
cfa1 <- TAM::IRT.linearCFA(mod1)
summary(cfa1)

# linear CFA in lavaan package
lavmodel <- "
    sc=~ sc1+sc2+sc3+sc4
    mj=~ mj1+mj2+mj3+mj4
    sc1 ~ 1
    sc ~~ mj
    "
mod1b <- lavaan::sem( lavmodel, data=dat, missing="fiml", std.lv=TRUE)
summary(mod1b, standardized=TRUE, fit.measures=TRUE )

#############################################################################
# EXAMPLE 2: Unidimensional confirmatory factor analysis data.Students
#############################################################################

data(data.Students, package="CDM")
# select variables
vars <- scan(nlines=1, what="character")
    sc1 sc2 sc3 sc4
dat <- data.Students[, vars]

#*** Model 1: 2PL model
mod1 <- TAM::tam.mml.2pl( dat )
summary(mod1)

# linear approximation CFA
cfa1 <- TAM::IRT.linearCFA(mod1)
summary(cfa1)

# linear CFA
lavmodel <- "
    sc=~ sc1+sc2+sc3+sc4
    "
mod1b <- lavaan::sem( lavmodel, data=dat, missing="fiml", std.lv=TRUE)
summary(mod1b, standardized=TRUE, fit.measures=TRUE )

## End(Not run)

TAM

Test Analysis Modules

v3.6-45

GPL (>= 2)

Authors

Alexander Robitzsch [aut,cre] (<https://orcid.org/0000-0002-8226-3132>), Thomas Kiefer [aut], Margaret Wu [aut]

Initial release

2021-04-22 14:35:52