This function approximates a fitted item response model by a linear confirmatory factor analysis. I.e., given item response functions, the expectation \(E(X_i | \theta_1, \ldots, \theta_D)\) is linearly approximated by \(a_{i1} \theta _1 + \ldots + a_{iD} \theta_D\). See Vermunt and Magidson (2005) for details.
IRT.linearCFA( object, group=1)# S3 method for IRT.linearCFA
summary(object, ...)
A list with following entries
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.
Data frame with standardized factor loadings.
Mean of factors
Standard deviations of factors
Fitted item response model for which the IRT.expectedCounts
method is defined.
Group identifier which defines the selected group.
Further arguments to be passed.
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
See tam.fa for confirmatory factor analysis in TAM.