  Latent variable models represent a useful tool for the analysis of complex
data when the constructs of interest are not observable. A problem related to
these models is that the integrals involved in the likelihood function cannot
be solved analytically. We propose a computational approach, referred to as
Dimension Reduction Method (DRM), that consists of a dimension reduction of the
multidimensional integral that makes the computation feasible in situations in
which the quadrature based methods are not applicable. We discuss the
advantages of DRM compared with other existing approximation procedures in
terms of both computational feasibility of the method and asymptotic properties
of the resulting estimators.
