In recent years several authors have viewed latent
trait models for binary data as special models for
contingency tables. This connection to contingency
table analysis is used as the basis for a survey of
various latent trait models. This article discusses estimation
of item parameters by conditional, direct,
and marginal maximum likelihood methods, and
estimation of individual latent parameters as opposed
to an estimation of the parameters of a latent
population density. Various methods for testing the
goodness of fit of the model are also described.
Several of the estimators and tests are applied to a
data set concerning consumer complaint behavior.