Quality of life (QOL) has become an important concept for health care. As QOL is a multidimensional concept that is best evaluated by a number of latent constructs, it is well recognized that latent variable models, such as exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) are useful tools for analyzing QOL data. Recently, QOL researchers have realized the potential of structural equation modeling (SEM), which is a generalization of EFA and CFA in formulating a regression type equation in the model for studying the effects of the latent constructs to the QOL or health-related QOL. However, as the items in a QOL questionnaire are usually measured on an ordinal categorical scale, standard methods in SEM that are based on the normal distribution may produce misleading results. In this article, we propose an approach that uses a threshold specification to handle the ordinal categorical variables. Then, on the basis of observed ordinal categorical data, a maximum likelihood (ML) approach for analyzing CFA and SEM is introduced. This approach produces the ML estimates of the parameters, estimates of the scores of latent constructs, and the Bayesian information criterion for model comparison. The methodologies are illustrated with a dataset that was obtained from the WHOQOL group.