This article deals with the problem of analyzing sets of ipsative variables using the common factor model. We demonstrate that the usual assumptions of the common factor model, especially the assumption of uncorrelated disturbances, are not appropriate for sets of ipsative variables. We develop a common factor model that takes into account the ipsative properties of such data and show how this model can be applied to any set of ipsative measures using the methods of confirmatory factor analysis. We then suggest that the application of this model may be useful in modeling the latent content of sets ofrankings and other measures that have the ipsative property as a result of the measurement procedure. Finally, we apply the model to Kohn's measures of parental values, using sample data from the General Social Surveys.
