Conjoint analysis is a rigorous survey technique used to understand healthcare preferences in the pharmaceutical and medical device industries.  In a traditional conjoint study, respondents are presented with a complete profile of all of the combination of attributes and features (or levels) for a particular product or service.  However, research involving a large number of attributes can be too burdensome for respondents and has been shown to elicit inaccurate responses.  This study describes a procedure used to link attributes from two or more different conjoint surveys that share at least one attribute.  Linked latent conjoint modeling can lessen the burden on respondents while allowing utility parameters to be estimated for a large number of attributes, all on the same interval scale. Conjoint survey data were linked using a partial profile design and parameters were calculated using maximum likelihood estimation for finite mixture modeling.  Several examples demonstrating the procedures used to link choice based survey data are provided.  In addition, results from a latent variable modeling of the linked survey data are reviewed.  Finally, to illustrate the flexibility of latent conjoint analysis, continuous and categorical covariates were simultaneously estimated to demonstrate the usefulness of latent modeling of conjoint data.