One of the major objectives in conjoint analysis has been to predict the choices made by a sample of individuals for a new item, which is described in terms of a set of attributes used in a conjoint study. Ratings-based conjoint studies involve the conversion of an individual’s stated utility for an item (or rating) to predict the probability of choice of an alternative under various conditions (e.g., when other alternatives are available). As described in Chap. 4 , such a prediction is made using preference data (ratings or rankings) collected on a set of hypothetical choice alternatives. A parallel stream of research pursues the path of choice experiments in which an individual makes a choice among a set of choice alternatives (a choice set), each of which is typically described by a set of attributes; several choice sets are presented to each individual. These choice data, across all the choice sets and all individuals, are then analyzed using a choice model (usually a multinomial logit model and sometimes a multinomial probit model) to obtain a function that relates the attribute levels to probability of choice. This approach has come to be known as choice-based conjoint analysis and has its roots in discrete choice analysis; these methods are also called “stated” choice methods (or stated choice experimental methods) because they represent the intended choices of respondents among hypothetical choice possibilities. This chapter describes these methods.