Discrete choice experiments (DCEs), also called stated or conjoint choice experiments, are widely used to quantify people’s preferences in fields as diverse as economics, marketing, transportation, health, psychology, environmental planning, and social, political and communication sciences. Given a set of predefined attributes of a product or an item, DCEs identify those attributes that matter most and indicate the most appealing levels for each. Typically, DCEs involve respondents choosing among hypothetical (occasionally real) alternative items presented in choice sets where the alternatives, also called profiles, are combinations of levels of different attributes. In most studies, the number of attributes is large (loosely speaking, more than five). To bridge the gap between the incorporation of many attributes in profiles and the increased cognitive load in choosing between profiles, we recommend using partial profile designs. As opposed to full profile designs, which vary the levels of all attributes in the profiles of the choice sets, partial profile designs vary the levels of only a subset of the attributes. Using real-life DCE case studies, we show how to construct full and partial profile designs by means of the recommended Bayesian design approach in JMP. We show how to analyse the resulting choice data, both on an aggregate level (by pooling the data) and on an individual level, when the number of choice sets evaluated by each respondent allows doing so.