Conjoint Analysis in R
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Conjoint Analysis is one of the most recent popular statistical techniques, which is being used in every kind of field to find the relative importance of preferences. Conjoint analysis engages the measurement of psychological judgments (consumer preferences, or accept abilities) or perceived similarities or differences between choice alternatives. Each problem of finding the preferences could be identified and solved by using the conjoint analysis methodology. Random Utility Model explains that the decision maker has a perfect discrimination capability. In this context if analyst has not the c...
Conjoint Analysis is one of the most recent popular statistical techniques, which is being used in every kind of field to find the relative importance of preferences. Conjoint analysis engages the measurement of psychological judgments (consumer preferences, or accept abilities) or perceived similarities or differences between choice alternatives. Each problem of finding the preferences could be identified and solved by using the conjoint analysis methodology. Random Utility Model explains that the decision maker has a perfect discrimination capability. In this context if analyst has not the complete information then the uncertainty always be in the decisions. Faisal Conjoint Model (FCM) is a the integrated model of conjoint analysis and random utility models. Its design is independent of design structure. It could be used for any research design, i.e., full profile, orthogonal, factorial, supersaturated, etc. Another important point about FCM is about its rank procedure. It works for every kind of ranks, i.e., unique ranks, percentage ranks, tied ranks, missing ranks etc.
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