A conjoint analysis applies a complex form of analysis of variance to the data obtained from each respondent. This analysis calculates a value (or utility) for each feature. Features with the highest values are judged the most important to respondents. It tries to identify the interdependencies which exist between a numbers of variables.
First we need to create an orthogonal design feeding in the factors and defining various levels for each one of them. This design then needs to be saved as a Plan File and another Data file needs to imported from the excel. After running the syntax file we get a lot of tables and charts depicting the utility of individual subjects for different factors as well the group as whole along with an importance summary explicitly stating the importance of each factor as ranked or rated by the subjects. The factors can be rated, ranked or arranged by the subjects.
Conjoint analysis can be used to investigate the attributes that influence individual investors when they make a decision to buy shares. In deciding to buy a particular stock, financial measures, such as dividend and price–earnings ratio are relevant. However, they are less important than the company's management or recent movements in the share's price. These variables can be factored in and then analysed according to find out whether these people are speculators or investors.
Conjoint analysis can also be used to probe into the factors that are responsible for Venture Capitalists decision making. Decision making is central to the ability of venture capitalists to predict those new ventures likely to succeed.
Conjoint analysis is a useful tool that helps reveal the relative importance of component attributes. To improve the predictive ability of this analysis, research participants should be grouped into similar segments based on objectives, values and/or other factors.
NAME: ASMITA KARANGE