We had a masters student working on a survey for us that offered respondents a series of choices between A and B retirement plans in the form of a conjoint analysis. We have the data back and now we don't know how to do the analysis. How do you get to utility charts from the choices?

- Jan

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Thu 22 Jun 2017, 08:03
(This post was last modified: Thu 22 Jun 2017, 08:23 by Saul Dobney.)
Saul this is an area of my interest also. I've made some basic research on the subject also, Wikipedia and this article: link:www.researchoptimus.com/article/what-is-conjoint-analysis.php. I am planning to use conjoint also. If I have 5 types of parameters, with 5 choices (categories), where's the line between using Logistical Regression and Bayes Tecniques. Can I use the same data to make the analysis and will the results differ from each other. Have in mind that my experience is only in simple linear regression models.

[Mod: Careful on adding links please - you're trying to promote an agency without saying you're working for them]

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Hierarchical Bayes can be seen as an extension of Logistic Regression - both are used for categorical decisions (ie choices instead of ratings). In the original form of choice-based conjoint, data is analysed at a sample-level, so bringing all the data from all respondents into one pot for analysis, which would then use logistic regression. For marketing purposes this has the problem that it treats the dataset as homogeneous and so potentially masking different subgroups in the data. A variety of methods were used to try to identify different groups (eg latent class) from choice-based data and in current times, HB is the now preferred method to get back to individual level utilities from choice-based data.

Thanks for this wonderful information.