We are new here, and have recently gotten a question that we have very much been struggling to answer. It is concerning a question regarding a conjoint analysis in which we have to incorporate an individual-specific intercept. We are working with the a regression model that includes:
- 15 Consumers (subjects)
- 22 Profiles
- 1 – 5 attributes
- amount of Levels varying from 2 levels to 3 levels
We already have all dummies needed to conduct a general conjoint analysis in SPSS. This question however is as follows:
while consumers’ preferences over different attributes and levels are more or less the same, they do have different baseline level of utility (constant terms). As a consequence, to derive more accurate estimation of consumers’ part-worth values, researchers need to make the intercept individual-specific (but the part-worth values are the same across consumers). In your analysis, try to think of a way to incorporate individual-specific intercept in the regression.
We have tried many different approaches but haven't fully figures out how to solve this problem. Can anybody maybe give us some tips? We highly appreciate it!
Best Answer
How many scenarios did each respondent see? I typically use hierarchical Bayes to estimate individual random effect discrete choice models, especially if I have a small sample. You can use covariates in the upper-level model (like country, respondent type, etc.) to further pool the data which adds more stability and statistical support to the individual-level random effects. The upper-level models can be used directly as well for even more stability. There are several programs that do this including R that you can call from SPSS. The package is called ChoiceModelR. I would also check out Elea Feit's book called "R for Marketing Research and Analytics" by Springer. A link with code examples and data below.
Hope this helps.
http://r-marketing.r-forge.r-project.org/