Any specific rules to follow for the same.

Do share details on the same

Thanks!

Visit Sawtooth Software Feedback to share your ideas on how we can improve our products.

Any specific rules to follow for the same.

Do share details on the same

Thanks!

+2 votes

Most important thing to remember is that the utilities should be normalized per respondent. That gives each respondent essentially the same "scale factor" or magnitude of the parameters.

There are multiple ways to do this. You can export the utilities from SMRT, through Run Manager + Export (and then choose the "Zero-centered Diffs" option to export the utilities. Zero-centered diffs subtracts the mean utility within each attribute, causing each attribute to have utilities that sum to zero (this really should automatically occur in most conjoint analysis approaches, such as CBC). Then, Zero-centered diffs scaling procedure multiplies all the zero-centered utilities by the constant for each individual such that the sum of the differences between min and max utilities within each attribute, across attributes, averages 100 per attribute.

There are other ways to normalize the parameters as well to give each respondent the same scaling. For example, one could find the multiplicative factor that gives each respondent the same standard deviation across all utilities.

There are multiple ways to do this. You can export the utilities from SMRT, through Run Manager + Export (and then choose the "Zero-centered Diffs" option to export the utilities. Zero-centered diffs subtracts the mean utility within each attribute, causing each attribute to have utilities that sum to zero (this really should automatically occur in most conjoint analysis approaches, such as CBC). Then, Zero-centered diffs scaling procedure multiplies all the zero-centered utilities by the constant for each individual such that the sum of the differences between min and max utilities within each attribute, across attributes, averages 100 per attribute.

There are other ways to normalize the parameters as well to give each respondent the same scaling. For example, one could find the multiplicative factor that gives each respondent the same standard deviation across all utilities.

+1 vote

Another thing to remember is that, because they sum to zero, utilities for attributes with part worth coding will create ill conditioned matrices for some analyses (e.g. regression, factor analysis). So if you have a 5 level attribute you'll need to drop one of its levels out of your regression or factor analysis to get the analysis to run.

This won't be a problem for cluster analysis, which isn't a multivariate technique and which therefore isn't affected in this way by ill conditioning.

This won't be a problem for cluster analysis, which isn't a multivariate technique and which therefore isn't affected in this way by ill conditioning.

...