How to Run a Boosted Varying Coefficients Model on MaxDiff Data

Performs multinomial logit on MaxDiff data, which is equivalent to a single-class latent class analysis.

The table below shows the output of a boosted varying coefficients model using MaxDiff data on technology companies. In this case, 2 covariates have been selected (likelihood of recommending Apple and Samsung) and a boosted 3-class latent class analysis over respondents is run at the end. The histograms show the distribution of preference shares across respondents.

Requirements

A document containing your MaxDiff respondent data.
A MaxDiff experimental design.

Please note these steps require a Displayr license.

Method

Add the latent class analysis to your project by selecting Anything > Advanced Analysis > MaxDiff > Multinomial Logit.
Select your experimental design. You can use an existing table, an R output, variables from a data set, or a URL. For this example, I'm using an existing table that I've added to my project by selecting Table > Paste or Enter Table and then adding data by clicking on the Paste or type data button in the object inspector.
The Design input table needs to be in a form similar to the one shown below. The 'Version' column is optional when there is only one version in the design. The 'Question' column is also optional. However, if these columns are included, they must have the names 'Version' and 'Question'. The columns after this contain the indices of the alternatives presented to the respondents.
Select the Version variable from your respondent data set. If you only have one version, then this can be left blank.
In Best selections, choose the variables in your data set that identify the options that were selected as best, or most preferred, for each task. The order of the variables you have selected should match the order from the design (i.e., the variable for the first task should be selected first, the variable from the second task should be selected next, and so on).
In Worst selections, choose the variables in your data set which identify the options that were selected as worst, or least preferred, in each task.
Click on Add alternative labels, and enter the alternative names in the first column of the spreadsheet editor. The order of the alternatives should match the order in the design.
In the Model section, choose the Number of classes. I've selected 3 classes for this example. From the Type menu, select Varying Coefficients.
Click the Calculate button to run the Latent Class Analysis model.
OPTIONAL: In the Save Variable(s) section, choose the variables you want to save. Choices are Class Membership, Class Membership Probabilities, Preference Shares, Proportion of Correct Predictions, RLH(Root Likelihood), Sawtooth-Style Preference Shares (K Alternatives), and Zero-Centered Utilities.
OPTIONAL: Select Advanced Analysis > MaxDiff > Diagnostic to request the following diagnostics: Class Parameters Table, Class Preference Shares Table, Parameter Statistics Table, Posterior Intervals Plot, and Trace Plots