How to Do MaxDiff Latent Class Analysis

Latent class analysis is used with MaxDiff studies for two quite different reasons:

To create segments. The output of latent class analysis is a small number of groups of respondents with different preferences. These groups can be treated as segments.
As an alternative to Hierarchical Bayes (the gold standard). However, latent class analysis makes very different assumptions and can occasionally outperform Hierarchical Bayes, particularly if data shows evidence of a small number of discrete segments.

For more details on MaxDiff methods, you can read our MaxDiff ebook in the Data Story Guide.

Requirements

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

Method

From the toolbar, go to Anything > Advanced Analysis > MaxDiff > Latent Class Analysis or in the Report tree, click +> Advanced Analysis > MaxDiff > Latent Class Analysis.
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 copying it from Excel and pasting it onto my page using Ctrl + v (or Cmd + v on a Mac).
In the object inspector , 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 that 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 4 classes for this example.
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