How to Use Hierarchical Bayes for MaxDiff

As computing has advanced, more computationally intensive methods for analyzing MaxDiff data have become possible. Hierarchical Bayes is the state-of-the-art technique for estimating coefficients for respondents in a MaxDiff study. This is seen as the "gold standard" in MaxDiff analysis, and is the most common method for estimating utilities for MaxDiff data, for more detail you can read our MaxDiff eBook.

Requirements

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

Note: If your MaxDiff data is from Alchemer or Qualtrics, see:

Method

From either the toolbar > Anything menu or the hover > + menu in the Report tree, select Advanced Analysis > MaxDiff > Hierarchical Bayes.
In the object inspector , 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).
Select the Version variable from your respondent data set. If you only have one version, 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, set the Number of classes to 1. This parameter controls the complexity of the model. If the data set contains discrete people, these segments may be missed if the number of classes is set to 1. A more complex model, which is one with more classes, is more flexible but takes longer to fit and may not necessarily provide better performance. If investigating more than one class, it is advisable to ensure it has better predictive accuracy than the one-class solution via cross-validation.
Iterations - The default value here is 100. This option controls how long the analysis runs. More iterations result in a longer computation time but often lead to better results. When using fewer iterations, the possibility exists that the model returns premature results. In addition, warning messages may appear about divergent transitions or that the Bayesian Fraction of Missing Information is low. However, the absence of warning messages does not mean that the number of iterations is sufficient.
Chains - This option specifies how many separate chains (independent analyses) to run, where the chains run in parallel, given the availability of multiple cores. Increasing the number of chains increases the quality of the results. It does, however, result in a longer running time if chains are queued up. Therefore the recommendation is to leave this option at its default value of 8. This makes full use of the 8 cores available when running R code in Displayr.
Maximum Tree Depth - As this is a very technical option, the practical implication is that this option should only be changed if a warning appears indicating that the maximum tree depth has been exceeded. The default value is 10 and a warning should not appear under most circumstances. If such a warning does appear, try first increasing the maximum tree depth to 12 rather than a larger number, as this could increase computation time significantly.
Leave other inputs at their default values.
Click the Calculate button to run the Hierarchical Bayes model.