## Introduction

This article describes how to use Hierarchical Bayes for analysis of MaxDiff data.

## Requirements

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

## Method

- Select
**Anything > Advanced Analysis****> MaxDiff > Hierarchical Bayes**. - 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 which I've added to my project by selecting
**Table > Paste or Enter Table**: - 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 which 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, 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 for. More iterations result in a longer computation time but often leads 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.

## See Also

How to Create MaxDiff Model Ensembles

How to Create a MaxDiff Model Comparison Table

How to Create a MaxDiff Experimental Design

How to Save Classes from a MaxDiff Latent Class Analysis

How to Save Respondent-Level Preference Shares from a MaxDiff Latent Class Analysis

How to Convert Alchemer MaxDiff Data for Analysis in Displayr

How to Create Trace Plots from a Hierarchical Bayes Analysis

How to Create a Table of Parameter Statistics from a Hierarchical Bayes Analysis

How to Create a Posterior Intervals Plot from a Hierarchical Bayes Analysis

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