How to Perform an Advanced Analysis of Experimental Data (MaxDiff)

This article walks through a case study of analyzing MaxDiff data using Hierarchical Bayes. For general How-To steps, see How to Use Hierarchical Bayes for MaxDiff.

Requirements

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

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

Method

Step 1: Opening the document

Create a new Displayr document and call it "Presidential MaxDiff".
Download the raw data file here.
Under Data Sources, click Add Data, choose Upload file, and select the data file that you just downloaded.

Step 2: Fitting the MaxDiff model

In the Report tree, select +> Advanced Analysis > MaxDiff > Hierarchical Bayes.
From the object inspector , go to Data > Experimental Design > Design source > Provide a URL.
In the Design URL box, specify the following URL: https://app.displayr.com/DataMart/File/b843f9f6-8180-49c9-b332-af993838a563/President_Experimental_Design.csv?company_id=751221
- MaxDiff is an experimental method, and its analysis requires both raw data and the experimental design.
In the Respondent Data section, select the Version variable: MDversion: MaxDiff Version [MDversion].
In Best selections, type MDmost and select the 10 variables. Make sure that you select them in the correct order from 1 to 10 as seen in the screenshot below:
In Worst selections, select the 10 MDleast variables. Again, make sure that you select them in the correct order from 1 to 10 as seen in the screenshot below:
In Experimental Design, click on Add Alternative labels.
Add these alternatives in the spreadsheet that opens, each on a separate line: Decent/ethical, Plain-speaking, Healthy, Successful in business, Good in a crisis, Experienced in government, Concerned for the welfare of minorities, Understands economics, Concerned about global warming, Concerned about poverty, Has served in the military, Multilingual, Entertaining, Male, From a traditional American background, Christian You will end up with a sheet that looks like the following:
Click OK.
Click Calculate.

This calculation is going to take about 10 minutes or so (it is doing a lot!).

When the calculation is finished, you will see an output like the one below. If you are interested in understanding what it means, we have lots of posts about MaxDiff in our Help Center.

Step 3: Extracting additional outputs from the model

Often when conducting an advanced analysis it is useful to extract certain things from the model, such as predictions, goodness-of-fit plots, and the like. The basic workflow for doing this in Displayr is to select the model output of interest, and then choose specific things to extract from menus.

Click on the output from the model and in the object inspector , select the diagnostics or other things to extract under Data > Diagnostics or Data > Save Variable(s). Note that there are various things to extract in both menus. See details about each option in the articles here.

How to Use Hierarchical Bayes for MaxDiff

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