This is the fourth in a series of blog posts designed as a DIY training course for using Displayr. This post presents the analysis of a relatively exotic experiment. Do not be concerned if you are unfamiliar with the technique (MaxDiff) and its data, as that is not the point of the post. This post is designed to introduce the basic workflow by which more advanced analyses are conducted in Displayr, and the basic principles illustrated in this post are applicable to most other types of modeling (e.g., machine learning, latent class analysis, regression).

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If you are really sure that advanced analysis is not your thing, skip ahead to Case Study: Visualizations.

You can view a video of this dashboard being created here.

Step 1: Opening the document

Step 2: Fitting the MaxDiff model

  • Open a new empty model from the menu:
    • In the Ribbon: Insert > More (Analysis) > Marketing > MaxDiff > Hierarchical Bayes

  • Feed it some data and customize the model, in the Object Inspector on the Inputs tab:
    • Design location: Provide a URL
    • Design URL: (MaxDiff is an experimental method, and its analysis requires both raw data and the experimental design).
    • Version: MDversion: MaxDiff Version [MDversion]
    • Best selections: Type mdmost and select the 10 variables. Make sure you select them in the correct order.
    • Worst selections: Select the 10 mdleast variables.
    • Add Alternative labels: Add these alternatives in the spreadsheet that opens: 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
  • This calculation is going to take about 10 minutes or so (it is doing a lot!). In the meantime, you may want to skip ahead and start on the next post in this series: Case Study: Visualizations.

When the calculation has 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 on our blog. However, there is no need to understand this output at this juncture, as the purpose of the post is to introduce the general workflow of using Displayr for advanced analyses.

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 Inputs > DIAGNOSTICS or Inputs > SAVE VARIABLE(S). Note that there are various things to extract in both menus. Click on Inputs > SAVE VARIABLE(S) > Save Preference Shares.

Once the calculation is complete, a new variable set will appear at the top of the Data Tree (that is, under Data Sets in the bottom left): Preference shares from max.diff.

Step 4: Writing up the results of the model

This post just illustrated the creation of the key outputs: the preference share variables. The next post in this series, series: Case Study: Visualizations, creates visualizations to explain the key outputs from this study.

Try it yourself

To see the document created by following the steps in this post, click here. Or...

Create your own MaxDiff Design