- A document containing your MaxDiff respondent data.
- The MaxDiff experimental design.
- Add the latent class analysis to your project by selecting Anything > 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 which I've added to my project by selecting Table > Paste or Enter Table: and then added data by clicking on the red Paste or type data button in the object inspector on the right.
- 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 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, choose the Number of classes. I've selected 4 classes for this example.
- Click the Calculate button to run the Latent Class Analysis model.
How to do Latent Class Analysis
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 Respondent-Level Preference Shares from a MaxDiff Latent Class Analysis
How to Convert Alchemer MaxDiff Data for Analysis in Displayr
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