UPCOMING WEBINAR: Run Research Your Way With AI Skills The main goal of Latent Class Analysis is to group respondents into segments or classes. There are two ways to apply this analysis in Displayr, 1) for segmentation using various variable sets to create a tree (see here) and 2) for segmentation based on MaxDiff data - which is used in this article. This article describes how to go from a MaxDiff Latent Class Analysis output:
To variables which contain class memberships for each respondent, based on the latent class analysis.
Requirements
- A document containing a MaxDiff Latent Class Analysis output.
Method
To create variables that contain estimated preference shares for each respondent, based on the latent class analysis:
1. Select your latent class analysis output.
2. From the Report tree select + > Advanced Analysis > MaxDiff > Save Variable(s) > Class Membership.
When shown in a SUMMARY table, the output will show a summary table of the new variable.
Next
How to do Latent Class Analysis
How to do MaxDiff 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