This article describes how to do a Latent Class Analysis in Displayr. Latent Class is a statistical technique for grouping together similar observations (i.e., creating segments).
A data set containing the variables that you want to use as inputs to the cluster analysis segmentation.
1. Login into Displayr and load a document.
2. Load the data set that contains the variables that you want to use as inputs to the Latent Class Analysis.
3. From the toolbar menu, select Anything > Advanced Analysis > Cluster > Latent Class Analysis.
4. On the next screen, select the variables that you want to include as inputs to the Latent Class Analysis from the Available data list. The selected variables will be displayed on the Data to display list. As an example, I've used a data set containing statements on a 5-point agree/disagree scale about attitudes around mobile technology.
5. Select the number of segments you want to create:
- Select Work out number of groups automatically if you want Displayr to determine the number of groups with the greatest differences, or
- Select Specify the number of groups and enter a value for the number of segments you want to create.
For this example, I've select the latter option and entered a value of 4.
6. OPTIONAL: Apply a filter if you want to create a segmentation for a specific subgroup.
7. OPTIONAL: Select a weight if you want the input variables weighted.
8. Click the Create Latent Class Analysis button.
The Latent Class output will then be generated. The first column shows the distribution of responses for the enter sample used in the analysis. Each additional column shows the response distributions for each of the segments.
A new single response variable is added to the bottom of the data set called "Latent Class Analysis" with a date/time stamp in the variable label. This variable contains the segment assigned to each respondent. Create a table using this variable to see the distribution of segment assignments.