Mixed-Mode Cluster Analysis
Mixed-Mode Cluster Analysis is cluster analysis which permits all of the different Question Types available in Q.
This algorithm has been included following requests by users. However, there are other algorithms in Q which are preferable:
- If the data being clustered is numeric, Segments - K-Means Cluster Analysis is preferable.
- Where the variables to be analyzed are not numeric, Latent Class Analysis is preferable.
Note preferable means that these other algorithms have marginally better qualities, but it is still possible to get a good solution with Mixed-Mode Cluster Analysis, and such a solution may be superior to those obtained via the other algorithms.
How to create a Mixed-Mode Cluster Analysis in Q
- The first step depends on which version of Q you are using:
- Q5.0 and later: Create > Segments > Mixed-Mode Cluster Analysis
- Older versions of Q: Create > Segments, click Advanced, and change the Objective setting to Clustering.
- Select the questions to be used to form the segments in the Questions to analyze dialog box.
- If necessary, modify the default options. Note that:
- By default Q will automatically select the number of segments using the Bayesian information criterion. You can alternatively specify a specific number of segments by selecting the Manual option. Alternatively, you can select a different information criteria by clicking Advanced.
- The Question Type of the questions that are analyzed determines how the latent class model is conducted. For example, when analyzing a Pick One - Multi any scale points are ignored and Q treats the data as categorical; if it is converted to a Ranking Q focuses on understanding relativities; if it is converted to Number - Multi Q treats the data as being numeric.
- Latent Class Analysis for a discussion of the options and interpretation of the outputs (Mixed-Mode Cluster Analysis and Latent Class Analysis share the same options and the same outputs).
- Statistical Model for Latent Class Analysis, Mixed-Mode Tree, and Mixed-Mode Cluster Analysis for a discussion of the underlying statistical model.