Identifying Groups in Data: Analyses Based on Dissimilarities Between Sequences
| TOC | Content (click for jumping to examples) |
|---|---|
| 4.1 Clustering sequences to uncover typologies | Crisp (or hard) clustering algorithms |
| 4.2 Illustrative application | Hierarchical clustering: Ward’s linkage |
| Partitional clustering: PAM | |
| Visualizing clustering options with MDS | |
| Comparison between different time granularities | |
| 4.3 “Construct validity” for typologies from cluster analysis to sequences | No code for this section |
| 4.4 Using typologies as dependent and independent variables | Clusters as outcomes |
| Clusters as predictors |
Chapter 4 considers how to use the dissimilarity matrices to identify groups in data by using different clustering techniques. The resulting typology is further analyzed either as a categorical independent or dependent variable within a regression framework. We recommend to read Chapter 4.3 carefully. as it contains important considerations on how to make informed decisions when identifying the number of clusters.
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