rChapter4-0.Rmd

---
title: "Chapter 04"
description: |
  Identifying Groups in Data: Analyses Based on Dissimilarities Between Sequences
output:
  distill::distill_article:
    toc: false
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE)
```


| TOC                           | Content (click for jumping to examples)  |
|-------------------------------------|---------------------------------------------|
| 4.1	Clustering sequences to uncover typologies                              | [Crisp (or hard) clustering algorithms](rChapter4-1.html) | 
| 4.2	Illustrative application                                                | [Hierarchical clustering: Ward’s linkage](rChapter4-2.html) | 
|                                                                             | [Partitional clustering: PAM](rChapter4-2.html#partitional-clustering-pam) |
|                                                                             | [Visualizing clustering options with MDS](rChapter4-2.html#visualizing-clustering-options-with-mds) |
|                                                                             | [Comparison between different time granularities](rChapter4-2.html#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](rChapter4-4.html) | 
|                                                                             | [Clusters as predictors](rChapter4-4.html#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.