Use of Clustering Methods to Understand More about the Case

Abstract

During the past seventy years, the field of cluster analysis has emerged, accompanied by a plethora of methods, algorithms, concepts, and terminology that are used in cluster-related research. We refer to cluster analysis (CA) as a general approach composed of several multivariate methods for delineating natural groups or clusters in data sets. In this paper, we describe the ability of CA to provide rich information about the individual case and highlight potential underlying social processes. First, we discuss the theory behind CA as well as differentiate between more and less familiar clustering approaches. Second, we illustrate the value of less familiar clustering techniques by comparing the results of a four wave growth mixture model of family variables versus clustering the same data with a more familiar two-step approach. The growth mixture modelling approach suggested a one-class cluster solution where all families shared similar growth trajectories in parenting practices and family relationship characteristics. However the two-step clustering approach suggested a four-class solution. Finally, we describe ways that CA allows researchers to model processes whose outcomes are the results of a combination of multiple factors and additional benefits of less familiar clustering methods.