Guidelines for Creating Clusters Using Grade Correspondence Analysis: Practical and Technical Issues

This document is intended as a practical guide for researchers using the Grade Correspondence Analysis (GCA) method of clustering data. The purpose of the guide is to highlight facets of GCA that are not immediately apparent or covered in other publications, and to ensure that all aspects of GCA can be exploited. It is based on experience gained and software used in the ESRC Analysis of Large and Complex Datasets project "Data enhancements to improve the scope and reliability of microsimulation models". See Taylor, Gomulka and Sutherland (2000).