Multivariate methods for index construction

Surveys, by their very nature, result in data structures that are multivariate. While recognizing the value of simple approaches to survey data analysis, the present chapter illustrates the benefits of a more in-depth analysis, for selected population subgroups through the application of multivariate techniques. Software packages are now available that make possible the application of these more advanced methods by survey researchers. This chapter demonstrates a range of situations where multivariate methods have a role to play in index construction and in initial stages of data exploration with specific subsets of the survey data, before further analysis is carried out to address specific survey objectives. The focus is mainly on methods that involve the simultaneous study of several key variables. In this context, multivariate methods allow a deeper exploration into possible patterns that exist in the data, enable complex interrelationships among many variables to be represented graphically, and provide ways of reducing the dimensionality of the data for summary and further analysis. The discussion on index construction uses the broader interpretation of multivariate methods to include regression-type methods. The emphasis throughout is on providing an overview of multivariate methods so that an appreciation of their value towards index construction can be obtained from a very practical point of view. It is aimed both at those engaged in large-scale household surveys and at survey researchers involved in research and development projects who may have little experience in the application of the analysis approaches described here. The use of these methods is illustrated with suitable examples and a discussion of how the results may be interpreted. Key terms: Index construction, multivariate methods, principal components, cluster analysis. Household Sample Surveys in Developing and Transition Countries