Influential analysis and presentation of survey data

Abstract

Surveys of human populations invariably yield data sets in a near-standard cases-by-variables format, where the variables divide into a set of biographical questions and a set of questions relating to the survey's direct objective. Analysis consists of investigating the interrelationships between these two sets of variables as well as exploring variation within each set. An approach is described using correspondence analysis and cluster analysis to reveal the patterns of covariation in a large data set. Correspondence analysis elicits new continuous constructs from the data, whereas cluster analysis elicits discrete constructs, and the two techniques often complement each other. The descriptions which they provide can influence one's understanding of the data by revealing the true complexity of features that are not always apparent in a formal modelling approach. Examples are presented in the context of readership surveys.