ABSTRACT
Multidimensional scalogram analysis is one of a sub-set of statistical procedures that look at the relationships between a large number of variables known, commonly, as multivariate analysis techniques. The most commonly used multivariate techniques are those that assume some underlying linearity in each of the variables and seek to relate those variables to some common latent dimensions, or factors. Factor analysis, therefore, can be seen as a procedure that looks at the correlations that every variable has with every other and seeks to establish the major dimensional components, the underlying vectors which characterize those relationships. Factor analysis, as a consequence, always leads to the identification of essentially independent dimensions that characterize the data under study. It operates on the shared variance amongst the different variables in order to develop what is in effect a list of constituents in which each plays a distinct, separate role in accounting for the way in which the variables vary in relation to each other.
