Scaling Methods: A Taxonomy

Jörg Blasius & Victor Thiessen

Scaling methods germinated in the pioneering work of Karl Pearson and Charles Spearman, who in the first decade of the 20th century developed methods to describe relationships between variables. Pearson created the correlation coefficient for metric data, while Spearman introduced the rank order correlation and an algorithm designed to document the existence of a general factor in intelligence research. Since then, these ideas have been extended to permit an (in theory) unlimited number of factors obtained from data ranging from crude categorical to refined metric and permitting the imposition of a variety of constraints on the variables. What all scaling methods have in common, however, is that they can be used to transform a set of observed variables to a smaller number of latent variables or factors (dimensions). Scaling methods differ with respect to the type of input data for which they are most appropriate. Principal component analysis (PCA) and ...

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