Factor analysis

This is a generic name given to a class of multivariate statistical methods that undertake data reduction and summarisation. The method analyses the interrelationships among a large number of variables and explains these variables in terms of their common underlying dimensions (factors). For example, a survey questionnaire may consist of 100 questions, but because not all the questions are the same, they do not all measure the basic underlying dimensions to the same extent. Factor analysis enables us to identify the separate dimensions being measured by the survey and obtain a factor loading for each variable on each factor.

Factor analysis may be used to discover a set of dimensions that underlie or underpin a set of variables (Figure 10.8). Applications for factor analysis include:

• uncovering the factors that influence advertising readership

• ascertaining which personal characteristics are associated with preferring one brand of product to another

• uncovering the important dimensions of product/service quality

• uncovering the factors that need to be taken into account in making decisions about such things as product design and promotion.








Germany Switzerland


\ France —■—^ \ Norway Austria







Infant mortality




Factor 1


Scatterplot of standard of living analysis


Scatterplot of standard of living analysis

The example in Table 10.10 shows a factor analysis of the standard of living data just examined. Factor 2 represents the health dimension and Factor 1 the spending power dimension.

For further information on factor analysis, see Hair et al.3 and Green et al.4

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