Factor analysis is a general name denoting a class of procedures primarily used for data reduction and summarization. In marketing research, there may be a large number of variables, most of which are correlated and which must be reduced to a manageable level. Relationships among sets of many interrelated variables are examined and represented in terms of a few underlying factors. For example, store image may be measured by asking respondents to evaluate stores on a series of items on a semantic differential scale. These item evaluations may then be analyzed to determine the factors underlying store image.
In analysis Of variance, multiple regression, and discriminant analysis, one variable is considered as the dependent or criterion variable, and the others as independent or predictor variables. However, no such distinction is made in factor analysis. Rather, factor analysis is an interdependence technique in that an entire set of interdependent relationships is examined.I Factor analysis is used in the following circumstances:
- . To identify underlying dimensions, or factors, that explain the correlations among a set of variables. For example, a set of lifestyle statements may be used to measure the psychographic profiles of consumers. These statements may then be factor analyzed to identify the underlying psychographic factors, as illustrated in the department store example. This is also illustrated in Figure 19.1 derived based on empirical analysis, where the seven psychographic variables can be represented by two factors. In this figure, factor I can be interpreted as somebody versus socialite, and factor 2 can be interpreted as sports versus movies/plays.
- To identify a new, smaller set of uncorrelated variables to replace the original set of correlated variables in subsequent multivariate analysis (regression or discriminant analysis). For example, the psychographic factors identified may be used as independent variables in explaining the differences between loyal and nonlocal consumers. Thus, instead of the seven correlated psychographic variables of Figure 19.1,wecan use the two uncorrelated factors, i.e., somebody versus socialite, and sports versus movies/plays, in subsequent analysis.
- To identify a smaller set of salient variables from a larger set for use in subsequent multivariate analysis. For example, a few of the original lifestyle statements that correlate highJy with the identified factors may be used as independent variables to explain the differences between the \o.yal and nonlocal users. Specifically, based on theory and empirical results (Figure 19.1), we can select home is best place and football as independent variables, and drop the other five variables to avoid problems due to multicollinearity (see Chapter 17
All these uses are exploratory in nature and, therefore, factor analysis is also called exploratory factor analysis (EFA). The technique IUIS numerous Iloplicacioos in mar-kicking research. For example:
• It can be used’ in market segmentation for identifying the underlying variables on which to group the customers. New car buyers might be grouped based on the relative emphasis they place on economy, convenience, performance, comfort, and luxury. This might result in five segments: economy seekers, convenience seekers, performance seekers, comfort seekers, and luxury seekers
• In product research, factor analysis can be employed to determine the brand attributes that influence consumer choice. Toothpaste brands might be evaluated in terms of protection against cavities, whiteness of teeth, taste, fresh breath, and price.
• In advertising studies, factor analysis can be used to understand the media consumption habits of the target market. The users of frozen foods may be heavy viewers of cable TV, see a lot of movies, and listen to country music.
• In pricing studies, it can be used to identify the characteristics of price-sensitive consumers. For e-ample, these consumers might be methodical, economy minded, and home centered.