Factor Analysis and Regression

The disadvantage of the Likert scale is that it gives equal weight to all statements. Some statements are more important than others, and some statements are highly correlated to others. Ideally a measure that represents the group of statements, should take these issues into consideration.

In studies where an understanding of the importance of each statement is desirable, researchers may adopt a two-step process — firstly to reduce the statements into a smaller set of uncorrelated factors, and subsequently to determine the importance of each factor.

The statistical technique used to determine the factors is called factor analysis. It reduces the statements to a smaller set of factors — those statements that are highly correlated (i.e., fluctuate together) are grouped into the same factor, and those that exhibit low or zero correlation with each other fall into different factors. A summary measure such as “Overall satisfaction with teller service” may then be regressed on the factors to determine their relative importance. For instance, in brand equity research, the equity index is regressed on the factors to derive the importance or contribution of each factor to brand equity.


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