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.
