ABSTRACT
The major purpose of factor analysis is the orderly simplification of a large number of inter-correlated measures to a few representative constructs or factors. Suppose that a researcher wants to identify the major dimensions underlying a number of personality tests. He begins by administering the personality tests to a large sample of people (N = 1000), with each test supposedly measuring a specific aspect of a person’s personality (e.g., ethnocentrism, authoritarianism, locus of control). Assume that there are 30 such tests, each consisting of 10 test items. What the researcher will end up with is a mass of numbers (i.e., 1000 × 30 × 10 = 300,000 scores) that will say very little about the dimensions underlying these personality tests. On average, some of the scores will be high, some will be low, and some intermediate, but interpretation of these scores will be extremely difficult if not impossible. This is where factor analysis comes in. It allows the researcher to “reduce” this mass of numbers to a few representative factors that can then be used for subsequent analysis.
