Factor Analysis

Another widely used procedure based on correlation is factor analysis. This procedure analyzes the intercorrelations among a large set of measures to identify a smaller number of common factors. Factors are hypothetical constructs assumed to underlie different types of psychological measures, such as intelligence, aptitude, achievement, personality, and attitude measures. Factor analysis indicates the extent to which tests or other instruments are measuring the same thing, enabling researchers to deal with a smaller number of constructs. Some factor analysis studies of intelligence tests, for example, have identified underlying verbal, numerical, spatial, memory, and reasoning factors.

The first steps in factor analysis involve selecting variables to be included in the analysis and developing a correlation matrix that shows the correlation of each measure with every other measure. There may be a very large number of correlations in the matrix. The matrix is then subjected to computations with a factor analysis computer program that produces clusters of variables that intercorrelate highly within the cluster but have low correlations with other clusters. These clusters are the factors, and the object is to identify a smaller number of separate underlying factors that can account for the covariation among the larger number of variables.