What statistical procedure identifies underlying clusters by correlating test scores?

Factor analysis is used to identify underlying structures or clusters within a set of data by examining the interrelationships between multiple variables. In the context of test scores, factor analysis would allow researchers to determine which scores are correlated and to group them into factors or clusters that represent a common underlying trait or ability.

This is particularly useful in psychology and social sciences, where different tests might measure various dimensions of intelligence, personality, or other constructs. By using factor analysis, researchers can simplify complex data sets and identify the latent variables that account for the observed correlations among the test scores, aiding in the understanding of the underlying constructs being measured.

In contrast, regression analysis is more about predicting a dependent variable from one or more independent variables rather than identifying clusters. The correlation coefficient measures the strength and direction of a relationship between two variables but does not himself group them into clusters. Variance analysis, often referred to as ANOVA, assesses the differences between group means but does not focus on revealing underlying relationships or clusters among variables. Thus, factor analysis is the appropriate method for identifying underlying clusters based on the correlations of test scores.