This chapter covers exploratory (EFA) and confirmatory (CFA) factor analysis as strategies for understanding the dimensionality of psychological constructs and of the instruments used to measure them. EFA is often used to identify underlying dimensions in a data set, especially in the early stages of instrument development and construct evaluation. EFA can help form hypotheses about psychological phenomena and constructs and the results can have clinical utility to the degree that the scale scores have unique predictive efficacies. Principal component analysis, a specific form of EFA, is used to reduce a large number of variables to a smaller number of underlying components. CFA is typically used to test hypotheses concerning the structure of a set of indicators, compare competing structural hypotheses, or examine the generalizability of a hypothesized factor structure across samples that differ on dimensions of individual difference. When applying the results of factor analysis in clinical decision-making, the clinician should consider the characteristics of the factor analysis sample, the variables included and their psychometric evidence, the data analytic procedure employed, and the amount of variance accounted for by the factors. The chapter ends with multiple recommendations for interpreting the results of factor analysis studies.