This chapter discusses three important multivariate statistical analysis methods: principal components analysis (PCA), factor analysis (FA), and cluster analysis (CA). PCA and FA are often used together for data reduction by structuring many variables into a limited number of components (factors). The techniques are particularly useful for eliminating variable collinearity and uncovering latent variables. Applications of the methods are widely seen in socioeconomic studies (also see case study 8 in Section 8.4). While the PCA and FA group variables, the CA classiﬁes many observations into categories according to similarity among their attributes. In other words, given a dataset as a table, the PCA and FA reduce the number of columns and the CA reduces the number of rows.