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A correlation analysis is a statistical procedure that evaluates the association between two sets of variables. The association between variables can be linear or nonlinear (→  Linear and Nonlinear Models of Causal Analysis ). In communication research, however, correlation analyses are mostly used to evaluate linear relationships. Sets of variables may include one or many variables. Associations between two variables (two sets of one variable) can be analyzed with a bivariate correlation analysis . Associations between one (dependent) variable and a set of two or more (independent) variables can be studied using multiple correlation (regression) analysis (→  Regression Analysis ; Discriminant Analysis ). Relationships between sets of many (independent and dependent) variables can be investigated using canonical correlation analysis (→  Factor Analysis ; Structural Equation ). Variables in each set can be measured on a nominal, ordinal, or interval level (→  Scales ; Measurement Theory ). There is a specific correlation analysis for any combination of measurement level and number of variables in each set. Associations between ordinal variables (i.e., variables that have been measured on an ordinal level) are usually analyzed with a nonparametric correlation analysis (→  Nonparametric Analysis ). All other combinations can be considered parametric linear correlation analyses ... log in or subscribe to read full text