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Definitions and measures of discrete emotions are thorny issues in research on affect, resulting from the multitude of emotion theories and approaches to this concept; there is no gold standard for measures of discrete emotions. The utility of measurement methods should be compared across multiple perspectives to allow some degree of cumulativeness. This study reported emotion data collected from a college sample (N = 113), using seven professionally produced videos as stimuli messages. Fear, anger, sadness, disgust, and happiness were measured with the self-report method and by analyzing recorded facial expressions with FaceReader™, an FACS-based computer software that automatically analyzes facial expressions for discrete emotions. Multilevel modeling analyses demonstrated initial evidence for the correspondence between emotions measured with both methods and convergent and discriminant validity for FaceReader™ as a method of measuring discrete emotions.

Correspondence between Two Methods of Measuring Discrete Emotions: Self-report versus Machine-coded Facial Displays