An Information Theoretic Approach for Identifying Shared Information and Asymmetric Relationships Among Variables

Abstract

Behavioral researchers are often faced with the need to identify complex multivariate relationships. Statistical information theory provides a framework for quantifying in a single value the proportion of total information in one set of measures (Y) explained by another set of measures (X). It also quantifies the amount of redundant information and allows for asymmetry of explained information between variables. The general information theoretic approach is presented and illustrated using measures of affect, cognition and behavior. A statistically significant and asymmetric information theoretic relationship is found among the variables: affect (like/dislike) provides a higher percentage of information about behavior (shopping frequency) than behavior does about affect. In addition, affect provides a higher percentage of information about behavior than does perceived location convenience.