Andrea Cataldo (UMass Amherst)

Time: 12:00pm to 1:15pm Wednesday Dec. 7  Location: Tobin 521B.
Title: A Bayesian Hierarchical Signal Detection Model for Rating Scale Data

Abstract: The rating scale is a predominant method of quantifying internal states. For instance, emotion is typically measured by asking individuals to rate how well each of several emotional words represents their current state. Group mean ratings are then compared to determine which emotion was rated highest. This method of analysis is limited in two ways: First, the finite number of stimuli available to target particular states, such as emotion, reduces within-subjects power. Second, comparing mean ratings is an insensitive measure of how individuals discriminate between possible states. This talk presents a Bayesian hierarchical signal detection model for rating scale data as a solution to both limitations: signal detection theory first offers a more sensitive discrimination measure, and the Bayesian hierarchical framework provides more reliable estimates at the individual level.