18th Annual Student Research Conference
Social Science
Predicting Mood in Everyday Situations
Jennifer E. Hopper*, Cheylynne Y. Bosley, Chinaka I. Agwu, Whitney B. Fancher, Alicia N. Lee, and Kate Pickett
Dr. Jeffrey Vittengl, Faculty Mentor
This study measures undergraduates’ accuracy and bias in predicting their own mood in everyday situations. Participants in this study are completing fully-structured, paper-and-pencil diary measures. In particular students are asked to identify 12 situations they expect to encounter during the next week. These situations should fit into one of four categories including; pleasant-social, pleasant-nonsocial, unpleasant-social, and unpleasant-nonsocial. This variation is being utilized to ensure reliability in both mood and content. After completing their predicted mood for each situation students are asked to transfer their situations to another packet. During the following week, participants record their actual mood after the situations occur. We hypothesize that participants will be relatively accurate in their mood predictions. However, we also hypothesize that participants will predict more intense mood than they actually experience. This study is intended to replicate and extend prior mood-bias research to naturally occurring, everyday experiences.
Keywords: Mood Prediction, Everyday Situations, Pleasant-social, Pleasant-nonsocial, Unpleasant-social, Unpleasant-nonsocial
Topic(s):Psychology
Presentation Type: Poster
Session: 29-81
Location: OP Lobby & Atrium
Time: 1:15