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Session Type: Symposium
This symposium highlights how application of advanced statistical analyses contributes to not only novel empirical results but novel conceptual thinking. The session includes an international team of experts from the US, Canada, Germany, and Hong Kong. The presented studies combine established theories such as emotions, expectancy-value theory, and self-determination theory with contemporary statistical analyses such as network analysis, sentiment analysis, dynamic structural equation modeling, piecewise growth modeling, and bifactor exploratory structural equation modeling to reveal new empirical insights and to expand common conceptualizations. The presenters and discussant will suggest practical implications of working with advanced statistics to contribute to the repair of education in terms of motivation and emotions.
Teachers’ Enthusiasm and their Natural Language in Class – a Sentiment Analysis Approach - Anne C. Frenzel, University of Munich; Hannah Kleen, DIPF | Leibniz Institute for Research and Information in Education; Anton Karl Georg Marx, University of Munich; David F. Sachs, Ludwig Maximilian University of Munich; Franziska Baier-Mosch, University Siegen; Mareike Kunter, DIPF | Leibniz Institute for Research and Information in Education
Exploring the Interplay Between Teachers’ Daily Emotional Labor and Daily Perceived Student Engagement through Network Analysis - Hui Wang, The Education University of Hong Kong; Shan Li, Lehigh University
Dynamics of Expectancies, Values, and Costs among College Students and their Relation to Motivational Development - John Keane, University of Michigan; Lisa Linnenbrink-Garcia, Michigan State University
Dissecting Motivational Processes Shaped by a Utility-Value Intervention in Chemistry: A Piecewise Growth Curve Analysis - Fani Lauermann, University of Bonn; Patrick N. Beymer, University of Cincinnati; Daria Katharina Benden, University of Bonn
Bifactor-ESEM Validation of Basic Psychological Need Satisfaction and Frustration in Classroom Assessment - Lia Daniels, University of Alberta; Kendra Lin Wells, University of Alberta