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41.027 - Recent Advances in Modeling Response Style Bias in Rating Scale Data
Sat, April 29, 8:15 to 10:15am, Henry B. Gonzalez Convention Center, Floor: River Level, Room 7BSession Type: Symposium
Abstract
This symposium examines response style bias in rating scale data using recently developed psychometric models. The symposium brings together renowned scholars in various disciplines from around the world to engage in invigorating discussion from a variety of perspectives, including model selection, bias detection, and bias correction. This session topic is significant because ignoring response bias can introduce systematic measurement error undetectable by traditional models, rendering incommensurability of ratings across respondents and invalidating any meaningful group comparisons. The session starts with a brief introduction to the formidable tasks in analyzing rating scale data, followed by six presentations each addressing a different aspect of the challenge on hand, and concluded by a world-renowned expert’s commentary on the recent advances and future directions.
Sub Unit
Chair
Papers
Measuring Response Styles in Likert Items Using Item Response Tree Models - Ulf Bockenholt, Northwestern University
Generalized Item Response Tree Models for Psychological Assessments - Minjeong Jeon, University of California at Los Angeles; Paul De Boeck, The Ohio State University
Identification of Response Styles in Attitude Measurement Using a Multiprocess Mixture Item Response Theory Model - Ryan Derickson, U.S. Department of Veteran Affairs; Lihshing Leigh Wang, University of Cincinnati; Jiaqi Zhang, SKT Education Group
Analyzing Response Styles and Trait-Based Rating Responses With Multiprocess Item Response Theory Models - Thorsten Meiser, University of Mannheim; Hansjoerg Plieninger, University of Mannheim
Differentiating Between Types of Response Styles and Valid Responses Using Mixture and Multiprocess Item Response Theory Models - Lale Khorramdel, ETS; Matthias Von Davier, National Board of Medical Examiners; Artur Pokropek, Polish Academy of Science
Separating Content-Related Effects From the Tendency Toward Middle or Extreme Categories in Rating Scale Data - Moritz Berger, University of Bonn; Gerhard Tutz, Ludwig-Maximilians-Universität München; Matthias Schmid, University of Bonn