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Issues of Measurement Invariance in Mixture Models for Educational Data
Sun, April 19, 8:15 to 10:15am, Virtual RoomSession Type: Symposium
Abstract
This organized symposium presents a collection of papers focused on both theoretical and methodological issues related to measurement invariance for mixture models. While measurement invariance is commonly discussed in the factor analytic context, less attention has been afforded to this issue in the mixture modeling context when dealing with multiple subgroups. We unpack the implications of measurement invariance and the assumptions it posits on the number and type of latent classes that emerge, issues and practical concerns around evaluating measurement invariance, and meaning and comparisons that can be made from partially invariant mixture models. Together, this symposium pushes the field forward by highlighting practical and methodological issues related to the use and comparisons of mixture models when considering multiple populations.
Sub Unit
Chair
Papers
Measurement Invariance in Mixture Modeling: Issues and Current Practices - Odelia Simon, University of California - Santa Barbara; Karen L. Nylund-Gibson, University of California - Santa Barbara
Using Mixture Modeling to Make Meaning of Differential Item Functioning - Daniel Katz, University of California - Santa Barbara
Structural Invariance in Multigroup Latent Class Analysis: Perception of Disability Status and Academic Expectations - Adam Garber, University of California - Santa Barbara; Karen L. Nylund-Gibson, University of California - Santa Barbara
Measurement Invariance in Latent Transition Models - Karen L. Nylund-Gibson, University of California - Santa Barbara; Katherine E. Masyn, Georgia State University
Validation and Calibration in Mixture Modeling: The Exploratory Factor Analysis/Confirmatory Factor Analysis of Mixture Models - Delwin Carter, University of California - Santa Barbara; Karen L. Nylund-Gibson, University of California - Santa Barbara
Integrative Data Analysis With Mixture Models: Person-Centered Approaches With Pooled Data - Katherine E. Masyn, Georgia State University; Adrian Bravo, William & Mary; Matthew R Pearson, University of New Mexico