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Advancements and Challenges in Cognitive Diagnostic Models and Mixture Modeling
Thu, April 24, 5:25 to 6:55pm MDT (5:25 to 6:55pm MDT), The Colorado Convention Center, Floor: Meeting Room Level, Room 302Session Type: Symposium
Abstract
This session tackles key issues in approaches that classify individuals into distinct categories or classes. By examining the identifiability and reliability of Cognitive Diagnostic Models (CDMs) under realistic conditions, we aim to enhance the validity of inferences drawn from these models. Extending Bonifay and Cai’s (2017) work on model complexity, we gain insights into the performance of various IRT and latent class analysis (LCA) models with randomly generated data. Additionally, we compare Latent Class Tree Analysis and traditional LCA in handling local independence violations, providing a nuanced view of class membership identification accuracy and reliability. Integrating machine learning-based imputation within growth mixture models addresses a critical gap, potentially revolutionizing missing data handling in longitudinal studies.
Sub Unit
Chair
Papers
Challenges with Identification and Estimation of Cognitive Diagnostic Models - Claudia J. Ventura, University of Connecticut; Eric Loken, University of Connecticut
Examining Model Complexity Due to Functional Form with Randomly Generated Data - Kirsten Reyna, University of Connecticut; Eric Loken, University of Connecticut
Evaluating the Performance of Standard Latent Class Analysis Compared to Tree-Based Latent Class Analysis - Richard Baidoo, University of Connecticut; Zachary K. Collier, University of Connecticut; Eric Loken, University of Connecticut
Imputation with Random Forest and Deep Learning for Bayesian Estimated Growth Mixture Models - Joshua Sukumar, University of Connecticut; Zachary K. Collier, University of Connecticut
Detecting Differential Effects Through Parallel Multiple-Mediator Mixture Models - Min Liu, Baylor University; Gregory R. Hancock, University of Maryland