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Session Type: Paper Session
This session discusses innovative approaches to analyzing educational data. The papers in this session discuss advanced statistical models and fitting methods for analyzing complex data. Latent Dirichlet Allocation, Cross-classified random effects modeling, Hidden Markov modeling, L-moments theory and their utility for analyzing educational data are discussed.
Uniform Quadratic and Logistic Quantile Mixture Polynomial Distributions Based on L-Moment Theory - Mohan Dev Pant, Eastern Virginia Medical School
Understanding Textual Data in Social Science Using Latent Dirichlet Allocation Model - Minju Hong, University of Arkansas at Fayetteville; Sunyoung Park, California Lutheran University
Comparison of Competing Approaches to Analyzing Cross-Classified Data - Young Ri Lee, The University of Texas at Austin; James Eric Pustejovsky, University of Wisconsin - Madison
Exploring Rater Accuracy Using Unfolding Models Combined With Topic Models Incorporating Supervised Latent Dirichlet Allocation - Jordan M. Wheeler, University of Georgia; Jue Wang, University of Science and Technology of China; George Engelhard, University of Georgia
Trajectories of Doctoral Students in the Biological Sciences: A Hidden Markov Model Approach - Jerry Michael Whitmore Jr, Boston University; Jordan Lawson, Boston College