Minjeong Jeon, University of California, Los Angeles

I will present a novel statistical framework for analyzing item response data. The proposed framework leverages ideas and tools from state-of-art latent space modeling approaches and aims to capture unknown, complex item and respondent dependence structures that may be undetectable with existing methods. Specifically, the proposed method understands item response data as a function of the distances between items and respondents, between items, or between respondents. The positions of individual items and respondents are displayed in a in a low-dimensional Euclidean space, showing sub-groups of items and respondents that may be too nearby or distant from each other. Since similarities and differences are explicitly modeled, the traditional (local) independence assumptions for items and for respondents are no longer needed in the proposed framework. I will first present a simple latent space Rasch model that explicitly incorporates the effects of item and respondent latent positions on the probability of a correct response and then explain a more flexible approach that directly estimates similarities and differences between items as well as between respondents. Lastly, I will describe a hierarchical extension of the latent space item response model that accommodates hierarchical data structures and captures dependence structures for higher-level units, such as classrooms and schools. Empirical examples are provided to illustrate the use of the proposed models in practice.

ABOUT THE SPEAKER

Dr. Jeon is an Assistant Professor of Advanced Quantitative Methods at the department of Education of UCLA. Prior to coming to UCLA, she was an Assistant Professor of Quantitative Psychology at the Ohio State University. She obtained her Ph.D in Quantitative Methods and Evaluation and MA in Statistics from UC Berkeley. Her research interests include developing, applying, and estimating a variety of statistical/latent variable models, such as multilevel models, structural equation models, item response theory models, and growth models. She is also interested in developing computational algorithms and software. Her recent interests include network analysis, item response tree/process models, and joint modeling of multivariate data (such as behavior, psychological, and neural data).