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Sun-Joo Cho

Associate Professor of Psychology and Human Development
Vanderbilt Data Science Institute & Data Science Minor Affiliate Faculty

Research topics include generalized latent variable models, generalized linear mixed effect models, and parameter estimation, with a focus on item response modeling.

Data complexity Dr. Cho has dealt with consists of (1) multiple manifest person categories such as a control group versus an experimental group in an experimental design, (2) multiple latent person categories (or mixtures or latent classes) such as a mastery group versus a non-mastery group in a cognitive test, (3) multiple item groups that may lead to multidimensionality such as number operation, measurement, and representation item groups in a math test, (4) multiple groups such as hospitals where patients are nested in a multilevel (or hierarchical) data structure, (5) multiple time points such as pretest and posttest in intervention studies, (6) intensive (many time points) ordinal, nominal, and count time series (e.g., from eye-tracking, fMRI, emotional responses, and N-of-1 or single case trials), (7) response processes (e.g., multinomial processing), (8) spatial dependence, and (9) multiple sequences (channels). 

Dr. Cho has collaborated with researchers from a wide variety of disciplines including reading education, math education, special education, psycholinguistics, clinical psychology, cognitive psychology, neuropsychology, audiology, medicine, and computer science. She serves on the editorial boards of Behavior Research Methods, International Journal of Testing, Journal of Educational Measurement, and Psychologial Methods. She was also named National Academy of Education/Spencer Postdoctoral Fellow (2013), Chancellor Faculty Fellow (2019-2021), and Association for Psychological Science (APS) Fellow (Quantitative Field, 2020 - ). Dr. Cho has current research projects funded by the National Science Foundation (NSF), the Institute of Education Sciences (IES), and the National Institutes of Health (NIH). 

Representative Publications

* denotes co-authors at Vanderbilt University.

Methodological Papers in Peer-Reviewed Journals  

 

Substantive Papers in Peer-Reviewed Journals 

 

Book Chapters

  • Brown-Schmidt, S.*, Naveiras, M.*, De Boeck, P., & Cho, S.-J. (invited). Statistical modeling of intensive categorical time series eye-tracking data using dynamic generalized linear mixed effect models with crossed random effects. A special issue of "Gazing toward the future: Advances in eye movement theory and applications", Psychology of learning and motivation series (Volume 73). [Funding was supported in part by the National Science Foundation (SES 1851690)]
  • De Boeck, P., & Cho, S.-J. (forthcoming). IRTree modeling of cognitive processes based on outcome and intermediate data. Maryland Assessment Research Center (MARC).
  • Cho, S.-J., Brown-Schmidt, S.*, Naveiras, M.*, & De Boeck, P. (forthcoming). A dynamic generalized mixed effect model for intensive binary temporal-spatio data from an eye tracking technique. Maryland Assessment Research Center (MARC).
  • De Boeck, P., Cho, S.-J., & Wilson, M. (2016). Explanatory item response models: An approach to cognitive assessment. In A. Rupp, & Leighton, J. (Eds.), Handbook of cognition and assessment (pp. 249-266). Harvard, MA: Wiley Blackwell.
  • Cohen, A. S., & Cho, S.-J. (2016). Information criteria. In W. J. van der Linden (Ed.), Handbook of item response theory, models, statistical tools, and applications (Vol. 2, pp. 363-378). Boca Raton, FL: Chapman & Hall/CRC Press.

 


Honors

  • Association for Psychological Science (APS) Fellow (2020)
  • Vanderbilt University Chancellor Faculty Fellow (2019-2021)
  • Vanderbilt University Provost Research Studios  (PRS) Award (2018)
  • Vanderbilt University Trans-Institutional Program (TIPs) Award (co-PI)  (2016-2018)

Study Title: Understanding digital dominance in teaching and learning: An interdisciplinary approach

  • Vanderbilt University Research Scholar Grant Award (2016)

Study Title: Multilevel reliability measures in a multilevel item response theory framework

Study Title: An application to simultaneous investigation of word and person contributions to word reading and lexical representations using random item response models

  • National Academy of Education/Spencer Postdoctoral Fellow (9/2013 - 6/2015)

Study Title: Evaluating educational programs with a new item response theory perspective 

Study Title: Latent transition analysis with a mixture IRT measurement model

  •  State of the Art Lecturer, Psychometric Society (2010)

Study Title: Random item response models