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Sun-Joo Cho

Professor of Psychology and Human Development
Vanderbilt Data Science Institute Affiliate Faculty; LIVE Learning Innovation Incubator Affiliate Faculty

Research topics include generalized latent variable models, generalized linear and nonlinear mixed-effects models, generalized additive mixed models, mixed-effects machine learning, parameter estimation, model assessment and selection, and model diagnostics, with a focus on item response, multilevel, and longitudinal/time-series modeling.

Data complexity Dr. Cho has dealt with consists of (1) multiple manifest person categories such as a control group versus a treatment group in an experimental design, (2) multiple latent person categories (or mixtures or latent classes) such as phenogroups, (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) repeated measures such as pretest and posttest in intervention studies, (6) intensive (many time points) binary, ordinal, nominal, and count time series (e.g., from ambulatory physiological recording, wearable devices, eye-tracking, emotional responses, experience sampling methods, ecological momentary assessment, dynamic treatment regimes, and N-of-1 or single case trials), (7) response processes (e.g., multinomial processing), (8) spatial dependence, (9) multiple sequences or multivariate time series from multi-sourced big process data, (10) nonlinear interactions,  (11) multiway categorical data, and (12) functional response time effects (e.g., in signal detection theory and item response theory).

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, medicine, and computer science (machine learning, deep learning, and AI applications for multimodal data). She is the Editor-in-Chief of the British Journal of Mathematical and Statistical Psychology, an associate editor of the Journal of Educational Measurement and Psychometrika, and a consulting editor of the Behavior Research Methods, Psychological Methods, and International Journal of Testing. She was also named a National Academy of Education/Spencer Postdoctoral Fellow (2013), a Vanderbilt Chancellor Faculty Fellow (2019-2021), and an Association for Psychological Science (APS) Fellow (Quantitative Field, 2020 - ). Dr. Cho has had research projects funded by the National Science Foundation (NSF), the National Institutes of Health (NIH) (e.g., NIMH), and the U.S. Department of Education Institute of Education Sciences (project website: LIVE).

Representative Publications

* denotes co-authors at Vanderbilt University or Vanderbilt University Medical Center.

Google Scholar, PubMed

Methodological Papers in Peer-Reviewed Journals   

- A tutorial on fitting a generalized additive logistic model to intensive binary time-series eye-tracking data using R can be found here. Illustrative data can be found here.

- A tutorial on fitting a dynamic tree-based item response model using R (Laplace approximation) can be found here.  
- Stan code for Bayesian analysis can be found here.
- A poster presented at the (virtual) International Meeting of the Psychometric Society 2020 can be found here. 
- Researchers in substantive areas may be interested in reading the following book chapter to apply a dynamic tree-based item response model:
Brown-Schmidt, S.*, Naveiras, M.*, De Boeck, P., & Cho, S.-J. (2020). 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).
- Some extensions of dynamic tree-based item response models are described in the following book chapter: 
De Boeck, P., & Cho, S.-J. (2020). IRTree modeling of cognitive processes based on outcome and intermediate data. In H. Jao & R. W. Lissitz (Eds.), Innovative psychometric modeling and methods (pp. 91-104). Charlotte, NC: Information Age Publishing. 

 

Substantive Papers in Peer-Reviewed Journals 

 

Book Chapters

 

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 

  • National Council on Measurement in Education (NCME) Award for an Outstanding Example of an Application of Educational Measurement Technology to a Specific Problem (2011)

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