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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). She is the Editor-in-Chief of the , 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  (2013), a Vanderbilt Chancellor Faculty Fellow (2019-2021), and an  (Quantitative Field, 2020 - ). Dr. Cho has had research projects funded by the , the National Institutes of Health (NIH) (e.g., ), and the .

Representative Publications

* denotes co-authors at 青青草视频 or 青青草视频 Medical Center.

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Methodological Papers in Peer-Reviewed Journals   

  • Cho, S.-J.,  Goodwin, A. P.*, Naveiras, M., & De Boeck, P. (in press). . Journal of Educational Measurement. [Funding was supported by the ; The data and the R code used in the illustration are available on the .]
  • Cho, S.-J., Brown-Schmidt, S.*, Clough, S.*, & Duff, M.* (in press). . Psychometrika. [This paper is part of a special issue on "Model Identification and Estimation for Longitudinal Data in Practice" in Psychometrika; Funding was supported by the NIDCD grant R01 NIH DC017926; The data and the R code used in the illustration are available on the .]
  • Cho, S.-J.,  Goodwin, A. P.*, Naveiras, M., & Salas, J.* (2024). . Journal of Educational Measurement, 61, 219-251.  [Funding was supported by the ; The data and the R code used in the illustration are available on the .]
  • Cho, S.-J.#, Wu, H.*#, & Naveiras, M. (2024). . British Journal of Mathematical and Statistical Psychology, 77, 289-315. #The first and second authors contributed equally to this work.
  • Cho, S.-J., Preacher, K. J.*, Yaremych, H.*, Naveiras, M.*, Fuchs, D.*, & Fuchs, L. S.* (2024). . Behavior Research Methods, 56, 2094–2113. [R code for parameter estimation and data visualization can be found in the.] 
  • Cho, S.-J. (2024). . Journal of the Royal Statistical Society Series C: Applied Statistics, 73, 123-142. R code and an example data set to fit the model can be found .
  • Naveiras, M.*, & Cho, S.-J. (2023). . Applied Psychological Measurement, 47, 478-495.  [Supplementary materials and R functions for Bayesian estimation can be found .]
  • Cho, S.-J., Brown-Schmidt, S.*, De Boeck, P., Naveiras, M.*, Yoon, S. O., & Benjamin, A. (2023).  Psychometrika, 88, 1056-1086. [Funding was supported by the National Science Foundation (SES 1851690); Data and R code can be found .] 
  • Cho, S.-J., De Boeck, P., Naveiras, M.*, & Ervin, H.* (2022). . Behavior Research Methods, 54, 2178-2220. [Funding was supported by the National Science Foundation (SES 1851690); R code for level-specific residual calculations and diagnostic measures, plots, and tests can be found .]
  • Cho, S.-J., Preacher, K. J.*, Yaremych, H.*, Naveiras, M.*, Fuchs, D.*, & Fuchs, L. S.* (2022). . British Journal of Mathematical and Statistical Psychology, 75, 493-521. [R code for parameter estimation and data visualization can be found in the supplementary materials.]
  • Cho, S.-J., Brown-Schmidt, S.*, De Boeck, P., & Naveiras, M.* (2022). . Psychological Methods, 27, 307-346. [Funding was supported by the National Science Foundation (SES 1851690); R code for parameter estimation and data visualization can be found in the paper.] 

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

  • Cho, S.-J., Naveiras, M.*, & Barton, E. E.* (2022). . Multivariate Behavioral Research, 57, 422-440. [Code for Bayesian implementation of a cubic regression spline in a vector Poisson log-normal additive model can be found in the paper; Supplementary materials are here.]
  • De Boeck, P., & Cho, S.-J. (2021). Psychometrika, 86, 712-716. [Patient-Reported Outcomes Measurement Information System (PROMIS) Special Section.]

  • Cho, S.-J., Watson, D. G.*, Jacobs, C., & Naveiras, M.* (2021). . Multivariate Behavioral Research, 56, 476-495. [Code for Bayesian analysis can be found in the paper.]

  • Cho, S.-J., Brown-Schmidt, S.*, De Boeck, P., & Shen, J.* (2020). . Psychometrika, 85, 154-184. [Funding was supported in part by the National Science Foundation (SES 1851690).] 

     

- A tutorial on fitting a dynamic tree-based item response model using R (Laplace approximation) can be found .  
- 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). . A special issue of "", 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.), (pp. 91-104). Charlotte, NC: Information Age Publishing. 

  • Cho, S.-J., Shen, J., & Naveiras, M.* (2019). 

    . Multivariate Behavioral Research, 54, 856-881.

  • Kim, S.-H., Cohen, A. S., Cho, S.-J., & Eom, H. J. (2019). . Applied Psychological Measurement, 43, 95-112.
  • Rights, J. D.*, Sterba, S. K.*, Cho, S.-J., & Preacher, K. J.* (2018). . Behaviormetrika, 45, 495-503.
  • Cho, S.-J., Brown-Schmidt, S.*, & Lee, W.-y.* (2018). . Psychometrika, 83, 751-771. [Supplemental materials can be found on .]
  • Cho, S.-J., & De Boeck., P. (2018). [Brief Reports] . Applied Psychological Measurement, 42, 169-172. [The derivation for N in the paper is applicable to generalized linear mixed models with crossed random effects.]
  • Lee, W.-y.*, Cho, S.-J., & Sterba, S. K.* (2018). . Applied Psychological Measurement, 42, 136-154.
  • Suh. Y., Cho, S.-J., & Bottge, B. (2018). . Applied Psychological Measurement, 42, 73-88.
  • Lee, W.-y.*, &  Cho, S.-J. (2017). . Journal of Educational Measurement, 54, 364-393.
  • Cho, S.-J., & Goodwin, A. P.* (2017). . Psychometrika, 82, 846-870. 
  • Cho, S.-J., De Boeck, P., & Lee, W.-y.* (2017). . Applied Psychological Measurement, 41, 353-371.
  • Cho, S.-J., & Suh, Y. (2017). [Software Notes] . Applied Psychological Measurement, 41, 155-157.
  • Lee, W.-y.*, & Cho, S.-J. (2017). . Applied Measurement in Education, 30, 129-146.
  • Cho, S.-J., Suh, Y., & Lee, W.-y.* (2016). . Applied Psychological Measurement, 40, 573-591. [Confirmatory multigroup multidimensional or bi-factor item response modeling was presented for DIF.]
  • Cho, S.-J., & Preacher, K. J.* (2016). . Educational and Psychological Measurement, 76, 771-786.
  • Cho, S.-J., Suh, Y., & Lee, W.-y.* (2016). . Educational Measurement: Issues and Practice, 35, 48-61.
  • Cho, S.-J., Preacher, K. J.*, & Bottge, B. A. (2015). . Applied Psychological Measurement, 39, 627-642. [The first author received the following financial support for the research, authorship, and publication of this article: National Academy of Education/Spencer Postdoctoral Fellowship.]
  • Cho, S.-J., & Bottge, B. A. (2015). . British Journal of Mathematical and Statistical Psychology, 68, 410-433. [The first author received the following financial support for the research, authorship, and publication of this article: National Academy of Education/Spencer Postdoctoral Fellowship.]
  • Paek, I., & Cho, S.-J. (2015). . Applied Psychological Measurement, 39, 135-143.
  • Suh, Y., & Cho, S.-J. (2014). . Applied Psychological Measurement, 38, 359-375.
  • Cho, S.-J., De Boeck, P., Embretson, S., & Rabe-Hesketh, S. (2014). . Psychometrika, 79, 84-104. [Alternating imputation posterior algorithm with adaptive quadrature was developed for multilevel crossed random effects such as a random item effect across items and a random item group effect across item groups in 2-parameter item response models.]
  • Cho, S.-J., Cohen, A. S., & Kim, S.-H. (2014). . Structural Equation Modeling: A Multidisciplinary Journal, 21, 375-395.
  • Cho, S.-J., Gilbert, J. K.*, & Goodwin, A. P.* (2013). . Psychometrika, 78, 830-855.
  • Cho, S.-J., Athay, M.*, & Preacher, K. J.* (2013). . British Journal of Mathematical and Statistical Psychology, 66, 353-381. [Supplementary results, lmer script, and data are posted on the website: .]   
  • Cho, S.-J., Cohen, A. S., & Kim, S.-H. (2013). .  Journal of Statistical Computation and Simulation, 83, 278-306.
  • Cho, S.-J., Cohen, A. S., & Bottge, B. A. (2013). . Psychometrika, 78, 576-600.
  • Suh, Y., Cho, S.-J., & Wollack, J. A. (2012). . Journal of Educational Measurement, 49, 285-311.
  • Cho, S.-J., Partchev, I., & De Boeck, P. (2012). . British Journal of Mathematical and Statistical Psychology, 65, 438-466. [Alternating imputation posterior algorithm with adaptive quadrature was developed for 1-parameter multidimensional random item response models.]
  • Cho, S.-J., & Suh, Y. (2012). [Software Notes] . Applied Psychological Measurement, 36, 147-148.
  • De Boeck, P., Cho, S.-J., & Wilson, M. (2011). . Applied Psychological Measurement, 35, 583-603.
  • Cho, S.-J., & Rabe-Hesketh, S. (2011). . Computational Statistics and Data Analysis, 55, 12-25.
  • Cho, S.-J., Cohen, A. S., Kim, S.-H., & Bottge, B. A. (2010). . Applied Psychological Measurement, 34, 583-604.
  • Cho, S.-J., & Cohen, A. S. (2010). . Journal of Educational and Behavioral Statistics, 35, 336-370.
  • Cho, S.-J., Li, F., & Bandalos, D. L. (2009). . Educational and Psychological Measurement. 69, 748-759.
  • Li, F., Cohen, A. S., Kim, S.-H., & Cho, S.-J. (2009). . Applied Psychological Measurement, 33, 353-373.

 

Substantive Papers in Peer-Reviewed Journals 

  • Clough, S.*, Brown-Schmidt, S.*, Cho, S.-J., & Duff, M.* (accepted). Reduced on-line speech gesture integration during multimodal language processing in adults with moderate-severe traumatic brain injury: Evidence from eye-tracking. Cortex. [Dynamic GLMM and IRTree models were applied.]
  • Bean, C. A. L.*, Mueller, S. B.*, Abitante, G.*, Ciesla, J. A., Cho, S.-J., & Cole, D. A.* (in press). Journal of Psychopathology and Behavioral Assessment. [Graded response models were applied.]
  • Hornsby, B. W.*, Camarata, S.*, Cho, S.-J., Davis, H.*, McGarrigle, R., & Bess, F. H.* (2023). . Ear and Hearing, 44, 1251-1261. [Graded response models and IRT DIF analyses were applied.]
  • Hornsby, B. W.*, Camarata, S*, Cho, S.-J., Davis, H.*, McGarrigle, R., & Bess, F. H.* (2022).   Journal of Speech Language and Hearing Research, 65, 2343-2363. [Graded response models and IRT DIF analyses were applied.]
  • Sunday, M. A.*, Tomarken, A.*, Cho, S.-J., & Gauthier, I.* (2022). . Journal of Experimental Psychology: General, 151, 676-694.
  • Hornsby, B. W.*, Camarata, S.*, Cho, S.-J., Davis, H.*, McGarrigle, R., & Bess, F. H.* (2021). .  Psychological Assessment, 33, 777–788 [Graded response models and IRT DIF analyses were applied.]
  • Brown-Schmidt, S.*, Cho, S.-J., Nozari, N., Klooster, N., & Duff, M.* (2021). . Neuropsychologica, 152, 107730. [Funding was supported in part by the National Science Foundation (SES 1851690); Dynamic generalized linear mixed-effects models were applied.] 
  • Goodwin, A. P.*, Cho, S.-J., Reynolds, D, Silverman, R., & Nunn, S. (2021). . Journal of Educational Psychology, 113, 27-48. [Multilevel factor models for complex multilevel designs and multilevel multivariate linear models were applied to data involving 745 teachers and 18,844 students from the Measures of Effective Teaching (MET) study.]
  • Goodwin, A. P.*, Cho, S.-J., Reynolds, D., Brady, K.*, & Salas, J. A.* (2020). . The American Educational Research Journal, 57, 1837-1867. [Explanatory item response models were applied.] 
  • Jacobs, C. L., Cho, S.-J., & Watson, D. G.* (2019). . Cognitive Science, 43, e12749 [Markov mixed-effect multinomial logistic regression model for nominal repeated measures was applied.]
  • Spencer, M.*, Cho, S.-J., & Cutting, L. E.* (2019). . Child Neuropsychology, 25, 198-216. [Multidimensional graded response models, IRT DIF, and explanatory item response models were applied.]
  • Levin, D. T.*, Seiffert, A.*, Cho, S.-J., & Carter, K.* (2018). . Cognitive Research: Principles and Implications, 3, 49. [Mixed-effects logistic regression models were applied.]
  • Nick, E. A.*, Cole, D. A.*, Cho, S.-J., Smith, D. K.*, Cater, T. G.*, & Zelkowitz, R.* (2018). . Psychological Assessment, 30, 1127-1143. [For IRT analyses, multidimensional graded response models and IRT DIF were applied.]
  • Cole, D. A.*, Goodman, S., Garber, J.*, Cullum, K. A., Cho, S.-J., Right, J. D.*, Felton, J. W., Jacquez, F. M., Korelitz, K. E.*, & Simon, H. F. M.* (2018). . Psychological Assessment, 30, 1065-1081. [For IRT analyses, multidimensional graded response models and IRT DIF were applied.]
  • Hornsby, B. W.*, Gustafson, S.*, Lancaster, H., Cho, S.-J., Camarata, S.*, & Bess, F. H.* (2017). . American Journal of Audiology, 26, 393-407. [Nonparametric ANOVA for a between-within design was applied.]
  • Goodwin, A. P.*, & Cho, S.-J. (2016). . Scientific Studies of Reading, 20, 490-514. [Generalized linear mixed modeling for doubly multilevel binary longitudinal data (Cho & Goodwin, 2017) was applied.]
  • Goodwin, A. P.*, Cho, S.-J., & Nichols, S.* (2016). . The Reading Teacher. [Generalized linear mixed modeling for doubly multilevel binary longitudinal data (Cho & Goodwin, 2017) was applied.]
  • Lee, W.-y.*, Cho, S.-J., McGugin, R. W.*, Van Gulick, A. B.*, & Gauthier, I.* (2015). . Journal of Vision, 15. [IRT DIF detection methods and multigroup item response models were applied.] 
  • Cho, S.-J., Wilmer, J., Herzmann, G., McGugin, R.*, Fiset, D., Van Gulick, A. B.*, Ryan, K.*, & Gauthier, I.* (2015). . Psychological Assessment, 27, 552-566. [Exploratory bi-factor item response models, explanatory item response models, and IRT DIF detection methods were applied.]
  • Bottge, B. A., Ma, X., Gassaway, L., Toland, M. D., Butler, M., & Cho, S.-J. (2014).. Exceptional Children, 80, 423-437. [Three-level hierarchical linear models for repeated measures were applied.]
  • Goodwin, A. P.*, Gilbert, J. K.*, Cho, S.-J., & Kearns, D. M. (2014). . Journal of Educational Psychology, 106, 448-468. [Explanatory multidimensional multilevel random item response models (Cho, Gilbert, & Goodwin, 2013) were applied.]
  • Miller, A. C., Davis, N.*, Gilbert, J. K.*, Cho, S.-J., Toste, J. R., Street, J.*, & Cutting, L. E.* (2014). . Learning Disabilities Research & Practice, 29, 25-35. [Linear and nonlinear models with nested and crossed random effects were applied.]
  • Bottge, B. A., & Cho, S.-J. (2013). Effects of enhanced anchored instruction on skills aligned to common core math standards. Learning Disabilities: A Multidisciplinary Journal, 19, 73-83. [Multilevel longitudinal item response models were applied.]
  • Goodwin, A. P.*, Gilbert, J. K.*, & Cho, S.-J. (2013). Morphological contributions to adolescent word reading: An item response approach. Reading Research Quarterly, 48, 39-60. [Random item response models and explanatory item response models were applied.]
  • Cole, D. A.*, Cho, S.-J., Martin, N. C.*, Youngstrom, E. A., Curry, J. F.,  Findling, R. L., Compas, B. E.*, Goodyer, I. M., Rohde, P., Weissman, M., Essex, M. J., Hyde, J. S., Forehand, R., Slattery, M. J., Felton, J. W.*, & Maxwell, M. A.* (2012). . Journal of Abnormal Psychology,121, 838-851. [Exploratory, explanatory, and multiple-group multidimensional graded response models were applied.]
  • Cho, S.-J., Bottge, B. A., Cohen, A. S., & Kim, S.-H. (2011). . Journal of Special Education, 45, 67-76.  [Mixture longitudinal item response model was applied.]

 

Book Chapters

  • Brown-Schmidt, S.*, Naveiras, M.*, De Boeck, P., & Cho, S.-J. (2020). . A special issue of "", 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. (2020). IRTree modeling of cognitive processes based on outcome and intermediate data. In H. Jao & R. W. Lissitz (Eds.),(pp. 91-104). Charlotte, NC: Information Age Publishing. 
  • Cho, S.-J., Brown-Schmidt, S.*, Naveiras, M.*, & De Boeck, P. (2020). A dynamic generalized mixed effect model for intensive binary temporal-spatio data from an eye tracking technique. In H. Jao & R. W. Lissitz (Eds.), (pp. 45-68). Charlotte, NC: Information Age Publishing. 
  • De Boeck, P., Cho, S.-J., & Wilson, M. (2016). Explanatory item response models: An approach to cognitive assessment. In A. Rupp & J. Leighton (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)
  • 青青草视频 Chancellor Faculty Fellow (2019-2021)
  • 青青草视频 Provost Research Studios  (PRS) Award (2018)
  • 青青草视频 Trans-Institutional Program (TIPs) Award (co-PI)  (2016-2018)

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

  • 青青草视频 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