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Sun-Joo Cho

Associate Professor of Psychology and Human Development

Research topics include generalized latent variable modeling and its 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 manifest item groups that may lead to multidimensionality such as number operation, measurement, and representation item groups in a math test, (4) multiple manifest person groups such as schools where students are nested in a multilevel (or hierarchical) data structure, (5) multiple time points such as pretest and posttest in intervention studies, and (6) intensive categorical longitudinal responses.

Dr. Cho has collaborated with researchers from a variety of disciplines including reading education, math education, special education, psycholinguistics, clinical psychology, cognitive psychology, neuropsychology, and audiology. She serves on the editorial boards of Journal of Educational Psychology, Behavior Research Methods, and International Journal of Testing

Representative Publications

* denotes co-authors at Vanderbilt University.

Methodological Papers in Refereed Journals 

  • Cho, S.-J., & De Boeck., P. (in press). [Brief Reports] A note on N in Bayesian information criterion (BIC) for item response models. Applied Psychological Measurement
  • Lee, W.-y.*, &  Cho, S.-J. (in press). Detecting differential item discrimination (DID) and the consequences of ignoring DID in multilevel item response models. Journal of Educational Measurement.
  • Lee, W.-y.*, Cho, S.-J., & Sterba, S. K.* (in press). Ignoring a multilevel structure in mixture item response models: Impact on parameter recovery and model selection. Applied Psychological Measurement.
  • Suh. Y., Cho, S.-J., & Bottge, B. (in press). A multilevel longitudinal nested logit model for measuring changes in correct response and error types. Applied Psychological Measurement.
  • Cho, S.-J., & Goodwin, A. P.* (in press). Modeling learning in doubly multilevel binary longitudinal data using generalized linear mixed models: An application to measuring and explaining word learning. Psychometrika
  • Cho, S.-J., De Boeck, P., & Lee, W.-y.* (2017). Evaluating testing, profile likelihood confidence interval estimation, and model comparisons for item covariate effects in linear logistic test models. Applied Psychological Measurement, 41, 353-371.
  • Cho, S.-J., & Suh, Y. (2017). [Software Notes] Obtaining fixed effects for between-within designs in explanatory longitudinal item response models using Mplus. Applied Psychological Measurement, 41, 155-157.
  • Lee, W.-y.*, & Cho, S.-J. (2017). Consequences of ignoring measurement invariance in longitudinal item response models. Applied Measurement in Education, 30, 129-146.
  • Choi, I. H., Paek, I., & Cho, S.-J. (2017). The impact of various class-distinction features on model selection in the mixture Rasch model. Journal of Experimental Education, 85, 411-424.
  • Cho, S.-J., Suh, Y., & Lee, W.-y.* (2016). After DIF items are detected: IRT calibration and scoring in the presence of DIF. 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). Measurement error correction formula for cluster-level group differences in cluster randomized and observational studies. Educational and Psychological Measurement, 76, 771-786.
  • Cho, S.-J., Suh, Y., & Lee, W.-y.* (2016). An NCME instructional module on latent DIF analysis using mixture item response models. Educational Measurement: Issues and Practice, 35, 48-61.
  • Cho, S.-J., Preacher, K. J.*, & Bottge, B. A. (2015). Detecting intervention effects in a cluster randomized design using multilevel structural equation modeling for binary responses. 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). Multilevel multidimensional item response model with a multilevel latent covariate. 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). A note on parameter estimate comparability across latent classes in mixture IRT modeling. Applied Psychological Measurement, 39, 135-143.
  • Suh, Y., & Cho, S.-J. (2014). Chi-square difference tests for detecting differential functioning in a multidimensional IRT model: A Monte Carlo study. Applied Psychological Measurement, 38, 359-375.
  • Cho, S.-J., De Boeck, P., Embretson, S., & Rabe-Hesketh, S. (2014). Additive multilevel item structure models with random residuals: Item modeling for explanation and item generation. 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). A mixture group bi-factor model for binary responses. Structural Equation Modeling: A Multidisciplinary Journal, 21, 375-395.
  • Cho, S.-J., Gilbert, J. K.*, & Goodwin, A. P.* (2013). Explanatory multidimensional multilevel random item response model: An application to simultaneous investigation of word and person contributions to multidimensional lexical quality. Psychometrika, 78, 830-855.
  • Cho, S.-J., Athay, M.*, & Preacher, K. J.* (2013). Measuring change for a multidimensional test using a generalized explanatory longitudinal item response model. British Journal of Mathematical and Statistical Psychology, 66, 353-381. [Supplementary results, lmer script, and data are posted on the website: http://quantpsy.org/pubs.htm.]   
  • Cho, S.-J., Cohen, A. S., & Kim, S.-H. (2013). Markov chain Monte Carlo estimation of a mixture item response theory model.  Journal of Statistical Computation and Simulation, 83, 278-306.
  • Cho, S.-J., Cohen, A. S., & Bottge, B. A. (2013). Detecting intervention effects using a multilevel latent transition analysis with a mixture IRT model. Psychometrika, 78, 576-600.
  • Suh, Y., Cho, S.-J., & Wollack, J. A. (2012). A comparison of item calibration procedures in the presence of test speededness. Journal of Educational Measurement, 49, 285-311.
  • Cho, S.-J., Partchev, I., & De Boeck, P. (2012). Parameter estimation of multiple item profiles models. 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] Bayesian analysis of item response models using WinBUGS 1.4.3. Applied Psychological Measurement, 36, 147-148.
  • De Boeck, P., Cho, S.-J., & Wilson, M. (2011). Explanatory secondary dimension modelling of latent DIF. Applied Psychological Measurement, 35, 583-603.
  • Cho, S.-J., & Rabe-Hesketh, S. (2011). Alternating imputation posterior estimation of models with crossed random effects. Computational Statistics and Data Analysis, 55, 12-25.
  • Cho, S.-J., Cohen, A. S., Kim, S.-H., & Bottge, B. A. (2010). Latent transition analysis with a mixture IRT measurement model. Applied Psychological Measurement, 34, 583-604.
  • Cho, S.-J., & Cohen, A. S. (2010). A multilevel mixture IRT model with an application to DIF. Journal of Educational and Behavioral Statistics, 35, 336-370.
  • Cho, S.-J., Li, F., & Bandalos, D. L. (2009). Accuracy of the parallel analysis procedure using polychoric correlations. Educational and Psychological Measurement. 69, 748-759.
  • Li, F., Cohen, A. S., Kim, S.-H., & Cho, S.-J. (2009). Model selection methods for mixture dichotomous IRT models. Applied Psychological Measurement, 33, 353-373.

 

Substantive Papers in Refereed Journals 

  • 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.* (in press). Validating parent and child forms of the parent perception inventory. Psychological Assessment.
  • Hornsby, B. W.*, Gustafson, S.*, Lancaster, H., Cho, S.-J., Camarata, S.*, & Bess, F. H.* (in press). Subjective fatigue in children with hearing loss using self- and parent- proxy reports. American Journal of Audiology. [Nonparametric ANOVA for a between-within design was applied.]
  • Goodwin, A. P.*, & Cho, S.-J. (2016). Unraveling vocabulary learning: Reader and item-level predictors of vocabulary learning within comprehension instruction for fifth and sixth-graders. Scientific Studies of Reading, 20, 490-514. [Generalized linear mixed modeling for doubly multilevel binary longitudinal data (Cho & Goodwin, in press) was applied.]
  • Goodwin, A. P.*, Cho, S.-J., & Nichols, S.* (2016). Ways to 'WIN' at word learning. The Reading Teacherhttp://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1936-2714/earlyview. [Generalized linear mixed modeling for doubly multilevel binary longitudinal data (Cho & Goodwin, in press) was applied.]
  • Lee, W.-y.*, Cho, S.-J., McGugin, R. W.*, Van Gulick, A. B.*, & Gauthier, I.* (2015). Differential item functioning analysis of the Vanderbilt Expertise Test for Cars (VETcar). Journal of Vision, 15http://jov.arvojournals.org/article.aspx?articleid=2449199. [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). Item response theory analyses of the Cambridge face memory test (CFMT). 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). Effects of blended instructional models on math performance. 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). Probing lexical representations: Simultaneous modeling of word and reader contributions to multidimensional lexical representations. Journal of Educational Psychology106, 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). Novel approaches to examine passage, student, and question effects on reading comprehension. 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). Are increased weight and appetite useful indicators of depression in children and adolescents?. 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). Detecting cognitive change in the math skills of low-achieving adolescents. Journal of Special Education, 45, 67-76.  [Mixture longitudinal item response model was applied.]

 

Book Chapters

  • 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

  • Vanderbilt University Trans-Institutional Program (TIPs) Award (co-PI)  (2016)

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

  • Vanderbilt University Research Scholar Grant Award (2016)
  • National Council on Measurement in Education (NCME) Bradley Hanson Award for Contributions to Educational Measurement (2016)
  • National Council on Measurement in Education (NCME) Award for an Outstanding Example of an Application of Educational Measurement Technology to a Specific Problem (2014)

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 Fellowship (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