213 A Hobbs
Ph.D., University of Georgia, Winter, 2007
Item Response Theory I (Graduate PSY-GS 326; Undergraduate PSY-PC 2590)
Item Response Theory II (Graduate PSY-GS 327)
Psychometric Methods (Undergraduate PSY-PC 2530)
Assistant 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, and (5) multiple time points such as pretest and posttest in intervention studies.
* denotes co-authors at Peabody College.
- Cho, S.-J., De Boeck, P., Embretson, S., & Rabe-Hesketh, S. (accepted). Additive multilevel item structure models with random residuals: Item modeling for explanation and item generation. Psychometrika. [Alternating imputation posterior algorithm with adaptive quadrature was developed for multilevel crossed random effects.]
- Bottge, B. & Cho, S.-J. (accepted). Assessing item-level effects of enhanced anchored instruction on problem solving. Learning Disabilities: A Multidisciplinary Journal. [Multilevel longitudinal item response models were applied.]
- Bottge, B., Ma, X., Toland, M., Gassaway, L., Butler, M., & Cho, S.-J. (accepted). Effects of blended instructional models on math performance. Exceptional Children. [Three-level hierarchical linear models for repeated measures were applied.]
- Cho, S.-J., Gilbert, J. K.*, & Goodwin, A. P.* (in press). Explanatory multidimensional multilevel random item response model: An application to simultaneous investigation of word and person contributions to multidimensional lexical quality. Psychometrika.
- Cho, S.-J., Cohen, A. S., & Bottge, B. (in press). Detecting intervention effects using a multilevel latent transition analysis with a mixture IRT model. Psychometrika.
- Cohen, A. S. & Cho, S.-J. (forthcoming). Information criteria. In W. J. van der Linden & R. K. Hambleton (Eds.), Handbook of item response theory, models, statistical tools, and applications. Boca Raton, FL: Chapman & Hall/CRC Press.
- Cho, S.-J., Cohen, A. S., & Kim, S.-H. (in press). A mixture group bi-factor model for binary responses. Structural Equation Modeling: A Multidisciplinary Journal.
- 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.
- 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.*, et al. (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.]
- 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.
- 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., Bottge, B., 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.]
- 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. (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.
- 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
- The College Board Research Fellowship (9/2006 - 8/2007)
Study Title: A multilevel mixture IRT model with an application to DIF