#### Contact Information

Email

(615) 322-8409

213 A Hobbs

#### Research Area

#### Education

Post-Doc., University of California, Berkeley (Educational Measurement and Statistics, 2007-2009)

Ph.D., University of Georgia (Educational Measurement and Statistics, Winter 2007)

M.A., Yonsei University, Seoul, South Korea (Statistics, 2003)

B.A., Yonsei University, Seoul, South Korea (Education, 2001)

#### Current Courses

Item Response Theory I (Graduate PSY-GS 8880; Undergraduate PSY-PC 3738)

Item Response Theory II (Graduate PSY-GS 8881)

Applied Bayesian Analysis for Latent Variable Modeling (Graduate PSY-GS 8850)

Psychometric Methods (Undergraduate PSY-PC 3722)

#### Advising

#### Definitely interested in accepting new graduate students for Fall 2017

#### Societies

National Council on Measurement in Education (NCME)

Psychometric Society

American Educational Research Association (AERA) Division D

# 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, and (5) multiple time points such as pretest and posttest in intervention studies.

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

#### Representative Publications

* denotes co-authors at Vanderbilt University.

**Methodological Papers in Refereed Journals **

**Cho, S.-J.**, Suh. Y., & Lee, W.-y.* (in press). After DIF items are detected: IRT calibration and scoring in the presence of DIF.*Applied Psychological Measurement*. [Confirmatory multigroup multidimensional or bi-factor item response modeling was presented for DIF.]**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.**, & 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.**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.**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 **

- Goodwin, A. P.*,
**Cho, S.-J.**, & Nichols, S.* (2016). Ways to 'WIN' at word learning.*The Reading Teacher*. http://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, 15*, http://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 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). 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. **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. (forthcoming). Explanatory latent variable models. In A. A. Rupp & J. P. Leighton (Eds.).*Handbook of cognition and assessment*. 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

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

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