Welcome from the Program Director
EDMS Students and Events
Upcoming Events

Chair > Welcome to the programs in Measurement, Statistics and Evaluation in the Department of Human Development and Quantitative methodology. We offer some of the nation's premiere graduate training in psychometric and statistical methods. Through our Ph.D, M.A., and Certificate programs, EDMS students become trained in modern analytical methods to prepare them for careers in academia and applied research. Our faculty are engaged in world-class research at the frontiers of our field, creating opportunities for our students to be mentored in all phases of the research process. I am honored to serve a department with such remarkable faculty, students, and alumni, and I encourage you learn more about what makes us so special.                                               -- Gregory R. Hancock

EDMS Photo

> EDMS welcomes Dr. Kyung (Chris) T. Han from Graduate Management Admission Council, for a MSMS presentation on March 9, 2015, at 11am. Click here for more details.

> EDMS RESEARCH DAY is March 27, 8:30-4:00. Click here for more details, and here for the schedule.

Some Recent Publications by EDMS Faculty and Students
News Items

> Chen, J., Choi, J., Weiss, B. A., & Stapleton, L. (2014). An empirical evaluation of mediation effect analysis with manifest and latent variables using Markov chain Monte Carlo and alternative estimation methods. Structural Equation Modeling: A Multidisciplinary Journal,21, 253-262. doi: 10.1080/10705511.2014.882688

> Mao, X., Harring, J. R., & Hancock, G. R. (2014). A note on the specification of error structures in latent interaction models. Educational and Psychological Measurement. DOI: 10.1177/0013164414537491.

> Kang, Y., & Harring, J. R., Li, M. (2014). Reexamining the impact of non-normality in two-group comparison procedures. The Journal of Experimental Education, DOI: 10.1080/00220973.2013.876605.

> Li, M., Harring, J. R., & Macready, G. B. (2014). Investigating the feasibility of using Mplus in the estimation of growth mixture models. Journal of Modern Applied Statistical Methods, 13, 484-513.

> Harring, J. R., & Blozis, S. A. (2014). Fitting correlated residual error structures in nonlinear mixed-effects models using SAS PROC NLMIXED. Behavior Research Methods46, 472-484.

> Harring, J. R., Weiss, B. A., & Li, M. (2015). Assessing spurious interaction effects in structural equation modeling: A cautionary note. Educational and Psychological Measurement.

> McNeish, D., & Harring, J. R. (in press). Clustered data with small sample sizes: Comparing the performance of model-based and design-based approaches. Communication in Statistics: Simulation and Computation.

> Chen, Y.-F.. & Jiao, H. (2014). Exploring the utility of background and cognitive variables in explaining latent differential item functioning: An example of the PISA 2009 reading assessment. Educational Assessment, 19, 77-96.

> Harring, J. R. (2014). A spline model for latent variables. Educational and Psychological Measurement. DOI: 10.1177/0013164413504295.

> Jiao, H., & Zhang, Y. (2014). A polytomous multilevel testlet model for testlet-based assessments with complex sampling designs. British Journal of Mathematical and Statistical Psychology. DOI: 10.1111/bmsp.12035

> Kohli, N., Harring, J. R., & Hancock, G. R. (in press). Piecewise linear-linear latent growth mixture models with unknown knots. Educational and Psychological Measurement.

> Lissitz, R. W., & Jiao, H. (Editors, 2014) Value added modeling and growth modeling with particular application to teacher and school effectiveness. Charlotte, NC: Information Age Publishing, Inc.

> Liu, M., & Hancock, G. R. (in press). Unrestricted mixture models for class identification in growth mixture modeling. Educational and Psychological Measurement. DOI: 10.1177/0013164413519798

> Mao, X., Harring, J. R., & Hancock, G. R. (2014). A note on the specification of error structures in latent interaction models. Educational and Psychological Measurement. DOI: 10.1177/0013164414537491.

> McNeish, D. M., & Stapleton, L.M. (in press). The effect of small sample size on two level model estimates: A review and illustration. Educational Psychology Review

> McNeish, D. M. (2014). Modeling sparsely clustered data: Design-based, model-based, and single-level methods. Psychological Methods. DOI: 10.1037/met0000024

> Patton, J. M., & Cheng, Y. (2014). Effects of item calibration error on applications of item response theory. In Y. Cheng & H.-H. Chang (Eds.), Advancing methodologies to support both summative and formative assessments. Charlotte, NC: Information Age Publishing.

> Sweet, T. M., Thomas, A. C., & Junker, B. W. (2014). Hierarchical mixed membership stochastic block models for multiple networks and experimental interventions. In E. Airoldi, D. Blei, E. Erosheva, & S. Fienberg (Eds.), Handbook on mixed membership models and their applications. Boca Raton, FL: Chapman & Hall/CRC.

> Yang, J. S., & Cai, L. (in press). Estimation of contextual effects through nonlinear multilevel latent variable modeling with a Metropolis-Hastings Robbins-Monro algorithm. Journal of Educational and Behavioral Statistics.

> Yuan, K.-H., Cheng, Y., & Patton, J. M. (2014). Information matrices and standard errors for MLEs of item parameters in IRT. Psychometrika, 79, 232-254.




> Congratulations to Ji An, recipient of a 2015 CTB/McGraw-Hill summer internship.

> Congratulations to Dandan Liao, who won the 2015 Student Paper Competition Award for the Joint Statistical Meeting to be held in Seattle in August.

> Congratulations to Chen Li, who was selected as a University of Maryland Global Graduate Fellow.

> Congratulations to Xiaying (James) Zheng, who was selected as one of the eight international teaching fellows on campus this year.
> Congratulations to Bob Lissitz on his appointment by the Governor to the Maryland College and Career-Ready Standards and Partnership for Assessment of Readiness for College and Careers, Implementation Review Workgroup.


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