Jeffrey R. Harring
Associate Professor

Short Professional Bio 
Return to topDr. Harring is Associate Professor of Measurement, Statistics, and Evaluation (EDMS) in the Department of Human Development and Quantitative Methodology at the University of Maryland. Prior to joining the the EDMS faculty in the fall of 2006, Dr. Harring received a M.S. degree in Statistics in 2004, and completed his Ph.D. in the Quantitative Methods Program within Educational Psychology in 2005both degrees coming from the University of Minnesota. Before that, Dr. Harring taught high school mathematics for 12 years.
Dr. Harring teaches a variety of graduatelevel quantitative methods courses including: General Linear Models I & II, Statistical Analysis of Longitudinal Data, Statistical Computing and Monte Carlo Simulation, Multivariate Data Analaysis and Finite Mixture Models in Measurement and Statistics.
Dr. Harring's research interests focus on applications of (i) statistical models for repeated measures data, (ii) linear and nonlinear structural equation models, (iii) multilevel models and (iv) statistical computing.
Courses 
Selected Publications 
Return to topHarring, J. R., & Houser, A. (in press). Longitudinal models for continuous repeated measures data. In A. A. Rupp, & J. Leighton (Eds.), Handbook of cognition and assessment (pp. XXX). New York: Wiley.
McNeish, D., & Harring, J. R. (in press). Class enumeration in growth mixture models when assumptions are violated: A synthesis and simulation. Journal of Classification.
Harring, J. R., McNeish, D., & Hancock, G. R. (in press). Using phantom variables in structural equation modeling to assess model sensitivity to external misspecification. Psychological Methods.
Harring, J. R., & Johnson, T. (in press). Twoway analysis of variance. In B. Frey (Ed.), The SAGE encyclopedia of educational research, measurement and evaluation (pp. XXX). Thousand Oaks, CA: SAGE Publications.
Harring, J. R., & Blozis, S. A. (2016). A note on recurring misconceptions when fitting nonlinear mixed models. Multivariate Behavioral Research. DOI: 10.1080/00273171.2016.1239522.
Blozis, S. A., & Harring, J. R. (2016). On the estimation of nonlinear mixedeffects models and latent curve models for longitudinal data. Structural Equation Modeling: A Multidisciplinary Journal. DOI: 10.1080/10705511.2016.1190932.
Harring, J. R., & Hodis, F. A. (2016). Applications of mixture modeling in educational psychology. Educational Psychologist, 51, 354367.
McNeish, D., & Harring, J. R. (2016). Correcting model fit criteria for small sample latent growth models with incomplete data. Educational and Psychological Measurement. DOI: 10.1177/0013164416661824.
Li, M., & Harring, J. R. (2016). Investigating methods of incorporating covariates in growth mixture modeling: A simulation study. Educational and Psychological Measurement. DOI: 10.1177/0013164416653789.
Kohli, N., Harring, J. R., & Zopluoglu, C. (2016). Estimation of the finite mixture of nonlinear random coefficient models. Psychometrika, 81, 851880. For preliminary simulation results click here.
Harring, J. R., & Liu, J. (2016). A Comparison of estimation methods for nonlinear mixed effects models under model misspecification and data sparseness: A simulation study. Journal of Moder Applied Statistical Methods. For supplementary information about estimation methods click here. For supplementary information about the ANOVA and classification trees click here. For supplementary information regarding tabulated outcome measures and graphics, click here.
Harring, J. R., & Stapleton, L. M., & Beretvas, S. N. (Eds.) (2015). Advances in multilevel modeling for educational research: Addressing practical issues found in realworld applications. Charlotte, NC: Information Age Publishing, Inc.
Harring, J. R., Beretvas, S. N., & Israni, A. (2015). A model for crossclassified nested repeated measures data. In J. R. Harring, L. M. Stapleton, & S. N. Beretvas (Eds.), Advances in multilevel modeling for educational research: Addressing practical issues found in realworld applications (pp. 229260). Charlotte, NC: Information Age Publishing, Inc.
Stapleton, L. M., Harring, J. R., & Lee, D. Y. (2015). Sampling weight considerations for multilevel modeling of panel data. In J. R. Harring, L. M. Stapleton, & S. N. Beretvas (Eds.), Advances in multilevel modeling for educational research: Addressing practical issues found in realworld applications (pp. 6396). Charlotte, NC: Information Age Publishing, Inc.
McNeish, D., & Harring, J. R. (2015). Clustered Data with Small Sample Sizes: Comparing the Performance of ModelBased and DesignBased Approaches. Communication in Statistics: Simulation and Computation. DOI: 10.1080/03610918.2014.983648.
Harring, J. R., Weiss, B. A., & Li, M. (2015). Assessing spurious interaction effects in structural equation modeling: A cautionary note. Educational and Psychological Measurement. DOI: 10.1177/0013164414565007.
Zopluoglu, C., Harring, J. R., & Kohli, N. (2014). FitPMM: An R routine to fit finite mixtures of piecewise mixedeffects models with unknown random knots. Applied Psychological Measurement, 38(7), 583584.
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.
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(1), 484513.
Harring, J. R. (2014). A spline model for latent variables. Educational and Psychological Measurement, 74, 197213. Annotated SAS PROC IML CODE for maximum likelihood estimation of the piecewise regression model.Harring, J. R., & Blozis, S. A. (2013). Fitting Correlated Residual Error Structures in Nonlinear MixedEffects Models Using SAS PROC NLMIXED. Behavioral Research Methods. doi: 10.3758/s134280130397z. Appendix with annotated SAS PROC NLMIXED CODE for running the five level1 error structures for NLME models.
Kohli, N., Harring, J. R., & Hancock, G. R. (2013). Estimating unknown knots in piecewise linearlinear latent growth mixture models. Educational and Psychological Measurement. DOI: 10.1177/0013164413496812.
Kohli, N., & Harring, J. R. (2013). Modeling growth in latent variables using a piecewise function. Multivariate Behavioral Research, 48, 370397.
Hancock, G. R., Harring, J. R., & Lawrence, F. R. (2013). Using latent growth models to evaluate longitudinal change. In G. R. Hancock & R. O. Mueller (Eds.), Structural equation modeling: A second course (2nd ed.) (pp. 307340). Greenwood, CT: Information Age Publishing, Inc.
Cudeck, R., & Harring, J. R. (2012). Estimating the correlation between two variables when individuals are measured repeatedly. In M. C. Edwards & R. C. MacCallum (Eds.), Current topics in the theory and application of latent variable models (pp. 1123). New York, NY: Routledge/Taylor & Francis.Harring, J. R., & Hancock, G. R. (Eds.) (2012). Advances in longitudinal methods in the social and behavioral sciences. Charlotte, NC: Information Age Publishing, Inc.
Harring, J. R. (2012). Finite mixtures of nonlinear mixed effects models. In J. R. Harring & G. R. Hancock (Eds.), Advances in longitudinal methods in the social and behavioral sciences (pp. 159192). Charlotte, NC: Information Age Publishing, Inc.
Dardick, W., & Harring, J. R. (2012). Automated path tracing for general linear models. Mutliple Linear Regression Viewpoints, 38, 3850. Accompanying SAS Program for APT paper.
Mislevy, J. L., Rupp, A. A., & Harring, J. R. (2012). Detecting local item dependence in polytomous adaptive data. Journal of Educational Measurement, 49, 127147.
Harring, J. R., Kohli, N., Silverman, R., & Speece, D. L. (2012). Fitting a secondorder conditionally linear mixed effects model as an SEM in Mplus. Structural Equation Modeling: A Multidisciplinary Journal, 19, 118136.
Harring, J. R., Weiss, B. A., & Hsu, J.C. (2012). A comparison of methods for estimating quadratic effects in structural equation models. Psychological Methods, 17, 193214.
Zieffler, A. S., Harring, J. R., & Long, J. D. (2011). Comparing groups: Randomization and bootstrap methods using R. New York: Wiley.
Cudeck, R., & Harring, J. R. (2010). Developing a random coefficient model for nonlinear repeated measures data. In S.M. Chow, E. Ferrer, & F. Hsieh (Eds.), Statistical methods for modeling human dynamics: An interdisciplinary dialogue (pp. 289318). New York: Routledge.
Choi, J., Harring, J. R., & Hancock, G. R. (2009). Latent growth modeling for logistic response functions. Multivariate Behavioral Research,44, 620645.
Harring, J. R. (2009). A nonlinear mixed effects model for latent variables. Journal of Educational and Behavioral Statistics,34, 293318.
Cudeck, R., Harring, J. R., & du Toit, S. H. C. (2009). Marginal maximum likelihood estimation of a latent variable model with interaction. Journal of Educational and Behavioral Statistics,34, 131144.
Blozis, S. A., Harring, J. R., & Mels, G. (2008). Using LISREL to fit nonlinear latent curve models. Structural Equation Modeling. A Multidisciplinary Journal, 15, 346369.
Cudeck, R., & Harring, J. R. (2007). The analysis of nonlinear patterns of change with random coefficient models. Annual Review of Psychology, 58, 615637.
Long, J. D., Harring, J. R., Brekke, J. S., Test, M. A., & Greenberg, J. (2007). Longitudinal construct validity of brief symptom inventory subscales in schizophrenia. Psychological Assessment, 19, 298308.
Blozis, S. A., Conger, K. J., & Harring, J. R. (2007). Nonlinear latent curve models for longitudinal data. International Journal of Behavioral Development: Special Issue on Longitudinal Modeling of Developmental Processes, 31, 340346.
Harring, J. R., Cudeck, R., & du Toit, S. H. C. (2006). Fitting partially nonlinear random coefficient models as SEMs.Multivariate Behavioral Research, 41, 579596.
Selected Presentations 
Return to topHarring, J. R. (2015, September). Continuity corrections in multiphase random coefficient models. Invited presentation at the Innovations in Latent Variable and Random Effects Modeling Conference, Minneapolis, MN.
McNeish, D. M., & Harring, J. R. (2015, May). Small sample robust model fit criteria in latent growth models with noninformative dropout. Presented at the Modern Modeling Methods Conference, University of Connecticut, Storrs, CT.
Harring, J. R., & Liu, J. (2015, May). Simulating data for mixture model studies: Considering measures of data overlap. Presented at the Modern Modeling Methods Conference, University of Connecticut, Storrs, CT.
Beretvas, S. N., Harring, J. R., & Israni, A. (2014, November). A crossclassified model for nested repeated measures data. Presentation at the Advances in Multilevel Modeling for Educational Research Conference, University of Maryland.
Stapleton, L. S., Harring, J. R., & Lee, D. (2014, November). Sampling weight considerations for multilevel modeling of panel data. Presentation at the Advances in Multilevel Modeling for Educational Research Conference, University of Maryland.Kohli, N., Harring, J. R., Zopluoglu, C. (2014, July). A finite mixture of nonlinear random coefficient models for continuous repeated measures data. Paper presented at the International Meeting of the Psychometric Society, Madison, WI.
Harring, J. R. (2014, January). The use of multiphase models in the social and behavioral sciences. Invited lecture. James Madison University, Va.
Blozis, S. A., & Harring, J. R. (2013, November). Using PROC NLMIXED to fit correlated residual error structures in nonlinear mixed models. Presented at the annual meeting of the Western Users of SAS Software Conference. Las Vegas, NV. (Best Paper Award for the Analysis and Statistics Section)
Kang, Y., & Harring, J. R. (2012, May). Investigating the Impact of NonNormality, Effect Size, and Sample Size on TwoGroup Comparison Procedures: An Empirical Study. Presented at the annual meeting of the American Educational Research Association (AERA), SIG: Educational Statisticians, Vancouver, BC. (Student Paper Award Winner)
Hancock, G. H., & Harring, J. R. (2011, May). Using Phantom Variables in Structural Equation Modeling to Assess Model Sensitivity to External Misspecification. Presented at the Modern Modeling Methods Conference, University of Connecticut.
Kohli, N., & Harring, J. R. (2011, April). Modeling growth in latent variables using a piecewise function. Presented at the annual meeting of the American Educational Research Association (AERA), SIG: Educational Statisticians, New Orleans, LA. (Student Paper Award Winner)
Harring, J. R. (2011, April). A piecewise regression model for latent variables. Presented at the annual meeting of the American Educational Research Association (AERA), SIG: Structural Equation Modeling, New Orleans, LA.
Cudeck, R., & Harring, J. R. (2010, September). On computing the correlation of repeated measures data. Presented at Current Topics in the Theory and Application of Latent Variable Models: A Conference Honoring the Scientific Contributions of Michael W. Browne, The Ohio State University, Columbus, OH.
Harring, J. R. (2010, June). Finite Mixtures of Nonlinear Mixed Effects Models. Presented at the Advances in Longitudinal Methods in the Social and Behavioral Sciences Conference, University of Maryland, College Park, MD.
Harring, J. R., & Choi, J. (2010, May). Nonlinear latent growth modeling using Markov chain Monte Carlo estimation. Presented at the annual meeting of the American Educational Research Association, Denver (AERA), SIG: Structural Equation Modeling, CO.
Harring, J. R., Weiss, B. A., & Hsu, J. C. (2010, May). A comparison of methods for estimating quadratic effects in nonlinear structural equation models. Presented at the annual meeting of the American Educational Research Association (AERA), SIG: Structural Equation Modeling, Denver, CO.
Workshops 
Longitudinal Data Analysis: A Latent Variable Perspective (2016)
A threeday workshop: Longitudinal Data Analysis: A Latent Variable Perspective was held at the University of Maryland, College Park from September 1416, 2016.
Finite Mixture Modeling: Statistical Methods for Correlated Data (2016)
A oneday postconference workshop: Modern Finite Mixture Modeling: Statistical Methods for Correlated Data to be held at the Modern Methods Modeling Conference, University of Connecticut on May 26, 2016.
Modern Statistical Techniques for Longitudinal Data Analysis (2015)
A threeday workshop: Modern Statistical Techniques for Longitudinal Data Analysis was held at the University of Miami from February 68, 2015.
Applied Data Analysis Using R (2014)
A threeday workshop: Applied Data Analysis Using R was held at the University of Maryland from August 1315, 2014.
GMM for Eduucational Researchers (2014)
A oneday workshop: Growth Mixture Modeling for Educational Researchers was held at the American Educational Research Association annual meeting in Philadelphia, PA on April 2, 2014.
Finite Mixture Modeling (2013)
A threeday workshop: Introduction to Finite Mixture Models was held at the University of Maryland from December 46, 2013.
CanAm Quantitative Research Symposium 
Friday, December 2, 2016
Dr. Ji Seung Yang (University of Maryland)Handling Nonnormal Latent Densities in Multilevel/Multidimensional Item Factor Models
Slides for Dr. Yang's presentation can be obtained by clicking here after Dec. 2, 2016
Friday, October 7, 2016
Dr. Brandon LeBeau (University of Iowa)Extending Accessibility of OpenSource Software to the Masses: A Shiny Case Study.
Slides for Dr. LeBeau's presentation can be obtained by clicking here (after Oct. 7)
Friday, April 1, 2016
Dr. Mark Gierl (University of Alberta)Using Automated Procedures to Generate Test Items: A Progress Report
Slides for Dr. Gierl's presentation can be obtained by clicking here
Friday, February 26, 2016
Dr. Tony Albano (University of Nebraska)Examining Bias in Testing With Multilevel and CrossClassified Item Response Modeling
Dr. Albano's presentation is based on the following paper.
Slides for Dr. Albano's presentation can be obtained by clicking here
Friday, December 4, 2015
Dr. Nidhi Kohli (University of Minnesota)Fitting a LinearLinear Piecewise Growth Mixture Model with Unknown Knots
Dr. Kohli's presentation is based on the following paper.
Slides for Dr. Kohli's presentation can be obtained by clicking here

