Dr. Harring is a Professor in the Measurement, Statistics, and Evaluation (EDMS) program 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 2005--both degrees coming from the University of Minnesota. Before that, Dr. Harring taught high school mathematics for 12 years.
Society of Multivariate Experimental Psychology. Elected Member (2017).
Recognized at the 9th Annual University-Wide Celebration of Scholarship and Research (2016). University of Maryland.
College of Education & Human Development (CEHD) Rising Alumni Award (2016). University of Minnesota.
University of Maryland Graduate Faculty Mentor of the Year Award (2015). University of Maryland.
College of Education Excellence in Graduate Mentoring Award (2015). University of Maryland.
College of Education Excellence in Teaching Award (2010). University of Maryland.
University of Maryland Graduate Student Mentor of the Year Award Nominee (2013-2014). University of Maryland.
Hancock, G. R., Harring, J. R., & Macready, G. B. (Eds.) (in press). Advances in latent class analysis: A festschrift for Chan Dayton. Charlotte, NC: Information Age Publishing, Inc.
Harring, J. R., & Stapleton, L. M., & Beretvas, S. N. (Eds.) (2015). Advances in multilevel modeling for educational research: Addressing practical issues found in real-world applications. Charlotte, NC: Information Age Publishing, Inc.
Harring, J. R., & Hancock, G. R. (Eds.) (2012). Advances in longitudinal methods in the social and behavioral sciences. Charlotte, NC: Information Age Publishing, Inc.
Zieffler, A. S., Harring, J. R., & Long, J. D. (2011). Comparing groups: Randomization and bootstrap methods using R. New York: Wiley.
Liu, J., & Harring, J. R. (in press). A systematic investigation of within-subject and between-subject covariance structures in growth mixture models. In G. R. Hancock, J. R. Harring & G. B. Macready (Eds.). A festschrift for Chan Dayton. Charlotte, NC: Information Age Publishing, Inc.
Harring, J. R., & Blozis, S. A. (in press). Modeling nonlinear longitudinal change with mixed effects models. In A. A. O’Connell & D. B. McCoach (Eds.), Multilevel modeling with introductory and advanced applications.
Harring, J. R., & Houser, A. (2016). Longitudinal models for continuous repeated measures data. In A. A. Rupp, & J. Leighton (Eds.), Handbook of cognition and assessment: Frameworks, methodologies, and applications (pp. 267-296). New York: Wiley.
Harring, J. R., Beretvas, S. N., & Israni, A. (2015). A model for cross-classified 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 real-world applications (pp. 217-246). Charlotte, NC: Information Age Publishing, Inc.
Stapleton, L. M., Harring, J. R., & Lee, D. (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 real-world applications (pp. 59-91). Charlotte, NC: Information Age Publishing, Inc.
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. 309-341). Greenwood, CT: 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. 159-192). Charlotte, NC: Information Age Publishing, Inc.
Man, K., & Harring, J. R. (in press). Negative binomial models for visual fixation counts on test items. Educational and Psychological Measurement.
Man, K., Harring, J. R., & Sinharay, S. (in press). Use of data mining methods to detect test fraud. Journal of Educational Measurement.
Blozis, S. A., & Harring, J. R. (2018). Fitting nonlinear mixed-effects models with alternative residual covariance structures. Sociological Research & Methods. DOI: 10.1177/0049124118789718.
Man, K., Harring. J. R., Ouyang, U., & Thomas, S. L. (2018). Response time based nonparametric Kullback-Leibler divergence measure for detecting aberrant test-taking behavior. International Journal of Testing, 18, 155-177.
Amaya, A., & Harring, J. R. (2017). Assessing the effect of social integration on unit nonresponse in household surveys. Journal of Survey Statistics and Methodology, 5, 480-508. http://dx.doi.org/10.1093/jssam/smx001.
Harring, J. R., McNeish, D., & Hancock, G. R. (2017). Using phantom variables in structural equation modeling to assess model sensitivity to external misspecification. Psychological Methods, 22, 616-631.
McNeish, D. M. & Harring, J. R. (2017). Clustered data with small sample sizes: Comparing the performance of model-based and design-based approaches. Communications in Statistics - Simulation and Computation, 46, 855-869.
McNeish, D., & Harring, J. R. (2017). The effect of model misspecification on growth mixture model class enumeration. Journal of Classification. http://dx.doi.org/10.1007/s00357-017-9233-y.
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 mixed-effects 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, 354-367.
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.
Kohli, N., Harring, J. R., & Zopluoglu, C. (2016). Estimation of the finite mixture of nonlinear random coefficient models. Psychometrika, 81, 851-880.
Panlilio, C., Harring, J. R., & Jones Harden, B. (in press). Fear regulation patterns of young children in foster care: An exploratory process-centered approach. Child Abuse & Neglect: Special Issue on Measurement of Child Maltreatment.
Rieger, S., Göllner, R., Spengler, M., Trautwein, U., Nagengast, B., Harring, J. R., & Roberts, B. W. (2018). The effects of getting a new teacher on the consistency of personality. Journal of Personality. https://onlinelibrary.wiley.com/doi/pdf/10.1111/jopy.12410.
Panlilio, C., Jones Harden, B., & Harring, J. R. (2017). School readiness of maltreated preschoolers and later school achievement: The role of emotion regulation, language, and context. Child Abuse & Neglect. Available online: http://dx.doi.org/10.1016/j.chiabu.2017.06.004.
Park, J., & Moser, D. K., Griffith, K., Harring, J. R., & Johantgen, M. (2017). Exploring symptom clusters in people with heart failure. Clinical Nursing Research. http://dx.doi.org/10.1177/1054773817729606.
Leech, K., Wei, R., Harring, J. R., & Rowe, M. L. (2018). A brief parent-focused intervention to improve preschoolers’ conversational skills and school readiness. Developmental Psychology, 54, 15-28.
Proctor, C. P., Harring, J. R., & Silverman, R. D. (2017). Linguistic interdependence between Spanish language and English language and reading: A longitudinal exploration from second through fifth grade. Bilingual Research Journal, 40, 372-391.
Prendez, J., & Harring, J. R. (2018, May). Exploring fungible parameter estimates in SEM. Presented at the Modern Modeling Methods Conference, University of Connecticut, Storrs, CT.
Cassiday, K., & Harring, J. R. (2018, April). A comparison of label switching algorithms. Paper presented at the American Educational Research Association Conference, New York, NY.
Man, K., Harring, J. R., Sinharay, S., & Jiao, H. (2018, April). Investigating the use of data mining methods in the detection of aberrant testing behavior. Paper presented at the National Council of Measurement in Education Conference, New York, NY.
Zou, J., Harring, J. R., Brandt, H., & Kelava, A. (2018, April). A multilevel IRT model with nonlinear effects. Paper presented at the National Council on Measurement in Education Conference, New York, NY.
Harring, J. R., Zou, J., Brandt, H., & Kelava, A. (2017, April). Nonlinear structural equation mixture models in a multilevel framework: An empirical study. Paper presented at the American Educational Research Association Conference, San Antonio, TX.
Lee, D. Y., Stapleton, L. M., & Harring, J. R. (2017, April). Respondent attrition in longitudinal analysis of panel data. Paper presented at the American Educational Research Association Conference, San Antonio, TX.
Harring, J. R. (2016, August). Exploring model-based covariance structures in finite mixture models. In S. K. Sterba (Chair), Advances in mixture modeling methods. Invited symposium at American Psychological Association conference. Denver, CO.
Man, K., & Harring, J. R. (2016, May). Aberrant behavior detection based on a multivariate mixture modeling approach. Presented at the Modern Modeling Methods Conference, University of Connecticut, Storrs, CT.
Calico, T., Rupp, A. A., & Harring, J. R. (2016, April). Aligning process, product and survey data: Bayes nets for a simulation-based assessment. Paper presented at the National Council of Measurement in Education Conference, Washington, D.C.
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.
Kang, Y., & Harring, J. R. (2012, May). Investigating the Impact of Non-Normality, Effect Size and Sample Size on Two-Group 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)
Institutes of Educational Sciences (Goal 3). Toggle Talk. $3,247,480
National Institute of Mental Health. Trajectories of Behavioral Inhibition and Risk for Anxiety. $3,551,633
Institute of Educational Sciences (Goal 2); NCER for English Learners. The CLAVES Intervention Project: Developing a Supplemental Intervention for Comprehension, Linguistic Awareness, and Vocabulary in English for Spanish Speakers. $1,500,000
Dr. Harring teaches quantitative methods courses making up the EDMS graduate-level statistics sequence: EDMS 645 - Quantitative Methods I, EDMS 646 - General Linear Model I, and EDMS 651 - General Linear Model II. Corresponding more to his own research interests, Dr. Harring has also taught advanced graduate seminars on longitudinal data analysis (EDMS 769L), statistical computing and Monte Carlo simulation (EDMS 779), and finite mixture models in measurement and statistics (EDMS 769M).