Laura M. Stapleton is Associate Dean for Research, Innovation, and Partnerships. She is also a Professor in Measurement, Statistics and Evaluation (EDMS) in the Department of Human Development and Quantitative Methodology at the University of Maryland and served as the Associate Director of the Research Branch of the Maryland State Longitudinal Data System Center from 2013-2018. She joined the faculty of the college in Fall 2011 after being on the faculty in Psychology at the University of Maryland, Baltimore County and in Educational Psychology at the University of Texas, Austin. She is the Director of the NSF Quantitative Research Methods Scholar program, currently serves as Associate Editor of AERA Open and each year teaches as part of the faculty of the National Center for Education Research funded Summer Research Training Institute on Cluster Randomized Trials at Northwestern University. Prior to earning her Ph.D. in Measurement, Statistics and Evaluation, she was an economist at the Bureau of Labor Statistics and, subsequently, conducted educational research at the American Association of State Colleges and Universities and as Associate Director of institutional research at the University of Maryland.
Stapleton, L. M., & #Johnson, T. L. (2019). Models to examine the validity of cluster-level factor structure using individual-level data. Advances in Methods and Practices in Psychological Science, 2, 312-329. https://doi.org/10.1177/2515245919855039
Wang, W., Liao, M., & Stapleton, L. M. (2019). Incidental second-level dependence in educational survey data with a nested data structure. Educational Psychology Review. doi.org/10.1007/s10648-019-09480-6
Bonnéry, D., #Feng, Y., Henneberger, A. K., #Johnson, T. L., Lachowicz, M., Rose, B. A., Shaw, T., Stapleton, L. M., Woolley, M. E., #Zheng, Y. (2019). The promise and limitations of synthetic data as a strategy to expand access to state-level multi-agency longitudinal data. Journal of Research on Educational Effectiveness. doi.org/10.1080/19345747.2019.1631421 Note: authors listed in alphabetical order
Hancock, G. R., Stapleton, L. M., & Mueller, R. (Eds.) (2018). The reviewer’s guide to quantitative methods in the social sciences, 2nd edition. New York, NY: Routledge.Leite, W. L., Stapleton, L. M., & Bettini, E. F. (2018). Propensity score analysis of complex survey data with structural equation modeling: A tutorial with Mplus. Structural Equation Modeling: A Multidisciplinary Journal.
Stapleton, L. M., & Kang, Y. (2018). Design effects of multilevel estimates from national probability samples. Sociological Methods & Research. Advance online publication. doi: 10.1177/0049124116630563
McNeish, D. M., Stapleton, L. M., & Silverman, R. (2017). On the unnecessary ubiquity of hierarchical linear modeling. Psychological Methods, 22,114-140. http://dx.doi.org/10.1037/met0000078
Stapleton, L. M., McNeish, D. M., & Yang, J.-S. (2016). Multi-level and single-level models for measured and latent variables when data are clustered. Educational Psychologist, 51, 317-330. doi: 10.1080/00461520.2016.1207178
Stapleton, L. M., Yang, J. S., & Hancock, G. R. (2016). Construct meaning in multilevel settings. Journal of Educational and Behavioral Statistics, 41, 481-520. doi: 10.3102/1076998616646200
Educational Testing Service, Princeton, New Jersey, Gulliksen Psychometric Fellowship, 2000 – 2001
AERA Division H, Outstanding Publication: Henneberger, A., Feng, Y., Johnson, T., Zheng, Y., Rose, B. A., Stapleton, L. M., Sweet, T., & Woolley, M. E. (2020). Statistically Modeling Multiple Membership in the Real World: Lessons from Statewide Longitudinal Data in Maryland
Outstanding Director of Graduate Studies, University of Maryland Graduate School, 2018
Faculty Mentor of the Year, University of Maryland Graduate School, 2017
College of Education Senate Award for Excellence in Teaching, 2015-16
Elected Membership to the Society for Multivariate Experimental Psychology, 2016
9th Annual University-Wide Celebration of Scholarship and Research Honoree, 2016
Outstanding Reviewer, American Educational Research Journal, 2015