Laura M. Stapleton is chair of the Department of Human Development and Quantitative Methodology and a professor of Quantitative Methodology: Measurement and Statistics. She previously served as the interim dean of the College of Education and Associate Dean for Research, Innovation, and Partnerships. She is the Director of the NSF-funded Quantitative Research Methods Scholars Program, which trains 20 early career scholars who focus on STEM education equity and access yearly. In 2021, the Governor appointed her to the seven-member Accountability and Implementation Board that is overseeing the enactment of the Blueprint for Maryland's Future that brings sweeping reform to the state's education system. 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 served as the Associate Director of the Research Branch of the Maryland State Longitudinal Data System Center from 2013-2018. 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.
Educational Testing Service, Princeton, New Jersey, Gulliksen Psychometric Fellowship, 2000 – 2001
Fellow of the American Educational Research Association, 2023
Society for Prevention Research, Abstract of Distinction. Henneberger, A.K., Rose, B.A., Stapleton, L.M., & Woolley, M.E. (2021). Innovations in Administrative Data Linkage and Access to Advance Prevention Science Research and Policy.
Finalist, National Assessment Governing Board member as Testing and Measurement Expert, 2021
AERA Division D, Significant Contribution to Educational Measurement and Research Methodology Award: Hancock, G.R., Stapleton, L.M., & Mueller, R. (Eds.) (2019). The reviewer’s guide to quantitative methods in the social sciences, 2nd edition. New York, NY: Routledge.
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
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
National Science Foundation; 2022 – 2025; Principal Investigator; BCSER Quantitative Research Methods for STEM Education Scholars Program
National Science Foundation; 2019 – 2022; Principal Investigator; Quantitative Research Methods for STEM Education Scholars Program (#1937745)
Institute for Education Sciences; National Center for Education Research, 2018-2020, Exploring Links Between Arts Education and Academic Outcomes in the International Baccalaureate, PI: Dr. Ken Elpus, UMCP
National Science Foundation; Developmental Science, 2017 – 2020, Promoting Intergroup Relationships and Reducing Prejudice in Childhood, PI: Dr. Melanie Killen, UMCP
Institute for Education Sciences; National Center for Education Research, 2016 – 2019, Cognitive and Motivational Contributors to Reading Comprehension in English Learners (ELs) and English Monolinguals (EMs): Different or Similar Growth Patterns? (R305A160280), PI: Dr. Ana Taboada, UMCP
Institute for Education Sciences; State Longitudinal Data Systems, 2015 – 2019, Feasibility of Synthetic Data for Population-Averaged and Cluster-Specific Analyses by Researchers Utilizing Integrated State Longitudinal Data Systems (R372A150045)
EDMS769D Modeling Educational Survey Data from Complex Sampling Designs
EDMS647 Causal Inference and Evaluation Methods
EDMS655 Multilevel Modeling