Xin Wei, Ph.D., has extensive experience in education policy research, experimental design, longitudinal data analysis, and psychometrics.

She currently leads the quantitative analysis on three U.S. Department of Education Investing in Innovation (i3) grants:

• Evaluation of the impact of training for new teachers, teacher leaders, and prospective principals on teacher and student outcomes in two districts in South Texas
• Examination of the impact of Collaborative Strategic Reading on improving student outcomes in Colorado
• Evaluation of the effectiveness of the Midwest expansion of the Child-Parent Center Education Program

She is also a co-principal investigator on two secondary data analysis grants funded by the National Science Foundation and Institute of Education Sciences to explore factors that contribute to success for students with autism. Wei also is applying growth modeling techniques using national longitudinal datasets to estimate children’s growth trajectory. She is a reviewer for the What Works Clearinghouse, using her knowledge of experimental design and statistical modeling to evaluate various educational intervention studies.

Wei directed the quantitative analysis of the Michigan Striving Readers project, a randomized controlled trial funded by the U.S. Department of Education investigating the effects of the Fusion Reading Intervention program on improving the reading achievement of striving readers in Michigan. She also worked as a statistician on a National Institute of Health-funded study linking stress and gene expression for youth.

Wei’s work can be found in peer-reviewed journals such as Exceptional Children, Educational Policy, Educational Measurement: Issues and Practice, Maternal and Child Health Journal, Psychoneuroendocrinology, Journal of Autism and Developmental Disorders, Journal of Research on Educational Effectiveness, Journal of Special Education, Remedial and Special Education, and others.

Before joining SRI, Wei designed cluster randomized experiments and quasi-experiments for education program evaluations, analyzed data from those studies using SAS and HLM, and reported on the results. She also has experience equating test scores for a state high-stakes test. She was a statistical consultant for the Social Science Data Services at Stanford University, where she gave workshops, provided consultation, and wrote documentation on the use of quantitative and qualitative statistical software.

For her dissertation, funded by the American Educational Research Association, she used HLM and regression discontinuity approaches to investigate whether the No Child Left Behind accountability policy is associated with improved student academic achievement and narrowing achievement gaps.

Wei received her Ph.D. in educational psychology with a focus on statistics and measurement and an M.S. degree in statistics from Stanford University, where she was a recipient of the prestigious Stanford Graduate Fellowship. She also holds an M.S. degree in human development from the University of Wisconsin-Madison and a B.A. degree in child development from Nanjing Normal University in China.

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