Qiu Wang

 Qiu Wang is an associate professor of quantitative research methodology at Syracuse University. His research interests include: 1) educational assessment, large-scale and big-data analyses using factor analysis, structural equation modeling, and data mining methods; 2) intervention effect estimation and propensity score matching in program evaluation through synthetic cohort design and measurement error modeling in math/science education; and 3) human development and changes in school settings with foci on empirical contemplative studies, technology and human-computer interaction, and individuals with special needs.

He has published relevant/collaborative works in more than 20 journals including Behaviormetrika, Psychoneuroendocrinology, Educational and Psychological Measurement, Developmental Psychology, Journal of Experimental Education, Computers & Education, and Journal of Speech, Language, and Hearing Research. He has been serving as the outside evaluator and/or consultant on several projects funded by non-profit and federal agents including DOE, IES, NIH, and NSF. He has served as a (Co-)PI, co-investigator, senior/statistician personal on 12 finished/on-going grants from agencies including internal Collaboration for Unprecedented Success and Excellence (CUSE) Grant Program, NSF, American Educational Research Association(AERA) Research Program, and the prestigious General Research Found in Hong Kong.He was an Assistant Professor and served as the co-director of Purdue University Psychometric Instruction/Investigation Laboratory (PUPIL) in the College of Education at Purdue. 

Education

• Ph.D. Michigan State University, Measurement and Quantitative Methods
• M.S. Michigan State University, Applied Statistics
• M.A. Peking University, China, Experimental Psychology
• B.A. Henan University, China, Psychology

Research & Scholarship

Quantitative Research Methodology

His research interests include:

• psychometrics and educational assessment using factor analysis;
• large-scale modeling and big-data analyses using empirical Bayes, structural equation modeling, data mining/classification methods, and measurement error modeling;
• intervention effect estimation and propensity score matching in program evaluation through synthetic cohort design in math/science education; and
• human development and changes in school settings with foci on empirical contemplative studies, technology use and human-computer interaction, minorities in poverty, individuals with special needs, and racial and gender differences.

Courses Taught

EDU 616 Understanding Educational Research

EDU 647 Statistical Thinking & Applications

EDU 737 Quantitative Research Design

EDU 791 Advanced Seminar in Quantitative Research Methods

EDU 886 Multivariate Research Methods

EDU 888 Structural Equation Modeling/Factor Analysis