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Qiu Wang

Qiu Wang
Associate Professor
Curriculum Vitae: Qiu Wang CV
Phone: 315.443.4763
Address: 350 Huntington Hall

  

Qiu Wang is an associate professor of quantitative research methodology at Syracuse University. His research interests include: 1) psychometrics and educational assessment using factor analysis; 2) large-scale modeling and big-data analyses using empirical Bayes, structural equation modeling, data mining/classification methods, and measurement error modeling; 3) intervention effect estimation and propensity score matching in program evaluation through synthetic cohort design in math/science education; and 4) 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.

He has published relevant/collaborative works in Behaviormetrika, Educational and Psychological Measurement, Developmental Psychology, Journal of Experimental Education, Journal of Career Assessment, Journal of Teacher Education, Computers & Education, Computers in Human Behavior, Educational technology research and development, Computers in the Schools, Journal of Educational Technology Development and Exchange, Journal of Learning Disabilities, and Remedial and Special Education.

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. He holds a Ph.D. in measurement and Quantitative Methods from Michigan State University (MSU), and master’s degrees in Experimental Psychology (Peking University, China) and in Applied Statistics (MSU).

 

 

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

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.