My research incorporates three connected themes: studying the fundamental issues in measurement, such as measurement invariance, differential item functioning (DIF) and outliers; developing and applying new statistical methods to education, psychology, health, and social behavior sciences; and educational assessments.

  • Initiated and leading a longitudinal study across four disciplines, education, psychology, psychometrics and computer science, to study the impact of robotics education on young children’s cognitive development and self-efficacy. Finished the data collection in year-1 and currently analyzing data collected from eye-tracking, cognitive assessments, survey and interviews
  • Actively collaborating and publishing papers with colleagues across disciplines including sports psychology, cognitive psychology, counselling psychology, biostatistics, science education, and computer science
  • Researching the application of propensity score to DIF, including application and simulation studies. Recently published four journal papers on this topic. Currently leading two projects extending this line of research to handle complex data
  • Published several journal papers on how outliers affect psychometric properties and models such as reliability estimates and exploratory factor analysis models
  • Integrated a Bayesian framework into my research, including Bayesian item response theory for investigating DIF, mixed effects models for studying response processes, and network meta-analysis
  • More recently exploring natural language processing for conversational analysis