Measurement
Measurement is the foundation of quantitative science, and it’s especially important in psychological and social sciences. If a variable was not measured with good precision and validity, any research results based on it is like picking out patterns from random noise, or even highly misleading when systematic biases are present.
Current projects
- New algorithms for adjusting parameter estimates for measurement noninvariance (Mark Lai & Winnie Tse)
- Measurement invariance with categorical data (Winnie Tse)
- Quantifying measurement noninvariance in a meaningful way (Yichi Zhang)
- Two-stage path analysis to adjust for measurement errors and measurement bias in data harmonization and integrative data analysis (Mark Lai, Winnie Tse, Jimmy Zhang, & Yixiao Li, funded by the National Science Foundation)
- Developing a Multidimensional Psychometric Framework on the Impact of Item Bias on Classification (Mark Lai & Yichi Zhang & Meltem Ozcan, Contract with Army Research Institute)
- Synthesis of invariance research (Jimmy Zhang & Haley Yue)
Multilevel Modeling
Multilevel modeling, also called mixed-effect models, is an extremely powerful framework with very broad applications for quantitative science. It makes efficient use of data by adaptively pooling information across clusters (i.e., schools, states, companies, persons, or any continuous groups, including continuous ones).
Current projects
- Reliability for clustered and longitudinal data (Mark Lai)
- Bias-correction for contextual and between-level effects (Mark Lai & Yichi Zhang)
- The Impact of Ignoring Parameter Uncertainty on Sample-Size Planning for Cluster-Randomized and Multisite Randomized Trials (Winnie Tse & Mark Lai, funded by Spencer Foundation)