Research Scientist

Technical skills: R, Python, SQL, Mplus, Stata, SPSS

Education

• Ph.D., Quantitative Methods and Computational Psychology, University of Southern California
• M.A., Quantitative Methods and Computational Psychology, University of Southern California
• B.S., Mathematics and Psychology, Dickinson College

Work Experience

Researcher @American Institutes for Research

• Led research projects supporting the largest nationally representative educational datasets with complex sampling design to inform real-world decision-making and policy development
• Evaluated the validity, reliability and impacts of measurement bias on key performance metrics using large survey datasets
• Presented actionable insights and recommendations to cross-functional teams including policymakers and applied researchers
• Collaborated with software engineers to build tools that train external researchers in statistical analysis of complex survey data

Publication

1. Lai, M. H. C., Zhang, Y., Ozcan, M., Tse, W. W.-Y., & Miles, A. (2025). fMACS: Generalizing dMACS effect size for measurement noninvariance with multiple groups and multiple grouping variables. Structural Equation Modeling: A Multidisciplinary Journal. Advance online publication. https://doi.org/10.1080/10705511.2025.2484812

2. Lai, M. H. C., Zhang, Y., & Ji, F. (2024). Adjusting for measurement error in cluster means in multilevel modeling: Two numerically stable alternatives to latent-mean centering. Multivariate Behavioral Research. Advance online publication. https://doi.org/10.1080/00273171.2024.2307034

3. Zhang, Y., & Lai, M. H. C. (2024). Evaluating two small-sample corrections for fixed-effects standard errors and inferences in multilevel models with heteroscedastic, unbalanced, clustered data. Behavior Research Methods. Advance online publication. https://doi.org/10.3758/s13428-023-02325-9

4. Ozturk, E. D., Zhang, Y., Lai, M. H. C., Sakamoto, M. S., Chanfreau-Coffinierd, C., & Merritt, V.C. (2023). Measurement invariance of the Neurobehavioral Symptom Inventory (NSI) in male and female Million Veteran Program (MVP) enrollees completing the Comprehensive Traumatic Brain Injury Evaluation (CTBIE). Assessment. Advance online publication. https://doi.org/10.1177/10731911231198214

5. Tse, W. W., Lai, M. H. C., & Zhang, Y. (2023). Does strict invariance matter? Valid group mean comparisons with ordered-categorical items. Behavior Research Methods. Advance online publication. https://doi.org/10.3758/s13428-023-02247-6

6. Zhang, Y., Kim, Y., & Zheng, X. (2023). Investigating measurement invariance in NAEP student questionnaire index items. [AIR-NAEP Working Paper]. Washington, DC: American Institutes for Research.

7. Zhang, Y., Lai, M. H. C., & Palardy, G. J. (2023). A Bayesian region of measurement equivalence (ROME) approach for establishing measurement invariance. Psychological Methods, 28(4), 993–1004. https://doi.org/10.1037/met0000455

8. Lai, M. H. C., & Zhang, Y. (2022). Classification accuracy of multidimensional tests: Quantifying the impact of noninvariance. Structural Equation Modeling, 29(4), 620–629. https://doi.org/10.1080/10705511.2021.1977936