Xiao Liu's main research interests center around three inter-related areas: (1) mediation analysis, (2) causal inference and propensity score methods and (3) longitudinal data analysis. Broadly, Xiao Liu is interested in developing, evaluating and applying quantitative methods for designing research studies, analyzing complex data and improving causal inference.
Ph.D. in Quantitative Psychology, University of Notre Dame, 2022
M.S. in Applied Statistics, University of Notre Dame, 2020
B.S. in Statistics, Renmin University of China, 2017
Focuses on mediation analysis, causal inference, and longitudinal data analysis.
Liu, X., Liu, F., Miller-Graff, L., Howell, K. & Wang, L. (2023). Causal inference for treatment effects in partially nested designs. Psychological Methods. doi:10.1037/met0000565.
Liu, X.., Zhang, Z.., Valentino, K.. & Wang, L.. (2023). The impact of omitting confounders in parallel process latent growth curve mediation models: Three sensitivity analysis approaches. Structural Equation Modeling: A Multidisciplinary Journal. doi:10.1080/10705511.2023.2189551.
Liu, X., Zhang, Z. & Wang, L. (2022). Bayesian hypothesis testing of mediation: Methods and the impact of prior odds specifications. Behavior Research Methods. doi:10.3758/s13428-022-01860-1.
Liu, X. & Wang, L. (2021). The impact of measurement error and omitting confounders on statistical inference of mediation effects and tools for sensitivity analysis. Psychological Methods, 26(3), 327–342. doi:10.1037/met0000345.
Liu, X. & Wang, L. (2019). Sample size planning for detecting mediation effects: a power analysis procedure considering uncertainty in effect size estimates. Multivariate Behavioral Research, 54(6), 822–839. doi:10.1080/00273171.2019.1593814.
