Xiao Liu's research focuses on developing and applying statistical methods for causal inference with complex data. This includes causal mediation analysis, propensity score methods, methods for clustered data, and longitudinal data analysis including growth curve modeling.
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
Statistical methods for causal inference, causal mediation analysis, propensity score methods, growth curve models, and methods with clustered data.
Liu, X. (2024). Propensity score weighting with missing data on covariates and clustered data structure.
Multivariate Behavioral Research, Advanced online publ. (
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Liu, X.., Zhang, Z.., Valentino, K.. & Wang, L.. (2024). The impact of omitting confounders in parallel process latent growth curve mediation models: Three sensitivity analysis approaches.
Structural Equation Modeling: A Multidisciplinary Journal,
31(1), 132–150. (
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Liu, X., Zhang, Z. & Wang, L. (2024). Detecting mediation effects with the Bayes factor: Performance evaluation and tools for sample size determination. Psychological Methods, (accepted).
Liu, X. & Wang, L. (2024). Causal mediation analysis with the parallel process latent growth curve mediation model. Structural Equation Modeling: A Multidisciplinary Journal, (accepted).
Liu, X., Liu, F., Miller-Graff, L., Howell, K. & Wang, L. (2023). Causal inference for treatment effects in partially nested designs.
Psychological Methods, Advanced online publ. (
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Liu, X., Zhang, Z. & Wang, L. (2023). Bayesian hypothesis testing of mediation: Methods and the impact of prior odds specifications.
Behavior Research Methods,
55(3), 1108–1120. (
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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. (
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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. (
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Outstanding Quantitative Dissertation Award, American Educational Research Association, Division D (2023)