Broadly, Xiao Liu is interested in developing and applying quantitative methods to help investigate various substantive research questions in education, psychology, and related social and behavioral sciences. Particularly, her research interests include the following interrelated areas of quantitative methods: causal inference, quasi-experimental design and analysis (e.g., propensity score methods), causal mediation analysis, experimental design and analysis, and clustered and/or longitudinal data analysis.
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
Quantitative methods for causal inference, experimental/quasi-experimental design and analysis, causal mediation analysis, clustered and/or longitudinal data analysis.
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., 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)
