Xiaohui LUO 罗晓慧
Ph.D. Candidate in Psychological Statistics and Psychometrics
Beijing Normal University
Visiting Researcher · University of Groningen
👋 About Me
Hi! I am Xiaohui Luo, a PhD candidate in Psychological Statistics and Psychometrics at Beijing Normal University (BNU). I am supervised by Prof. Hongyun Liu at the Dynamic Modeling & Assessment in Psychology (DyMAP) Lab.
In 2026, I am conducting a one-year research visit at the University of Groningen under the guidance of Prof. Laura Bringmann in the LaBlab.
💡 Research Focus
My research aims to bridge methodological innovation, AI and machine learning, and precision behavioral health.
Explore my research & publications →
🌟 Recent Highlights
Awarded the National Scholarship for Ph.D. students (my fifth National Scholarship across my undergraduate and doctoral studies)!
Our paper on two-method measurement planned missing designs is now published in Behavior Research Methods! The study develops time-embedded adaptations for intensive longitudinal studies. Read the paper.
Our paper on non-varying individuals is now published in Behavior Research Methods! From an idea in my final undergraduate year to publication at the start of PhD year four. Read the paper.
More
This project began in my final undergraduate year and reached publication as I entered the fourth year of my PhD. This long journey makes the paper especially meaningful to me. I am deeply grateful to the reviewers, associate editors, and editor-in-chief, whose thoughtful feedback throughout a challenging review process helped shape an early idea into a stronger paper and deepened my understanding of the problem.
First-authored paper accepted in Psychological Methods (IF: 11.5). This tutorial introduces Bayesian evaluation of latent variable models using the R package bleval, covering information criteria and Bayes factors. Read the paper.
More
This was my first experience developing an R package. It grew out of model-evaluation questions we encountered in another project that did not work out as hoped. What began as a side task in Bayesian latent variable modeling eventually became this tutorial and bleval, a reminder that a side task can become a meaningful contribution in its own right.
I am grateful to Prof. Edgar C. Merkle for his guidance on the tutorial and package development, to my advisors Prof. Hongyun Liu and Prof. Yang Liu for their mentorship, and to my co-first-author Jieyuan Dong for the teamwork.
Awarded the SAA 2026 Travel Scholarship for active participation in the Society for Ambulatory Assessment Conference in Vienna. Truly honored and looking forward to presenting our work there!
Earlier
First-authored paper accepted in Psychometrika. This work develops a bias-correction method via stochastic approximation for two-stage estimation in differential equation models (likely the most technically demanding project of my PhD journey so far). Read the paper.
More
This project brought together my mentors’ expertise in bias correction, statistical inference, and differential equation modeling. Working through the problem together showed me the value of discussing ideas with people who approach them from different perspectives.
I am deeply grateful to Prof. Hongyun Liu, Prof. Yueqin Hu, and Prof. Yang Liu for their patient guidance throughout this technically demanding project.
Editorial Assistant for Acta Psychologica Sinica (心理学报) and Psych Journal. Truly honored to support the editorial process of these journals!
Visiting Researcher at University of Groningen, The Netherlands. So excited about all the new experiences, collaborations, and perspectives this visit brings!
CAST Youth Science and Technology Talent Program (Doctoral Project) (Selected as one of 13 Ph.D. students nationwide by the Chinese Psychological Society)
National Scholarship for Ph.D. students
Best Paper Award, International Society for Data Science and Analytics (ISDSA) Annual Meeting. The paper behind this award finally found its home in Psychometrika (accepted Mar 2026)! Read the paper.
First-authored paper accepted in Psychological Methods (IF: 11.5). So thrilled that our work on timescale mismatch between variables in intensive longitudinal data and possible solutions has been accepted in this flagship methodological journal! Read the paper.
📬 Feel free to reach out to discuss research collaborations, methodological questions, or potential opportunities!
Last updated: Nov 2026