This section highlights my research outputs including manuscripts under review, published work, and conference presentation. In other words: what happens after the model converges.
Embedded Files
Publications
Publications
He, S., & Lee, W. (accepted). Identifying Latent Profiles of Multicultural Acceptance Among Multicultural Adolescents: A Machine Learning Informed Latent Profile Analysis.
He, S., & Lee, W. (under review). A Dynamic Perspective on Clinical Treatment Evaluation Using Dynamic Structural Equation Modeling.
He, S., & Lee, W. (2022). Generalized linear mixed models for studies using different sets of stimuli across conditions. Frontiers in Psychology. 13 doi: 10.3389/fpsyg.2022.955722
Collaborative Projects
Collaborative Projects
2024.09–2025.05, “Comparison of multidimensional scaling results according to data characteristics: Similarity and proximity data” (PI: Wooyeol Lee), Researcher, funded by the National Research Foundation of Korea
2022.03–2023.06, “Predicting suicidal crisis: converging evidence from a population-based cohort study and cognitive control experiments” (PI: Sungeun You), Researcher, funded by the National Research Foundation of Korea
Conference Presentations
Conference Presentations
He, S. (2025, December). Bayesian Joint Location-Scale Models for Dynamic Psychological Processes with Amplitude-Based Predictors. The Psychological Resolution of BK21 Social Inequality in 2025: EQUALS Winter Academic Study Presentation. [Presentation Excellence Award]
He, S., & Lee, W., Han, Y.(2025, November). Comparing Stimulus Spaces of the Individual Differences Scaling (INDSCAL) and Unfolding Models: An Exploratory Analysis Across Data Types [poster session]. 2025 Fall Conference of the Korean Society for Psychological Measurement and Assessment, Daegu, South Korea.
He, S. (2025, June). Dynamic Multilevel Location–Scale Models for evaluating dynamic treatment effects in Intensive Longitudinal Data. The Psychological Resolution of BK21 Social Inequality in 2025: EQUALS Summer Academic Study Presentation. [Presentation Excellence Award]
He, S. & Lee, W (2025, April). Evaluating Dynamic Treatment Effects via Random Residual Variance in DSEM: A Simulation Study [poster session]. Korean Society for Psychological Measurement and Assessment – 2025 Spring Conference, Seoul, South Korea.
He, S. & Lee, W (2024, August). Importance of Random Residual Variance in the Dynamic Structural Equation Modeling (DSEM) Framework [poster session]. American Psychological Association 2024 Convention, Seattle, WA, United States.
He, S., & Lee, W (2023, February). Establishment of experimental stimulus effects in a generalized linear mixed-effects model [paper presentation]—the 2nd BK21 Project Group (Team) Regional University Psychology Department Joint Academic Conference.
He, S. (2022, June). Dynamic structural equation model (DSEM) for Ecological Momentary Assessment [paper presentation]. The Psychological Resolution of BK21 Social Inequality in 2022: EQUALS Summer Academic Study Presentation. [Presentation Excellence Award]
He, S., & Lee, W. (2021, December). Generalized linear mixed-effects model for experimental designs having non-repeated items [paper presentation]. The Psychological Resolution of BK21 Social Inequality in 2021: EQUALS Winter Academic Study Presentation.
He, S., & Lee, W. (2021, April). Results of ignoring the heterogeneity due to the random effect of the item [poster session]. The 2021 online spring conference of the Korean Society of Psychological Measurement and Assessment. [Presentation Excellence Award]
Ongoing Research
Ongoing Research
In prep for submission
Lee, S.H, He, S., Ahn, J.K. For Whom Does Imagery Rescripting Work? Moderators of Treatment Response in a Dismantling Trial of Group CBT for Social Anxiety Disorder.
Kim, J.H, He, S., You, S.E. Relationship between Posttraumatic Stress Disorder Symptoms and Suicide Crisis Syndrome: A network analysis.
Manuscript in preparation
He, S., Kim, Y., Lee, W., Han, Y. Recent Extensions of Item Response Theory (IRT) Models.
He, S., & Han, Y. Evaluating the Interchangeability of INDSCAL and Unfolding Models
He, S., & Lee, W. Intensive Longitudinal Data.
Kim, S.H, He, S., You, S. Machine Learning for suicide risk prediction.
DBT study
In progress
He, S., & Lee, W. APIM in the Dynamic Structural Equation Modeling.
He, S., & Lee, W. Causal effects in psychological observational data.
He, S., Han, Y. & Lee, W. Validity study: A meta-analysis.
He, S., & Lee, W. Modeling residual variance as a predictor: A new perspective.
He, S. A cutoff point study.
He, S. Dynamic mixture modeling.
He, S. Dynamic survival analysis modeling.
He, S. Bayesian modeling of cyclical changes in within-person variability
Past Research
Past Research
He, S., & Lee, W. 2023.09–2024.08, Importance of Random Residual Variance in the Dynamic Structural Equation Modeling (DSEM) Framework (APA).
APA_poster_shuncheng he.pdfHe, S., & Lee, W. 2022.01 – 2023.08, “Introduction to Dynamic Structural Equation Models for Ecological Momentary Assessment Data”, Master's topic.
He, S., & Lee, W. 2021 – 2022, “Generalized linear mixed-effects model for experimental designs having non-repeated items,” published in Frontiers in Psychology.
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