My research interests lie at the intersection of psychometrics and quantitative methods in psychology. I am particularly interested in longitudinal data modeling, with a focus on traditional and intensive designs. My work encompasses various analytical strategies, including linear growth curve modeling and advanced methods tailored for intensive longitudinal data, such as dynamic structural equation modeling (DSEM). I am especially interested in capturing, modeling, and interpreting temporal structures in psychological processes. In addition, as I continue to deepen my training, I am also interested in applying diverse statistical tools to address practical research problems in psychology.

More specifically, the research topics I am concerned with include the following:

1. Longitudinal data modeling (applied in psychology, education, and social sciences) 

• Dynamic Structural Equation Modeling (DSEM) is a statistical framework that combines time-series analysis with multilevel modeling, allowing researchers to capture both within-person dynamics and between-person differences in intensive longitudinal data. Current work includes modeling random residual variance (RRV) and amplitude as indicators of variability, as well as developing user-friendly R packages (e.g., psyDSEM). 

• Longitudinal nonlinear data capture time-based trajectories that simple linear functions cannot adequately describe. Such patterns often appear in psychology and health studies—for instance, symptom reduction that accelerates and then plateaus, or periodic fluctuations in mood. I am particularly interested in exploring appropriate statistical approaches, such as nonlinear mixed-effects models, spline methods, or GAMM, to capture these nonlinear processes. 

2. Psychometric models (applied in psychology and education) 

• I plan to advance Item Response Theory (IRT) by developing extended models, including multidimensional, explanatory, and mixture IRT, and by creating IRT-based longitudinal reliability indices and time-varying information functions to quantify measurement precision in longitudinal studies.

3. Quantitative methods in experimental psychology (applied in psychology, natural sciences, and medicine) 

• GLMM joint modeling to process experimental data

Using GLMM-based joint modeling frameworks to capture multiple processes simultaneously (e.g., accuracy and response time)

• • Causal Inference in Experimental Psychology      

Bridging modern causal methodologies with experimental designs to better identify and interpret underlying psychological processes. 

4. Semiparametric/nonparametric statistics 

• Semiparametric = parametric core + flexible parts. Keeps interpretable coefficients (e.g., group effects) while letting other relationships be smooth/unknown, reducing model misspecification.

• Nonparametric = minimal functional assumptions. Let the data shape the function, capturing heterogeneity and nonlinear dynamics that rigid models miss.