Research

My research focuses on the development and application of quantitative methods for complex research problems in education, psychology, and the social sciences. I integrate educational measurement, psychometrics, statistical modeling, and machine learning to address questions involving measurement, large-scale data, longitudinal studies, and other complex data sets.

Research Areas

Educational Measurement and Psychometrics

My research in educational and psychological measurement examines how latent constructs are measured and how the quality, validity, and fairness of assessments can be evaluated. Areas of interest include item response theory (IRT), differential item functioning, scale development and validation, polytomous and rating-scale data, and methodological issues affecting measurement and inference.

Large-Scale Assessment and Survey Data

I conduct research using large-scale assessments and survey data, including national and international assessment programs such as NAEP and PISA. My work addresses complex sample designs, sampling weights, variance estimation, plausible values, multilevel structures, missing data, and the analysis and interpretation of large-scale assessment data. I am also interested in methodological issues arising from digitally based assessments and assessment process data.

Machine Learning Applications in Education and Psychology

I investigate how machine learning can complement traditional statistical and psychometric approaches in education and psychology. Applications include prediction, feature selection, classification, nonlinear relationships, model comparison, and the identification of heterogeneous patterns in complex and high-dimensional data.

An important focus of this work is the relationship between data-driven machine learning methods and theory- and measurement-driven research. Rather than treating machine learning as a replacement for traditional methods, I am interested in understanding when and how computational approaches can provide useful additional evidence while maintaining interpretability and substantive meaning.

Longitudinal and Complex Data Analysis

I develop and apply methods for analyzing longitudinal, multilevel, and otherwise complex data structures. This includes research involving repeated measurements, within-person variability, person-specific processes, multilevel models, and complex dependencies among observations.

My applied work has included longitudinal and intensive repeated-measures data in education, psychology, health, and human development, as well as large-scale assessment and survey data.

Missing Data and Psychometric Harmonization

I study methodological approaches for handling missing data and integrating measures across studies, instruments, and populations. This includes missing responses in assessments, missing-data methods for longitudinal and complex datasets, and psychometric approaches to harmonizing measures when data are collected using different instruments or measurement frameworks.

Advanced Quantitative Methodology

A broader goal of my research is to develop, evaluate, and apply advanced quantitative methods for complex research problems. This includes multilevel modeling, longitudinal analysis, missing-data methods, machine learning, and other modern statistical approaches.

I am particularly interested in methodological choices that reflect both the structure of the data and the substantive research question, rather than applying statistical methods independently of the research context.

Selected Research Themes

Across these areas, several themes connect my methodological and applied work:

  • Measurement: Developing and evaluating methods for valid and meaningful measurement.
  • Complex data: Developing methods for large-scale, longitudinal, multilevel, and high-dimensional data.
  • Computational methods: Integrating machine learning and modern computational approaches with statistical and psychometric methods.
  • Reproducibility: Developing statistical software and reproducible workflows that make quantitative methods more accessible and usable.
  • Interdisciplinary applications: Applying quantitative methodology to research questions in education, psychology, health, and human development.