The LCDS Lab trains students, postdoctoral scholars, and collaborators to connect rigorous quantitative methodology with applied education decisions.
Training Philosophy
The lab’s training model emphasizes statistical theory and derivation, careful study design, transparent assumptions, reproducible workflows, clear writing, and communication with both methodological and applied audiences. Students and collaborators learn to move between statistical methodology, real education data, software-supported workflows, and partner-facing evidence.
Skills Students Develop
- Statistical theory and derivation
- Statistical programming
- Causal inference and evaluation
- Multilevel modeling
- Longitudinal data analysis
- Difference-in-differences
- Monte Carlo simulation
- Power and sample-size planning
- Education data science and AI
- Cost-effectiveness analysis
- Research writing and dissemination
Training Pathway
Foundation
Build core skills in statistical theory and derivation, quantitative methods, programming, research design, causal reasoning, and education evaluation.
Project Involvement
Contribute to funded projects through data preparation, simulation studies, literature reviews, software examples, or applied analyses.
Scholarly Leadership
Develop conference presentations, manuscripts, software documentation, dissertation work, and partner-facing research products.
Courses and Teaching
- EDF 7474 Multilevel Models – Spring 2021, Spring 2023, Spring 2024, Spring 2025, Spring 2026.
- EDF 7405 Advanced Quantitative Foundations of Educational Research – Fall 2020, Spring 2021, Fall 2021, Summer 2022, Fall 2022, Fall 2024.
- EDF 6938 Special Topic: Multilevel Randomized Trials – Fall 2021.
- Special topics in causal evaluation, multilevel modeling, longitudinal designs, and statistical power analysis.
Workshops, Seminars, and Events
Workshops
Hands-on workshops on the design and analysis of longitudinal studies using randomized controlled trials and difference-in-differences designs, with planned training opportunities at SREE, AERA, AEFP, and APPAM in the coming years.
Seminars
Research talks and invited presentations on longitudinal methods, causal evaluation, education data science, AI, and education evaluation.
Reading and Methods Group
The lab will organize reading groups on recent developments in difference-in-differences in Fall 2026 and causal machine learning in Spring 2027.
Upcoming workshops, seminars, and reading group meetings will be shared here when dates are confirmed.
Training Areas
- Statistical theory and derivation
- Causal inference and evaluation
- Multilevel modeling
- Longitudinal design and power
- Heterogeneous treatment effects
- Statistical programming and simulation
- Education data science and AI
- Digital learning systems
- Cost-effectiveness and implementation
Public Resources
As lab resources mature, this page will link to workshop materials, software examples, reading lists, tutorials, and reproducible code connected to LCDS Lab projects.