The LCDS Lab’s methodological research portfolio develops statistical tools, study-design guidance, software, and training resources for education research. These projects focus on longitudinal design, power and sample-size planning, causal inference, heterogeneous treatment effects, mediation, cost-effectiveness, and related methods.
External Grants
NSF CAREER: Longitudinal Studies in STEM Education
Funder: National Science Foundation
Period: 2024-2029
Role: Principal Investigator
Amount: $1,248,938
Statistical power analysis and optimal sample size planning for longitudinal studies in STEM education.
Multilevel Randomized Cost-Effectiveness Trials
Funder: National Science Foundation
Period: 2020-2026
Role: Co-Principal Investigator and UF Campus PI
Amount: $1,274,904
Statistical frameworks and tools for planning multilevel randomized cost-effectiveness trials.
Mediation Effects in Experimental Studies
Funder: Spencer Foundation
Period: 2022-2023
Role: Principal Investigator
Amount: $50,000
Optimal design of experimental studies investigating mediation effects with individual-level mediators.
Internal Grants
Causal Discovery for Online Learning Platforms
Funder: UF HDOSE Strategic Reinvestment Fund
Period: 2026-2027
Role: Principal Investigator
Amount: $15,000
Causal discovery and high-dimensional treatment methods for evaluating online learning platforms.
Enhancing Longitudinal Program Evaluations
Funder: UF Lastinger Center for Learning
Period: 2025-2026
Role: Principal Investigator
Amount: $93,808
Estimating design parameters and illustrating advanced quasi-experimental methods using UF Lastinger Center data.
Propensity Score-Based Quasi-Experimental Study Planning
Funder: UF HDOSE Strategic Reinvestment Fund
Period: 2025-2026
Role: Principal Investigator
Amount: $14,984
Sample size and power analysis methods for propensity score-based quasi-experimental studies.
Machine Learning for Causal Heterogeneous Effects
Funder: UF HDOSE Strategic Reinvestment Fund
Period: 2022-2023
Role: Principal Investigator
Amount: $12,500
Comparing machine learning methods to detect causal heterogeneous treatment effects in multilevel randomized trials.
Binary Outcomes in Multilevel Cost-Effectiveness Trials
Funder: University of Alabama Research Grants Committee
Period: 2018-2020
Role: Principal Investigator
Amount: $5,964
Power analysis for multilevel randomized cost-effectiveness trials with binary effectiveness measures.