MacKenzie Deforest
Research Fellow @National Institute For Student Success
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WORK HISTORY
Research Fellow @National Institute For Student Success
Utilize a Multilevel Model to explore student success program impacts between and within different careers and those impacts on work force outcomes. Currently working with four separate program evaluations, with populations varying from alumni to alumni- Collaborate with external partners and manage all data sharing agreements- Use R and Python coding to clean and analyze specific trends within the data prior to running the multilevel model- Explore current methodological literature surrounding causal inference in multi-site observational studies to evaluate the potential average treatment effect on participants and the average treatment effect on the treated- Explore methodological literature in multilevel modeling to ensure methodologically sound decisions prior to analysis- Check model assumptions and adjust models as needed for assumption violations- Write up findings and submit to grant funder, the National Institute for Student Success, and leading peer reviewed journals to disseminate key findings.
EDUCATION
Georgia State University
Graduate Certificate, Artificial Intelligence Business Innovation
Georgia State University
Master of Science - MS, Clinical Rehabilitation Counseling
Massachusetts Institute of Technology
Certificate Program, Data Science and Machine Learning: Making Data-Driven Decisions
University of Georgia - Franklin College of Arts and Sciences
Bachelor’s Degree, Psychology/Neuroscience
Georgia State University
Doctor of Philosophy - PhD, Research, Measurement, and Statistics
ABOUT MACKENZIE DEFOREST
As a Research Fellow and Data Analyst at the National Institute for Student Success (NISS), my work centers on transforming complex educational data into actionable insights that inform institutional strategies and improve student outcomes. I currently lead a research initiative focused on workforce outcomes, where I analyze post-graduation employment and salary trends using cross-classified multilevel modeling. This project allows me to examine how factors such as academic major, career field, and student characteristics intersect to influence long-term success—offering valuable guidance to institutional leaders and advisors.In this role, I have expanded my expertise in advanced statistical techniques, including multilevel modeling, while continuing to build on my skills in predictive analytics, machine learning, and data visualization. My models help stakeholders better understand academic and postsecondary outcomes, equipping them with the evidence needed to drive equity-centered, data-informed decision-making.Leveraging strong competencies in SQL and statistical programming, I contribute to the university\'s mission of improving enrollment, retention, and graduation outcomes through data transparency and strategic insight. My commitment to methodological rigor and impactful reporting aligns closely with my ongoing doctoral research in Research, Measurement, and Statistics. I believe in using data not only to reflect current realities but to unlock new possibilities that enhance the student experience.
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