Ming Li, PhD

Education

• PhD, Measurement, Statistics, and Evaluation (2015)
  University of Maryland, College Park (MD)
• MS, Educational Psychology, Concentration in Assessment, Evaluation, and Testing (2009)
  George Mason University (VA)

About Me

As Director of Institutional Research and Analytics at Widener University, I advance institutional planning and informed decision-making through data analytics, assessment, and reporting. I also serve as the university’s Accreditation Liaison Officer (ALO) to the Middle States Commission on Higher Education (MSCHE), coordinating accreditation-related activities and communication between the university and the Commission. My work includes analyzing institutional trends and student outcomes, developing reports and data visualizations, contributing to strategic priorities and university-wide initiatives, providing decision support and data consultation, conducting survey research, and promoting the accuracy, consistency, and integrity of institutional data.

Drawing on my experience in statistical modeling, data science, measurement theory, survey methodology, and educational assessment, I enjoy translating complex information into clear, actionable insights. I value the opportunity to collaborate with colleagues across the university and contribute to efforts that strengthen student success and continuous improvement.

Personal Insight for Students

I enjoy helping students see how data can be used to explore meaningful questions and better understand the world around them. My advice is to remain curious, ask questions, and seek opportunities to connect with faculty and staff. Building relationships, trying new experiences, and exploring different interests can help you discover a path that aligns with your strengths and goals.

Research Interests

My research interests focus on applying a range of analytical and assessment approaches to better understand student experiences, educational outcomes, and institutional effectiveness. I am particularly interested in predictive analytics, longitudinal and multilevel modeling, mixed methods, and data visualization. Through this work, I aim to identify meaningful patterns, examine relationships over time, and deepen understanding of the factors that shape students’ educational experiences and success.