Montserrat V
- Research Program Mentor
PhD at Indiana University at Bloomington
Expertise
Statistical analysis, including regression, longitudinal analysis, and machine learning using R. Statistical analysis in topics such as politics, education, public health, and economics. Research design, including experimental design and survey design. Comparability studies, including propensity score matching and measurement invariance.
Bio
Montserrat recently obtained her Quantitative Research Methodology Ph.D. at Indiana University Bloomington (IUB). In 2020, she obtained an MS in Applied Statistics from IUB. Her research interests focus on fairness in assessment, particularly those assessments comparing results of highly heterogeneous populations. She has collaborated on several research projects focusing on fairness-related topics in education and health. She is highly interested in improving equity through the use of quantitative analysis.Project ideas
Health Equity in Underprivileged Communities
This project aims to empower inexperienced students to explore the concept of health equity by utilizing open-source data to investigate disparities in healthcare access and outcomes in underprivileged communities. Students will learn how to gather, analyze, and interpret data, ultimately contributing to a deeper understanding of health disparities and the potential for positive change. Learning Outcomes: Enhanced data gathering and analysis skills. - Improved understanding of health equity and disparities. - Awareness of the socio-economic determinants of health. - The ability to visualize and present data effectively. - A sense of empowerment to advocate for positive change in underprivileged communities.
Bridging the Learning Gap with Open-Source Data
This project is designed for exploring educational equity by using open-source data to investigate disparities in educational access and outcomes in diverse communities. Students will learn how to gather, analyze, and visualize data, enabling them to better understand the complexities of educational equity and contribute to positive change in their communities. Learning Outcomes: - Enhanced data collection and analysis skills. - Improved understanding of educational equity and disparities. - Awareness of the factors contributing to disparities in education. - The ability to visualize and present data effectively. - Empowerment to advocate for positive change in the field of education.