Graduate Student Association (GSA)

Lindsey Insco holds a Bachelor of Science in Chemistry from Xavier University (2022) and a Master of Science in Criminal Justice from the University of Cincinnati (2023). Lindsey is currently a doctoral student in Criminal Justice at the University of Cincinnati. Her research examines life-course patterns of offending, drug overdose trends and response efforts, and broader characteristics of terrorism and counterterrorism. 

Esnart is an international doctoral student in the college of education.  Her program concentration is  education community-based action research. Her research interests revolve around the intersection of early childhood education, emergency education, and the rights of girls.

Amota Ataneka Merang is a doctoral candidate in Quantitative Research Methodologies whose work bridges psychometrics, causal inference, and machine learning. His dissertation develops new methods for estimating causal effects with latent variables, constructs that are central across many fields but cannot be measured directly (e.g., achievement, burnout, motivation, depression, self-efficacy, trust, belonging, quality of life). Existing latent variable methods often rely on strong parametric assumptions, while machine learning methods are flexible but typically ignore measurement error. Amota’s work addresses both problems by developing causal machine learning methods that account for measurement error while relaxing assumptions about complex nonlinear and interaction effects.

His dissertation is supported by the National Academy of Education/Spencer Dissertation Fellowship, the most prestigious dissertation fellowship in education. As of July 1, 2026, his paper providing the theoretical foundation and motivation for this work has been accepted for publication pending minor revisions at Psychological Methods, one of the leading and most selective journals of the American Psychological Association. He also has a related article under revision at the Journal of Educational Psychology that applies these modern causal machine learning methods to real-world educational settings. Amota is currently developing a targeted after generative framework for item response theory (IRT), allowing researchers to use machine learning for estimating causal effects when outcomes and covariates are measured using binary or categorical test and survey items.

A second line of Amota’s research focuses on the design of adequately powered studies when researchers are interested not only in main treatment effects, but also in indirect and mediation effects in multilevel and multisite settings. His published work in this area develops principles, formulas, and practical guidance for estimating statistical power and required sample sizes in complex mediation settings. This includes methods for planning studies that involve clustering, multiple sites, and indirect effects, as well as empirically derived design parameter values drawn from large-scale real-world datasets.

A third line of his research agenda focuses on QuantCrit and the development of equitable quantitative methodologies for evaluating institutions and interventions serving remote and historically marginalized communities. This work is deeply connected to his personal journey from subsistence living in Nikunau, a very remote atoll in Kiribati, to doctoral training in the United States through formal education. Growing up in a context where conventional indicators of “success” often failed to capture local realities shaped his interest in questioning how quantitative systems define disadvantage, achievement, and institutional performance. In this area, he developed Critical Data Envelopment Analysis (Critical DEA), a QuantCrit framework for evaluating homogeneous entities such as schools, hospitals, banks, and ports in ways that center equity, local context, and community-defined strengths.

Finally, Amota is also writing a book chapter for the Oxford Handbook of Impact Evaluation and regularly presents his work at research conferences: American Educational Research Association (AERA), Modern Modeling Methods (M3), the American Evaluation Association (AEA), and Society for Research on Educational Effectiveness (SREE). 

Catherine is a doctoral student in the School of Criminal Justice at the University of Cincinnati. She holds a Bachelor's degree in Psychology (2016) and a Master’s in Applied Behavioral Science with a concentration in criminal justice (2020) from Wright State University. Her research interests lie in corrections specifically focusing on offender rehabilitation, prisoner reentry, in-prison programming, and specialty courts.

Tiffany is a highly motivated and experienced educator passionate about early childhood education. She is currently a doctoral student in Educational Studies at the University of Cincinnati, where she is concentrating in Curriculum Studies and Teacher Education. Tiffany holds undergraduate degrees in Early Childhood and Inclusive Early Childhood Education and a master's degree in Curriculum & Instruction with a focus on STEM Learning. Her research interests include mathematics and science education, STEM/STEAM learning, and play-based learning.