Graduate Student Association (GSA)

Lindsey Insco received her Bachelor's in chemistry from Xavier University in 2022 and her Master's in criminal justice from the University of Cincinnati in 2023. Lindsey is currently a doctoral student of criminal justice at the University of Cincinnati. Her research interests include life course criminology, drug overdoses, and, more generally, terrorism. 

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 is a doctoral researcher supervised by Professor Benjamin Kelcey. His research focuses on developing new algorithms for modeling latent factors in causal inference frameworks. Latent factors are variables that cannot be measured directly such as intelligence, depression, anxiety, pain tolerance, immune system strength, ecosystem resilience, climate resilience, fish population health, love, happiness, poverty, community support and so forth. In contrast, observed variables like height, gender, weight, or temperature can be measured directly and with relatively little error.

Modeling latent factors is challenging because we only see them indirectly through survey items, test scores, behaviors, or indicators which introduces measurement error. This problem is not small—it is a critical limitation of nearly every algorithm in use today. For more than 100 years, researchers have relied on regression, structural equation models (SEMs), and more recently, structural-after-measurement (SAM) methods. Yet none of these approaches, on their own, can fully address both measurement error and the need for correct model specification. They either assume the measurement model is perfectly specified or treat the observed indicators as if they were error-free, which is rarely true in practice.

Amota’s work is designed to close this gap. By integrating machine learning with latent variable modeling, his research aims to develop the first generation of algorithms that directly tackle both measurement error and model specification challenges. This innovation is not just technical—it is essential for improving how we estimate causal effects in education, medicine, biology, and other fields where hidden constructs drive real-world outcomes.

Amota also contributes to the development of methods and tools for complex randomized study designs (a gold standard design for studying cause-and-effects) such as cluster randomized trials (CRTs), multisite design, partially nested CRTs, and Multiple-Group Individually-Randomized Group Trials. Amota has presented his work at the American Educational Research Association (AERA), American Evaluation Association, and the 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.