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 specializing in causal machine learning and psychometrics. His dissertation develops new algorithms for causal inference with latent variables — constructs that are important to us because they influence policy, practice, and theory development but cannot be measured directly (e.g., depression, burnout, resilience, emotions, affect, satisfaction, student achievement, motivation, pain severity, social capital, self-efficacy, mobility, trust, belonging, stigma, food insecurity, quality of life, organizational culture, radicalization, etc.). Such constructs are ubiquitous across psychology, education, health sciences, economics, political science, public health, climate science, and beyond, appearing as both outcomes and predictors in the same study. For example, a researcher estimating the causal effect of a treatment on depression must often account for prior mental health conditions (e.g., anxiety and depressive symptoms) as confounders — all of which are latent (i.e., they cannot be directly measured the way height or weight can). Similarly, a researcher studying the effect of a school intervention on student achievement must contend with the fact that both the outcome (achievement) and key confounders (e.g., prior achievement, motivation, and socioeconomic disadvantage) are latent. The same challenge arises in health research when estimating the effect of a pain management program on quality of life while adjusting for pain severity and psychological distress.

Despite their pervasiveness across research disciplines, existing causal inference methods handle latent variables poorly: traditional and contemporary approaches (e.g., Causal Forests, Structural Equation Modeling) either ignore measurement error entirely or impose strong parametric assumptions about functional form, requiring that all nonlinear terms and interaction effects be correctly specified in advance. In practice, these assumptions are routinely violated or remain unverifiable because the true relationships among observed and latent variables are complex and largely unknown. Machine learning and data-adaptive methods relax these model specification constraints, but they do so while ignoring the measurement error inherent in latent constructs. Bridging statistics, machine learning, and quantitative methodology, Amota's work addresses both of these limitations simultaneously. The new machine learning methods he is developing account for measurement error and relax model specification assumptions, enabling cause-and-effect analysis with latent variables in nonexperimental and observational settings, even when the functional relationships among variables are unknown or complex. The result methods integrate psychometric theory, causal inference, and machine learning — one that simultaneously accommodates the flexibility needed to capture complex relationships and the rigor needed to handle constructs that are measured with error. This dissertation work is being supported ($27,500) by the National Academy of Education/Spencer Dissertation Fellowship, the most prestigious dissertation fellowship in the field of Education. 

A second line of his research work addresses the development and design of adequately powered studies when researchers care about not only main effects but also indirect effects in multilevel and multisite settings. This research directly responds to a well-documented problem in educational, health, psychotherapy research: investigators routinely underpowered their main and mediation effect studies because they apply single-level power formulas to clustered data, or rely on standardized conventions (e.g., Cohen's d) rather than empirically grounded design parameters. His peer-reviewed publications in this area include: (1) Designing Multisite Randomized Trials to Detect (Conditional) Indirect Effects (American Journal of Evaluation, 2026, (2) Design and Analysis of Multisite Cluster-Randomized Trials Targeting (Conditional) Mediation Effects (Journal of Experimental Education, 2025), (3) Design Parameter Values for Planning Mediation Studies with Teacher and Student Mathematics Outcomes (Journal of Research on Educational Effectiveness, 2024), and (4) Evaluations of Literacy-Based Programs: Empirical Values for Designing Studies Probing Mediation (Evaluation Review, 2026). These papers developed methods (principles and expressions) to predict statistical power and sample size in complex settings specific for mediation effects and provided empirically derived design parameter values (drawn from real large-scale datasets) for such settings. A related work is providing PowerUpR Shiny App to implement power analysis for standard and complex data structures (see Causal Evaluation & Ataneka et al., 2023).

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.

Amota is also writing a book chapter for the Oxford Handbook of Impact Evaluation and regularly presents his work at premier 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.