This book presents a staged, pedagogically driven framework for modeling dependence in longitudinal binary data, integrating modern AI-assisted computational workflows throughout the analysis. Rather than treating correlation, hierarchy, and endogeneity as technical afterthoughts, the book positions dependence as the central structural feature of longitudinal data.

The text develops a six-stage modeling progression:

1.       Pooled logistic regression (independence)

2.       Clustered standard errors and generalized estimating equations (GEE) (correlation)

3.       Two-stage feedback models (endogeneity)

4.       Joint hierarchical models (correlation + feedback + hierarchy

5.       Bayesian joint hierarchical models (full uncertainty propagation)

6.       Integrative synthesis and model comparison.

A defining feature of the book is its simulation-first pedagogy. Each modeling stage is motivated through controlled simulation studies that allow readers to observe bias, RMSE, coverage failures, and inferential distortions before introducing more advanced methods. The framework is then applied to real longitudinal health dataset from the Philippines IFPRI Child Health and Nutrition Survey, demonstrating how modeling decisions materially affect scientific conclusions.

The book’s primary contributions are:

·         A unified framework linking independence, correlation, feedback, hierarchy, and Bayesian inference;

·         Clear treatment of endogenous covariates and dynamic feedback in binary longitudinal data;

·         Practical guidance for hierarchical and Bayesian modeling, including multiple membership structures;

·         Responsible AI-assisted analysis through prompt-based code generation, reproducible workflows, and verification of AI-generated results.

Niloofar Ramezani is an Associate Professor at Virginia Commonwealth University. Her research focuses on longitudinal data analysis, hierarchical modeling, and applied biostatistics, with applications in public health and biomedical research. She has published in peer-reviewed statistical and health-science journals and has extensive experience teaching graduate-level statistics and biostatistics courses.

Lori Selby is a doctoral candidate in the School of Mathematical and Statistical Sciences at Arizona State University. Her research interests include longitudinal modeling, hierarchical and Bayesian methods, and applied statistical computing. She has contributed to simulation studies and pedagogical development in advanced statistical modeling.

Jeffrey R. Wilson is Professor of Statistics and Biostatistics at Arizona State University, where he serves as Associate Dean for Research in the W. P. Carey School of Business. He is a Fellow of the American Statistical Association. His research spans longitudinal data analysis, generalized estimating equations, hierarchical and Bayesian modeling, feedback mechanisms, and applied methodology in health, education, and management sciences. He has published extensively in leading statistical and interdisciplinary journals and has substantial experience in graduate education, methodological training, and textbook development.