This book is an invaluable resource for all researchers interested in understanding practical applications of longitudinal regression methods. The book aims to equip the readers with practical applications of longitudinal regression with case studies derived from administrative datasets such as Australian Government’s Person Level Integrated Data Asset (PLIDA) datasets. In providing practical examples re. longitudinal regressions, a variety of administrative data would be used including examples not only from PLIDA but from other sources like the Institute for Employment Research, Germany. These practical examples based on these data sources are generalizable to other data sources readers would come across and use in their own analytical research.
The author emphasizes the applications of longitudinal regression methods, using substantial empirical illustrations, designed to help users of social research and data analysis to better analyze and understand linked administrative datasets. The book discusses numerous SAS procedures such as PROC GLM for estimating fixed effects linear models, PROC LOGISTIC for estimating fixed effects logistic regression models, PROC PHREG for estimating fixed effects Cox regression models and PROC GENMOD for estimating fixed effects Poisson regression models. The reader learns about the critical need for proper handling of longitudinal data, including issues related to data privacy, anonymization, and ethical considerations. By providing real-world case studies and practical examples, the book seeks to bridge the gap between theoretical knowledge and practical implementation, offering valuable guidance for researchers and practitioners.
The primary audience for this book comprises social researchers and academics who engage in advanced analytical research utilizing longitudinal datasets across a variety of domains, including income, employment, health, social security, and education. The book is also well-suited for statisticians, demographers, public policy analysts, and graduate students who focus on longitudinal studies to understand trends and patterns in population dynamics.
Table of Contents:
1. Foundations of Longitudinal Data Analysis: Designs, Trajectories, and Causal Leverage 2. Administrative Longitudinal Data: Architecture, Governance, and Harmonisation 3. Synthetic Panels for Methods: From Data Engineering to Estimation Strategy 4. Exploratory Analysis of Longitudinal Data: Quality Audits, Visualisation, and Pre-Modelling Decisions 5. Within-Subject Inference: Fixed-Effects Models for Wage Inequality 6. Between-Subject Heterogeneity: Random-Effects Models for Education and Mortality 7. Population-Averaged Inference: Generalised Estimating Equations for Income Dynamics and Upward Mobility
About the Author :
Taylan Akay, Ph.D. currently works at Department of Defence as a data specialist. He also works at UNSW Canberra at ADFA as a Post‑Doctoral Senior Research Associate, advancing AI ethics for autonomous weapon systems. Dr Akay’s career spans rigorous analytic roles across the Department of Defence, Social Services, and Home Affairs, where he has crafted scalable machine learning models, driven cloud migration strategies and implemented enterprise data governance frameworks. Based in Canberra, he holds a PhD in Econ from RMIT University and a Master’s in Applied Finance from Monash University.
