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Full Description
This book is an invaluable resource for all researchers interested in understanding practical applications of longitudinal regression methods. This book aims to equip readers with practical applications of longitudinal regression using case studies derived from administrative datasets such as the Australian government's Person Level Integrated Data Asset (PLIDA). In providing practical examples concerning longitudinal regressions, a variety of administrative data is used, including examples not only from PLIDA but also from other sources like the Institute for Employment Research, Germany. These practical examples based on these data sources are generalisable to other data sources that readers may come across and use in their analytical research.
The author emphasises the applications of longitudinal regression methods, using substantial empirical illustrations, designed to help users of social research and data analysis to better analyse and understand linked administrative datasets. This 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. Readers learn about the critical need for the proper handling of longitudinal data, including issues related to data privacy, anonymisation, and ethical considerations. By providing real‑world case studies and practical examples, this book bridges 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, utilising longitudinal datasets across a variety of domains, including income, employment, health, social security, and education. This 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.
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.
