1st Edition
Applied Linear Regression for Longitudinal Data With an Emphasis on Missing Observations
This book introduces best practices in longitudinal data analysis at intermediate level, with a minimum number of formulas without sacrificing depths. It meets the need to understand statistical concepts of longitudinal data analysis by visualizing important techniques instead of using abstract mathematical formulas. Different solutions such as multiple imputation are explained conceptually and consequences of missing observations are clarified using visualization techniques. Key features include the following:
- Provides datasets and examples online
- Gives state-of-the-art methods of dealing with missing observations in a non-technical way with a special focus on sensitivity analysis
- Conceptualises the analysis of comparative (experimental and observational) studies
It is the ideal companion for researchers and students in epidemiological, health, and social and behavioral sciences working with longitudinal studies without a mathematical background.
1. Scientific Framework of Data Analysis
2. Revisiting and Shortcomings of Standard Linear Regression Models
3. An Introduction to the Analysis of Longitudinal Data
4. Model Building for Longitudinal Data Analysis
5. Analysis of a Pre/Post Measurement Design
6. Analysis of Longitudinal Life-Event Studies
7. Analysis of Longitudinal Experimental Studies
Biography
Frans E.S. Tan is an associate professor (retired) of methodology and statistics at Maastricht University, The Netherlands.
Shahab Jolani is an assistant professor of methodology and statistics at Maastricht University, The Netherlands.
"Overall, the book is well written. It is clear and allows the reader understanding the main concepts behind models for longitudinal data analysis, with few effort from a technical viewpoint. The examples used to illustrate the methods covered in the textbook are numerous and also rather easy to follow. This helps the reader learn how to proceed with a full longitudinal data analysis."
Maria Francesca Marino, University of Florence, Italy, The American Statistician, February 2024.
