Applied Linear Regression for Longitudinal Data With an Emphasis on Missing Observations PDF

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 researcher and students in epidemiological, health, and social and behavioral sciences working with longitudinal studies without a mathematical background.

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.

Authors: Frans E.S. Tan, Shahab Jolani