Applied Linear Regression for Longitudinal Data With an Emphasis on Missing Observations
Applied Linear Regression for Longitudinal Data With an Emphasis on Missing Observations by Frans E. S. Tan, published by CRC Press in 2022, is a comprehensive guide designed for those interested in longitudinal data analysis. This 226-page book aims to introduce best practices at an intermediate level, focusing on visualizing statistical concepts rather than relying heavily on complex mathematical formulas. It addresses the challenges posed by missing observations and provides insights into various solutions, including multiple imputation.
Readers will find that this book emphasizes the importance of understanding statistical techniques through visualization, making it accessible for researchers and students in fields such as psychology, health, and social sciences. It includes datasets and examples available online, along with state-of-the-art methods for handling missing data in a straightforward manner. The text also conceptualizes the analysis of both experimental and observational studies, ensuring that those without a strong mathematical background can effectively engage with longitudinal studies.
Official synopsis Publisher
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.
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