Walt Stroup is an Emeritus Professor of Statistics. He served on the University of Nebraska statistics faculty for over 40 years, specializing in statistical modeling and statistical design. He is a Fellow of the American Statistical Association, winner of the University of Nebraska Outstanding Teaching and Innovative Curriculum Award and author or co-author of three books on mixed models and their extensions.

Marina Ptukhina (Pa-too-he-nuh), PhD, is an Associate Professor of Statistics at Whitman College. She is interested in statistical modeling, design and analysis of research studies and their applications. Her research includes applications of statistics to economics, biostatistics and statistical education. Ptukhina earned a PhD in Statistics from the University of Nebraska-Lincoln, a Master of Science degree in Mathematics from Texas Tech University and a Specialist degree in Management from The National Technical University "Kharkiv Polytechnic Institute."

Julie Garai, PhD, is a Data Scientist at Loop. She earned her PhD in Statistics from the University of Nebraska-Lincoln and a bachelor's degree in Mathematics and Spanish from Doane College. Dr Garai actively collaborates with statisticians, psychologists, ecologists, forest scientists, software engineers, and business leaders in academia and industry. In her spare time, she enjoys leisurely walks with her dogs, dance parties with her children, and playing the trombone.

Preface to the Second Edition

Part 1: Essential Background

1. Modeling Basics

2. Design Matters

3. Setting the Stage

Part 2: Estimation and Inference Theory

4. Pre-GLMM Estimation and Inference Basics

5. GLMM Estimation

6. Inference, Part I

7. Inference, Part II

Part 3: Applications

8. Treatment and Explanatory Variable Structure

9. Multi-Level Models

10. Best Linear Unbiased Prediction

11. Counts

12. Rates and Proportions

13. Zero-inflated and Hurdle Models

14. Multinomial Data

15. Time-to-Event Data

16. Smoothing Splines and Additive Models

17. Correlated Errors, part 1: Repeated Measures

18. Correlated Errors, part 2: Spatial Variability

19. Bayesian Implementation of GLMM

20. Four Bayesian GLMM Examples

21. Precision, Power, Sample Size and Planning