Recommended Textbooks:

  • Milliken, G.A., & Johnson, D.E. (2009). Analysis of Messy Data Volume 1: Designed Experiments, Second Edition (2nd ed.). Chapman and Hall/CRC. [link]
  • Stroup, W.W., Ptukhina, M., & Garai, J. (2024). Generalized Linear Mixed Models: Modern Concepts, Methods and Applications (2nd ed.). Chapman and Hall/CRC. [link]Note: First edition of this book has a very nicely written Chapter 2!

Recommended software-related resources:

  • R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. [link]
  • Wickham et al. (2023). R for Data Science (2nd ed.). O’Reilly Media. [link]

Recommended Applied Textbooks:

  • Agriculture/Natural Resources:
    • eds B. Glaz and K.M. Yeater (2018). Applied Statistics in Agricultural, Biological, and Environmental Sciences. American Society of Agronomy, Inc. Soil Science Society of America, Inc. Crop Science Society of America, Inc. [link]
    • Gbur, E.E., Stroup, W.W., McCarter, K.S., Durham, S., Young, L.J., Christman, M., West, M., and Kramer, M. (2020). Analysis of generalized linear mixed models in the agricultural and natural resources sciences (Vol. 156). John Wiley & Sons. [link]
  • Social Science:
    • Gelman, A. and Hill, J. (2006). Data Analysis Using Regression and Multilevel/Hierarchical Models (1st ed.). Cambridge University Press. [link]

Recommended blogs:

  • Statistical Modeling, Causal Inference, and Social Science. Gelman et al. [link]