What are the most popular references for mixed models? What are the most popular references (especially texts) you think in the area of mixed models? Or what are those you think were helpful to you when you learnt this topic?
 A: Here are some of my favourite resources on mixed effects models:
INTRODUCTION TO LINEAR MIXED MODELS by Gabriela K Hajduk
https://ourcodingclub.github.io/tutorials/mixed-models/
LEMMA (Learning Environment for Multilevel Methodology and Applications) online multilevel modelling course
http://www.bristol.ac.uk/cmm/learning/online-course/
Dr. Sean Anderson's GLMM Course Notes:
https://github.com/seananderson/glmm-course
An Introduction to Mixed Models for Experimental Psychology by Henrik Singmann and David Kellen
https://cran.r-project.org/web/packages/afex/vignettes/introduction-mixed-models.pdf
To be continued...
A: SAS for Mixed Models
Even if you don't use SAS, still very good.  Covers a lot of different modeling methods for various designs.  Also details the theory behind each method.
A: I've looked at and read a lot of the available books on this topic. My top two are, in order,

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*Multilevel and Longitudinal Modeling Using Stata (Volumes 1 and 2) by Rabe-Hesketh & Skrondal.

Much of what I know about multilevel and longitudinal modeling can be owed to this two-volume set. Although Rabe-Hesketh and Skrondal present Stata-specific code and output, you do not need to be a Stata user to benefit and learn from these books, which are incredibly comprehensive. They cover everything from linear versions of these models (Vol. 1) to generalized versions (Vol. 2). They illustrate how these models compare to similar modeling approaches for clustered data used in other fields, including econometric fixed effects models and generalized estimating equations. They describe intuitions behind the modeling, walk you through various formulas, employ simulations to illustrate concepts, and are subject-area agnostic. You will see examples from education, health science, business, economics, etc., both in the chapters as they illustrate the models and in the abundant and challenging problem sets. I cannot recommend these books highly enough. Version 4 is expected to be published in 2021 from Stata Press.


*Multilevel Analysis by Snijders & Bosker.

This is a fantastic, relatively compact introduction and tour of multilevel modeling. The book is so thick with details and explanations of things that other books gloss over but are critical for understanding these models. This book is not tied to any particular software. Sometimes they use Stata, sometimes MLwiN, sometimes HLM but they never show you code. Their code and data is available online for reference.
A: Raudenbush & Bryk (2002) is a widely cited reference (20k hits on Google Scholar)
Hox (2010) is also popular, and also accessible (not as technical as Raudenbush & Bryk [2002])
