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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?

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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])

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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...

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    $\begingroup$ Hi @Isabella -- I've gone ahead and merged these two questions so that all of the answers are preserved in one canonical location. (+1) The merge is good for your answer! It means that your answer will be more easily found when folks look for references for mixed models, and will be able to reach a larger audience. You didn't do anything wrong. One of the roles of the diamond mod team is to do tidying like merging threads and identifying duplicates. I understand why it can be a source of friction; sometimes it feels a bit counter-intuitive and like the software is mad at you. Sorry! $\endgroup$
    – Sycorax
    May 28, 2021 at 17:57
  • $\begingroup$ Thank you, @Sycorax! 😊 $\endgroup$ May 28, 2021 at 18:09
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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.

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  • $\begingroup$ This is a "big" reference book! Thank you for suggestion! $\endgroup$
    – askming
    Dec 15, 2013 at 5:03
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I've looked at and read a lot of the available books on this topic. My top two are, in order,

  1. 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.

  1. 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.

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