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Multilevel Modeling in Plain Language

Multilevel Modeling in Plain Language

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November 2015 | 160 pages | SAGE Publications Ltd

Have you been told you need to do multilevel modeling, but you can't get past the forest of equations? Do you need the techniques explained with words and practical examples so they make sense?

Help is here! This book unpacks these statistical techniques in easy-to-understand language with fully annotated examples using the statistical software Stata. The techniques are explained without reliance on equations and algebra so that new users will understand when to use these approaches and how they are really just special applications of ordinary regression. Using real life data, the authors show you how to model random intercept models and random coefficient models for cross-sectional data in a way that makes sense and can be retained and repeated. 

This book is the perfect answer for anyone who needs a clear, accessible introduction to multilevel modeling.

Chapter 1: What Is Multilevel Modeling and Why Should I Use It?
Mixing levels of analysis  
Theoretical reasons for multilevel modeling  
What are the advantages of using multilevel models?  
Statistical reasons for multilevel modeling  
Assumptions of OLS  
How this book is organized  
Chapter 2: Random Intercept Models: When intercepts vary
A review of single-level regression  
Nesting structures in our data  
Getting starting with random intercept models  
What do our findings mean so far?  
Changing the grouping to schools  
Adding Level 1 explanatory variables  
Adding Level 2 explanatory variables  
Group mean centring  
Model fit  
What about R-squared?  
A further assumption and a short note on random and fixed effects  
Chapter 3: Random Coefficient Models: When intercepts and coefficients vary
Getting started with random coefficient models  
Trying a different random coefficient  
Fanning in and fanning out  
Examining the variances  
A dichotomous variable as a random coefficient  
More than one random coefficient  
A note on parsimony and fitting a model with multiple random coefficients  
A model with one random and one fixed coefficient  
Adding Level 2 variables  
Residual diagnostics  
First steps in model-building  
Some tasters of further extensions to our basic models  
Where to next?  
Chapter 4: Communicating Results to a Wider Audience
Creating journal-formatted tables  
The fixed part of the model  
The importance of the null model  
Centring variables  
Stata commands to make table-making easier  
What do you talk about?  
Models with random coefficients  
What about graphs?  
Cross-level interactions  
Parting words  


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I started to read the book with vivid interest because of the subject that too often does not find enough space in books which provide an overview of the most used statistical methods  leaving out those who are somewhat a little bit more elaborate. After a while I found that I had read many pages, as a story, in a short time, and, rethinking to the title of the book, I remembered there was a part saying “…. In plain language”. This is really genuine.

The Authors do really introduce the subject in a very friendly way, propose examples which facilitate the reader to better  understand and explain the output of Stata.  I suggest the book both to students and instructors who want a specific text on this subject. On the one hand, students will be not afraid of formula, considering that the book is centred on the understanding of the subjects, on the other hand, instructors will benefit in reviewing the path of the multilevel analysis very quickly.

It is a book for those who have some knowledge of statistic but I think that this aspect is definitely clear to the reader. The book is really complete in all the phases of a multilevel analysis, the “plain approach” helps the reader to grasp the idea,  follow the Stata commands and outputs and, finally, to interpret the findings. I think that the Authors were very skillful in preparing this book and added a very useful resource, in particular, for those who use Stata for their analysis.

Dr. Gabriele Messina
University of Siena

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