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Multilevel Modeling in Plain Language
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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

 
Software

 
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

 
Interactions

 
Model fit

 
What about R-squared?

 
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

 
Shrinkage

 
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

 

Supplements

Click for online resources
https://study.sagepub.com/robsonandpevalin

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

Sage College Publishing

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