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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

Karen Robson

Karen Robson is Assistant Professor in the Department and Marketing and Hospitality at Central Michigan University. She holds a BSc (Honsd) in Psychology from Queen’s University, and an MA in Psychology, an MBA and PhD from Simon Fraser University. Karen’s research investigates consumer innovativeness, including how consumers repurpose or use market offerings in ways not intended by the manufacturer and the intellectual property law implications of this practice. A recipient of the Joseph-Armand Bombardier Doctoral Scholarship, her work has appeared in journals such as MIS Quarterly Executive, Business Horizons, Journal of Marketing... More About Author

David Pevalin

David Pevalin is Professor in the School of Health and Human Sciences and Dean of Postgraduate Research and Education at the University of Essex. He previously served in the Merchant Navy, the City of London Police and the Royal Hong Kong Police. He studied part time at the University of Hong Kong before graduate studies at the University of Calgary, Canada. He returned to the UK in 1999 as Senior Research Officer at the Institute for Social and Economic Research at the University of Essex and joined his current School in 2003 after obtaining his PhD. He co-authored (with Karen Robson) The Stata Survival Manual (Open University Press), co... More About Author

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