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Multilevel Modeling in Plain Language, by Karen Robson, David Pevalin
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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.
- Sales Rank: #310518 in eBooks
- Published on: 2015-11-02
- Released on: 2015-11-02
- Format: Kindle eBook
Review
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.
About the Author
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 Education, Journal of Advertising Research, and Journal of Public Affairs.
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-edited (with David Rose) The Researcher’s Guide to the National Statistics Socio-economic Classification (Sage), and authored research reports for the Department of Work and Pensions and the Health Development Agency. He has published papers in the Journal of Health and Social Behavior, British Journal of Sociology, Lancet, Public Health, and Housing Studies.
Most helpful customer reviews
1 of 1 people found the following review helpful.
Recommended to folks new to multilevel analysis
By Amazon Customer
I liked this book and recommend it to novices in the subject-matter. After attempting to grasp multilevel modeling from other four or five books, I felt I really could understand the essence and logic of it after reading this one. Worked examples of multilevel models application using software is another significant plus.
0 of 0 people found the following review helpful.
"These equations must mean something in words. Why can't they just use the words?"
By Probabilistic Person
This is one of the clearest and most useful advanced statistics books I have read in over 50 years of interest. Recommend for all non-experts (and perhaps even some experts) in this these methods and also recommend you run the examples in programs Stata (especially) or R as you read (using other programs like SAS or SPSS is doable and helped by links recommended in the book).
I am 90+% through the book in 10 days and feel like I understand and can begin to apply almost all of the content. Am now ready and eager to tackle more advanced texts like "Multilevel and Longitudinal Modeling Using Stata, Volumes I and II, Third Edition", by Sophia Rabe-Hesketh and Anders Skrondal .
Starting as an engineer with only a rough acquaintance with multilevel regression and no practical modeling experience; thanks to the book, I now see many ways to apply multilevel regression either to greatly improve or to justify single-level models I had developed and audited.
0 of 0 people found the following review helpful.
The authors' writing is easy to follow and clearly written
By Joseph Simpson
A must read for doctoral students who, at any time in their research, might encounter data that are "nested" (e.g. students in classrooms, individuals in groups, or employees in companies). The authors' writing is easy to follow and clearly written. The examples in the book provide readers with an easy to follow application of the concepts discussed by the authors. This book is not meant to be a technical exposition on the subject, but an easy to read and apply guide for those who are interested in using multilevel modeling in their research.
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