Multilevel Modeling in Plain Language

by Karen Robson, David Pevalin

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.

158 pages, Kindle Edition

First published December 17, 2015

Reviews

[An Te · Rating 4 out of 5]

An extremely helpful primer for those contemplating using multilevel modelling with their data. It goes through the assumptions of an OLS model vs the random intercepts and random coefficient models from modelling for multilevel effects. This particular edition is geared towards those using STATA. It is helpful to have the STATA output placed in line with the text, with the broad outline of what the assumptions are of the proffered models. It's left much food for thought but this has been a welcome book for a novitate to these techniques. Further readings and references are provided throughout.

[Quiet_Inside · Rating 4 out of 5]

haha, very plain