With a real focus on the practical, this book provides students with a step-by-step approach, plenty of real-life examples, and downloadable data and exercises on the accompanying study website to help take the fear and intimidation out of 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

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.