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?

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 organizedChapter 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 effectsChapter 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