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.
This book is the perfect answer for anyone who needs a clear, accessible introduction to multilevel modeling.
Applications in STATA®, IBM® SPSS®, SAS®, R, & HLMTM G. David Garson. addition to the editor, and the five chapters by the editor use different examples and have different content.) Garson, G. David. (2013b).
This book provides clear and simple examples illustrating how to interpret and visualize a wide variety of regression models.
... controlling for variable X. It is analogous to the usual intraclass correlation coefficient , but now controlling for X. The formula for the ( non - residual , or raw ) intraclass correlation coefficient was just the same ...
Robson, Karen & Pevalin, David. (2016). Multilevel modeling in plain language. College Station, TX: Stata Press. Rockwood, Nicholas J. & Hayes, Andrew F. (2017). MLmed: An SPSS macro for multilevel mediation and conditional process ...
For researchers who want a sophisticated discussion of multilevel models in plain language , this is their monograph . -Michael S. Lewis - Beck Series Editor MULTILEVEL MODELING DOUGLAS A. LUKE Saint Louis University School of vi.
Gelfand, A. E., Sahu, S. K., and Carlin, B. P. (1995). Efficient parameterisations for normal linear mixed models. Biometrika, (82):479–488. Gelman, A. (2005). Analysis of variance: Why it is moreimportant than ever.
... Multilevel modeling in plain language. Sage. Sampson, R. J. (2012). Great American city: Chicago and the enduring neighborhood effect. University of Chicago Press. Sampson, R. J., Morenoff, J. D., & Gannon-Rowley, T. (2002). Assessing ...
This text will be perfect for all advanced students and researchers in social and personality psychology using social psychophysiological methods as part of their studies or research.
... analysis, Main results, Author's conclusions. Plain Language Summary: Plain language title, Summary text The review: Background Objectives Methods Criteria for considering studies for this review Types of studies Types of participants ...