Multilevel Modeling is a concise, practical guide to building models for multilevel and longitudinal data. Author Douglas A. Luke begins by providing a rationale for multilevel models; outlines the basic approach to estimating and evaluating a two-level model; discusses the major extensions to mixed-effects models; and provides advice for where to go for instruction in more advanced techniques. Rich with examples, the Second Edition expands coverage of longitudinal methods, diagnostic procedures, models of counts (Poisson), power analysis, cross-classified models, and adds a new section added on presenting modeling results. A website for the book includes the data and the statistical code (both R and Stata) used for all of the presented analyses.
Chapter 7.: Guidance
Learning had to be digested. You didn’t just have to know, you have to comprehend.
—Terry Pratchett, Unseen Academicals, 2009
Recommendations for Presenting Results
Given the complexities of building, testing, and interpreting multilevel and other types of mixed-effects models, it is sometimes challenging to figure out the best way to present model results. Every analytic situation will have its own challenges, so it is impossible to have a set of specific directions that can always be followed to produce effective model results tables and figures. However, if the following general recommendations are kept in mind while designing the tables and figures, you will be more likely to produce effective analytic dissemination products.
- Make sure that the random effects and multilevel structure of the model are clear.
- Treat a ...