- Subject index
In this important new Handbook, the editors have gathered together a range of leading contributors to introduce the theory and practice of multilevel modeling.
The Handbook establishes the connections in multilevel modeling, bringing together leading experts from around the world to provide a roadmap for applied researchers linking theory and practice, as well as a unique arsenal of state-of-the-art tools. It forges vital connections that cross traditional disciplinary divides and introduces best practice in the field.
Part I establishes the framework for estimation and inference, including chapters dedicated to notation, model selection, fixed and random effects, and causal inference; Part II develops variations and extensions, such as nonlinear, semiparametric and latent class models; Part III includes discussion of missing data and robust methods, assessment of fit and ...
Chapter 22: Robust Methods for Multilevel Analysis
Robust Methods for Multilevel Analysis
What are robust statistical methods? Robust methods are statistical procedures for estimation and establishing confidence intervals that are not very sensitive to violations of the assumptions of the underlying statistical methodology. Examples from standard univariate statistics are the finding that, in general, parametric tests are not very sensitive to violations of the normality assumption, provided the sample size is not too small. If the violation is extreme, as in having a single extreme outlier in the data, robustness does not apply. Obviously, if we define robustness as insensitivity to violations of assumptions, the next question is: which assumptions? This chapter examines the common assumptions of multilevel models, and discusses ...