- 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 25: Multilevel Models: Is GEE a Robust Alternative in the Presence of Binary Endogenous Regressors?
Multilevel Models: Is GEE a Robust Alternative in the Presence of Binary Endogenous Regressors?
Generalized Estimating Equations (GEE) have been used to analyze clustered univariate response data in its many varied forms. This includes panel data on a sequence of outcomes for different subjects and clinical trials data of different subjects obtained from a clustered sample design. For nested 2-level clustered data, we typically have j = 1,…, J clusters of i = 1, Nj responses (yij), with the associated covariate vectors, xij = [xijk], k = 1, K. For longitudinal data, j typically denotes a subject, and i = 1,…, Nj the time-ordered sequence of responses ...