- 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 23: Missing Data
The problem of dealing with missing values is common throughout statistical work and is particularly present whenever human subjects are enrolled. Respondents may refuse participation or may be unreachable. Patients in clinical and epidemiological studies may withdraw their initial consent without further explanation. Early work on missing values was largely concerned with algorithmic and computational solutions to the induced lack of balance or deviations from the intended study design (Afifi and Elashoff, 1966; Hartley and Hocking, 1971). More recently, general algorithms such as Expectation–Maximization (EM) (Dempster et al., 1977) and data imputation and augmentation procedures (Rubin, 1987; Tanner and Wong, 1987), combined with powerful computing resources, have ...