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Leave A Reply Cancel Reply. Survival models are for data where the outcome is the occurrence or nonoccurrence of a certain event, in a certain observation period. If the event has not occurred when the observation period ends, the outcome is said to be censored, since we do not know whether or not the event has taken place after the observation period ended.
Chapter Nine discusses cross-classified models. Some data are multilevel in nature, but do not have a neat hierarchical structure. Multilevel models for such cross-classified data can be formulated, and estimated with standard software provided that it can handle restrictions on estimated parameters. Chapter Ten discusses multilevel regression models for multivariate outcomes. These can also be used to estimate models that resemble confirmative factor analysis, and to assess the reliability of multilevel measurements.
A different approach to multilevel confirmative factor analysis is treated in chapter Thirteen. Chapter Eleven describes a variant of the multilevel regression model that can be used in meta-analysis. It resembles the weighted regression model often recommended for meta-analysis. Using standard multilevel regression procedures, it is a flexible analysis tool, especially when the meta-analysis includes multivariate outcomes. Chapter Twelve deals with the sample size needed for multilevel modeling, and the problem of estimating the power of an analysis given a specific sample size.
An obvious complication in multilevel power analysis is that there are different sample sizes at the distinct levels, which should be taken into account. Chapter Thirteen treats some advanced methods of estimation and assessing significance. It discusses the profile likelihood method, robust standard errors for establishing confidence intervals, and multilevel bootstrap methods for estimating bias-corrected point- estimates and confidence intervals.
Multilevel models for covariance structures, or multilevel structural equation models SEM , are a powerful tool for the analysis of multilevel data. Recent versions of structural equation modeling software such as Eqs, Lisrel, Mplus all include at least some multilevel features.
The general statistical model for multilevel covariance structure analysis is quite complicated. It explains how multilevel confirmatory factor models can be estimated with either conventional SEM software or using specialized programs. In addition, it deals with issues of calculating standardized coefficients and goodness-of-fit indices in multilevel structural models. Chapter Fifteen extends this to path models. Chapter Sixteen describes structural models for latent curve analysis.
This is a SEM approach to analyzing longitudinal data, which is very similar to the multilevel regression models treated in Chapter Five. Multilevel Analysis; Techniques and Applications 3 This book is intended as an introduction to the world of multilevel analysis. Most of the chapters on multilevel regression analysis should be readable for social scientists who have a good general knowledge of analysis of variance and classical multiple regression analysis.
Some of these chapters contain material that is more difficult, but this is generally a discussion of specialized problems, which can be skipped at first reading. An example is the chapter on longitudinal models, which contains a prolonged discussion of techniques to model specific structures for the covariances between adjacent time points.
This discussion is not needed to understand the essentials of multilevel analysis of longitudinal data, but it may become important when one is actually analyzing such data. Conversely, in addition to an adequate background in structural equation modeling, the chapters on multilevel structural equation modeling do not require knowledge of advanced mathematical statistics.
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