This book presents the state of the art in multilevel analysis, with an emphasis on more advanced topics. These topics are discussed conceptually, analyzed mathematically, and illustrated by empirical examples. Multilevel analysis is the statistical analysis of hierarchically and non-hierarchically nested data. The simplest example is clustered data, such as a sample of students clustered within schools. Multilevel data are especially prevalent in the social and behavioral sciences and in the biomedical sciences. The chapter authors are all leading experts in the field. Given the omnipresence of multilevel data in the social, behavioral, and biomedical sciences, this book is essential for empirical researchers in these fields.

This book presents the state of the art in multilevel analysis, with an emphasis on more advanced topics. These topics are discussed conceptually, analyzed mathematically, and illustrated by empirical examples. Multilevel analysis is the statistical analysis of hierarchically and non-hierarchically nested data. The simplest example is clustered data, such as a sample of students clustered within schools. Multilevel data are especially prevalent in the social and behavioral sciences and in the biomedical sciences. The chapter authors are all leading experts in the field. Given the omnipresence of multilevel data in the social, behavioral, and biomedical sciences, this book is essential for empirical researchers in these fields.

This is the first accessible and practical guide to using multilevel models in social research. Multilevel approaches are becoming increasingly important in social, behavioral, and educational research and it is clear from recent developments that such models are seen as being more realistic, and potentially more revealing, than ordinary regression models. While other books describe these multilevel models in considerable detail none focuses on the practical issues and potential problems of doing multilevel analyses that are covered in Introducing Multilevel Modeling.

Because the archaeology of West Mexico has received little attention from researchers, large segments of the region's prehistoric ceramic sequences have long remained incomplete. This book goes far toward filling that gap by analyzing a collection of potsherds excavated in the 1960s and housed since then, though heretofore unanalyzed, at UCLA. The authors employ the rarely used statistical technique known as correspondence analysis to sequence the Long-Glassow collection of artifacts.

The book explains how correspondence analysis works and how it can be applied in archaeology. In addition to describing the archaeological sites in north central Jalisco where the collection comes from, the authors provide an ethnohistorical overview including information on the earliest Spanish explorers to reach the sites. They sequence more than seventy ceramic types and derive a master sequence from more than ten thousand potsherds. In addition to Mesoamerican archaeologists, the audience will also include other archaeologists concerned with ceramic analysis or the application of statistics to archaeology.

Authors Ita G. G. Kreft and Jan de Leeuw have provided the first accessible and practical guide to using multilevel models in social research. Multilevel approaches are becoming increasingly important in social, behavioral, and educational research and it is clear from recent developments that such models are seen as more realisticùand potentially more revealingùthan ordinary regression models. While other books describe these multilevel models in considerable detail none focus on the practical issues and potential problems of doing multilevel analyses that are covered in Introducing Multilevel Modeling. The authorsÆ approach is user-oriented, keeping formal mathematics and statistics to a minimum. Other key features include the use of worked examples using real data sets, analyzed using the leading computer package for multilevel modeling.