Multilevel Analysis: Techniques and Applications

Multilevel Analysis: Techniques and Applications

Multilevel Analysis: Techniques and Applications

Multilevel Analysis: Techniques and Applications

Synopsis

This book is an introduction to multilevel analysis for applied researchers featuring models for hierarchical or nested data. This book presents two types of models: The multilevel regression and multilevel covariance structures models. Despite the book being an introduction, it includes a discussion of many extensions and special applications. As an introduction, it will be useable in courses in a variety of fields, such as psychology, education, sociology, and business. The various extensions and special applications make it useful to researchers who work in applied or theoretical research, and to methodologists that have to consult with these researchers. The basic models and examples are discussed in non-technical terms; the emphasis is on understanding the methodological and statistical issues involved in using these models. Some of the extensions and special applications contain more technical discussions, either because that is necessary for understanding what the model does, or as an introduction to more advanced treatments. Thus, the book will be useful as an introduction and as a standard reference for a large variety of applications.

Excerpt

To err is human, to forgive divine; but to include errors into your design is statistical.

—Leslie Kish

This book is intended as an introduction to multilevel analysis for applied researchers. The term 'multilevel' refers to a hierarchical or nested data structure, usually people within organizational groups, but the nesting may also consist of repeated measures within people, or respondents within clusters as in cluster sampling. The expression multilevel model or multilevel analysis is used as a generic term for all models for nested data. This book presents two multilevel models: the multilevel regression model and a model for multilevel covariance structures.

Multilevel modeling used to be only for specialists. However, in the past decade, multilevel analysis software has become available that is both powerful and relatively accessible for applied researchers. As a result, there is a surge of interest in multilevel analysis, as evidenced by the appearance of several reviews and monographs, applications in different fields ranging from psychology and sociology to medicine, and a thriving Internet discussion list with more than 1400 subscribers. Despite it being an introduction, the book includes a discussion of many extensions and special applications. As an introduction, it should be useable in courses in a variety of fields, such as psychology, education, sociology and business. The various extensions and special applications should make it useful to researchers who work in applied or theoretical research, and to methodologists who have to consult with these researchers. The basic models and examples are discussed in nontechnical terms; the emphasis is on understanding the methodological and statistical issues involved in using these models. Some of the extensions and special applications contain discussions that are more technical, either because that is necessary for understanding what the model does, or as a helpful introduction to more advanced treatments in other texts. Thus, in addition to its role as an introduction, the book should be useful as a standard reference for a large variety of applications. It assumes that readers have a basic knowledge of social science statistics, including analysis of variance and multiple regression analysis. The section about multilevel structural equation models assumes a basic understanding of ordinary structural equation modeling.

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