An Introduction to Multilevel Modeling Techniques

An Introduction to Multilevel Modeling Techniques

An Introduction to Multilevel Modeling Techniques

An Introduction to Multilevel Modeling Techniques

Synopsis

This book provides a broad overview of basic multilevel modeling issues and illustrates techniques building analyses around several organizational data sets. Although the focus is primarily on educational and organizational settings, the examples will help the reader discover other applications for these techniques. Two basic classes of multilevel models are developed: multilevel regression models and multilevel models for covariance structures--are used to develop the rationale behind these models and provide an introduction to the design and analysis of research studies using two multilevel analytic techniques--hierarchical linear modeling and structural equation modeling.

Excerpt

The book by Heck and Thomas presents an introduction to the statistical method of multilevel data modeling. Many data sets can be termed multilevel because they are organized and described through various levels of aggregation. the term hierarchical has also been used as an appropriate way to describe such data. For example, data collected about the attitudes and beliefs of employees toward sexual harassment in the workplace can be hierarchically organized by specific department or by organization. Because it is not possible for an individual employee to be in more than one department or for the department to be in more than one organization, the employees are said to be nested within departments and the departments are nested within organizations.

Until recently, the most common approach for the statistical analysis of multilevel data would be to first either aggregate data to the group level or disaggregate data to the individual level. Unfortunately, neither approach is adequate for a proper analysis of multilevel data. the statistical method of multilevel modeling (sometimes also referred to as hierarchical, random-co- efficient, or variance-component modeling) allows for the appropriate analysis of multilevel data. Two major obstacles have hindered the more widespread use of multilevel modeling: the complexity of the original treatments of the theory provided by its developers, and the lack of tailored computer programs for performing a multilevel analysis. Although tailored computer programs for calculating a multilevel analysis are becoming more widely available and some commercial statistical packages now provide procedures for computing the estimates needed in a multilevel analysis, few nontechnical introductions to the method have appeared in the literature.

In this volume, Heck and Thomas make the theory and methods of multilevel analysis available to anyone who has mastered the most basic rudiments of regression analysis. Although the book is written at an introductory level, the . . .

Search by... Author
Show... All Results Primary Sources Peer-reviewed

Oops!

An unknown error has occurred. Please click the button below to reload the page. If the problem persists, please try again in a little while.