Ronald H. Heck - An Introduction to Multilevel Modeling Techniques (The Quantitative Methodology Series)
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An Introduction to Multilevel Modeling Techniques (The Quantitative Methodology Series)
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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.
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An Introduction to Multilevel Modeling Techniques Quantitative Methodology Series
author
:
Heck, Ronald H.; Thomas, Scott Loring.
publisher
:
Lawrence Erlbaum Associates, Inc.
isbn10 | asin
:
0805829636
print isbn13
:
9780805829631
ebook isbn13
:
9780585218328
language
:
English
subject
Social sciences--Mathematical models, Social sciences--Research--Mathematical models.
publication date
:
2000
lcc
:
H61.25.H43 2000eb
ddc
:
300/.1/5118
subject
:
Social sciences--Mathematical models, Social sciences--Research--Mathematical models.
Page i
An Introduction to Multilevel Modeling Techniques
Page ii
QUANTITATIVE METHODOLOGY SERIES
Methodology for Business and Management
George A. Marcoulides, Series Editor
Heck/Thomas An Introduction to Multilevel Modeling Techniques
Duncan/Duncan/Strycker/Li/Alpert An Introduction to Latent Variable Growth Curve Modeling
Marcoulides Modern Methods for Business Research
Page iii
An Introduction to Multilevel Modeling Techniques
Ronald H. Heck and Scott L. Thomas
University of Hawai'i at Manoa
Page iv
Copyright 2000 by Lawrence Erlbaum Associates, Inc.
All rights reserved. No part of this book may be reproduced in any form, by photostat, microfilm, retrieval system, or any other means, without prior written permission of the publisher.
Library of Congress Cataloging-in-Publication Data
Heck, Ronald H. An introduction to multilevel modeling techniques / Ronald H. Heck and Scott L. Thomas p. cm.(Quantitative methodology series) Includes bibliographical references and index. ISBN 0-8058-2963-6 (alk. paper) 1. Social sciencesMathematical models. 2. Social sciencesResearchMathematical models. I. Heck, Ronald H.; Thomas, Scott Loring. II. Title. III. Series. H61.25.H43 1999 300'.1'5118dc21 99-19115 CIP
Books published by Lawrence Erlbaum Associates are printed on acid-free paper, and their bindings are chosen for strength and durability.
Printed in the United States of America
10 9 8 7 6 5 4 3 2 1
Page v
Series Editor's Introduction
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-coefficient, 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
Page vi
examples do an excellent job covering necessary computational procedures and available software in detail. The steps from the simple models to the more complicated ones are clearly described and illustrated, and thus the reader can begin at a relatively low level and proceed slowly to the more general class of models described in later chapters. I believe readers will find this book to be an excellent research companion for conducting multilevel analyses. This book provides an important addition to the Quantitative Methodology Series.
GEORGE A. MARCOULIDES CALIFORNIA STATE UNIVERSITY, FULLERTON
Page vii
Contents
Series Editor's Introduction
George A. Marcoulides
v
Preface
ix
1 An Introduction to Multilevel Models in Organizational Research
1
2 The Development of Multilevel Modeling Techniques
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