Handbook of Statistical Modeling for the Social and Behavioral Sciences PDF
Preview Handbook of Statistical Modeling for the Social and Behavioral Sciences
Handbook of Statistical Modeling for the Social and Behavioral Sciences Handbook of Statistical Modeling for the Social and Behavioral Sciences Edited by Gerhard Arminger Bergische Universitiit Wuppertal Wuppertal, Germany Clifford C. Clogg Late of Pennsylvania State University University Park, Pennsylvania and Michael E. Sobel University ofA rizona Tucson, Arizona SPRINGER SCIENCE+BUSINESS MEDIA, LLC Library of Congress Cataloging-in-Publication Data Handbook of statlstlcal modellng for the soclal and behaVloral SClences / edlted by Gerhard Armlnger, Cllfford C. Clogg, and Mlchael E. SobeI. p. cm. Includes bibllographlca I references and lndex. ISBN 978-1-4899-1294-7 ISBN 978-1-4899-1292-3 (eBook) DOI 10.1007/978-1-4899-1292-3 1. Soclal sciences--Statlstlcal methods. 2. Psychology- -Statlstlcal methods. 1. Armlnger, Gerhard. II. Clogg, Cllfford C. III. SobeI, Michael E. HA29.H2487 1994 300' .1'5195--dc20 94-43088 CIP ISBN 978-1-4899-1294-7 © 1995 Springer Science+Business Media New York Originally published by Plenum Press, New York in 1995 Softcover reprint ofthe hardcover 1st edition 1995 109876543 Ali rights reserved No part of this book may be reproduced, stored in a retrieval system, or transmitted in any fOfm Of by any means, electronic, mechanical, photocopying, microfilming, recording, or otherwise, without written permission from the Publisher To Paula W eiBenbacher To the Memory of Richard G. Clogg To the Memory of Irvin Sobel and Peggy Sobel Contributors Gerhard Arminger, Department of Economics, Bergische Universitat-GH Wuppertal, D-42097 Wuppertal, Germany Michael W. Browne, Department of Psychology, Ohio State University, 142 Townshend Hall, 1885 Neil Avenue Mall, Columbus, Ohio43210, USA Clifford C. Clogg~· Department of Sociology and Department of Statistics, Pennsylvania State University, University Park, Pennsylvania 16802, USA Alfred Hamerle, Lehrstuhl fiir Statistik, U niversitat Regensburg, U niversitatsstr. 31, D-93053 Regensburg, Germany Cheng Hsiao, Department of Economics, University of Southern California, Los Angeles, California 90089--0253, USA Roderick J. A. Little, Department of Biostatistics, University of Michigan, 1420 Wash ington Heights, Ann Arbor, Michigan 48109-2029, USA Nicholas T. Longford, Educational Testing Service, Princeton, New Jersey 08541, USA Trond Petersen, Walter A. Haas School of Business, University of California, Berkeley, California 94720, USA Gerd Ronning, Abteilung Statistik und Okonometrie I, Department of Economics, Eber hard-Karls-Universitat, Mohlstr. 36, D-72074 Tiibingen, Germany Nathaniel Schenker, Department of Biostatistics, UCLA School of Public Health, 10833 Le Conte Avenue, Los Angeles, California 90024-1772, USA Michael E. Sobel, Department of Sociology, University of Arizona, Tucson, Arizona 85721, USA t Deceased vii Foreword It is a pleasure to be able to contribute a foreword to this impressive handbook on quan titative methods for the analysis of data. Too often attempts such as this appear to consist of rather disconnected chapters on favorite, but possibly narrow, topics of distinguished contributors. Not so with this one! The editors have done an admirable job of blending contributions from distinguished researchers into a coherent package. Throughout the chapters, simple but realistic examples are used to introduce fundamen tal ideas, and the individual authors do an especially good job of relating more advanced procedures to more basic ones, which should already be familiar to most researchers. Also, all chapters indicate, at least to some extent, the availability of software for implementing the procedures being discussed; this enterprise is always a bit hazardous in that software is constantly being born, modified, and dying, but the choice to include such references is clearly preferable to excluding them. The selection of topics is also excellent for a researcher approaching data already col lected. The general focus on conceptual parametric modeling is on-target, as such models allow the formulation of crisp scientific hypotheses, and for the natural estimation of effects and intervals for them in addition to statistical tests. As these models and their applications become more extended and developed, I expect to see more full probability (Bayesian) modelling being used so that nuisance parameters and small sample complications can be more systematically handled. Eventually, this will lead to even more discussion of compu tational issues, including inference by simulation, especially iterative simulation. This is a forward-looking book with many fine contributions-congratulations to the editors and to the other authors of individual chapters. DONALD B. RUBIN Professor and Chairman Department ofS tatistics Harvard University ix IN MEMORIAM Clifford C. Clogg passed away on May 7, 1995. Cliff was an excellent colleague, and we shall miss working with him. But most of all, we mourn the loss of a very dear and special friend. G.A. M.E.S. Preface This is a research handbook and a reference work for quantitative methodologists, ap plied statisticians, empirical researchers, and graduate students in disciplines such as com munications, demography, economics, education, geography, political science, psychology, statistics, and sociology. Although the focus is on models and methods for the social and behavioral sciences, this volume should also be of interest to epidemiologists and others in the health sciences, as well as to business researchers, especially those engaged in organi zational or marketing research. Researchers in these fields face a number of common modeling problems. First, much or even most of1he research in these substantive fields is based on nonexperimental data, such as surveys and quasi-experiments. Second, variables to be modeled are usually mea sured with error. Failing to take measurement error into account typically leads to infer ences that are not reliable. Third, measurements in these disciplines may be quantitative and continuous at one extreme or categorical and nominal at another. Fourth, in recent years, longitudinal data have become more common, and special methods are needed to take full advantage of the information in such data sets. Each of the chapters in this handbook addresses one or more of the above issues. In Chapter 1, Sobel considers the difficulties that arise in attempting to use nonexperimental data to draw causal inferences, while in Chapter 2, Little and Schenker discuss modern methods for dealing with another ubiquitous problem: missing data. In modeling data, researchers usually attempt to describe how one or more specified dependent variables is or are related to independent variables or predictors, and the inferences that are made can depend heavily on auxiliary assumptions. For example, the assumption of normal ho moscedastic errors in regression analysis needs to be examined. Arminger's chapter on mean structures focuses on the construction of parametric models for the relation between a set of predictor variables and one or more specified dependent variables; here the relation ship is to be described by the "mean structure," and one wants to use models and methods that are valid with as few auxiliary assumptions as possible. Browne andArminger's chap ter on mean and covariance structure models discusses latent variable models for the case where both the observed and unobserved variables are metrical, and the case where one or more of the observed variables are ordered categorical and the unobserved variables are metrical. Sobel's chapter on discrete data focuses primarily on log-linear models for contin gency tables, where all the observed variables in the analysis are categorical, while Clogg's chapter on the latent class model focuses on latent variable models where both the observed and unobserved variables are discrete; he also briefly takes up the case where the observed variables are discrete and the unobserved variable is metrical, as in the Rasch model. xii Preface xiii The next three chapters focus on methods for longitudinal data. Hsiao considers models for the analysis of panel data; here measurements on the sample of respondents are taken on several occasions. He shows how to take advantage of the panel setup to answer questions that cannot be addressed with data from a cross-sectional study, or that can be answered more confidently with panel data. Panel data are also the focus of the chapter by Hamerle and Ronning; whereas Hsiao is concerned with the case where the specified dependent variable is metrical, Hamerle and Ronning take up the case where the dependent variable is discrete. Finally, Petersen considers dynamic models for the analysis of event histories, or survival models, in which a respondent can change states at arbitrary times in the study interval. Longford's chapter considers multilevel or hierarchical models. In many ways, these models are similar to the panel-data models discussed by Hsiao. However, in panel stud ies (without missing data) the observations are fully crossed (time by person), whereas in multilevel models, the observations are nested within larger clusters, e.g., persons within schools within school districts. The observations within a cluster tend to be more similar than those in different clusters, and Longford shows how to estimate models that take this clustering into account. A number of other important topics were excluded from the volume. For example, there is no chapter on model selection principles; but each chapter addresses this topic in context. Given the focus on modeling, we did not include material on the collection of data, or on sampling design. We also focused on parametric modeling, excluding, for example, consideration of nonparametric estimation of regression functions and graphical procedures. In addition, we do not discuss time series models, as there is already a large literature on the subject. Similarly, a number of other topics were excluded, including spatial models and network analysis, the latter somewhat specific to sociology. Finally, the subject of graphical modeling is not considered, despite a large statistical literature on the topic. These and other topics are surely important, but it would have been difficult to include all of them in a one-volume handbook. In order that the handbook be accessible and useful to empirical workers and advanced graduate students, we asked our authors to follow a common format. Each chapter in troduces the models in a simple context, illustrating the types of problems and data for which the models are useful. We encouraged our authors to use examples throughout their chapters and to draw upon familiar models or procedures to motivate their contributions. In addition, all the chapters include material on software that can be used to estimate the models studied. Each chapter is largely self-contained, thereby allowing a researcher who wants to "study a certain type of model useful in his or her work to do so by focusing on a particular chapter, without having to study the rest of the handbook in depth. Similarly, instructors can easily organize an advanced-graduate level course around one or more of the handbook's themes by focusing attention on several of the chapters. For example, a one semester course on longitudinal analysis might take up the chapters by Hsiao, Hamerle and Ronning, and Petersen. At the same time, the handbook is also intended for use by those interested in the more technical aspects of these subjects. Therefore, we also asked our authors to prepare reviews that represent the "state of the art" in their area. Not only did all of our authors do so, but many of the chapters also contain original material.
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