In addition, he reviews log-linear models, latent trait models, and a number of restricted latent class models in detail as well as for the estimation of parameters for these models.

This volume is based on an international conference held at the Institute for Science Education (IPN) in Kiel in August 1985. The IPN is a national research institute for science education of the Federal Republic of Germany associated with the University of Kiel. The aim of this conference-to treat latent trait and latent class models under comparative points of view as well as under application aspects-was realized in many stimulating contributions and very different ways. We asked the authors of these papers to work out their contributions for publication here, not only because many of the papers present new material, but also because the time is ripe for a comprehen sive volume, working up the widespread literature of the past ten years in this field. We have tried to compile a volume that will be of interest to statistically oriented researchers in a variety of disciplines, including psychology, sociology, education, political science, epidemiology, and the like. Although the chapters assume a reasonably high level of methodo logical sophistication, we hope that the book will find its way into advanced courses in the above fields. We are grateful to the IPN for organizing the conference, to our contributors for their untiring efforts in revising their chapters for publication, and to the staff of Plenum Publishing Corporation for helping to make this book a reality.

Contributors thoroughly survey the most important statistical models used in empirical reserch in the social and behavioral sciences. Following a common format, each chapter introduces a model, illustrates the types of problems and data for which the model is best used, provides numerous examples that draw upon familiar models or procedures, and includes material on software that can be used to estimate the models studied. This handbook will aid researchers, methodologists, graduate students, and statisticians to understand and resolve common modeling problems.

What is latent class analysis? If you asked that question thirty or forty years ago you would have gotten a different answer than you would today. Closer to its time of inception, latent class analysis was viewed primarily as a categorical data analysis technique, often framed as a factor analysis model where both the measured variable indicators and underlying latent variables are categorical. Today, however, it rests within much broader mixture and diagnostic modeling framework, integrating measured and latent variables that may be categorical and/or continuous, and where latent classes serve to define the subpopulations for whom many aspects of the focal measured and latent variable model may differ. For latent class analysis to take these developmental leaps required contributions that were methodological, certainly, as well as didactic. Among the leaders on both fronts was C. Mitchell “Chan” Dayton, at the University of Maryland, whose work in latent class analysis spanning several decades helped the method to expand and reach its current potential. The current volume in the Center for Integrated Latent Variable Research (CILVR) series reflects the diversity that is latent class analysis today, celebrating work related to, made possible by, and inspired by Chan’s noted contributions, and signaling the even more exciting future yet to come.

"The Handbook of Methodological Approaches to Community-Based Research is intended to aid the community-oriented researcher in learning about and applying cutting-edge quantitative, qualitative, and mixed methods approaches"--

Applied Latent Class Analysis introduces several innovations in latent class analysis to a wider audience of researchers. Many of the world's leading innovators in the field of latent class analysis contributed essays to this volume, each presenting a key innovation to the basic latent class model and illustrating how it can prove useful in situations typically encountered in actual research.