Wissen gewinnt immer mehr an Bedeutung. Die Folgen für das Verhältnis von Bildung und Beschäftigung sind allerdings erst ansatzweise geklärt. Deshalb werden in diesem Buch ausgewählte Bezüge zwischen Bildung, Weiterbildung und beruflichen Karrieren anhand des sozio-ökonomischen Panels nachgezeichnet. Im Einzelnen geht es um den Wert von Bildungsabschlüssen bei der Statuszuweisung, die Entwicklung der Karrieremobilität, um Ungleichheiten in der Weiterbildungsbeteiligung und um den Nutzen beruflicher Weiterbildung für den sozialen Aufstieg. Die Ergebnisse werfen ein kritisches Licht auf den vielbeschworenen Anspruch des "lebenslangen Lernens".
Bringing to life the most widely used quantitative measurements and statistical techniques in marketing, this book is packed with user-friendly descriptions, examples and study applications. The process of making marketing decisions is frequently dependent on quantitative analysis and the use of specific statistical tools and techniques which can be tailored and adapted to solve particular marketing problems. Any student hoping to enter the world of marketing will need to show that they understand and have mastered these techniques. A bank of downloadable data sets to compliment the tables provided in the textbook are provided free for you here
Statistical science as organized in formal academic departments is relatively new. With a few exceptions, most Statistics and Biostatistics departments have been created within the past 60 years. This book consists of a set of memoirs, one for each department in the U.S. created by the mid-1960s. The memoirs describe key aspects of the department’s history -- its founding, its growth, key people in its development, success stories (such as major research accomplishments) and the occasional failure story, PhD graduates who have had a significant impact, its impact on statistical education, and a summary of where the department stands today and its vision for the future. Read here all about how departments such as at Berkeley, Chicago, Harvard, and Stanford started and how they got to where they are today. The book should also be of interests to scholars in the field of disciplinary history.
An imaginative introduction to statistics, reorienting the course towards an understanding of statistical thinking and its meaning and use in daily life and work. Gudmund Iversen and Mary Gergen bring their years of experience and insight into teaching the subject, incorporating such innovations and insights as a sustained emphasis on the process of statistical analysis and what statistics can and cannot do as well as careful exposition of the ideas of developing statistical and graphical literacy. In the spirit of contemporary pedagogy and by using technology, the authors break down the traditional barriers of statistical formulas and lengthy computations encountered by students without strong quantitative skills. Further, formulas are grouped at the end of each chapter along with related problems, and, with only algebra as a prerequisite, the book is ideal for students in the liberal arts and the behavioural and social sciences.
"This book provides an outstanding introduction to. . . using association models developed primarily by Leo Goodman. . . . This well-written book provides a careful and generally clear introduction to association models. . . . the authors have achieved their aims well. They make a strong case for the usefulness of association models in a variety of applications. Clogg. . . and Shihadeh have provided sociologists with an introduction filled with wise advice about analyzing associations between ordinal variables." --Alan Agresti in Contemporary Sociology "This is a very useful book about. . . statistical models for ordinal variables. Reading this book. . . your reviewer was pleased to find a clear and succinct account explaining a variety of association models. . . . These models are the 'RC' models. . . . it is to statistical methods for the social sciences that this book. . . is aimed. . . . This is not a total beginner's book, however. . . and I thought the pace a little faster than leisurely. . . . a fine resource of clear description and explanation of the use of statistical models for ordinal data. . . ." --M. C. Jones in Journal of the Royal Statistical Society "This book is worthwhile reading for statisticians who have scattered training in ordinal data analysis and want to pull this training into a coherent overview. It is a fine supplement to other more mathematical books in the area. . . . After reading the book, the reader will have a clear understanding of the role of odds ratios in ordinal data analysis." --Technometrics "Includes a concise but clear review of criteria for assessing goodness-of-fit. . . . I found this volume an accessible unification of work in the area. I recommend it." --International Statistical Institute How should data involving response variables of many ordered categories be analyzed? What technique is the most useful in analyzing partially ordered variables regarded as dependent variables? Addressing these and other related concerns in social and survey research, this book carefully explores the statistical analysis of data involving dependent variables that can be coded into discrete, ordered categories. Through an analysis of ordinal variables, the authors cover the general procedures for assessing goodness-of-fit, review the independence model and the saturated model, define measures of association, demonstrate the logit version of the model and the jackknife method for contingency tables, and explain associated models for two-way tables as well as logit-type regression models.
A unified treatment of the most useful models for categorical and limited dependent variables (CLDVs) is provided in this book. Throughout, the links among the models are made explicit, and common methods of derivation, interpretation and testing are applied. In addition, the author explains how models relate to linear regression models whenever possible.
Logistic Regression is designed for readers who have a background in statistics at least up to multiple linear regression, who want to analyze dichotomous, nominal, and ordinal dependent variables cross-sectionally and longitudinally.
Event history analysis has been a useful method in the social sciences for studying the processes of social change. However, a main difficulty in using this technique is to observe all relevant explanatory variables without missing any variables. This book presents a general approach to missing data problems in event history analysis which is based on the similarities between log-linear models, hazard models and event history models. It begins with a discussion of log-rate models, modified path models and methods for obtaining maximum likelihood estimates of the parameters of log-linear models. The author then shows how to incorporate variables with missing information in log-linear models - including latent class models, m
Logistic Regression Models for Ordinal Response Variables provides applied researchers in the social, educational, and behavioral sciences with an accessible and comprehensive coverage of analyses for ordinal outcomes. The content builds on a review of logistic regression, and extends to details of the cumulative (proportional) odds, continuation ratio, and adjacent category models for ordinal data. Description and examples of partial proportional odds models are also provided. This book is highly readable, with lots of examples and in-depth explanations and interpretations of model characteristics.
The pioneering texts in quantitative history were written over two decades ago, but as a command of methodological context, computer experience, and statistical literacy have become increasingly important to the study of history, the need for an introduct
Categorical data are quantified as either nominal variables--distinguishing different groups, for example, based on socio-economic status, education, and political persuasion--or ordinal variables--distinguishing levels of interest, such as the preferred politician or the preferred type of punishment for committing burglary. This new book is a collection of up-to-date studies on modern categorical data analysis methods, emphasizing their application to relevant and interesting data sets. This volume concentrates on latent class analysis and item response theory. These methods use latent variables to explain the relationships among observed categorical variables. Latent class analysis yields the classification of a group of respondents according to their pattern of scores on the categorical variables. This provides insight into the mechanisms producing the data and allows the estimation of factor structures and regression models conditional on the latent class structure. Item response theory leads to the identification of one or more ordinal or interval scales. In psychological and educational testing these scales are used for individual measurement of abilities and personality traits. The focus of this volume is applied. After a method is explained, the potential of the method for analyzing categorical data is illustrated by means of a real data example to show how it can be used effectively for solving a real data problem. These methods are accessible to researchers not trained explicitly in applied statistics. This volume appeals to researchers and advanced students in the social and behavioral sciences, including social, developmental, organizational, clinical and health psychologists, sociologists, educational and marketing researchers, and political scientists. In addition, it is of interest to those who collect data on categorical variables and are faced with the problem of how to analyze such variables--among themselves or in relation to metric variables.
"This is a thorough and sophisticated treatment of structural equation modeling (SEM). The book assumes a strong background in statistics and matrix algebra. The author clearly is knowledgeable and has put much thought into the pros and cons of standard applications of SEM. The chapters on multilevel and growth modeling are an excellent feature of the book." --Rick H. Hoyle, University of Kentucky Through the use of detailed, empirical examples, this exciting book presents an advanced treatment of the foundations of structural equation modeling (SEM) and demonstrates how SEM can provide a unique lens on problems in the social and behavioral sciences. The author begins with an introduction to recursive and non-recursive models, estimation, testing, and the problem of measurement in observed variables. Kaplan then explores the issue of group differences in structural models, statistical assumptions in structural modeling (from sampling to missing data and specification error), the assessment of statistical power and model modification in the context of model evaluation, and SEM applied to complex data structures such as those obtained from clustered random sampling. The book concludes with a discussion of recent developments in latent variable growth curve modeling and a critique of the conventional practice of structural modeling in light of recent developments in econometric modeling. Features/Benefits: - Shows how SEM can be used to answer substantive questions by weaving a small set of substantive examples throughout the book - Explains recent developments in structural equation modeling applied to complex sampling, such as multilevel SEM and latent variable growth - Provides a critique of the standard practice of structural equation modeling throughout the book and discusses an alternative approach in light of recent developments in econometric modeling.