Bücher der Reihe Quantitative Applications in the Social Sciences

Although clustering--the classifying of objects into meaningful sets--is an important procedure, cluster analysis as a multivariate statistical procedure is poorly understood. This volume is an introduction to cluster analysis for professionals, as wel

Updates to this new edition include: more coverage of regression assumptions and model fit; additional material on residual analysis; more examples of transformations; and the inclusion of the measures of tolerance and VIF within the discussion about collinearity.

As budgets tighten and costs increase, it is becoming even more necessary that workable social programmes are shown to be worthy of support. This book presents one approach to evaluation -- multiattribute utility technology -- which stresses that evaluations should be comparative, and that all the different constituencies served by a programme and its different goals have to be kept in mind.

This volume shows how odds ratios can be used as a framework for understanding log-linear models. Moving systematically from the paradigmatic 2x2 case to more complicated cases, the author defines the odds ratio and demonstrates how it is a measure of association for tabular analysis.

Written in nontechnical language, this popular and practical volume has been completely updated to bring readers the latest advice on major issues involved in longitudinal research. It covers: research design strategies; methods of data collection; how longitudinal and cross-sectional research compares in terms of consistency and accuracy of results.

Outlines a set of techniques that enable a researcher to discuss the "hidden structure" of large data bases. These techniques use proximities, measures which indicate how similar or different objects are, to find a configuration of points which reflects the structure in the data.

A statistical method which will appeal to two groups in particular: those who are currently using the more traditional technique of exploratory factor analysis; and those who are interested in the analysis of covariance structures, commonly known as the LISREL model. The first group will find that this technique may be more appropriate to the analysis of their research problems; while the second group will find that confirmatory factor analysis is a useful first step to understanding the LISREL model, for this book, and its companion volume, Covariance Structure Models, are designed to be read consecutively. The proofs presented are simple, but the reader must feel comfortable with matrix algebra in order to understand the model.

This volume introduces the theory, method, and applications of one type of conjoint analysis technique. These techniques are used to study individual judgement and decision processes. Based upon Information Integration Theory, metric conjoint analysis allows for evaluation of multi-attribute alternatives based on interval level data. The model, which justifies use of metric conjoint methods and the statistical techniques drawn from it, are the core of this monograph. Also described are applications of the model in marketing, psychology, economics, sociology, planning, and other disciplines, all of which relate to forecasting the decision-making behavior of individuals.

Offers researchers a guide for selecting the best statistical model to use, as well as discussing such topics as contextual analysis with absolute/relative effects, and the choice between regression coefficients as fixed parameters or as random variables.

The second edition of this book provides a conceptual understanding of analysis of variance. It outlines methods for analysing variance that are used to study the effect of one or more nominal variables on a dependent, interval level variable. The book presumes only elementary background in significance testing and data analysis.

Discusses the innovative log-linear model of statistical analysis. This model makes no distinction between independent and dependent variables, but is used to examine relationships among categoric variables by analyzing expected cell frequencies.

An introduction to the underlying principles, central concepts, and basic techniques for conducting and understanding exploratory data analysis -- with numerous social science examples.

This paper considers the possible effects of making inferences about individuals from aggregate data. It assumes a knowledge of regression analysis, and explores the utility of techniques designed to make the inferences in causal modelling more reliable, including a comparison between ecological regression models and ecological correlation.

How do we group different subjects on a variety of variables? Should we use a classification procedure in which only the concepts are classified (typology), one in which only empirical entities are classified (taxonomy), or a combination of both? Kenneth D Bailey addresses these questions and shows how classification methods can be used to improve research. Beginning with an exploration of the advantages and disadvantages of classification procedures, the book covers topics such as: clustering procedures including agglomerative and divisive methods; the relationship among various classification techniques; how clustering methods compare with related statistical techniques; classification resources; and software packages for use in clustering techniques.

Monte Carlo simulation is a method of evaluating substantive hypotheses and statistical estimators by developing a computer algorithm to simulate a population, drawing multiple samples from this pseudo-population, and evaluating estimates obtained from these samples. This book explains the logic behind the method and demonstrates its uses for research.

Repeated surveys allow researchers the opportunity to analyze changes in society as a whole. This book includes: a discussion of the classic issue of how to separate cohort, period and age effects; methods for modelling aggregate trends; and methods for estimating cohort replacement's contribution to aggregate trends.

Secondary analysis has assumed a central position in social science research as existing survey data and statistical computing programmes have become increasingly available. This volume presents strategies for locating survey data and provides a comprehensive guide to US social science data archives, describing several major data files. The book also reviews research designs for secondary analysis.

Empirical researchers, for whom Iversen's volume provides an introduction, have generally lacked a grounding in the methodology of Bayesian inference. As a result, applications are few. After outlining the limitations of classical statistical inference, the author proceeds through a simple example to explain Bayes' theorem and how it may overcome these limitations. Typical Bayesian applications are shown, together with the strengths and weaknesses of the Bayesian approach. This monograph thus serves as a companion volume for Henkel's Tests of Significance (QASS vol 4).

Quantile Regression establishes the seldom recognized link between inequality studies and quantile regression models. Though separate methodological literatures exist for each subject matter, the authors explore the natural connections between this increasingly sought-after tool and research topics in the social sciences.

Provides students and researchers with a practical introduction to conducting analyses of interaction effects in the context of multiple regression. This title includes coverage on the analysis of three-way interactions in multiple regression analysis.

Introducing content analysis methods from a social science perspective, this book focuses on the reliability and viability of these coding procedures and their associated category schemes. This text is intended for those who have a basic knowledge of research methods or data analysis.

An introduction to a variety of techniques that may be used in the analysis of data from a panel study -- information obtained from a large number of entities at two or more points in time. The focus of this volume is on analysis rather than problems of sampling or design, and its emphasis is on application rather than theory.

Drawing on recent "event history" analytical methods from biostatistics, engineering and sociology, this book explains how longitudinal data can be used to study the causes of deaths, crimes, wars, and many other human-related events.

With empirical examples which are both plentiful and well chosen to teach the technique, this book provides a thorough guide to latent class scaling models for binary response variables.

This book provides a systematic and practical guide for researchers wishing to set up their own factorial survey design or to analyze factorial survey data.

An elementary introduction to significance testing, this paper provides a conceptual and logical basis for understanding these tests.

Describes the mathematical and logical foundations at a level that does not presume advanced mathematical or statistical skills. It illustrates how to do factor analysis with several of the more popular packaged computer programs.

Offers students a brief and accessible approach to systematically quantifying various types of narrative data they can collect during a research process.

This monograph is not statistical. It looks instead at pre-statistical assumptions about dependent variables and causal order. Professor Davis spells out the logical principles that underlie our ideas of causality and explains how to discover causal direction, irrespective of the statistical technique used. He stresses throughout that knowledge of the `real world'' is important and repeatedly challenges the myth that causal problems can be solved by statistical calculations alone.