Bücher der Reihe Quantitative Applications in the Social Sciences

This book explores a variety of graphical displays that are useful for visualizing multivariate data. The basic problem involves representing information that varies along several dimensions when the display medium is inherently two-dimensional. In order to address this problem, Jacoby introduces and explores the concept of a `data space'.

Clearly reviews the properties of important contemporary measures of association and correlation. Liebetrau devotes full chapters to measures for nominal, ordinal, and continuous (interval) data, paying special attention to the sampling distributions needed to determine levels of significance and confidence intervals. Valuable discussions also focus on the relationships between various measures, the sampling properties of their estimators and the comparative advantages and disadvantages of different approaches.

Explains the most widely used methods for analyzing cross-classified data on occupational origins and destinations. Hout reviews classic definitions, models, and sources of mobility data, as well as elementary operations for analyzing mobility tables. Tabular and graphic displays illustrate the discussion throughout.

Krippendorff introduces social scientists to information theory and explains its application for structural modeling. He discusses key topics such as: how to confirm an information theory model; its use in exploratory research; and how it compares with other approaches such as network analysis, path analysis, chi square and analysis of variance.Information Theory simplifies and clarifies a complex but powerful statistical method for analysing multivariate qualitative data. It will serve both as a textbook and as a sourcebook for researchers in communication theory, information theory and systems theory.

Significance testing - a core technique in statistics for hypothesis testing - is introduced in this volume. Mohr first reviews what is meant by sampling and probability distributions and then examines in-depth normal and t-tests of significance. The uses and misuses of significance testing are also explored.

Introduces the concept of expert systems development as a model for the acquisition, representation and validation of knowledge about relatively limited domains. Case studies derive from the authors' own development experiences in the social sciences.

This book builds on John Fox's previous volume in the QASS Series, Non Parametric Simple Regression. In this book, the reader learns how to estimate and plot smooth functions when there are multiple independent variables.

This book offers an overview of the central ideas in calculus and gives examples of how calculus is used to translate many real-world phenomena into mathematical functions. Beginning with an explanation of the two major parts of calculus - differentiation and integration - Gudmund R Iversen illustrates how calculus is used in statistics: to distinguish between the mean and the median; to derive the least squares formulas for regression co-efficients; to find values of parameters from theoretical distributions; and to find a statistical p-value when using one of the continuous test variables such as the t-variable.

Offers a unified framework to help students and researchers to analyze and understand any social science data that are organized in cross-classified formats.

Through developing a decomposition analysis of the inequality measures and promoting their effective use in research, this book provides readers with a step-by-step understanding of the inequality measures that are currently used.

Latent class analysis is a powerful tool for analyzing the structure of relationships among categorically scored variables. It enables researchers to explore the suitability of combining two or more categorical variables into typologies or scales. It also provides a method for testing hypotheses regarding the latent structure among categorical variables.

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This new edition presents an up-to-date description of differential item functioning. It describes varying procedures for addressing this process in practical testing contexts and presents useful examples and studies that readers may employ as a guide in their own work

This book demonstrates how to estimate and interpret fixed-effects models in a variety of different modeling contexts.

Looking at the multigraph representations of loglinear models, this is a clear, introductory text on the area of graphical models and is an ideal text for those new to the field.

This text covers the consequences of violating one of the key assumptions of Ordinary Least Squares regression (equal error variances), diagnostic tools to assess the existence of the problem of heteroskedasticity, and statistical techniques to analyse the data correctly.

Defines the distinctive set of psychometric and operational principles which, when combined with specialized statistical applications of correlation and factor-analysis techniques, provide researchers with a systematic and rigorously quantitative means for examining human subjectivity.

This text guides readers through the specification and identification of simultaneous equation models, how to assess the quality of the estimates and how to correctly interpret results.

Part of the 'little green books' QASS series, this text provides a clear introduction to ordinal item response theory.

With the format of the text mirroring the steps needed to be taken to solve multivariate general linear model problems, this clear and accessible guide introduces readers to this area of statistics.

This book provides a conceptual systematization and a practical tool for the randomization of between-subjects and within-subjects experimental designs.

Explaining the techniques and applications of exponential random graph modeling (ERGM) for social scientists, this is a uniquely sophisticated volume for examining social systems.

Presents the fundamentals of regression analysis, from its meaning to uses, in a concise, easy-to-read, and non-technical style.

Introduction to Power Analysis: Two-Group Studies provides readers with the background, examples, and explanation they need to read technical papers and materials that include complex power analyses.  This clear and accessible guide explains the components of test statistics and their sampling distributions, and author Eric Hedberg walks the reader through the simple and complex considerations of this research question. Filled with graphics and examples, the reader is taken on a tour of power analyses from covariates to clusters, seeing how the complicated task of comparing two groups, and the power analysis, can be made easy.

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.