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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.

This book provides a nontechnical introduction to Multiple Correspondence Analysis

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.

As one of the only texts introducing fractal analysis and the social processes involved to social science readers, this is a must-have book for those looking to gain an understanding of this area of analysis.

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.

Assuming no prior knowledge, this book is geared toward social science readers. It illustrates concepts using well known international, comparative, and national examples of spatial regression analysis. Each example is presented alongside relevant data and code, which is also available on a Web site maintained by the authors.

Explaining the theoretical underpinning of generalized linear models, this text enables researchers to decide how to select the best way to adapt their data for this type of analysis, with examples to illustrate the application of GLM.

Multilevel Structural Equation Modeling by Bruno Castanho Silva, Constantin Manuel Bosancianu, and Levente Littvay serves as a minimally technical overview of multilevel structural equation modeling (MSEM) for applied researchers and advanced graduate students in the social sciences. As the first book of its kind, this title is an accessible, hands-on introduction for beginners of the topic. The authors predict a growth in this area, fueled by both data availability and also the availability of new and improved software to run these models. The applied approach, combined with a graphical presentation style and minimal reliance on complex matrix algebra guarantee that this volume will be useful to social science graduate students wanting to utilize such models.

The book brings together material on the analysis of limited and bounded variables that is scattered across the literature in several disciplines, and presents it in a style that is both more accessible and up-to-date. The authors provide worked examples in each chapter using real datasets from a variety of disciplines. The software used for the examples include R, SAS, and Stata. The data, software code, and detailed explanations of the example models are available on an accompanying website.

Pampel's book offers readers the first "nuts and bolts" approach to doing logistic regression through the use of careful explanations and worked out examples. This book will enable readers to use and understand logistic regression techniques and will serve as a foundation for more advanced treatments of the topic.

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.

Correlation matrices (along with their unstandardized counterparts, covariance matrices) underlie the majority the statistical methods that researchers use today. A correlation matrix is more than a matrix filled with correlation coefficients. The value of one correlation in the matrix puts constraints on the values of the others, and the multivariate implications of this statement is a major theme of the volume. Alexandria Hadd and Joseph Lee Rodgers cover many features of correlations matrices including statistical hypothesis tests, their role in factor analysis and structural equation modeling, and graphical approaches. They illustrate the discussion with a wide range of lively examples.

The ideal primer for students and researchers across the social sciences who wish to master the necessary maths in order to pursue studies involving advanced statistical methods