Books in the Quantitative Applications in the Social Sciences series

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.

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.

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

This book presents methods for describing and analyzing dependency and irregularity in long time series. Irregularity refers to cycles that are similar in appearance, but unlike seasonal patterns more familiar to social scientists, repeated over a time scale that is not fixed. Until now, the application of these methods has mainly involved analysis of dynamical systems outside of the social sciences, but this volume makes it possible for social scientists to explore and document fractal patterns in dynamical social systems.

This book introduces current perspectives on Rasch measurement theory with an emphasis on developing Rasch-based scales. Authors George Engelhard Jr and Jue Wang introduce Rasch measurement theory step by step, with chapters on scale construction, evaluation, maintenance, and use. Points are illustrated and techniques are demonstrated through an extended example: The Food Insecurity Experience (FIE) Scale.

Researchers in the social sciences and beyond are dealing more and more with massive quantities of text data requiring analysis, from historical letters to the constant stream of content in social media. Traditional texts on statistical analysis have focused on numbers, but this book will provide a practical introduction to the quantitative analysis of textual data. Using up-to-date R methods, this book will take readers through the text analysis process, from text mining and pre-processing the text to final analysis. It includes two major case studies using historical and more contemporary text data to demonstrate the practical applications of these methods. Currently, there is no introductory how-to book on textual data analysis with R that is up-to-date and applicable across the social sciences. Code and a variety of additional resources are available on an accompanying website for the book.

Measurement connects theoretical concepts to what is observable in the empirical world, and is fundamental to all social and behavioral research. In this volume, J. Micah Roos and Shawn Bauldry introduce a popular approach to measurement: confirmatory factor analysis, with examples in every chapter draw from national survey data. Data to replicate the examples are available on a companion website, along with code in R, Stata, and Mplus.