Bøger i Quantitative Applications in the Social Sciences serien

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.

Survey Questions is a highly readable guide to the principles of writing survey questions. The authors review recent research on survey questions, consider the lore of professional experience and finally present those findings which have the strongest implications on writing these questions.

This book provides researchers with an overview of rating scale analysis along with practical guidance on how to conduct such analyses with their own survey data. Author Stefanie A. Wind presents three categories of methods: Rasch models; non-Rasch Item Response Theory (IRT) models; and non-parametric models, together with practical examples.

Although longitudinal social network data are increasingly collected, there are few guides on how to navigate the range of available tools for longitudinal network analysis. Author Scott Duxbury assumes that the reader is familiar with network measurement, description, and notation, and is versed in regression analysis, but is likely unfamiliar with statistical network methods. The goal of the book is to guide readers towards choosing, applying, assessing, and interpreting a longitudinal network model, and each chapter is organized with a specific data structure or research question in mind. A companion website includes data and R code to replicate the examples in 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.

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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.

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.

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

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.

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.

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.

In recent years the loglinear model has become the dominant form of categorical data analysis as researchers have expanded it into new directions. This book shows researchers the applications of one of these new developments - how uniting ordinary loglinear analysis and latent class analysis into a general loglinear model with latent variables can result in a modified LISREL approach. This modified LISREL model will enable researchers to analyze categorical data in the same way that they have been able to use LISREL to analyze continuous data. Beginning with an introduction to ordinary loglinear modelling and standard latent class analysis, the author explains the general principles of loglinear modelling with latent variables, the application of loglinear models with latent variables as a causal model as well as a tool for the analysis of categorical longitudinal data, the strengths and limitations of this technique, and finally, a summary of computer programs that are available for executing this technique.

Recent advances in statistical methodology and computer automation are making canonical correlation analysis available to more and more researchers. This volume explains the basic features of this sophisticated technique in an essentially non-mathematical introduction which presents numerous examples. Thompson discusses the assumptions, logic, and significance testing procedures required by this analysis, noting trends in its use and some recently developed extensions.

Reviews the sampling methods used in surveys such as simple random sampling, systematic sampling, stratification, cluster and multi-stage sampling, sampling with probability proportional to size, two-phase sampling, replicated sampling, panel designs, and non-probability sampling.

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.

A practical introduction to multi-level modelling, this title offers an introduction to HLM and illustrations of how to use this technique to build models for hierarchical and longitudinal data.

Aimed at readers with minimal experience in computer programming, this book provides a theoretical and methodological rationale for using ABM in the social sciences. It concludes with practical advice about how to design and create ABM, a discussion of validation procedures, and some guidelines about publishing articles based on ABM.

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.

Expands on the 1982 edition and adds new basic social network developments of the past twenty-five years.

Considers the techniques needed for exploring problems that compromise a regression analysis and for determining whether certain assumptions appear reasonable. The text covers such topics as the problem of collinearity in multiple regression, non-normality of errors and non-constant error variance.

Carol A. Chapelle shows readers how to design validation research for tests of human capacities and performance. Any test that is used to make decisions about people or programs should have undergone extensive research to demonstrate that the scores are actually appropriate for their intended purpose. Argument-Based Validation in Testing and Assessment is intended to help close the gap between theory and practice, by introducing, explaining, and demonstrating how test developers can formulate the overall design for their validation research from an argument-based perspective.

Several decades of psychometric research have led to the development of sophisticated models for multidimensional test data, and in recent years, multidimensional item response theory (MIRT) has become a burgeoning topic in psychological and educational measurement. Considered a cutting-edge statistical technique, the methodology underlying MIRT can be complex, and therefore doesn¿t receive much attention in introductory IRT courses. However author Wes Bonifay shows how MIRT can be understood and applied by anyone with a firm grounding in unidimensional IRT modeling. His volume includes practical examples and illustrations, along with numerous figures and diagrams. Brief snippets of R code are interspersed throughout the text (with the complete R code included on an accompanying website) to guide readers in exploring MIRT models, estimating the model parameters, generating plots, and implementing the various procedures and applications discussed throughout the book.

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.

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.

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.

A concise, introductory text on propensity score methods that is easy to comprehend by those who have a limited background in statistics, and is practical enough for researchers to quickly generalize and apply the methods.

Taking the reader step-by-step through the intricacies, theory and practice of regression analysis, Damodar N. Gujarati uses a clear style that doesn't overwhelm the reader with abstract mathematics.