Large and complex datasets are becoming prevalent in the social and behavioral sciences and statistical methods are crucial for the analysis and interpretation of such data. This series aims to capture new developments in statistical methodology with particular relevance to applications in the social and behavioral sciences. It seeks to promote appropriate use of statistical, econometric and psychometric methods in these applied sciences by publishing a broad range of reference works, textbooks and handbooks.
The scope of the series is wide, including applications of statistical methodology in sociology, psychology, economics, education, marketing research, political science, criminology, public policy, demography, survey methodology and official statistics. The titles included in the series are designed to appeal to applied statisticians, as well as students, researchers and practitioners from the above disciplines. The inclusion of real examples and case studies is therefore essential.
Please contact us if you have an idea for a book for the series.
By Derek Briggs
December 25, 2022
This book is about the process of measuring and evaluating student growth in educational settings. Using large-scale assessments to measure student growth has increasingly become a major point of emphasis in systems of educational accountability. The complete process of designing, calibrating, ...
By Ali Uenlue
December 15, 2022
By Jun Xu
November 24, 2022
Modern Applied Regressions creates an intricate and colorful mural with mosaics of categorical and limited response variable (CLRV) models using both Bayesian and Frequentist approaches. Written for graduate students, junior researchers, and quantitative analysts in behavioral, health, and social ...
By Kathleen M. Gates, Sy-Miin Chow, Peter C. M. Molenaar
November 15, 2022
This book focuses on a span of statistical topics relevant to researchers who seek to conduct person-specific analysis of human data. The primary audience are students and researchers in psychometrics, quantitative psychology, psychophysiology and neurocognition. This book can be used for both ...
By David A. Armstrong, Ryan Bakker, Royce Carroll, Christopher Hare, Keith T. Poole, Howard Rosenthal
August 01, 2022
With recent advances in computing power and the widespread availability of preference, perception and choice data, such as public opinion surveys and legislative voting, the empirical estimation of spatial models using scaling and ideal point estimation methods has never been more accessible.The ...
By Jocelyn E. Bolin
July 29, 2022
Regression Analysis in R: A Comprehensive View for the Social Sciences covers the basic applications of multiple linear regression all the way through to more complex regression applications and extensions. Written for graduate level students of social science disciplines this book walks readers ...
By Rudolf Debelak, Carolin Strobl, Matthew D. Zeigenfuse
June 07, 2022
An Introduction to the Rasch Model with Examples in R offers a clear, comprehensive introduction to the Rasch model along with practical examples in the free, open-source software R. It is accessible for readers without a background in psychometrics or statistics, while also providing detailed ...
By Holmes Finch
March 25, 2022
Researchers in the social sciences are faced with complex data sets in which they have relatively small samples and many variables (high dimensional data). Unlike the various technical guides currently on the market, Applied Regularization Methods for the Social Sciences provides and overview of a ...
By Barry Schouten, Jan van den Brakel, Bart Buelens, Deirdre Giesen, Annemieke Luiten, Vivian Meertens
September 28, 2021
Mixed-mode surveys have become a standard at many statistical institutes. However, the introduction of multiple modes in one design goes with challenges to both methodology and logistics. Mode-specific representation and measurement differences become explicit and demand for solutions in data ...
By John P. Hoffmann
September 14, 2021
Research in social and behavioral sciences has benefited from linear regression models (LRMs) for decades to identify and understand the associations among a set of explanatory variables and an outcome variable. Linear Regression Models: Applications in R provides you with a comprehensive treatment...
By Ryan Kennedy, Philip D. Waggoner
March 09, 2021
Introduction to R for Social Scientists: A Tidy Programming Approach introduces the Tidy approach to programming in R for social science research to help quantitative researchers develop a modern technical toolbox. The Tidy approach is built around consistent syntax, common grammar, and stacked ...
Edited
By Ian Foster, Rayid Ghani, Ron S. Jarmin, Frauke Kreuter, Julia Lane
November 18, 2020
Big Data and Social Science: Data Science Methods and Tools for Research and Practice, Second Edition shows how to apply data science to real-world problems, covering all stages of a data-intensive social science or policy project. Prominent leaders in the social sciences, statistics, and computer ...
