Chapman & Hall/CRC Statistik in den Sozial- und VerhaltenswissenschaftenReihe
Diese Reihe befasst sich mit der entscheidenden Rolle statistischer Methoden zur Analyse großer, komplexer Datensätze in den Sozial- und Verhaltenswissenschaften. Sie erfasst wegweisende Entwicklungen in der statistischen Methodik und betont deren Relevanz für Bereiche wie Soziologie, Psychologie, Wirtschaftswissenschaften und Politikwissenschaft. Die Sammlung fördert die fundierte Anwendung statistischer, ökonometrischer und psychometrischer Werkzeuge durch Nachschlagewerke, Lehrbücher und Handbücher, die reale Beispiele und Fallstudien enthalten. Sie richtet sich an angewandte Statistiker, Studenten und Forscher in diesen vielfältigen Disziplinen.
Focusing on the complexities of modeling age, period, and cohort effects in aggregate-level data, this book introduces various strategies such as constrained estimation and the innovative s-constraint approach. It emphasizes the geometric and algebraic aspects of these methods, enhancing readers' comprehension of statistical issues in APC analysis. By exploring the relationships among common techniques, the book provides a comprehensive framework for understanding and applying APC modeling effectively.
Focusing on ordered regression models, this book delves into cumulative, stage, and adjacent classes, along with variations influenced by the parallel regression assumption. It emphasizes the benefits of using ordered regression for analyzing ordinal outcomes compared to linear and binary models. Additionally, the text provides guidance on interpreting and presenting results, supported by empirical examples from the social and behavioral sciences, making it a valuable resource for researchers in these fields.
Both Traditional Students and Working Professionals Acquire the Skills to Analyze Social Problems. Big Data and Social Science: A Practical Guide to Methods and Tools shows how to apply data science to real-world problems in both research and the practice. The book provides practical guidance on combining methods and tools from computer science, statistics, and social science. This concrete approach is illustrated throughout using an important national problem, the quantitative study of innovation. The text draws on the expertise of prominent leaders in statistics, the social sciences, data science, and computer science to teach students how to use modern social science research principles as well as the best analytical and computational tools. It uses a real-world challenge to introduce how these tools are used to identify and capture appropriate data, apply data science models and tools to that data, and recognize and respond to data errors and limitations. For more information, including sample chapters and news, please visit the author's website.
