Applied Regularization Methods for the Social Sciences

Applied Regularization Methods for the Social Sciences provides an overview of a variety of models and clear examples of hands-on application of regularization techniques for statistical analyses in the social sciences. It covers frequentist and Bayesian approaches, includes extended examples, and has application exercises for instructors and independent researchers.

Format: Hardback
Length: 297 pages
Publication date: 09 March 2022
Publisher: Taylor & Francis Ltd

Researchers in the social sciences often encounter complex data sets characterized by relatively small samples and numerous variables (high-dimensional data). In contrast to the abundance of technical guides available in the market, Applied Regularization Methods for the Social Sciences offers a comprehensive overview of a diverse range of models, accompanied by clear and practical examples of their practical application. This book is designed to cater to social science researchers who seek to leverage regularization techniques to address the challenges posed by high-dimensional data.

Each chapter in this text delves into a specific application of regularization methods, providing a user-friendly technical description that facilitates understanding. The subsequent examples offer a thorough demonstration of the methods in action, allowing readers to grasp their practical implications.

Key Features:

• A user-friendly and accessible presentation of regularization methods, making them suitable for researchers with varying levels of statistical expertise.
• Coverage of regularization-based approaches for a wide array of statistical analyses commonly employed in the social sciences, encompassing both univariate and multivariate models.
• Extensive development of extended examples utilizing multiple software packages, including R, SAS, and SPSS, to facilitate a hands-on understanding of the methods.
• A dedicated website featuring all datasets and software scripts used in the examples, enabling readers to replicate and extend the analyses.
• Inclusion of both frequentist and Bayesian regularization approaches, providing researchers with the flexibility to choose the most appropriate method based on their research objectives and data characteristics.
• Application exercises for each chapter, designed to assist instructors in teaching the concepts and methodologies covered in the book. These exercises can also be utilized by independent researchers as a means to practice and reinforce their learning.

By presenting a comprehensive and practical guide to applied regularization methods, Applied Regularization Methods for the Social Sciences serves as a valuable resource for social science researchers seeking to enhance their analytical capabilities and address the complexities of high-dimensional data. Whether you are a novice or an experienced researcher, this book will provide you with the tools and insights necessary to effectively analyze and interpret social science data.

Weight: 588g
Dimension: 163 x 240 x 23 (mm)
ISBN-13: 9780367408787