Over the last several decades, mathematical models have become central to the study of social evolution, both in biology and the social sciences. But students in these disciplines often seriously lack the tools to understand them. A primer on behavioral modeling that includes both mathematics and evolutionary theory, Mathematical Models of Social Evolution aims to make the student and professional researcher in biology and the social sciences fully conversant in the language of the field. Teaching biological concepts from which models can be developed, Richard McElreath and Robert Boyd introduce readers to many of the typical mathematical tools that are used to analyze evolutionary models and end each chapter with a set of problems that draw upon these techniques. Mathematical Models of Social Evolution equips behaviorists and evolutionary biologists with the mathematical knowledge to truly understand the models on which their research depends. Ultimately, McElreath and Boyd’s goal is to impart the fundamental concepts that underlie modern biological understandings of the evolution of behavior so that readers will be able to more fully appreciate journal articles and scientific literature, and start building models of their own.

This book combines psychological and mathematical perspectives to analyse how qualitative mathematics can be used to create models of social and psychological processes.

This book is devoted to the power of mathematical modelling to give an answer to a broad diversity of real problems including medicine, finance, social behavioural problems and many engineering problems. Mathematical modelling in social sciences is very recent and comes with special challenges such as the difficulty to manage human behaviour, the role of the model hypothesis with the objectivity/subjectivity and the proper understanding of the conclusions. In this book, the reader will find several behavioural mathematical models that in fact may be understood as the so-called epidemiological models in the sense that they deal with populations instead of individuals.

"Highly recommended. . . . This is an important book in putting the burgeoning field of sociodynamics on a solid footing."—Journal of Artificial Societies and Social Simulation This text deals with general modelling concepts in the social sciences, their applications, and their mathematical methods. The author's well-organized approach offers a clear, coherent introduction to terminology, approaches, and goals in modelling. Appropriate for advanced undergraduates and graduate students, it requires a solid background in algebra and calculus. The three-part treatment begins by addressing general modelling concepts, the second part provides applications, and the third discusses mathematical method. Topics include population dynamics, group interaction, political transitions, evolutionary economics, and urbanization. Guiding students through a series of practical applications that illustrate the methods' potential scope, the text concludes with a detailed look at mathematical methods.

Structural Equation Modeling is a statistical method increasingly used in scientific studies in the fields of Social Sciences. It is currently a preferred analysis method, especially in doctoral dissertations and academic researches. Many universities do not include this method in the curriculum, so students and scholars try to solve these problems using books and internet resources. This book aims to guide the researcher in a way that is free from math expressions. It teaches the steps of a research program using structured equality modeling practically. For students writing theses and scholars preparing academic articles, this book aims to analyze systematically the methodology of studies conducted using structural equation modeling methods in the social sciences. In as simple language as possible, it conveys basic information. It consists of two parts: the first gives basic concepts of structural equation modeling, and the second gives examples of applications.

Featuring a practical approach with numerous examples, the second edition of Categorical Data Analysis for the Behavioral and Social Sciences focuses on helping the reader develop a conceptual understanding of categorical methods, making it a much more accessible text than others on the market. The authors cover common categorical analysis methods and emphasize specific research questions that can be addressed by each analytic procedure, including how to obtain results using SPSS, SAS, and R, so that readers are able to address the research questions they wish to answer. Each chapter begins with a "Look Ahead" section to highlight key content. This is followed by an in-depth focus and explanation of the relationship between the initial research question, the use of software to perform the analyses, and how to interpret the output substantively. Included at the end of each chapter are a range of software examples and questions to test knowledge. New to the second edition: The addition of R syntax for all analyses and an update of SPSS and SAS syntax. The addition of a new chapter on GLMMs. Clarification of concepts and ideas that graduate students found confusing, including revised problems at the end of the chapters. Written for those without an extensive mathematical background, this book is ideal for a graduate course in categorical data analysis taught in departments of psychology, educational psychology, human development and family studies, sociology, public health, and business. Researchers in these disciplines interested in applying these procedures will also appreciate this book’s accessible approach.