Statistics For The Behavioural Sciences

Written for students studying in a variety of social science areas, not solely the psychology student, this book is designed to give each student a conceptual understanding of the basic statistical procedures used in behavioral sciences.

Do you find statistics overwhelming and confusing? Have you ever wished for someone to explain the basics in a clear and easy-to-follow style? This accessible textbook gives a step-by-step introduction to all the topics covered in introductory statistics courses for the behavioural sciences, with plenty of examples discussed in depth, based on real psychology experiments utilising the statistical techniques described. Advanced sections are also provided, for those who want to learn a particular topic in more depth. Statistics for the Behavioural Sciences: An Introduction begins with an introduction to the basic concepts, before providing a detailed explanation of basic statistical tests and concepts such as descriptive statistics, probability, the binomial distribution, continuous random variables, the normal distribution, the Chi-Square distribution, the analysis of categorical data, t-tests, correlation and regression. This timely and highly readable text will be invaluable to undergraduate students of psychology, and students of research methods courses in related disciplines, as well as anyone with an interest in the basic concepts and tests associated with statistics in the behavioural sciences.

This book demonstrates the importance of computer-generated statistical analyses in behavioral science research, particularly those using the R software environment. Statistical methods are being increasingly developed and refined by computer scientists, with expertise in writing efficient and elegant computer code. Unfortunately, many researchers lack this programming background, leaving them to accept on faith the black-box output that emerges from the sophisticated statistical models they frequently use. Building on the author’s previous volume, Linear Models in Matrix Form, this text bridges the gap between computer science and research application, providing easy-to-follow computer code for many statistical analyses using the R software environment. The text opens with a foundational section on linear algebra, then covers a variety of advanced topics, including robust regression, model selection based on bias and efficiency, nonlinear models and optimization routines, generalized linear models, and survival and time-series analysis. Each section concludes with a presentation of the computer code used to illuminate the analysis, as well as pointers to packages in R that can be used for similar analyses and nonstandard cases. The accessible code and breadth of topics make this book an ideal tool for graduate students or researchers in the behavioral sciences who are interested in performing advanced statistical analyses without having a sophisticated background in computer science and mathematics.

This student-oriented text presents the basics for professors who need to get through the text quickly and who therefore give priority to the essentials of applied statistics. The text aims to captures the insight and classroom lecture tactics of statistics teachers.

Revision of the classic text in the field, adding two new chapters and thoroughly updating all others. The original structure is retained, and the book continues to serve as a combined text/reference.

This field-leading introduction to statistics text for students in the behavioral and social sciences continues to offer straightforward instruction, accuracy, built-in learning aids, and real-world examples. The goals of STATISTICS FOR THE BEHAVIORAL SCIENCES, 10th Edition are to teach the methods of statistics and convey the basic principles of objectivity and logic that are essential for science -- and valuable in everyday life. Authors Frederick Gravetter and Larry Wallnau help students understand statistical procedures through a conceptual context that explains why the procedures were developed and when they should be used. Students have numerous opportunities to practice statistical techniques through learning checks, examples, step-by-step demonstrations, and problems. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.