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Cited by more than 300 scholars, Statistical Reasoning in the Behavioral Sciences continues to provide streamlined resources and easy-to-understand information on statistics in the behavioral sciences and related fields, including psychology, education, human resources management, and sociology. Students and professionals in the behavioral sciences will develop an understanding of statistical logic and procedures, the properties of statistical devices, and the importance of the assumptions underlying statistical tools. This revised and updated edition continues to follow the recommendations of the APA Task Force on Statistical Inference and greatly expands the information on testing hypotheses about single means. The Seventh Edition moves from a focus on the use of computers in statistics to a more precise look at statistical software. The “Point of Controversy” feature embedded throughout the text provides current discussions of exciting and hotly debated topics in the field. Readers will appreciate how the comprehensive graphs, tables, cartoons and photographs lend vibrancy to all of the material covered in the text.
How to Write a Masters Thesis is a comprehensive manual on how to conceptualize and write a five-chapter masters thesis, including the introduction, literature review, methodology, results, and discussionnclusion. Very often, a theory-practice gap exists for students who have taken the prerequisite methods and statistics courses in their masters program but who have yet to understand how to apply and translate what they've learned about the research process with their first major project. Yvonna Bui demystifies this process by integrating the language learned in these prerequisite courses into a step-by-step guide for developing one's own thesis/project.
Statistical Power Analysis is a nontechnical guide to power analysis in research planning that provides users of applied statistics with the tools they need for more effective analysis. The Second Edition includes: * a chapter covering power analysis in set correlation and multivariate methods; * a chapter considering effect size, psychometric reliability, and the efficacy of "qualifying" dependent variables and; * expanded power and sample size tables for multiple regression/correlation.
This book provides a showcase for "best practices" in teaching statistics and research methods in two- and four-year colleges and universities. A helpful resource for teaching introductory, intermediate, and advanced statistics and/or methods, the book features coverage of: ways to integrate these courses how to promote ethical conduct how to create writing intensive programs novel tools and activities to get students involved strategies for teaching online courses and computer applications guidance on how to create and maintain helpful web resources assessment advice to help demonstrate that students are learning tips on linking diversity to research methodology. This book appeals to veteran and novice educators and graduate students who teach research methods and/or statistics in psychology and other behavioral sciences and serves as an excellent resource in related faculty workshops. Downloadable resources with activities that readers can customize is included.
This mid-level book introduces and explains statistical concepts and principles clearly, assuming minimal mathematical sophistication but avoiding a "cookbook" approach. The book also presents a broader outlook on hypothesis testing by including such often-neglected concepts as statistical power, indices and other techniques.
Research Methods and Statistics provides a seamless introduction to the subject, identifying various research areas and analyzing how one can approach them statistically. The text provides a solid empirical foundation for undergraduate psychology majors, and prepares the reader to think critically, and evaluate psychological research and claims they might hear in the news or popular press. The text can be used in all statistics, methods and experimental psychology courses.
Mounting failures of replication in social and biological sciences give a new urgency to critically appraising proposed reforms. This book pulls back the cover on disagreements between experts charged with restoring integrity to science. It denies two pervasive views of the role of probability in inference: to assign degrees of belief, and to control error rates in a long run. If statistical consumers are unaware of assumptions behind rival evidence reforms, they can't scrutinize the consequences that affect them (in personalized medicine, psychology, etc.). The book sets sail with a simple tool: if little has been done to rule out flaws in inferring a claim, then it has not passed a severe test. Many methods advocated by data experts do not stand up to severe scrutiny and are in tension with successful strategies for blocking or accounting for cherry picking and selective reporting. Through a series of excursions and exhibits, the philosophy and history of inductive inference come alive. Philosophical tools are put to work to solve problems about science and pseudoscience, induction and falsification.
Taken literally, the title "All of Statistics" is an exaggeration. But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like non-parametric curve estimation, bootstrapping, and classification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analysing data.