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Description: Incorporating a hands-on pedagogical approach, Nonparametric Statistics for Social and Behavioral Sciences presents the concepts, principles, and methods used in performing many nonparametric procedures. It also demonstrates practical applications of the most common nonparametric procedures using IBM's SPSS software. This text is the only current nonparametric book written specifically for students in the behavioral and social sciences. Emphasizing sound research designs, appropriate statistical analyses, and accurate interpretations of results, the text: Explains a conceptual framework for each statistical procedure Presents examples of relevant research problems, associated research questions, and hypotheses that precede each procedure Details SPSS paths for conducting various analyses Discusses the interpretations of statistical results and conclusions of the research With minimal coverage of formulas, the book takes a nonmathematical approach to nonparametric data analysis procedures and shows students how they are used in research contexts. Each chapter includes examples, exercises, and SPSS screen shots illustrating steps of the statistical procedures and resulting output.
The use of statistical tests in research; Choosing an appropriate statistical test; The one-sample case; The case of two related samples; The case of two independent samples; The case of k related samples; The case of k independent samples; Measures of correlatin and their tests of significance.
Statistics for the Behavioral Sciences is an introduction to statistics text that will engage students in an ongoing spirit of discovery by illustrating how statistics apply to modern-day research problems. By integrating instructions, screenshots, and practical examples for using IBM SPSS® Statistics software, the book makes it easy for students to learn statistical concepts within each chapter. Gregory J. Privitera takes a user-friendly approach while balancing statistical theory, computation, and application with the technical instruction needed for students to succeed in the modern era of data collection, analysis, and statistical interpretation.
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.
A practical and understandable approach to nonparametric statistics for researchers across diverse areas of study As the importance of nonparametric methods in modern statistics continues to grow, these techniques are being increasingly applied to experimental designs across various fields of study. However, researchers are not always properly equipped with the knowledge to correctly apply these methods. Nonparametric Statistics for Non-Statisticians: A Step-by-Step Approach fills a void in the current literature by addressing nonparametric statistics in a manner that is easily accessible for readers with a background in the social, behavioral, biological, and physical sciences. Each chapter follows the same comprehensive format, beginning with a general introduction to the particular topic and a list of main learning objectives. A nonparametric procedure is then presented and accompanied by context-based examples that are outlined in a step-by-step fashion. Next, SPSS® screen captures are used to demonstrate how to perform and recognize the steps in the various procedures. Finally, the authors identify and briefly describe actual examples of corresponding nonparametric tests from diverse fields. Using this organized structure, the book outlines essential skills for the application of nonparametric statistical methods, including how to: Test data for normality and randomness Use the Wilcoxon signed rank test to compare two related samples Apply the Mann-Whitney U test to compare two unrelated samples Compare more than two related samples using the Friedman test Employ the Kruskal-Wallis H test to compare more than two unrelated samples Compare variables of ordinal or dichotomous scales Test for nominal scale data A detailed appendix provides guidance on inputting and analyzing the presented data using SPSS®, and supplemental tables of critical values are provided. In addition, the book's FTP site houses supplemental data sets and solutions for further practice. Extensively classroom tested, Nonparametric Statistics for Non-Statisticians is an ideal book for courses on nonparametric statistics at the upper-undergraduate and graduate levels. It is also an excellent reference for professionals and researchers in the social, behavioral, and health sciences who seek a review of nonparametric methods and relevant applications.
“...a very useful resource for courses in nonparametric statistics in which the emphasis is on applications rather than on theory. It also deserves a place in libraries of all institutions where introductory statistics courses are taught." –CHOICE This Second Edition presents a practical and understandable approach that enhances and expands the statistical toolset for readers. This book includes: New coverage of the sign test and the Kolmogorov-Smirnov two-sample test in an effort to offer a logical and natural progression to statistical power SPSS® (Version 21) software and updated screen captures to demonstrate how to perform and recognize the steps in the various procedures Data sets and odd-numbered solutions provided in an appendix, and tables of critical values Supplementary material to aid in reader comprehension, which includes: narrated videos and screen animations with step-by-step instructions on how to follow the tests using SPSS; online decision trees to help users determine the needed type of statistical test; and additional solutions not found within the book.
Interactive Statistics for the Behavioral Sciences is an engaging tour of the topics covered in most behavioral science statistics textbooks: descriptive statistics, the logic of hypothesis testing, t tests, power analysis, confidence intervals, analysis of variance, correlation/regression, and nonparametric inferential statistics. Yet, it employs a radically different pedagogical approach. Without wholly abandoning the tradition of using a printed textbook to supplement classroom or online instruction, this system has at its core an interactive set of components that run through Web browsers such as Internet Explorer or Netscape Navigator. Working through these components, students create their own customized learning experience, rather than passively reading a printed text. The end result is students who can better master and even enjoy a subject that many approach with trepidation.
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 text is written for a one-semester introduction to statistical analysis course in sociology, criminal justice, social work, or psychology in which students will be exposed to the basic concepts and procedures in statistical analysis as it is applied in the social sciences. In Statistical Analysis in the Behavioral Sciences, Raymondo offers a text that provides clear and student-friendly explanations of statistical concepts, in which as much emphasis is placed on the purpose and interpretation of statistical analysis as on the traditional approach of how to perform statistical procedures. Through his clear and conversational writing style, by going beyond a surface interpretation, and by using "real-life" data, Raymondo sparks students' interest and understanding.
This text provides the reader with a single book where they can find accounts of a number of up-to-date issues in nonparametric inference. The book is aimed at Masters or PhD level students in statistics, computer science, and engineering. It is also suitable for researchers who want to get up to speed quickly on modern nonparametric methods. It covers a wide range of topics including the bootstrap, the nonparametric delta method, nonparametric regression, density estimation, orthogonal function methods, minimax estimation, nonparametric confidence sets, and wavelets. The book’s dual approach includes a mixture of methodology and theory.