J. P. Verma – författare

This book introduces the use of statistics to solve a variety of problems in exercise science and health and provides readers with a solid foundation for future research and data analysis.Statistics for Exercise Science and Health with Microsoft Office Excel: Aids readers in analyzing their own data using the presented statistical techniques combined with ExcelFeatures comprehensive coverage of hypothesis testing and regression models to facilitate modeling in sports scienceUtilizes Excel to enhance reader competency in data analysis and experimental designsIncludes coverage of both binomial and poison distributions with applications in exercise science and healthProvides solved examples and plentiful practice exercises throughout in addition to case studies to illustrate the discussed analytical techniquesContains all needed definitions and formulas to aid readers in understanding different statistical concepts and developing the needed skills to solve research problems

Introduces the applications of repeated measures design processes with the popular IBM® SPSS® softwareRepeated Measures Design for Empirical Researchers presents comprehensive coverage of the formation of research questions and the analysis of repeated measures using IBM SPSS and also includes the solutions necessary for understanding situations where the designs can be used. In addition to explaining the computation involved in each design, the book presents a unique discussion on how to conceptualize research problems as well as identify appropriate repeated measures designs for research purposes.Featuring practical examples from a multitude of domains including psychology, the social sciences, management, and sports science, the book helps readers better understand the associated theories and methodologies of repeated measures design processes. The book covers various fundamental concepts involved in the design of experiments, basic statistical designs, computational details, differentiating independent and repeated measures designs, and testing assumptions. Along with an introduction to IBM SPSS software, Repeated Measures Design for Empirical Researchers includes: A discussion of the popular repeated measures designs frequently used by researchers, such as one-way repeated measures ANOVA, two-way repeated measures design, two-way mixed design, and mixed design with two-way MANOVACoverage of sample size determination for the successful implementation of designing and analyzing a repeated measures studyA step-by-step guide to analyzing the data obtained with real-world examples throughout to illustrate the underlying advantages and assumptionsA companion website with supplementary IBM SPSS data sets and programming solutions as well as additional case studiesRepeated Measures Design for Empirical Researchers is a useful textbook for graduate- and PhD-level students majoring in biostatistics, the social sciences, psychology, medicine, management, sports, physical education, and health. The book is also an excellent reference for professionals interested in experimental designs and statistical sciences as well as statistical consultants and practitioners from other fields including biological, medical, agricultural, and horticultural sciences.J. P. Verma, PhD, is Professor of Statistics and Director of the Center for Advanced Studies at Lakshmibai National Institute of Physical Education, India. Professor Verma is an active researcher in sports modeling and data analysis and has conducted many workshops on research methodology, research designs, multivariate analysis, statistical modeling, and data analysis for students of management, physical education, social science, and economics. He is the author of Statistics for Exercise Science and Health with Microsoft® Office Excel®, also published by Wiley.

A step-by-step approach to problem-solving techniques using SPSS® in the fields of sports science and physical educationFeaturing a clear and accessible approach to the methods, processes, and statistical techniques used in sports science and physical education, Sports Research with Analytical Solution using SPSS® emphasizes how to conduct and interpret a range of statistical analysis using SPSS. The book also addresses issues faced by research scholars in these fields by providing analytical solutions to various research problems without reliance on mathematical rigor.Logically arranged to cover both fundamental and advanced concepts, the book presents standard univariate and complex multivariate statistical techniques used in sports research such as multiple regression analysis, discriminant analysis, cluster analysis, and factor analysis. The author focuses on the treatment of various parametric and nonparametric statistical tests, which are shown through the techniques and interpretations of the SPSS outputs that are generated for each analysis. Sports Research with Analytical Solution using SPSS® also features: Numerous examples and case studies to provide readers with practical applications of the analytical concepts and techniquesPlentiful screen shots throughout to help demonstrate the implementation of SPSS outputsIllustrative studies with simulated realistic data to clarify the analytical techniques coveredEnd-of-chapter short answer questions, multiple choice questions, assignments, and practice exercises to help build a better understanding of the presented conceptsA companion website with associated SPSS data files and PowerPoint® presentations for each chapter Sports Research with Analytical Solution using SPSS® is an excellent textbook for upper-undergraduate, graduate, and PhD-level courses in research methods, kinesiology, sports science, medicine, nutrition, health education, and physical education. The book is also an ideal reference for researchers and professionals in the fields of sports research, sports science, physical education, and social sciences, as well as anyone interested in learning SPSS.

Comprehensively teaches the basics of testing statistical assumptions in research and the importance in doing soThis book facilitates researchers in checking the assumptions of statistical tests used in their research by focusing on the importance of checking assumptions in using statistical methods, showing them how to check assumptions, and explaining what to do if assumptions are not met.Testing Statistical Assumptions in Research discusses the concepts of hypothesis testing and statistical errors in detail, as well as the concepts of power, sample size, and effect size. It introduces SPSS functionality and shows how to segregate data, draw random samples, file split, and create variables automatically. It then goes on to cover different assumptions required in survey studies, and the importance of designing surveys in reporting the efficient findings. The book provides various parametric tests and the related assumptions and shows the procedures for testing these assumptions using SPSS software. To motivate readers to use assumptions, it includes many situations where violation of assumptions affects the findings. Assumptions required for different non-parametric tests such as Chi-square, Mann-Whitney, Kruskal Wallis, and Wilcoxon signed-rank test are also discussed. Finally, it looks at assumptions in non-parametric correlations, such as bi-serial correlation, tetrachoric correlation, and phi coefficient. An excellent reference for graduate students and research scholars of any discipline in testing assumptions of statistical tests before using them in their research studyShows readers the adverse effect of violating the assumptions on findings by means of various illustrationsDescribes different assumptions associated with different statistical tests commonly used by research scholarsContains examples using SPSS, which helps facilitate readers to understand the procedure involved in testing assumptionsLooks at commonly used assumptions in statistical tests, such as z, t and F tests, ANOVA, correlation, and regression analysisTesting Statistical Assumptions in Research is a valuable resource for graduate students of any discipline who write thesis or dissertation for empirical studies in their course works, as well as for data analysts.

This book addresses sample size and power in the context of research, offering valuable insights for graduate and doctoral students as well as researchers in any discipline where data is generated to investigate research questions.