Advanced Statistics for Kinesiology and Exercise Science

Advanced Statistics for Kinesiology and Exercise Science is the first textbook to cover advanced statistical methods in the context of the study of human performance. Divided into three distinct sections, the book introduces and explores in depth both analysis of variance (ANOVA) and regressions analyses, including chapters on: - preparing data for analysis; - one-way, factorial, and repeated-measures ANOVA; - analysis of covariance and multiple analyses of variance and covariance; - diagnostic tests; - regression models for quantitative and qualitative data; - model selection and validation; - logistic regression Drawing clear lines between the use of IBM SPSS Statistics software and interpreting and analyzing results, and illustrated with sport and exercise science-specific sample data and results sections throughout, the book offers an unparalleled level of detail in explaining advanced statistical techniques to kinesiology students. Advanced Statistics for Kinesiology and Exercise Science is an essential text for any student studying advanced statistics or research methods as part of an undergraduate or postgraduate degree programme in kinesiology, sport and exercise science, or health science.

Moh H. Malek, Ph.D., FACSM, FNSCA, CSCS,*D, is an Associate Professor (tenured) of Physical Therapy in the Department of Health Care Sciences at the Eugene Applebaum College of Pharmacy and Health Sciences at Wayne State University in Detroit, Michigan, USA. Jared W. Coburn, Ph.D., FASCM, FNSCA, CSCS,*D, is a Professor (tenured) of Kinesiology in the Department of Kinesiology at California State University, Fullerton, California, USA. William D. Marelich, Ph.D., is Professor (tenured) of Psychology in the Department of Psychology at California State University, Fullerton, and a consulting statistician for Health Risk Reduction Projects, Integrative Substance Abuse Programs (ISAP), Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at the University of California, Los Angeles, USA.

Part 1: ANOVA analysis 1. What's stats got to do with it? 2. Organizing the data 3. Review of one-way analysis of variance (ANOVA) 4. Two- and three-way factorial ANOVA 5. Mixed or between-within factorial ANOVA 6. Analysis of covariance (ANCOVA) Part 2: Regression analysis 7. Diagnostic tests for regression 8. Basic multiple regression analysis 9. Multiple regression models for quantitative and categorical data 10. Regression model validation 11. Logistic regression Part 3: Special statistical procedures 12. Multivariate analysis of variance (MANOVA)

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