Advanced statistics for kinesiology and exercise science : a practical guide to ANOVA and regression analyses

Author(s)

Bibliographic Information

Advanced statistics for kinesiology and exercise science : a practical guide to ANOVA and regression analyses

Moh H. Malek, Jared W. Coburn and William D. Marelich

Routledge, 2019

  • : hbk
  • : pbk

Available at  / 3 libraries

Search this Book/Journal

Note

Includes bibliographical references and index

Description and Table of Contents

Description

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.

Table of Contents

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)

by "Nielsen BookData"

Details

Page Top