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• Cover Page
• Half Title page
• Title Page
• Copyright Page
• Dedication
• Contents
• List of figures
• List of tables
• Preface
• Illustration Credits
• Acknowledgements
• 1 Data, information and statistics
• Introduction
• The Nature of Data and Information
• Abstraction and communication
• Data types, variables, values and constants
• Scales of measurement
• Units of analysis
• Independent and dependent variables
• Parameters, Statistics and Samples
• Statistics in Research
• The normative paradigm
• Research Design
• General research issues
• Survey research
• Experimental research
• Measurement issues
• Misuse of Statistics
• Naïve use
• Intentional misuse of statistics
• Summary
• 2 Using this book
• Introduction
• Guidance for Readers
• Recommended use of material within different modules
• Chapter structure
• Slides
• Exercises
• Project exercises
• SPSS
• Comparing SPSS and Microsoft Excel
• Creating an SPSS data sheet
• Logical partitioning of a data sheet
• Computing new variables
• Notation
• Equation Notation
• Equations and expressions
• Multiplication
• Power
• Arrays
• Precedence of arithmetic operators
• Magnitude
• Set notation
• Sums
• Two uses of the bar accent
• Summary
• 3 Descriptive statistics
• Introduction
• Descriptive Statistics for Nominal Variables
• Frequency profiles and the mode
• Example: European soccer cities
• SPSS
• Reporting results
• Descriptive Statistics for Ordinal Variables
• Median, minimum, maximum, lower and upper quartiles
• Example: European soccer cities
• SPSS
• Reporting results
• Descriptive Statistics for Interval and Ratio Variables
• Mean, standard deviation, median, range and inter-quartile range
• Example: European soccer cities
• SPSS
• Reporting results
• Summary
• Exercises
• Exercise 3.1 Relative age in Grand Slam singles tennis
• Exercise 3.2 Burnout potential in student athletes
• Exercise 3.3. Fitness survey
• Project Exercise
• Exercise 3.4 Descriptive statistics for height and body mass of your class
• 4 Standardized scores
• Introduction
• Norms
• Quantiles
• Purpose
• Example: English National Superleague Netball performance
• SPSS
• Z-Scores
• Purpose
• Example
• T-Scores
• Purpose
• Example: Marathon running performance
• Stanines
• Purpose
• Example: Male finishing times in the 2011 London Marathon
• Summary
• Exercises
• Exercise 4.1 Deciles to interpret netball performance
• Exercise 4.2 Quartiles for netball performance
• Exercise 4.3. Decile norms for fitness test performances
• Exercise 4.4 Using z-scores to compare running event performances
• Project Exercise
• Exercise 4.5 Fitness assessment
• 5 Probability
• Introduction
• Probability in Research
• Experiments
• Terminology
• Multistep Experiments
• Probability in multistep experiments
• Assigning Probabilities
• The classical method
• The relative frequency method
• The subjective method
• Counting Rules
• Complex sample spaces
• Counting for combinations
• Counting for permutations
• Events and their Probabilities
• Events
• Rules for computing event probabilities
• Events and set operations
• Conditional probability
• Dependent and independent events
• Probabilistic Modelling
• Summary
• Exercises
• Exercise 5.1 Probability of an experimental outcome
• Exercise 5.2 Throwing two six-sided dice
• Exercise 5.3 Retrospective probability in tennis
• Exercise 5.4 Probabilistic modelling of a tennis point
• Exercise 5.5 Combinations and permutations
• Project Exercise
• Exercise 5.6 Probability of upsets in sport
• 6 Data distributions
• Introduction
• Discrete Probability Distributions
• The uniform discrete probability distribution
• The binomial distribution
• The Poisson distribution
• Continuous Probability Distributions
• The uniform continuous probability distribution
• The normal probability distribution
• T distributions
• F distributions
• Chi square distributions
• Issues with Distributions
• Summary
• Exercises
• Exercise 6.1 Probabilities and percentiles in a normal distribution
• Exercise 6.2 Distribution of tennis performance variables in women’s and men’s singles
• Exercise 6.3. English FA Premier League soccer
• Exercise 6.4 Stanines
• Exercise 6.5 Chance of winning the toss more than other teams
• Exercise 6.6 Athletic burnout questionnaire
• Project Exercise
• Exercise 6.7 British Indoor Rowing performance
• 7 Hypothesis testing
• Introduction
• Hypotheses
• The role of hypotheses in research studies
• Qualities of hypotheses
• Sampling
• Populations and samples
• A practical exercise in sampling
• The coin tossing spreadsheet
• Central Limit Theorem
• A practical exercise sampling from a normally distributed population
• Central Limit Theorem
• Hypothesis Testing
• Significance, Power and Effect
• Selecting A Test
• Single variable testing
• Testing for relationships between variables
• Testing for differences between independent groups
• Testing for differences between conditions
• Testing multiple dependent variables
• Predictive modelling
• Other statistical procedures
• Summary
• Exercises
• Exercise 7.1 Determine the sampling distribution of mean
• Exercise 7.2. Determine the confidence interval
• Exercise 7.3 What test to use when?
• Project Exercise
• Exercise 7.4 Coin tossing exercise
• 8 Correlation
• Introduction
• Pearson’s r
• Purpose of the test
• Coefficient of determination, r2
• Example: Relationships between anthropometric measures and estimated V̇O2 max
• SPSS
• Presentation of results
• Partial Correlations
• Purpose of the test
• Example: Confounding influence of distance covered by soccer players on the relationship between body mass and the number of path changes performed
• SPSS
• Presenting results
• Non-Parametric Correlations
• Purpose of the tests
• SPSS
• Presentation of results
• Summary
• Exercises
• Exercise 8.1. Anthropometric variables and estimated V̇O2 max
• Exercise 8.2. Variables related to margin of victory in soccer matches
• Exercise 8.3. Serving in tennis
• Project exercise
• Exercise 8.4. Correlation between stature, body mass and estimated V̇O2 max
• Exercise 8.5. Efficacy of World ranking in professional tennis
• 9 Linear Regression
• Introduction
• Bivariate Linear Regression
• Purpose of the test
• Interpolation and extrapolation
• Uses of linear regression
• Assumptions
• Significance
• Example: Middle distance running
• SPSS
• Presentation of results
• Multiple Linear Regression
• Purpose of the test
• Assumptions
• Significance
• Example: Predicting the outcomes of international soccer matches
• SPSS
• Presentation of results
• Stepwise and sequential methods
• Summary
• Exercises
• Exercise 9.1. Predictive model of 3000m time in terms of 1500m time
• Exercise 9.2. Multiple linear regression prediction of international soccer matches (knock out stages)
• Project Exercise
• Exercise 9.3. Relation between different event performances
• Exercise 9.4. Performance prediction in rugby union
• 10 T-Tests
• Introduction
• The One-Sample T-Test
• Purpose of the test
• Assumptions
• Example: Indoor rowing performances at national championships
• SPSS
• Reporting results
• The Independent Samples T-Test
• Purpose of the test
• Assumptions
• Example: A quasi-experimental study on the effectiveness of specific intermittent high intensity training
• SPSS
• Reporting results
• The Paired Samples T-Test
• Purpose of the test
• Assumptions
• Example: Effect of instructional and motivational self-talk on sit-up performance
• SPSS
• Reporting results
• Summary
• Exercises
• Exercise 10.1. Specific training experiment
• Exercise 10.2. Comparing the percentage of points won in tennis when the first serve is in and when a second serve is required
• Exercise 10.3. Dominant vs non-dominant Y balance test
• Project Exercise
• Exercise 10.4. Gender effect on indoor rowing strategy
• Exercise 10.5. Home advantage in soccer
• 11 Analysis of Variances
• Introduction
• The One-Way Anova Test
• Purpose of the test
• Assumptions
• Example: Dietary intake of prepubescent female aesthetic athletes
• SPSS
• Presenting results
• Bonferroni-adjusted, post hoc tests
• The General Linear Model
• Repeated Measures Anova
• Purpose of the test
• Assumptions
• Example: Work-rate during different quarters of a netball match
• SPSS
• Presenting results
• Analysis of Covariance (Ancova)
• Purpose of the test
• Assumptions
• Example: 60m sprint time of team game athletes
• SPSS
• Presenting results
• Random Factors
• Summary
• Exercises
• Exercise 11.1. Body mass adjusted energy intake and protein, carbohydrate and fat within the diet of female prepubescent aesthetic athletes
• Exercise 11.2. 400m hurdle performance
• Exercise 11.3. Daily energy intake adjusted for body mass
• Project Exercise
• Exercise 11.4. Positional effect on the height of soccer players
• 12 Factorial ANOVA
• Introduction
• The Between–Between Design
• Purpose of the test
• Assumptions
• Example: Children’s activity in the playground during morning break
• SPSS
• Reporting Results
• The Within–Within Desing
• Purpose of the test
• Assumptions
• Example: Fluid loss with and without a wetsuit
• SPSS
• Reporting the Results
• The Mixed Design
• Purpose of the test
• Assumptions
• Example: Level and quarter effect on work-rate in netball
• SPSS
• Reporting the results
• ANOVA Tests with more than Two Factors
• Summary
• Exercises
• Exercise 12.1. Gender and age effect on playground activity at lunch time
• Exercise 12.2. Gender and surface effect on inter-serve time in Grand Slam tennis
• Exercise 12.3. Venue and period effect on work-rate in professional soccer
• Exercise 12.4. 400m hurdles performance
• Exercise 12.5. Three-way ANOVA with the soccer player tracking data
• Exercise 12.6. Three-way ANOVA to analyse activity in the playground
• Project Exercise
• Exercise 12.7. Training and competition hours done by athletes in different types of sport
• 13 Multivariate ANOVA
• Introduction
• Single Factor MANOVA Tests (Between-Subjects Effects)
• Purpose of the test
• Assumptions
• Example: Fitness testing of women’s Gaelic footballers
• SPSS
• Reporting results
• Factorial MANOVA Tests (Between-Subjects Effects)
• Purpose
• Assumptions
• Example: Gender and sport type effect on burnout
• SPSS
• Reporting results
• Repeated Measures MANOVA Tests
• Purpose
• Assumptions
• Example: Comparing anxiety before training and competitive matches
• SPSS
• Reporting the results
• Mixed Factorial MANOVA Tests
• Purpose of the test
• Assumptions
• Example: Comparing anxiety before training and competitive matches
• SPSS
• Reporting the results
• MANCOVA Tests
• Summary
• Exercises
• Exercise 13.1. Effect of exercise participation on wellbeing
• Exercise 13.2. Gender and type of sport effect on behavioural regulation in sport
• Exercise 13.3. The effect of gender, type of sport and type of match on anxiety (modified SAS2)
• Project Exercise
• Exercise 13.4. Training and competition hours done by male and female athletes
• 14 Non-Parametric Tests
• Introduction
• Mann–Whitney U Test
• Purpose of the test
• Example: Comparing rally lengths between women’s and men’s Grand Slam singles tennis
• SPSS
• Presentation of results
• Wilcoxon Signed Ranks Test
• Purpose of the test
• Example: Percentage of points won in tennis when the first serve is in and when a second serve is required
• SPSS
• Presentation of results
• Kruskal–Wallis H Test
• Purpose of the test
• Assumptions
• Example: Surface effect on rally duration in Grand Slam tournaments
• SPSS
• Presentation of results
• Friedman test
• Purpose of the test
• Example: High-intensity activity performed during the four quarters of a netball match
• SPSS
• Summary
• Exercises
• Exercise 14.1. Service dominance in women’s and men’s singles tennis
• Exercise 14.2. Burnout potential of individual sport and team sport athletes
• Exercise 14.3. Comparing worry and somatic anxiety within student athletes
• Exercise 14.4. Surface effect in men’s and women’s singles tennis
• Exercise 14.5. Heart rate response during the four different quarters of club level netball matches
• Project Exercise
• Exercise 14.6. Home advantage in sport
• Exercise 14.7. Surface effect on double faults played in Grand Slam singles tennis
• Exercise 14.8. Preferred equipment for cardio-vascular exercise in the gym
• 15 Chi Square
• Introduction
• The chi square goodness of fit test
• Purpose of the test
• Assumptions
• Example: Relative age in women’s Grand Slam singles tennis
• SPSS
• Presentation of results
• Chi Square Test of Independence
• Purpose of the test
• Assumptions
• Example: Overseas players in Europe’s ‘Big Four’ soccer leagues
• SPSS
• Reporting results
• Summary
• Exercises
• Exercise 15.1. Relative age in women’s tennis
• Exercise 15.2. Relative age in men’s tennis
• Exercise 15.3. Injuries in women’s netball
• Project Exercise
• Exercise 15.4. Relative age of athletes
• Exercise 15.5. Proportion of unseeded players in the 3rd round of Grand Slam tennis tournaments
• 16 Statistical Classification
• Introduction
• Discriminant Function Analysis
• Purpose of the test
• Assumptions
• Example: Predicting outcomes of pool matches of the 2010 FIFA World Cup
• SPSS and Excel
• Binary Logistic Regression
• Purpose of the test
• Assumptions
• Example: Predicting outcomes of the knockout matches of the 2010 FIFA World Cup
• SPSS and Excel
• Summary
• Exercises
• Exercise 16.1. FIFA World Cup 2010
• Exercise 16.2. Violating the assumptions
• Exercise 16.3. Satisfying assumption of no outliers in predictor variables
• Exercise 16.4. Pre-tournament forecast
• Project Exercise
• Exercise 16.5. Predicting outcomes of soccer matches
• Exercise 16.6. Predicting the outcomes of international rugby union matches
• 17 Cluster Analysis
• Introduction
• Hierarchical Cluster Analysis
• Purpose of cluster analysis
• Assumptions
• First example: Score-line effect on net strategy in tennis
• SPSS
• Reporting results
• Second example: Customer preference for a flavouring ingredient in energy drinks
• SPSS
• Reporting results
• Discussion point
• Summary
• Exercise
• Exercise 17.1. Athletes with different perceptions of anxiety
• Project Exercise
• Exercise 17.2. Perceived performance in different modules
• 18 Data Reduction Using Principal Components Analysis
• Introduction
• Principal Components Analysis
• Purpose of principal components analysis
• Assumptions
• Example: Perceptions of sports tourism
• SPSS
• Using the component scores
• Reporting results
• Summary
• Exercises
• Exercise 18.1. Six component solution
• Project Exercise
• Exercise 18.2. Sports tourism
• 19 Reliability
• Introduction
• Measurement Issues
• Validity
• Objectivity
• Reliability
• Types Of Reliability Study
• What is reliability?
• Inter-rater reliability
• Test–retest reliability
• Parallel forms reliability
• Internal consistency
• Reliability Studies
• Meaningful Reliability Assessment
• Selecting Reliability Statistics
• Factors
• Multiple participant reliability studies
• Multiple retest reliability studies
• Single case reliability studies
• Cross-validation
• Internal consistency of components
• Producing Reliability Statistics
• Kappa
• Weighted kappa
• Correlation coefficients
• Absolute error
• Percentage error
• Root mean squared error
• Limits of agreement
• Ninety-five per cent ratio limits of agreement
• Change of the mean and standard error of measurement (typical error)
• Coefficient of variation
• Intraclass correlation coefficient
• Cronbach’s alpha
• Summary
• Exercises
• Exercise 19.1. Kappa for agreement in netball performance assessment
• Exercise 19.2. Kappa for decision accuracy in netball performance assessment
• Exercise 19.3. Reliability of split times in middle distance athletics
• Exercise 19.4. Y-Balance test performed with eyes open and using the non-dominant leg
• Exercise 19.5. Scoring in amateur boxing
• Exercise 19.6. Internal consistency of the Behavioural Regulation instrument (BRSQ)
• Exercise 19.7. Internal consistency of a wellbeing construct
• 20 Statistical Power
• Introduction
• What Is Statistical Power?
• Murphy et al.’s (2009) Model of Statistical Power
• Four Applications of Power Analysis
• Determining power levels
• Determining sample size
• Determining sensitivity of studies
• Determining criteria for statistical significance
• Summary
• Exercises
• Exercise 20.1. Desired relative seriousness
• Exercise 20.2. Determining power for a training study
• Exercise 20.3. Verifying observed power
• Exercise 20.4. Power curves
• Project Exercise
• Exercise 20.5. Research project planning
• References
• Index