Sports Research with Analytical Solution using SPSS E-Book

J. P. Verma

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 548

Veröffentlichungsjahr: 2016

Beschreibung

A step-by-step approach to problem-solving techniques using SPSS® in the fields of sports science and physical education Featuring 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 techniques * Plentiful screen shots throughout to help demonstrate the implementation of SPSS outputs * Illustrative studies with simulated realistic data to clarify the analytical techniques covered * End-of-chapter short answer questions, multiple choice questions, assignments, and practice exercises to help build a better understanding of the presented concepts * A 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.

Leseprobe

Table of Contents

COVER

TITLE PAGE

PREFACE

ABOUT THE COMPANION WEBSITE

ACKNOWLEDGMENTS

1 INTRODUCTION TO DATA TYPES AND SPSS OPERATIONS

1.1 INTRODUCTION

1.2 TYPES OF DATA

1.3 IMPORTANT DEFINITIONS

1.4 DATA CLEANING

1.5 DETECTION OF ERRORS

1.6 HOW TO START SPSS?

1.7 EXERCISE

2 DESCRIPTIVE PROFILE

2.1 INTRODUCTION

2.2 EXPLANATION OF VARIOUS DESCRIPTIVE STATISTICS

2.3 APPLICATION OF DESCRIPTIVE STATISTICS

2.4 COMPUTATION OF DESCRIPTIVE STATISTICS USING SPSS

2.5 INTERPRETATIONS OF THE RESULTS

2.6 DEVELOPING PROFILE CHART

2.7 SUMMARY OF SPSS COMMANDS

2.8 EXERCISE

2.9 CASE STUDY ON DESCRIPTIVE ANALYSIS

3 CORRELATION COEFFICIENT AND PARTIAL CORRELATION

3.1 INTRODUCTION

3.2 CORRELATION MATRIX AND PARTIAL CORRELATION

3.3 APPLICATION OF CORRELATION MATRIX AND PARTIAL CORRELATION

3.4 CORRELATION MATRIX WITH SPSS

3.5 PARTIAL CORRELATION WITH SPSS

3.6 SUMMARY OF THE SPSS COMMANDS

3.7 EXERCISE

3.8 CASE STUDY ON CORRELATION

4 COMPARING MEANS

4.1 INTRODUCTION

4.2 ONE-SAMPLE t-TEST

4.3 TWO-SAMPLE t-TEST FOR UNRELATED GROUPS

4.4 PAIRED t-TEST FOR RELATED GROUPS

4.5 ONE-SAMPLE t-TEST WITH SPSS

4.6 TWO-SAMPLE t-TEST FOR INDEPENDENT GROUPS WITH SPSS

4.7 PAIRED t-TEST FOR RELATED GROUPS WITH SPSS

4.8 SUMMARY OF SPSS COMMANDS FOR t-TESTS

4.9 EXERCISE

4.10 CASE STUDY

5 INDEPENDENT MEASURES ANOVA

5.1 INTRODUCTION

5.2 ONE-WAY ANALYSIS OF VARIANCE

5.3 ONE-WAY ANOVA WITH SPSS (EQUAL SAMPLE SIZE)

5.4 ONE-WAY ANOVA WITH SPSS (UNEQUAL SAMPLE SIZE)

5.5 TWO-WAY ANALYSIS OF VARIANCE

5.6 TWO-WAY ANOVA USING SPSS

5.7 SUMMARY OF THE SPSS COMMANDS

5.8 EXERCISE

5.9 CASE STUDY ON ONE-WAY ANOVA DESIGN

5.10 CASE STUDY ON TWO-WAY ANOVA

6 REPEATED MEASURES ANOVA

6.1 INTRODUCTION

6.2 ONE-WAY REPEATED MEASURES ANOVA

6.3 ONE-WAY REPEATED MEASURES ANOVA USING SPSS

6.4 TWO-WAY REPEATED MEASURES ANOVA

6.5 TWO-WAY REPEATED MEASURES ANOVA USING SPSS

6.6 SUMMARY OF THE SPSS COMMANDS FOR ONE-WAY REPEATED MEASURES ANOVA

6.7 SUMMARY OF THE SPSS COMMANDS FOR TWO-WAY REPEATED MEASURES ANOVA

6.8 EXERCISE

7 ANALYSIS OF COVARIANCE

7.1 INTRODUCTION

7.2 CONCEPTUAL FRAMEWORK OF ANALYSIS OF COVARIANCE

7.3 APPLICATION OF ANCOVA

7.4 ANCOVA WITH SPSS

7.5 SUMMARY OF THE SPSS COMMANDS

7.6 EXERCISE

7.7 CASE STUDY ON ANCOVA DESIGN

8 NONPARAMETRIC TESTS IN SPORTS RESEARCH

8.1 INTRODUCTION

8.2 CHI-SQUARE TEST

8.3 GOODNESS OF FIT WITH SPSS

8.4 TESTING INDEPENDENCE OF TWO ATTRIBUTES

8.5 TESTING ASSOCIATION WITH SPSS

8.6 MANN–WHITNEY U TEST: COMPARING TWO INDEPENDENT SAMPLES

8.7 WILCOXON SIGNED-RANK TEST: FOR COMPARING TWO RELATED GROUPS

8.8 KRUSKAL–WALLIS TEST

8.9 FRIEDMAN TEST

8.10 SUMMARY OF THE SPSS COMMANDS

8.11 EXERCISE

9 REGRESSION ANALYSIS AND MULTIPLE CORRELATIONS

9.1 INTRODUCTION

9.2 UNDERSTANDING REGRESSION EQUATION

9.3 APPLICATION OF REGRESSION ANALYSIS

9.4 MULTIPLE REGRESSION ANALYSIS WITH SPSS

9.5 SUMMARY OF SPSS COMMANDS FOR REGRESSION ANALYSIS

9.6 EXERCISE

10 APPLICATION OF DISCRIMINANT FUNCTION ANALYSIS

10.1 INTRODUCTION

10.2 BASICS OF DISCRIMINANT FUNCTION ANALYSIS

10.3 ASSUMPTIONS IN DISCRIMINANT ANALYSIS

10.4 WHY TO USE DISCRIMINANT ANALYSIS

10.5 STEPS IN DISCRIMINANT ANALYSIS

10.6 APPLICATION OF DISCRIMINANT FUNCTION ANALYSIS

10.7 DISCRIMINANT ANALYSIS USING SPSS

10.8 SUMMARY OF THE SPSS COMMANDS FOR DISCRIMINANT ANALYSIS

10.9 EXERCISE

10.10 CASE STUDY ON DISCRIMINANT ANALYSIS

11 LOGISTIC REGRESSION FOR DEVELOPING LOGIT MODEL IN SPORT

11.1 INTRODUCTION

11.2 UNDERSTANDING LOGISTIC REGRESSION

11.3 APPLICATION OF LOGISTIC REGRESSION IN SPORTS RESEARCH

11.4 ASSUMPTIONS IN LOGISTIC REGRESSION

11.5 STEPS IN DEVELOPING LOGISTIC MODEL

11.6 LOGISTIC ANALYSIS USING SPSS

11.7 INTERPRETATION OF FINDINGS

11.8 SUMMARY OF THE SPSS COMMANDS FOR LOGISTIC REGRESSION

11.9 EXERCISE

11.10 CASE STUDY ON LOGISTIC REGRESSION

12 APPLICATION OF FACTOR ANALYSIS

12.1 INTRODUCTION

12.2 TERMINOLOGIES USED IN FACTOR ANALYSIS

12.3 ASSUMPTIONS IN FACTOR ANALYSIS

12.4 STEPS IN FACTOR ANALYSIS

12.5 APPLICATION OF FACTOR ANALYSIS

12.6 FACTOR ANALYSIS WITH SPSS

12.7 SUMMARY OF THE SPSS COMMANDS FOR FACTOR ANALYSIS

12.8 EXERCISE

12.9 CASE STUDY ON FACTOR ANALYSIS

APPENDIX

BIBLIOGRAPHY

INDEX

END USER LICENSE AGREEMENT

List of Tables

Chapter 01

TABLE 1.1 Data on Anthropometric Parameters Obtained on College Badminton Players

Chapter 02

TABLE 2.1 Growth Data Obtained on School Boys

TABLE 2.2 Tests of Normality for the Data on Memory Recall

TABLE 2.3 Data on Physiological Parameters Obtained on University Hockey Players

TABLE 2.4 Output Showing Values of Different Statistics of Physiological Parameters

TABLE 2.5 Selected Statistics of the Physiological Parameters of University Hockey Players

TABLE 2.6 Standard Scores of the Physiological Parameters

TABLE 2.7 Transformed Standard Scores of the Physiological Parameters

TABLE 2.8 Data on Physiological Parameters of College Basketballers

TABLE 2.9 Tests of normality

TABLE 2.10 Descriptive statistics

Chapter 03

TABLE 3.1 Data on Physical Performance

TABLE 3.2 Descriptive Statistics

TABLE 3.3 Correlation Matrix

TABLE 3.4 Descriptive Statistics

TABLE 3.5 Partial Correlation Between 100-meter (X

1

) and 30-meter (X

7

) Performance After Controlling the Effect of Leg Strength (X

2

) and Standing Broad Jump (X

5

)

TABLE 3.6 Data on Self-Concept and Physical Parameters of Swimmers

TABLE 3.7 Data Format Used in SPSS

TABLE 3.8 Correlation Matrix

TABLE 3.9 Correlations r

12.36

TABLE 3.10 Correlations r

13.26

TABLE 3.11 Correlations r

16.23

Chapter 04

TABLE 4.1 Fat% of Football Players

TABLE 4.2 One-Sample Statistics

TABLE 4.3 One-Sample t Test

TABLE 4.4 t-Table for the Data on Fat%

TABLE 4.5 Data on Flexibility in Inches

TABLE 4.6 Descriptive Statistics of the Groups

TABLE 4.7 F and t Table for Testing the Equality of Variances and Equality of Means of Two Independent Groups

TABLE 4.8 t-Table for the Data on Flexibility Along with F Value

TABLE 4.9 Weights of Women in lb

TABLE 4.10 Paired Sample Statistics

TABLE 4.11 Paired t-Test Table

TABLE 4.12 Breath-Holding Capacity (in sec) of players

TABLE 4.13 Data Format Used in SPSS

TABLE 4.14 Independent Samples Test

TABLE 4.15 Data Format Used in SPSS for Anaerobic Capacity (in sec)

TABLE 4.16 Paired Samples Test

Chapter 05

TABLE 5.1 Data on Muscular Strength in Three Different Treatment Groups

TABLE 5.2 Data on Anxiety

TABLE 5.3 Descriptive Statistics for the Data on Anxiety in Different Sport Group

TABLE 5.4 ANOVA Table for the Data on Anxiety

TABLE 5.5 Post Hoc Comparison of Means Using Tukey HSD Test

TABLE 5.6 Means of the Groups with Graphics

TABLE 5.7 Data on Self-Concept

TABLE 5.8 Format of Data Feeding in Data View

TABLE 5.9 Descriptive Statistics for the Data on Self-Concept of Different Positional Players of Soccer

TABLE 5.10 ANOVA Table for the Data on Self-Concept

TABLE 5.11 Post Hoc Comparison of Means Using Scheffe Test

TABLE 5.12 Means of the Groups with Graphics

TABLE 5.13 Adolescents’ Data on Fitness Test

TABLE 5.14 Descriptive Statistics

TABLE 5.15 Levene’s Test of Equality of Error Variances

TABLE 5.16 Two-Way ANOVA Table Generated by the SPSS

TABLE 5.17 Two-Way ANOVA Table for the Data on Fitness Score

TABLE 5.18 Pairwise Comparison of Sport Groups

TABLE 5.19 Pairwise Comparison of Different Diet Groups

TABLE 5.20 F-Table for Testing the Effect of Diet in Each Sport Category

TABLE 5.21 Pairwise Comparison of Means in Sport Category

TABLE 5.22 F-Table for Testing the Effect of Sport in Each Diet Category

TABLE 5.23 Data on Shoulder Flexibility in Inches

TABLE 5.24 Data on Lifestyle Evaluation

TABLE 5.25 Data on Back Strength in kg

TABLE 5.26 Data Format used in SPSS for Back Strength (in kg)

TABLE 5.27 ANOVA

TABLE 5.28 Multiple Comparisons

TABLE 5.29 Data on Mood After the Treadmill Exercise in Each Treatment Group

TABLE 5.30 Data Format used in SPSS for Back Strength (in kg)

TABLE 5.31 Levene’s Test of Equality of Error Variances

TABLE 5.32 Tests of Between-Subjects Effects

TABLE 5.33 Descriptive Statistics

TABLE 5.34 Mean Mood Scores for Different Music Groups in Each Gender Group

TABLE 5.35 Mean Mood Scores for Different Gender Groups in Each Music Group

Chapter 06

TABLE 6.1 Data on VO

2

max (in ml/kg/min) Obtained on the Subjects at Different Duration During Cardio-Intervention Program

TABLE 6.2 Test of Normality

TABLE 6.3 Descriptive Statistics

TABLE 6.4 Mauchly’s Test of Sphericity

TABLE 6.5 F-Table for Testing Significance of Within-Subjects Effects

TABLE 6.6 Pair-Wise Comparison of Marginal Means

TABLE 6.7 Mean Score of VO

2

max in Different Time Period

TABLE 6.8 Data on Recovery Time in Minutes

TABLE 6.9 Test of Normality

TABLE 6.10 Descriptive Statistics

TABLE 6.11 Mauchly’s Test of Sphericity

TABLE 6.12 F-Table for Testing Significance of Within-Subjects Effects

TABLE 6.13 Estimates of Marginal Mean Recovery Time in Different Environment

TABLE 6.14 Pair-Wise Comparison of Marginal Means of Recovery Time in Each Environmental Group

TABLE 6.15 Estimates of Marginal Mean Recovery Time in Each Intervention Group

TABLE 6.16 Pair-Wise Comparison of Marginal Mean Recovery Time in Different Intervention Groups

TABLE 6.17 Estimates of Mean Recovery Time in Each Cell (

Environment

 × 

Intervention

)

TABLE 6.18 Data on Weight Reduction (in lb) During Tennis Match While Consuming Different Types of Drinks

TABLE 6.19 Data on Stress Under All the Treatment Conditions

TABLE 6.20 Test of Normality

TABLE 6.21 Descriptive Statistics

TABLE 6.22 Mauchly’s Test of Sphericity

TABLE 6.23 Tests of within-subjects effects

TABLE 6.24 Pairwise Comparisons

Chapter 07

TABLE 7.1 Data on Resting Pulse Rate (Beat/min) Before and After the Treatment

TABLE 7.2 Mean and Standard Deviation of Different Post-treatment Groups

TABLE 7.3 Adjusted Mean and Standard Error of Different Post-treatment Groups

TABLE 7.4 Tests “Between-Subjects” Effects

TABLE 7.5 ANCOVA Table for the Post-treatment Data on Resting Pulse Rate

TABLE 7.6 Pair-Wise Comparisons

TABLE 7.7 Pair-Wise Comparisons of Post-treatment Group Means of the Data on Resting Pulse Rate Shown with Graphics

TABLE 7.8 Data on Vertical Jump Performance (in Inches) in Different Depth Jump Height Group During Pre- and Post-treatment Testing

TABLE 7.9 Data on Strength Index in Different Judo Practice Groups Before and After Treatment

TABLE 7.10 Data Format Used in SPSS for Strength Index

TABLE 7.11 Tests of Between-Subjects Effects

TABLE 7.12 Pair-Wise Comparisons

TABLE 7.13 Estimates: Adjusted Mean and Standard Error of Different Groups After Post-treatment

Chapter 08

TABLE 8.1 Summary of Student’s Response About Their Preferences

TABLE 8.2 Observed and Expected Frequencies of the Response for Different Track Suit Brands

TABLE 8.3 Chi-Square for the Data on Brand Option

TABLE 8.4 Observed Frequencies (f

o

)

TABLE 8.5 Expected Frequencies (f

e

)

TABLE 8.6 Statement: Cigarette Contains Nicotine

TABLE 8.7 Response on Drinking Coffee for Relaxation

TABLE 8.8 Gender * Response Cross Tabulation

TABLE 8.9 Chi-Square for the Data on Gender * Options

TABLE 8.10 Contingency Coefficient for the Data on Gender * Options

TABLE 8.11 Data on VO

2

max (in ml.kg.min

−1

)

TABLE 8.12 Ranks

TABLE 8.13 Test Statistics

TABLE 8.14 Data on Weight Obtained on Housewives

TABLE 8.15 Descriptive Statistics

TABLE 8.16 Ranks

TABLE 8.17 Test Statistics

TABLE 8.18 Data on Flexibility (in Inches)

TABLE 8.19 Ranks of Different Groups

TABLE 8.20 Test Statistics

TABLE 8.21 Data on Strength (in lb)

TABLE 8.22 Descriptive Statistic

TABLE 8.23 Test Statistics

TABLE 8.24 Responses on “Coach Uses Innovative Practices”

TABLE 8.25 Responses of the Students About Their Subject Preferences

TABLE 8.26 Performance of the Students on Fitness Test

TABLE 8.27 Gender * Min_Mus_Fit Cross Tabulation

TABLE 8.28 Chi-Square for the Data on Gender * Min_Mus_Fit

TABLE 8.29 Contingency Coefficient for the Data on Gender * Min_Mus_Fit

Chapter 09

TABLE 9.1 Data on Physical and Anthropometric Variables Along with Playing Ability of Badminton Players

TABLE 9.2 Descriptive Statistics for Different Variables of Badminton Players

TABLE 9.3 Correlation Matrix

TABLE 9.4 Model Summary Along with the Values of R and R

2

TABLE 9.5 ANOVA Table Showing F Values for All the Models

TABLE 9.6 Regression Coefficients of Selected Variables in Different Models Along with Their t Values and Partial Correlations

TABLE 9.7 Data of Badminton Players on Their Physical and Physiological Parameters and Playing Ability

TABLE 9.8 Data Format Used in SPSS

TABLE 9.9 Regression Coefficients

TABLE 9.10 Model Summary

TABLE 9.11 ANOVA

Chapter 10

TABLE 10.1 Data on Physical and Anthropometric Parameters on Junior and Senior College Male Basketballers

TABLE 10.2 Group Statistics: Mean and Standard Deviation of All Parameters in Different Groups

TABLE 10.3 Box’s M Test

TABLE 10.4 Unstandardized Canonical Discriminant Function Coefficients

TABLE 10.5 Eigenvalues Table

TABLE 10.6 Wilks’ Lambda and Chi-Square Test

TABLE 10.7 Classification Matrix

TABLE 10.8 Standardized Canonical Discriminant Function Coefficients

TABLE 10.9 Functions at Group Centroids

Table 10.10 Data on the Group Cohesion Parameters Obtained on the High-Performer and Low-Performer Volleyballers

TABLE 10.11 Data Format Used in SPSS for Discriminant Analysis

TABLE 10.12 Box’s M Test

TABLE 10.13 Canonical Discriminant Function Coefficients

TABLE 10.14 Standardized Canonical Discriminant Function Coefficients

TABLE 10.15 Functions at Group Centroids

TABLE 10.16 Wilks’ Lambda

TABLE 10.17 Classification Results

Chapter 11

TABLE 11.1 Result of Different Basketball Matches in a Tournament Along with Selected Match Statistics

TABLE 11.2 Case Processing Summary

TABLE 11.3 Dependent Variable Encoding

TABLE 11.4 Categorical Variables Coding

TABLE 11.5 Classification Table (Model without Predictors)

TABLE 11.6 Variables in the Equation

TABLE 11.7 Variables Not in the Equation

TABLE 11.8 Omnibus Tests of Model Coefficients

TABLE 11.9 Model Summary

TABLE 11.10 Hosmer–Lemeshow Test

TABLE 11.11 Classification Table

TABLE 11.12 Variables in the Equation

TABLE 11.13 Data of the National-Level Men Cricketers

TABLE 11.14 Data Format Used in SPSS for Logistic Regression

TABLE 11.15 Classification Table

TABLE 11.16 Variables Not in the Equation

TABLE 11.17 Omnibus Tests of Model Coefficients

TABLE 11.18 Model Summary

TABLE 11.19 Hosmer–Lemeshow Test

TABLE 11.20 Classification Table

TABLE 11.21 Variables in the Equation

Chapter 12

TABLE 12.1 Data on Selected Physical and Physiological Parameters Obtained on Swimmers

TABLE 12.2 Descriptive Statistics

TABLE 12.3 Correlation Matrix for the Data on Selected Physical and Physiological Parameters of Swimmers

TABLE 12.4 KMO and Bartlett’s Test

TABLE 12.5 Total Variance Explained

TABLE 12.6 Component Matrix: Unrotated Factor Solution

TABLE 12.7 Rotated Component Matrix: Varimax Rotated Solution

TABLE 12.8 Factor 1: Physical Factor

TABLE 12.9 Factor 2: Growth Factor

TABLE 12.10 Test Battery for Screening the Swimmers

Table 12.11 Data on Physical Parameters of the College Hockey Players

TABLE 12.12 Data Format in SPSS with Physical and Physiological Parameters Obtained on Yoga Practitioners

TABLE 12.13 KMO and Bartlett’s Test

TABLE 12.14 Total Variance Explained

TABLE 12.15 Component Matrix

TABLE 12.16 Rotated Component Matrix

TABLE 12.17 Factor 1

TABLE 12.18 Factor 2

TABLE 12.19 Factor 3

TABLE 12.20 Factor 4

Appendix

TABLE A.1 The Normal Curve Area Between the Mean and a Given z Value

TABLE A.2 Critical Values of “t”

TABLE A.3 F-Table: Critical Values at 0.05 Level of Significance

TABLE A.4 F-Table: Critical Values at 0.01 Level of Significance

TABLE A.5 Critical Values of Chi-Square

TABLE A.6 Critical Values of the Correlation Coefficient

TABLE A.7 Critical Values of Studentized Range Distribution (q) for Family-wise ALPHA = 0.05

List of Illustrations

Chapter 01

FIGURE 1.1 Sequence of commands for starting SPSS package.

FIGURE 1.2 Commands for initiating SPSS.

FIGURE 1.3 Screen showing the option for creating/opening data file.

FIGURE 1.4 Blank format for defining variables.

FIGURE 1.5 Defining variables and their characteristics.

FIGURE 1.6 Defining code of nominal variable.

FIGURE 1.7 Variables along with their characteristics for the data shown in Table 1.1.

FIGURE 1.8 Format of data entry in most of the applications.

Chapter 02

FIGURE 2.1 Distribution of IQ scores of the IIT and engineering students. (a) Negatively skewed curve and (b) positively skewed curve.

FIGURE 2.2 Distribution with different types of kurtosis.

FIGURE 2.3 Command sequence for testing normality and identifying outliers.

FIGURE 2.4 Option for selecting variables and detecting outliers.

FIGURE 2.5 Option for computing the Shapiro–Wilk test and Q–Q plot.

FIGURE 2.6 (a–c) Normal Q–Q plot for the data on growth parameter.

FIGURE 2.7 (a–c) Boxplot for all three variables: (a) age, (b) height, and (c) weight.

FIGURE 2.8 Defining variables along with their characteristics.

FIGURE 2.9 Method of data entry in

Data View

.

FIGURE 2.10 Command sequence for computing descriptive statistics.

FIGURE 2.11 Selection of variables for descriptive analysis.

FIGURE 2.12 Option for different statistics to be computed.

FIGURE 2.13 Physiological profiles of university hockey players.

FIGURE 2.14 Boxplot showing outlier in weight data.

FIGURE 2.15 Boxplot showing outlier in fat data.

Chapter 03

FIGURE 3.1 Defining variables along with their characteristics.

FIGURE 3.2 Data file for the correlation matrix.

FIGURE 3.3 Commands sequence for computing correlation matrix.

FIGURE 3.4 Variable selection for computing correlation matrix.

FIGURE 3.5 Option for computing correlation matrix and other statistics.

FIGURE 3.6 Command sequence for computing partial correlation.

FIGURE 3.7 Variable selections in partial correlation.

FIGURE 3.8 Option selection in computing partial correlation and other statistics.

Chapter 04

FIGURE 4.1 Variable along with its characteristics for the data shown in Table 4.1.

FIGURE 4.2 Screen showing entered data for the fat% in

Data View

.

FIGURE 4.3 Command sequence in computing one-sample t-test.

FIGURE 4.4 Selection of variable in one-sample t-test.

FIGURE 4.5 Selecting options for computing one-sample t-test.

FIGURE 4.6 Defining code of nominal variable.

FIGURE 4.7 Defining variables along with their characteristics.

FIGURE 4.8 Format of data entry in

Data View

.

FIGURE 4.9 Command sequence in two-sample t-test.

FIGURE 4.10 Selection of variables in two-sample t-test.

FIGURE 4.11 Screen showing option for choosing significance level.

FIGURE 4.12 Defining variables along with their characteristics.

FIGURE 4.13 Data Entry format in paired t-test.

FIGURE 4.14 Command sequence in paired t-test.

FIGURE 4.15 Selecting variables in paired t-test.

Chapter 05

FIGURE 5.1 Defining variables along with their characteristics.

FIGURE 5.2 Data file of anxiety for one-way ANOVA.

FIGURE 5.3 Command sequence for one-way ANOVA.

FIGURE 5.4 Selection of variables in one-way ANOVA.

Figure 5.5 Selecting option for post hoc test and significance level.

FIGURE 5.6 Option for computing descriptive statistics.

FIGURE 5.7 Selection of variables in one-way ANOVA.

FIGURE 5.8 Defining variables along with their characteristics.

FIGURE 5.9 Data file of fitness data for two-way ANOVA.

FIGURE 5.10 Command sequence for two-way ANOVA.

FIGURE 5.11 Selection of variables in two-way ANOVA.

FIGURE 5.12 Options for post hoc test results.

FIGURE 5.13 Options for various outputs in two-way ANOVA.

FIGURE 5.14 Options for generating means plot.

FIGURE 5.15 Marginal means plot of

Sport

.

FIGURE 5.16 Marginal means plot of

Diet

.

FIGURE 5.17 Option for splitting data file for simple effect of

Diet

.

FIGURE 5.18 Selection of variables for generating simple effect of

Diet

.

FIGURE 5.19 Options for post hoc test and significance level.

FIGURE 5.20 Options for descriptive statistics.

FIGURE 5.21 Marginal means plot of

Sport

 × 

Diet

.

FIGURE 5.22 Option for splitting data file for simple effect of

Sport

.

FIGURE 5.23 Selection of variables for simple effect of

Sport

.

FIGURE 5.24 Marginal means plot of

Diet

 × 

Sport

.

FIGURE 5.25 Means plots.

Chapter 06

FIGURE 6.1 Data file of VO

2

max in one-way repeated measures ANOVA.

FIGURE 6.2 Command sequence in one-way repeated measures ANOVA.

FIGURE 6.3 Screen for defining variables.

FIGURE 6.4 Screen for adding variables in the analysis.

FIGURE 6.5 Screen showing option for selecting variables and means plot.

FIGURE 6.6 Option for computing descriptive statistics and pair-wise comparison of means using Bonferroni correction.

FIGURE 6.7 Marginal means plot.

FIGURE 6.8 Data file in two-way repeated ANOVA.

FIGURE 6.9 Defining independent and dependent variables.

FIGURE 6.10 Selecting variables in two-way repeated ANOVA.

FIGURE 6.11 Selecting options for means plot.

FIGURE 6.12 Selecting options for computing descriptive statistics and pair-wise comparison of means using Bonferroni correction.

FIGURE 6.13 Means plot of recovery time in different environmental groups.

FIGURE 6.14 Means plot of recovery time in different intervention groups.

FIGURE 6.15 Means plot in

Intervention

 × 

Environment

.

FIGURE 6.16 Means plot in

Environment

 × 

Intervention

.

FIGURE 6.17 Means plot.

Chapter 07

FIGURE 7.1 Defining variables along with their characteristics.

FIGURE 7.2 Data file of resting pulse rate for analysis of covariance.

FIGURE 7.3 Command sequence for analysis of covariance.

FIGURE 7.4 Selection of variables for ANCOVA.

FIGURE 7.5 Option for selecting model.

FIGURE 7.6 Options for various outputs in ANCOVA.

FIGURE 7.7 Means plot.

Chapter 08

FIGURE 8.1 Defining variable along with their characteristics.

FIGURE 8.2 Data file of brand response for goodness of fit.

FIGURE 8.3 Selecting variable for ‘Weight cases by’ option.

FIGURE 8.4 Option for selecting variable.

FIGURE 8.5 Option for descriptive statistics.

FIGURE 8.6 Defining variables along with their characteristics.

FIGURE 8.7 Data file of coffee data for chi-square.

FIGURE 8.8 Selecting variable for ‘Weight cases by’ option.

FIGURE 8.9 Option for selecting variables for chi-square.

FIGURE 8.10 Option for computing chi-square and contingency coefficient.

FIGURE 8.11 Option for computing observed and expected frequencies.

FIGURE 8.12 Defining independent and dependent variables.

FIGURE 8.13 Data file of VO

2

max for Mann–Whitney U test.

FIGURE 8.14 Selecting independent and dependent variables.

FIGURE 8.15 Selecting option for descriptive statistics.

FIGURE 8.16 Defining variables.

FIGURE 8.17 Data file for Wilcoxon signed-rank test.

FIGURE 8.18 Selecting pre- and post-test variables.

FIGURE 8.19 Selecting option for descriptive statistics.

FIGURE 8.20 Defining variables.

FIGURE 8.21 Data file of flexibility for Kruskal–Wallis test.

FIGURE 8.22 Selecting independent and dependent variables and option for descriptive statistics.

FIGURE 8.23 Option for descriptive statistics.

FIGURE 8.24 Defining variables.

FIGURE 8.25 Data file of strength scores for Friedman test.

FIGURE 8.26 Selecting variables and defining option for descriptive statistics.

Chapter 09

FIGURE 9.1 Defining variables along with their characteristics.

FIGURE 9.2 Screen showing data entered for all the variables in the data view.

FIGURE 9.3 Command sequence for regression analysis.

FIGURE 9.4 Selection of variables in regression analysis.

FIGURE 9.5 Selection of options in computing different outputs in regression analysis.

Chapter 10

FIGURE 10.1 Defining variables in discriminant analysis.

FIGURE 10.2 Screen showing data in the data view.

FIGURE 10.3 Screen showing SPSS commands for discriminant analysis.

FIGURE 10.4 Screen showing selection of variables for discriminant analysis.

FIGURE 10.5 Screen showing the options for statistics and discriminant coefficients.

FIGURE 10.6 Screen showing the options for classification matrix.

FIGURE 10.7 Means of the transformed group centroids.

FIGURE 10.8 Means of the transformed group centroids.

Chapter 11

FIGURE 11.1 Logistic function.

FIGURE 11.2 Data file of match statistics in basketball for logistic regression analysis.

FIGURE 11.3 Command sequence for logistic regression.

FIGURE 11.4 Selecting dependent and independent variables in logistic regression.

FIGURE 11.5 Option for generating Hosmer–Lemeshow goodness-of-fit and confidence intervals.

Chapter 12

FIGURE 12.1 Scree plot.

FIGURE 12.2 Defining variables along with their characteristics.

FIGURE 12.3 Data file of physical and physiological variables for factor analysis.

FIGURE 12.4 Command sequence for factor analysis.

FIGURE 12.5 Selection of variables in factor analysis.

FIGURE 12.6 Selection of options for correlation matrix and initial factor solution.

FIGURE 12.7 Option for scree plot.

FIGURE 12.8 Option for factor rotation.

FIGURE 12.9 Scree plot for the data obtained on swimmers.

FIGURE 12.10 Scree plot for the data obtained on yoga practitioners.

Guide

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SPORTS RESEARCH WITH ANALYTICAL SOLUTION USING SPSS®

J. P. Verma

Centre for Advanced StudiesLakshmibai National Institute of Physical Education

Copyright © 2016 by John Wiley & Sons, Inc. All rights reserved

Published by John Wiley & Sons, Inc., Hoboken, New JerseyPublished simultaneously in Canada

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