Sports Research with Analytical Solution using SPSS
1st Edition
Published on 24. May 2016
Book
Hardback
400 pages
978-1-119-20671-2 (ISBN)
Description
A step-by-step approach to problem-solving techniques using SPSS (R) 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 (R) 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 (R) 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 (R) presentations for each chapter
Sports Research with Analytical Solution using SPSS (R) 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.
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 (R) 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 (R) 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 (R) presentations for each chapter
Sports Research with Analytical Solution using SPSS (R) 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.
More details
Language
English
Place of publication
New York
United States
Target group
Professional and scholarly
Product notice
sewn/stitched
Cloth over boards
Dimensions
Height: 240 mm
Width: 161 mm
Thickness: 26 mm
Weight
754 gr
ISBN-13
978-1-119-20671-2 (9781119206712)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
E-Book
03/2016
Wiley
€111.99
Available for download
E-Book
03/2016
Wiley
€111.99
Available for download
Person
J. P. Verma, PhD, is Professor of Statistics and Director of the Center for Advanced Studies at Lakshmibai National Institute of Physical Education. Dr. Verma is an active researcher and expert in data analysis and sports statistics and has conducted many workshops on research methodology, research designs, multivariate analysis, statistical modeling, and data analysis for students in management, physical education, social science, and economics. He is the author of seven additional books including Repeated Measures Design for Empirical Researchers and Statistics for Exercise Science and Health with Microsoft (R) Office Excel (R), both published by Wiley.
Content
Preface xv
About the Companion Website xviii
Acknowledgments xix
1 Introduction to Data Types and SPSS Operations 1
1.1 Introduction 1
1.2 Types of data 2
1.2.1 Qualitative Data 2
1.2.2 Quantitative Data 3
1.3 Important definitions 4
1.3.1 Variable 4
1.4 Data Cleaning 4
1.5 Detection of Errors 5
1.5.1 Using Frequencies 5
1.5.2 Using Mean and Standard Deviation 5
1.5.3 Logic Checks 5
1.5.4 Outlier Detection 5
1.6 How to Start Spss? 6
1.6.1 Preparing Data File 7
1.7 Exercise 10
1.7.1 Short Answer Questions 10
1.7.2 Multiple Choice Questions 11
2 Descriptive Profile 14
2.1 Introduction 14
2.2 Explanation of Various Descriptive Statistics 16
2.2.1 Mean 16
2.2.2 Variance 16
2.2.3 Standard Error of Mean 17
2.2.4 Skewness 17
2.2.5 Kurtosis 18
2.2.6 Percentiles 19
2.3 Application of Descriptive Statistics 19
2.3.1 Testing Normality of Data and Identifying Outliers 20
2.4 Computation of Descriptive Statistics Using Spss 25
2.4.1 Preparation of Data File 25
2.4.2 Defining Variables 26
2.4.3 Entering Data 26
2.4.4 SPSS Commands 26
2.5 Interpretations of the Results 29
2.6 Developing Profile Chart 31
2.7 Summary of Spss Commands 33
2.8 Exercise 33
2.8.1 Short Answer Questions 33
2.8.2 Multiple Choice Questions 34
2.9 Case Study on Descriptive Analysis 36
3 Correlation Coefficient and Partial Correlation 41
3.1 Introduction 41
3.2 Correlation Matrix and Partial Correlation 43
3.2.1 Product Moment Correlation Coefficient 43
3.2.2 Partial Correlation 45
3.3 Application of Correlation Matrix and Partial Correlation 46
3.4 Correlation Matrix with Spss 46
3.4.1 Computation in Correlation Matrix 46
3.4.2 Interpretations of Findings 51
3.5 Partial Correlation with Spss 51
3.5.1 Computation of Partial Correlations 52
3.5.2 Interpretation of Partial Correlation 55
3.6 Summary of the Spss Commands 56
3.6.1 For Computing Correlation Matrix 56
3.6.2 For Computing Partial Correlations 57
3.7 Exercise 57
3.7.1 Short Answer Questions 57
3.7.2 Multiple Choice Questions 57
3.7.3 Assignment 60
3.8 Case Study on Correlation 60
4 Comparing Means 65
4.1 Introduction 65
4.2 One-Sample t-Test 66
4.2.1 Application of One-Sample t-Test 67
4.3 Two-Sample t-Test for Unrelated Groups 67
4.3.1 Assumptions While Using t-Test 67
4.3.2 Case I: Two-Tailed Test 68
4.3.3 Case II: Right Tailed Test 68
4.3.4 Case III: Left Tailed Test 69
4.3.5 Application of Two-Sample t-Test 70
4.4 Paired t-Test for Related Groups 70
4.4.1 Case I: Two-Tailed Test 71
4.4.2 Case II: Right Tailed Test 71
4.4.3 Case III: Left Tailed Test 72
4.4.4 Application of Paired t-Test 73
4.5 One-Sample t-Test with Spss 73
4.5.1 Computation in t-Test for Single Group 74
4.5.2 Interpretation of Findings 77
4.6 Two-Sample t-Test for Independent Groups with Spss 78
4.6.1 Computation in Two-Sample t-Test 79
4.6.2 Interpretation of Findings 83
4.7 Paired t-Test for Related Groups with Spss 85
4.7.1 Computation in Paired t-Test 86
4.7.2 Interpretation of Findings 89
4.8 Summary of Spss Commands for t-Tests 90
4.8.1 One-Sample t-Test 90
4.8.2 Two-Sample t-Test for Independent Groups 90
4.8.3 Paired t-Test 91
4.9 Exercise 91
4.9.1 Short Answer Questions 91
4.9.2 Multiple Choice Questions 91
4.9.3 Assignment 93
4.10 Case Study 94
5 Independent Measures Anova 100
5.1 Introduction 101
5.2 One-Way Analysis of Variance 101
5.2.1 One-Way ANOVA Model 102
5.2.2 Post Hoc Test 102
5.2.3 Application of One-Way ANOVA 103
5.3 One-Way Anova with Spss (Equal Sample Size) 103
5.3.1 Computation in One-Way ANOVA (Equal Sample Size) 104
5.3.2 Interpretation of Findings 107
5.4 One-Way Anova with Spss (Unequal Sample Size) 110
5.4.1 Computation in One-Way ANOVA (Unequal Sample Size) 111
5.4.2 Interpretation of Findings 114
5.5 Two-Way Analysis of Variance 115
5.5.1 Assumptions in Two-Way Analysis of Variance 116
5.5.2 Hypotheses in Two-Way ANOVA 116
5.5.3 Factors 117
5.5.4 Treatment Groups 117
5.5.5 Main Effect 117
5.5.6 Interaction Effect 117
5.5.7 Within-Groups Variation 117
5.5.8 F-Statistic 117
5.5.9 Two-Way ANOVA Table 118
5.5.10 Interpretation 118
5.5.11 Application of Two-Way Analysis of Variance 118
5.6 Two-Way Anova Using Spss 119
5.6.1 Computation in Two-Way ANOVA 121
5.6.2 Interpretation of Findings 126
5.7 Summary of the Spss Commands 137
5.7.1 One-Way ANOVA 137
5.7.2 Two-Way ANOVA 138
5.8 Exercise 138
5.8.1 Short Answer Questions 138
5.8.2 Multiple Choice Questions 139
5.8.3 Assignment 142
5.9 Case Study on One-Way Anova Design 143
5.10 Case Study on Two-Way Anova 147
6 Repeated Measures Anova 153
6.1 Introduction 153
6.2 One-Way Repeated Measures Anova 154
6.2.1 Assumptions in One-Way Repeated Measures ANOVA 155
6.2.2 Application in Sports Research 155
6.2.3 Steps in Solving One-Way Repeated Measures ANOVA 156
6.3 One-Way Repeated Measures Anova Using Spss 157
6.3.1 Computation in the One-Way Repeated Measures ANOVA 157
6.3.2 Interpretation of Findings 161
6.3.3 Findings of the Study 165
6.3.4 Inference 166
6.4 Two-Way Repeated Measures Anova 166
6.4.1 Assumptions in Two-Way Repeated Measures ANOVA 166
6.4.2 Application in Sports Research 167
6.4.3 Steps in Solving Two-Way Repeated Measures ANOVA 167
6.5 Two-Way Repeated Measures Anova Using Spss 168
6.5.1 Computation in Two-Way Repeated Measures ANOVA 170
6.5.2 Interpretation of Findings 173
6.5.3 Findings of the Study 181
6.5.4 Inference 181
6.6 Summary of the Spss Commands for One-Way Repeated Measures Anova 182
6.7 Summary of the Spss Commands for Two-Way Repeated Measures Anova 182
6.8 Exercise 183
6.8.1 Short Answer Questions 183
6.8.2 Multiple Choice Questions 183
6.8.3 Assignment 185
6.9 Case Study on Repeated Measures Design 186
7 Analysis of Covariance 190
7.1 Introduction 190
7.2 Conceptual Framework of Analysis of Covariance 191
7.3 Application of ANCOVA 192
7.4 ANCOVA with Spss 193
7.4.1 Computation in ANCOVA 194
7.5 Summary of the Spss Commands 201
7.6 Exercise 202
7.6.1 Short Answer Questions 202
7.6.2 Multiple Choice Questions 202
7.6.3 Assignment 203
7.7 Case Study on ANCOVA Design 204
8 Nonparametric Tests in Sports Research 209
8.1 Introduction 209
8.2 Chi-Square Test 211
8.2.1 Testing Goodness of Fit 211
8.2.2 Yates' Correction 212
8.2.3 Contingency Coefficient 212
8.3 Goodness of Fit with Spss 212
8.3.1 Computation in Goodness of Fit 213
8.3.2 Interpretation of Findings 216
8.4 Testing Independence of Two Attributes 216
8.4.1 Interpretation 218
8.5 Testing Association with Spss 219
8.5.1 Computation in Chi-Square 219
8.5.2 Interpretation of Findings 223
8.6 Mann-Whitney U Test: Comparing Two Independent Samples 224
8.6.1 Computation in Mann-Whitney U Statistic Using SPSS 224
8.6.2 Interpretation of Findings 226
8.7 Wilcoxon Signed-Rank Test: For Comparing Two Related Groups 227
8.7.1 Computation in Wilcoxon Signed-Rank Test Using SPSS 228
8.7.2 Interpretation of Findings 230
8.8 Kruskal-Wallis Test 231
8.8.1 Computation in Kruskal-Wallis Test Using SPSS 232
8.8.2 Interpretation of Findings 234
8.9 Friedman Test 234
8.9.1 Computation in Friedman Test Using SPSS 235
8.9.2 Interpretation of Findings 237
8.10 Summary of the Spss Commands 237
8.10.1 Computing Chi-Square Statistic (for Testing Goodness of Fit) 237
8.10.2 Computing Chi-Square Statistic (for Testing Independence) 238
8.10.3 Computation in Mann-Whitney U Test 238
8.10.4 Computation in Wilcoxon Signed-Rank Test 239
8.10.5 Computation in Kruskal-Wallis Test 239
8.10.6 Computation in Friedman Test 239
8.11 Exercise 240
8.11.1 Short Answer Questions 240
8.11.2 Multiple Choice Questions 241
8.11.3 Assignment 243
8.12 Case Study on Testing Independence of Attributes 243
9 Regression Analysis and Multiple Correlations 246
9.1 Introduction 246
9.2 Understanding Regression Equation 247
9.2.1 Methods of Regression Analysis 247
9.2.2 Multiple Correlation 248
9.3 Application of Regression Analysis 248
9.4 Multiple Regression Analysis with Spss 249
9.4.1 Computation in Regression Analysis 249
9.4.2 Interpretation of Findings 254
9.5 Summary of Spss Commands for Regression Analysis 259
9.6 Exercise 259
9.6.1 Short Answer Questions 259
9.6.2 Multiple Choice Questions 260
9.6.3 Assignment 261
9.7 Case Study on Regression Analysis 263
10 Application of Discriminant Function Analysis 267
10.1 Introduction 268
10.2 Basics of Discriminant Function Analysis 268
10.2.1 Discriminating Variables 268
10.2.2 Dependent Variable 268
10.2.3 Discriminant Function 268
10.2.4 Classification Matrix 269
10.2.5 Stepwise Method of Discriminant Analysis 269
10.2.6 Power of Discriminating Variable 269
10.2.7 Canonical Correlation 269
10.2.8 Wilks' Lambda 270
10.3 Assumptions in Discriminant Analysis 270
10.4 Why to Use Discriminant Analysis 270
10.5 Steps in Discriminant Analysis 271
10.6 Application of Discriminant Function Analysis 272
10.7 Discriminant Analysis Using Spss 274
10.7.1 Computation in Discriminant Analysis 274
10.7.2 Interpretation of Findings 279
10.8 Summary of the Spss Commands for Discriminant Analysis 284
10.9 Exercise 284
10.9.1 Short Answer Questions 284
10.9.2 Multiple Choice Questions 285
10.9.3 Assignment 286
10.10 Case Study on Discriminant Analysis 288
11 Logistic Regression for Developing Logit Model in Sport 293
11.1 Introduction 293
11.2 Understanding Logistic Regression 294
11.3 Application of Logistic Regression in Sports Research 295
11.4 Assumptions in Logistic Regression 297
11.5 Steps in Developing Logistic Model 297
11.6 Logistic Analysis Using Spss 297
11.6.1 Block 0 299
11.6.2 Block 1 299
11.6.3 Computation in Logistic Regression with SPSS 299
11.7 Interpretation of Findings 304
11.7.1 Case Processing and Coding Summary 304
11.7.2 Analyzing Logistic Models 305
11.8 Summary of the Spss Commands for Logistic Regression 310
11.9 Exercise 310
11.9.1 Short Answer Questions 310
11.9.2 Multiple Choice Questions 311
11.9.3 Assignment 312
11.10 Case Study on Logistic Regression 313
12 Application of Factor Analysis 319
12.1 Introduction 319
12.2 Terminologies Used in Factor Analysis 320
12.2.1 Principal Component Analysis 320
12.2.2 Eigenvalue 320
12.2.3 Kaiser Criterion 321
12.2.4 The Scree Test 321
12.2.5 Communality 321
12.2.6 Factor Loading 322
12.2.7 Varimax Rotation 322
12.3 Assumptions in Factor Analysis 322
12.4 Steps in Factor Analysis 323
12.5 Application of Factor Analysis 323
12.6 Factor Analysis with Spss 324
12.6.1 Computation in Factor Analysis Using SPSS 326
12.7 Summary of the Spss Commands for Factor Analysis 336
12.8 Exercise 336
12.8.1 Short Answer Questions 336
12.8.2 Multiple Choice Questions 337
12.8.3 Assignment 338
12.9 Case Study on Factor Analysis 339
Appendix 346
Bibliography 360
Index 368
About the Companion Website xviii
Acknowledgments xix
1 Introduction to Data Types and SPSS Operations 1
1.1 Introduction 1
1.2 Types of data 2
1.2.1 Qualitative Data 2
1.2.2 Quantitative Data 3
1.3 Important definitions 4
1.3.1 Variable 4
1.4 Data Cleaning 4
1.5 Detection of Errors 5
1.5.1 Using Frequencies 5
1.5.2 Using Mean and Standard Deviation 5
1.5.3 Logic Checks 5
1.5.4 Outlier Detection 5
1.6 How to Start Spss? 6
1.6.1 Preparing Data File 7
1.7 Exercise 10
1.7.1 Short Answer Questions 10
1.7.2 Multiple Choice Questions 11
2 Descriptive Profile 14
2.1 Introduction 14
2.2 Explanation of Various Descriptive Statistics 16
2.2.1 Mean 16
2.2.2 Variance 16
2.2.3 Standard Error of Mean 17
2.2.4 Skewness 17
2.2.5 Kurtosis 18
2.2.6 Percentiles 19
2.3 Application of Descriptive Statistics 19
2.3.1 Testing Normality of Data and Identifying Outliers 20
2.4 Computation of Descriptive Statistics Using Spss 25
2.4.1 Preparation of Data File 25
2.4.2 Defining Variables 26
2.4.3 Entering Data 26
2.4.4 SPSS Commands 26
2.5 Interpretations of the Results 29
2.6 Developing Profile Chart 31
2.7 Summary of Spss Commands 33
2.8 Exercise 33
2.8.1 Short Answer Questions 33
2.8.2 Multiple Choice Questions 34
2.9 Case Study on Descriptive Analysis 36
3 Correlation Coefficient and Partial Correlation 41
3.1 Introduction 41
3.2 Correlation Matrix and Partial Correlation 43
3.2.1 Product Moment Correlation Coefficient 43
3.2.2 Partial Correlation 45
3.3 Application of Correlation Matrix and Partial Correlation 46
3.4 Correlation Matrix with Spss 46
3.4.1 Computation in Correlation Matrix 46
3.4.2 Interpretations of Findings 51
3.5 Partial Correlation with Spss 51
3.5.1 Computation of Partial Correlations 52
3.5.2 Interpretation of Partial Correlation 55
3.6 Summary of the Spss Commands 56
3.6.1 For Computing Correlation Matrix 56
3.6.2 For Computing Partial Correlations 57
3.7 Exercise 57
3.7.1 Short Answer Questions 57
3.7.2 Multiple Choice Questions 57
3.7.3 Assignment 60
3.8 Case Study on Correlation 60
4 Comparing Means 65
4.1 Introduction 65
4.2 One-Sample t-Test 66
4.2.1 Application of One-Sample t-Test 67
4.3 Two-Sample t-Test for Unrelated Groups 67
4.3.1 Assumptions While Using t-Test 67
4.3.2 Case I: Two-Tailed Test 68
4.3.3 Case II: Right Tailed Test 68
4.3.4 Case III: Left Tailed Test 69
4.3.5 Application of Two-Sample t-Test 70
4.4 Paired t-Test for Related Groups 70
4.4.1 Case I: Two-Tailed Test 71
4.4.2 Case II: Right Tailed Test 71
4.4.3 Case III: Left Tailed Test 72
4.4.4 Application of Paired t-Test 73
4.5 One-Sample t-Test with Spss 73
4.5.1 Computation in t-Test for Single Group 74
4.5.2 Interpretation of Findings 77
4.6 Two-Sample t-Test for Independent Groups with Spss 78
4.6.1 Computation in Two-Sample t-Test 79
4.6.2 Interpretation of Findings 83
4.7 Paired t-Test for Related Groups with Spss 85
4.7.1 Computation in Paired t-Test 86
4.7.2 Interpretation of Findings 89
4.8 Summary of Spss Commands for t-Tests 90
4.8.1 One-Sample t-Test 90
4.8.2 Two-Sample t-Test for Independent Groups 90
4.8.3 Paired t-Test 91
4.9 Exercise 91
4.9.1 Short Answer Questions 91
4.9.2 Multiple Choice Questions 91
4.9.3 Assignment 93
4.10 Case Study 94
5 Independent Measures Anova 100
5.1 Introduction 101
5.2 One-Way Analysis of Variance 101
5.2.1 One-Way ANOVA Model 102
5.2.2 Post Hoc Test 102
5.2.3 Application of One-Way ANOVA 103
5.3 One-Way Anova with Spss (Equal Sample Size) 103
5.3.1 Computation in One-Way ANOVA (Equal Sample Size) 104
5.3.2 Interpretation of Findings 107
5.4 One-Way Anova with Spss (Unequal Sample Size) 110
5.4.1 Computation in One-Way ANOVA (Unequal Sample Size) 111
5.4.2 Interpretation of Findings 114
5.5 Two-Way Analysis of Variance 115
5.5.1 Assumptions in Two-Way Analysis of Variance 116
5.5.2 Hypotheses in Two-Way ANOVA 116
5.5.3 Factors 117
5.5.4 Treatment Groups 117
5.5.5 Main Effect 117
5.5.6 Interaction Effect 117
5.5.7 Within-Groups Variation 117
5.5.8 F-Statistic 117
5.5.9 Two-Way ANOVA Table 118
5.5.10 Interpretation 118
5.5.11 Application of Two-Way Analysis of Variance 118
5.6 Two-Way Anova Using Spss 119
5.6.1 Computation in Two-Way ANOVA 121
5.6.2 Interpretation of Findings 126
5.7 Summary of the Spss Commands 137
5.7.1 One-Way ANOVA 137
5.7.2 Two-Way ANOVA 138
5.8 Exercise 138
5.8.1 Short Answer Questions 138
5.8.2 Multiple Choice Questions 139
5.8.3 Assignment 142
5.9 Case Study on One-Way Anova Design 143
5.10 Case Study on Two-Way Anova 147
6 Repeated Measures Anova 153
6.1 Introduction 153
6.2 One-Way Repeated Measures Anova 154
6.2.1 Assumptions in One-Way Repeated Measures ANOVA 155
6.2.2 Application in Sports Research 155
6.2.3 Steps in Solving One-Way Repeated Measures ANOVA 156
6.3 One-Way Repeated Measures Anova Using Spss 157
6.3.1 Computation in the One-Way Repeated Measures ANOVA 157
6.3.2 Interpretation of Findings 161
6.3.3 Findings of the Study 165
6.3.4 Inference 166
6.4 Two-Way Repeated Measures Anova 166
6.4.1 Assumptions in Two-Way Repeated Measures ANOVA 166
6.4.2 Application in Sports Research 167
6.4.3 Steps in Solving Two-Way Repeated Measures ANOVA 167
6.5 Two-Way Repeated Measures Anova Using Spss 168
6.5.1 Computation in Two-Way Repeated Measures ANOVA 170
6.5.2 Interpretation of Findings 173
6.5.3 Findings of the Study 181
6.5.4 Inference 181
6.6 Summary of the Spss Commands for One-Way Repeated Measures Anova 182
6.7 Summary of the Spss Commands for Two-Way Repeated Measures Anova 182
6.8 Exercise 183
6.8.1 Short Answer Questions 183
6.8.2 Multiple Choice Questions 183
6.8.3 Assignment 185
6.9 Case Study on Repeated Measures Design 186
7 Analysis of Covariance 190
7.1 Introduction 190
7.2 Conceptual Framework of Analysis of Covariance 191
7.3 Application of ANCOVA 192
7.4 ANCOVA with Spss 193
7.4.1 Computation in ANCOVA 194
7.5 Summary of the Spss Commands 201
7.6 Exercise 202
7.6.1 Short Answer Questions 202
7.6.2 Multiple Choice Questions 202
7.6.3 Assignment 203
7.7 Case Study on ANCOVA Design 204
8 Nonparametric Tests in Sports Research 209
8.1 Introduction 209
8.2 Chi-Square Test 211
8.2.1 Testing Goodness of Fit 211
8.2.2 Yates' Correction 212
8.2.3 Contingency Coefficient 212
8.3 Goodness of Fit with Spss 212
8.3.1 Computation in Goodness of Fit 213
8.3.2 Interpretation of Findings 216
8.4 Testing Independence of Two Attributes 216
8.4.1 Interpretation 218
8.5 Testing Association with Spss 219
8.5.1 Computation in Chi-Square 219
8.5.2 Interpretation of Findings 223
8.6 Mann-Whitney U Test: Comparing Two Independent Samples 224
8.6.1 Computation in Mann-Whitney U Statistic Using SPSS 224
8.6.2 Interpretation of Findings 226
8.7 Wilcoxon Signed-Rank Test: For Comparing Two Related Groups 227
8.7.1 Computation in Wilcoxon Signed-Rank Test Using SPSS 228
8.7.2 Interpretation of Findings 230
8.8 Kruskal-Wallis Test 231
8.8.1 Computation in Kruskal-Wallis Test Using SPSS 232
8.8.2 Interpretation of Findings 234
8.9 Friedman Test 234
8.9.1 Computation in Friedman Test Using SPSS 235
8.9.2 Interpretation of Findings 237
8.10 Summary of the Spss Commands 237
8.10.1 Computing Chi-Square Statistic (for Testing Goodness of Fit) 237
8.10.2 Computing Chi-Square Statistic (for Testing Independence) 238
8.10.3 Computation in Mann-Whitney U Test 238
8.10.4 Computation in Wilcoxon Signed-Rank Test 239
8.10.5 Computation in Kruskal-Wallis Test 239
8.10.6 Computation in Friedman Test 239
8.11 Exercise 240
8.11.1 Short Answer Questions 240
8.11.2 Multiple Choice Questions 241
8.11.3 Assignment 243
8.12 Case Study on Testing Independence of Attributes 243
9 Regression Analysis and Multiple Correlations 246
9.1 Introduction 246
9.2 Understanding Regression Equation 247
9.2.1 Methods of Regression Analysis 247
9.2.2 Multiple Correlation 248
9.3 Application of Regression Analysis 248
9.4 Multiple Regression Analysis with Spss 249
9.4.1 Computation in Regression Analysis 249
9.4.2 Interpretation of Findings 254
9.5 Summary of Spss Commands for Regression Analysis 259
9.6 Exercise 259
9.6.1 Short Answer Questions 259
9.6.2 Multiple Choice Questions 260
9.6.3 Assignment 261
9.7 Case Study on Regression Analysis 263
10 Application of Discriminant Function Analysis 267
10.1 Introduction 268
10.2 Basics of Discriminant Function Analysis 268
10.2.1 Discriminating Variables 268
10.2.2 Dependent Variable 268
10.2.3 Discriminant Function 268
10.2.4 Classification Matrix 269
10.2.5 Stepwise Method of Discriminant Analysis 269
10.2.6 Power of Discriminating Variable 269
10.2.7 Canonical Correlation 269
10.2.8 Wilks' Lambda 270
10.3 Assumptions in Discriminant Analysis 270
10.4 Why to Use Discriminant Analysis 270
10.5 Steps in Discriminant Analysis 271
10.6 Application of Discriminant Function Analysis 272
10.7 Discriminant Analysis Using Spss 274
10.7.1 Computation in Discriminant Analysis 274
10.7.2 Interpretation of Findings 279
10.8 Summary of the Spss Commands for Discriminant Analysis 284
10.9 Exercise 284
10.9.1 Short Answer Questions 284
10.9.2 Multiple Choice Questions 285
10.9.3 Assignment 286
10.10 Case Study on Discriminant Analysis 288
11 Logistic Regression for Developing Logit Model in Sport 293
11.1 Introduction 293
11.2 Understanding Logistic Regression 294
11.3 Application of Logistic Regression in Sports Research 295
11.4 Assumptions in Logistic Regression 297
11.5 Steps in Developing Logistic Model 297
11.6 Logistic Analysis Using Spss 297
11.6.1 Block 0 299
11.6.2 Block 1 299
11.6.3 Computation in Logistic Regression with SPSS 299
11.7 Interpretation of Findings 304
11.7.1 Case Processing and Coding Summary 304
11.7.2 Analyzing Logistic Models 305
11.8 Summary of the Spss Commands for Logistic Regression 310
11.9 Exercise 310
11.9.1 Short Answer Questions 310
11.9.2 Multiple Choice Questions 311
11.9.3 Assignment 312
11.10 Case Study on Logistic Regression 313
12 Application of Factor Analysis 319
12.1 Introduction 319
12.2 Terminologies Used in Factor Analysis 320
12.2.1 Principal Component Analysis 320
12.2.2 Eigenvalue 320
12.2.3 Kaiser Criterion 321
12.2.4 The Scree Test 321
12.2.5 Communality 321
12.2.6 Factor Loading 322
12.2.7 Varimax Rotation 322
12.3 Assumptions in Factor Analysis 322
12.4 Steps in Factor Analysis 323
12.5 Application of Factor Analysis 323
12.6 Factor Analysis with Spss 324
12.6.1 Computation in Factor Analysis Using SPSS 326
12.7 Summary of the Spss Commands for Factor Analysis 336
12.8 Exercise 336
12.8.1 Short Answer Questions 336
12.8.2 Multiple Choice Questions 337
12.8.3 Assignment 338
12.9 Case Study on Factor Analysis 339
Appendix 346
Bibliography 360
Index 368