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More About This Title Repeated Measures Design for Empirical Researchers
English
Introduces the applications of repeated measures design processes with the popular IBM® SPSS® software
Repeated Measures Design for Empirical Researchers presents comprehensive coverage of the formation of research questions and the analysis of repeated measures using IBM SPSS and also includes the solutions necessary for understanding situations where the designs can be used. In addition to explaining the computation involved in each design, the book presents a unique discussion on how to conceptualize research problems as well as identify appropriate repeated measures designs for research purposes.
Featuring practical examples from a multitude of domains including psychology, the social sciences, management, and sports science, the book helps readers better understand the associated theories and methodologies of repeated measures design processes. The book covers various fundamental concepts involved in the design of experiments, basic statistical designs, computational details, differentiating independent and repeated measures designs, and testing assumptions. Along with an introduction to IBM SPSS software, Repeated Measures Design for Empirical Researchers includes:
- A discussion of the popular repeated measures designs frequently used by researchers, such as one-way repeated measures ANOVA, two-way repeated measures design, two-way mixed design, and mixed design with two-way MANOVA
- Coverage of sample size determination for the successful implementation of designing and analyzing a repeated measures study
- A step-by-step guide to analyzing the data obtained with real-world examples throughout to illustrate the underlying advantages and assumptions
- A companion website with supplementary IBM SPSS data sets and programming solutions as well as additional case studies
Repeated Measures Design for Empirical Researchers is a useful textbook for graduate- and PhD-level students majoring in biostatistics, the social sciences, psychology, medicine, management, sports, physical education, and health. The book is also an excellent reference for professionals interested in experimental designs and statistical sciences as well as statistical consultants and practitioners from other fields including biological, medical, agricultural, and horticultural sciences.
J. P. Verma, PhD, is Professor of Statistics and Director of the Center for Advanced Studies at Lakshmibai National Institute of Physical Education, India. Professor Verma is an active researcher in sports modeling and data analysis and has conducted many workshops on research methodology, research designs, multivariate analysis, statistical modeling, and data analysis for students of management, physical education, social science, and economics. He is the author of Statistics for Exercise Science and Health with Microsoft® Office Excel®, also published by Wiley.
English
English
Preface xv
1 Foundations of Experimental Design 1
Introduction 1
What is Experimental Research? 2
Design of Experiment and its Principles 3
Randomization 3
Replication 4
Blocking 4
Statistical Designs 5
Completely Randomized Design 5
Randomized Block Design 6
Matched Pairs Design 8
Latin Square designs 8
Factorial Experiment 9
Terminologies in Design of Experiment 10
Subject 11
Experimental Unit 11
Factor and Treatment 11
Criterion Variable 12
Variation and Variance 12
Experimental Error 12
External Validity 13
Internal Validity 13
Considerations in Designing an Experiment 13
Systematic Variance 14
Extraneous Variance 14
Randomization Method 15
Elimination Method 15
Matching Group Method 15
Adding Additional Independent Variable 16
Statistical Control 16
Error Variance 17
Exercise 17
Assignment 18
Bibliography 18
2 Analysis of Variance and Repeated Measures Design 21
Introduction 21
Understanding Variance and Sum of Squares 22
One Way Analysis of Variance for Independent Measures Design 24
Assumptions 24
Illustration I 25
Partitioning of Total Variation in the Design 26
Computation 26
Explanation 27
Partitioning of SS and Degrees of Freedom 27
Computation 27
Results 29
Post-Hoc Analysis 29
Means Plot 31
Repeated Measures Design 31
When to Use Repeated Measures ANOVA 32
Assumptions 32
Solving Repeated Measures Design With One-Way ANOVA 33
Illustration II 34
Hypothesis Construction 34
Layout Design 35
One-Way Repeated Measures ANOVA Model 36
Computation in Repeated Measures Design with One-Way ANOVA 36
Explanation 37
Computation 37
Testing Sphericity Assumption 39
Correcting for Degrees of Freedom 41
Results 43
Pair-Wise Comparison of Means 43
Bonferroni Correction 44
Effect Size 45
Exercise 46
Assignment 47
Bibliography 48
3 Testing Assumptions in Repeated Measures Design Using SPSS 51
Introduction 51
First Step in Using SPSS 52
Assumptions 53
Testing Normality 54
Test of Normality 57
Q–Q Plot for Normality 57
Box-plot for Identifying Outliers 59
Testing Sphericity 60
Remedial Measures when Assumption Fails 62
Transforming Nonnormal Data into Normal 62
Choice of Design and Sphericity 63
Sample Size Determination 64
Important Terms 64
Confidence Interval 64
Confidence Level 65
Power of the Test 66
Sample Size Determination on the Basis of Cost 67
Sample Size Determination on the Basis of Accuracy Factor 67
Sample Size in Estimating Mean 67
Sample Size in Hypothesis Testing 68
Exercise 68
Assignment 69
Bibliography 70
4 One-Way Repeated Measures Design 73
Introduction to Design 73
Advantage of One-Way Repeated Measures Design 74
Weakness of Repeated Measures Design 74
Application 74
Layout Design 75
Case I: When the Levels of Within-Subjects Variable are Different Treatments 75
Case II: When the Levels of Within-Subjects Variable are Different Time Durations 76
Steps in Solving One-Way Repeated Measures Design 77
Illustration 77
Testing Assumptions 77
Layout Design 78
Distribution of Variation and Degrees of Freedom 79
Hypothesis Construction 80
Level of Significance 80
Solving One-Way Repeated Measures Design Using SPSS 81
SPSS Output and Interpretation 83
Descriptive Statistics 83
Testing Sphericity 84
Testing Significance of Within-Subjects Effect 86
How to Report the Findings 88
Inference 88
Exercise 88
Assignment 89
Bibliography 90
5 Two-Way Repeated Measures Design 91
Introduction 91
Advantages of Using Two-Way Repeated Measures Design 92
Assumptions 92
Layout Design 93
Case I: When Levels of Within-Subjects Variable are Different Treatment Conditions 93
Case II: When the Levels of the Within-Subjects Variable are Different Time Durations 94
Application 94
Steps in Solving Two-Way Repeated Measures Design 95
Illustration 97
Layout Design 97
Distribution of Variation and Degrees of Freedom 98
Research Questions 100
Hypotheses Construction 100
Level of Significance 101
Solving Repeated Measures Design with Two-Way ANOVA Using SPSS 101
SPSS Output and Interpretation 104
Testing Assumptions 105
Data Type 106
Independence of Measurement 106
Normality 106
Sphericity 106
Descriptive Statistics 106
Testing Main Effect of Music (Within-Subjects) 106
Pairwise Comparison of Marginal Means of Music Groups 108
Means Plot of Music 108
Testing Main Effect of Environment (Within-Subjects) 108
Testing Significance of Interaction (Environment × Music) 108
Type I Error for Simple Effect 110
Simple Effect of Environment (Within-Subjects) 110
Simple Effect of Music (Within-Subjects) 116
How to Report the Findings 120
Assumptions 120
Testing Main Effects 120
Testing Simple Effects 121
Inference 121
Exercise 121
Assignment 122
Bibliography 124
6 Two-Way Mixed Design 125
Introduction 125
Advantages of Two-Way Mixed Design 127
Assumptions 127
Application 128
Layout Design 129
Case I: When Levels of the Within-Subjects Factor are Different Treatment Conditions 129
Case II: When Levels of the Within-Subjects Factor are Different Time Period 130
Steps in Solving Mixed Design with Two-Way ANOVA 131
Illustration 132
Layout Design 132
Distribution of Variation and Degrees of Freedom 134
Research Questions 135
Hypothesis Construction 136
Level of Significance 136
Solving Mixed Design with Two-Way ANOVA using SPSS 137
SPSS Outputs and Interpretation 140
Testing Assumptions 141
Assumption of Normality 141
Homogeneity of Variance Covariance Matrices 142
Homogeneity of Variance 142
Sphericity Assumption 143
Descriptive Statistics 143
Testing Main Effect of Movie (Within-Subjects) 144
Pair-Wise Comparison of Marginal Means of Movie Groups 144
Means Plot of Movie 145
Testing Main Effect of Age (Between-Subjects) 145
Pair-Wise Comparison of Marginal Means of Age Groups 146
Means Plot of Age 146
Testing Significance of Interaction (Movie × Age) 147
Simple Effect of Movie (Within-Subjects) 147
Simple Effect of Age (Between-Subjects) 151
How to Report the Findings 155
Assumptions 155
Testing Main Effects 156
Testing Simple Effects 156
Inference 157
Exercise 157
Assignment 158
Bibliography 159
7 One-Way Repeated Measures MANOVA 161
Introduction 161
When to Use Repeated Measures MANOVA? 162
Why to Use Repeated Measures MANOVA? 162
Assumptions 163
Application 164
Layout Design 165
Case I: When Levels of Within-Subjects Factor are Different Treatment Conditions 165
Case II: When Levels of Within-Subjects Factor are Different Time Period 166
Steps in Solving One-Way Repeated Measures MANOVA 166
Illustration 167
Layout Design 167
Research Questions 168
Hypotheses Construction 168
Level of Significance 170
Solving One-Way Repeated Measures MANOVA Design with SPSS 170
SPSS Output and Interpretation 173
Descriptive Statistics 174
Testing Assumptions 174
Testing Correlation 174
Testing Normality 176
Testing Outliers 176
Multivariate Testing 178
Univariate Testing 181
Testing Sphericity 181
Pair-Wise Comparison of Marginal Means 181
Means Plot of Maths 181
Means Plot of English 181
Means Plot of Reasoning 182
How to Report the Findings 183
Assumptions 183
Testing Multivariate Effect 183
Testing Univariate Effect 183
Inference 184
Exercise 184
Assignment 186
Bibliography 186
8 Mixed Design with Two-Way MANOVA 189
Introduction 189
What Happens in MANOVA Experiment 190
Assumptions 191
Multivariate Analysis 191
Univariate Analysis 192
Layout Design 192
Case I: When the Levels of Within-Subjects Factor are Different Treatment Conditions 192
Case II: When the Levels of the Within-Subjects Factor are Different Time Periods 193
Application 193
Steps in Solving Mixed Design with Two-Way MANOVA 194
Illustration 196
Layout Design 196
Research Questions 198
Hypotheses Construction 198
Level of Significance 200
Solving Mixed Design with Two-Way MANOVA Using SPSS 200
SPSS Output and Interpretation 204
Multivariate Outcome 204
Main Effect of Each Dependent Variable 204
Simple Effect of Each Dependent Variable 205
Testing Assumptions 206
Data Type 206
Testing Correlations 206
Testing Normality 210
Testing Outliers 210
Homogeneity of Variances 211
Homogeneity of Variance Covariance Matrices 211
Sphericity Assumption for Within-Subjects Conditions 211
Multivariate Testing 213
Univariate Testing 215
Main Effect of Between-Subjects Factor (Sex) 215
Main Effect of Within-Subjects Factor (Chocolate) 215
Level of Significance for Simple Effect 219
Simple Effect on Taste 219
Simple Effect on Crunchiness 227
Simple Effect on Flavor 231
Means Plots (Sex × Chocolate) 233
How to Report Findings 235
Assumptions 236
Multivariate Effects 237
Univariate Main Effects 237
Univariate Simple Effects 237
Inference 238
Exercise 238
Assignment 240
Bibliography 240
Appendix 243
Index 255
