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<p>Preface xv</p> <p>Illustration Credits xix</p> <p><b>1 Foundations of Experimental Design 1</b></p> <p>Introduction 1</p> <p>What is Experimental Research? 2</p> <p>Design of Experiment and its Principles 3</p> <p>Randomization 3</p> <p>Replication 4</p> <p>Blocking 4</p> <p>Statistical Designs 5</p> <p>Completely Randomized Design 5</p> <p>Randomized Block Design 6</p> <p>Matched Pairs Design 8</p> <p>Latin Square designs 8</p> <p>Factorial Experiment 9</p> <p>Terminologies in Design of Experiment 10</p> <p>Subject 11</p> <p>Experimental Unit 11</p> <p>Factor and Treatment 11</p> <p>Criterion Variable 12</p> <p>Variation and Variance 12</p> <p>Experimental Error 12</p> <p>External Validity 13</p> <p>Internal Validity 13</p> <p>Considerations in Designing an Experiment 13</p> <p>Systematic Variance 14</p> <p>Extraneous Variance 14</p> <p>Randomization Method 15</p> <p>Elimination Method 15</p> <p>Matching Group Method 15</p> <p>Adding Additional Independent Variable 16</p> <p>Statistical Control 16</p> <p>Error Variance 17</p> <p>Exercise 17</p> <p>Assignment 18</p> <p>Bibliography 18</p> <p><b>2 Analysis of Variance and Repeated Measures Design 21</b></p> <p>Introduction 21</p> <p>Understanding Variance and Sum of Squares 22</p> <p>One Way Analysis of Variance for Independent Measures Design 24</p> <p>Assumptions 24</p> <p>Illustration I 25</p> <p>Partitioning of Total Variation in the Design 26</p> <p>Computation 26</p> <p>Explanation 27</p> <p>Partitioning of SS and Degrees of Freedom 27</p> <p>Computation 27</p> <p>Results 29</p> <p>Post-Hoc Analysis 29</p> <p>Means Plot 31</p> <p>Repeated Measures Design 31</p> <p>When to Use Repeated Measures ANOVA 32</p> <p>Assumptions 32</p> <p>Solving Repeated Measures Design with One-Way ANOVA 33</p> <p>Illustration II 34</p> <p>Hypothesis Construction 34</p> <p>Layout Design 35</p> <p>One-Way Repeated Measures ANOVA Model 36</p> <p>Computation in Repeated Measures Design with One-Way ANOVA 36</p> <p>Explanation 37</p> <p>Computation 37</p> <p>Testing Sphericity Assumption 39</p> <p>Correcting for Degrees of Freedom 41</p> <p>Results 43</p> <p>Pair-Wise Comparison of Means 43</p> <p>Bonferroni Correction 44</p> <p>Effect Size 45</p> <p>Exercise 46</p> <p>Assignment 47</p> <p>Bibliography 48</p> <p><b>3 Testing Assumptions in Repeated Measures Design Using SPSS 51</b></p> <p>Introduction 51</p> <p>First Step in Using SPSS 52</p> <p>Assumptions 54</p> <p>Testing Normality 54</p> <p>Test of Normality 57</p> <p>Q–Q Plot for Normality 57</p> <p>Box-plot for Identifying Outliers 57</p> <p>Testing Sphericity 59</p> <p>Remedial Measures When Assumption Fails 62</p> <p>Transforming Nonnormal Data into Normal 62</p> <p>Choice of Design and Sphericity 63</p> <p>Sample Size Determination 64</p> <p>Important Terms 64</p> <p>Confidence Interval 64</p> <p>Confidence Level 65</p> <p>Power of the Test 66</p> <p>Sample Size Determination on the Basis of Cost 67</p> <p>Sample Size Determination on the Basis of Accuracy Factor 67</p> <p>Sample Size in Estimating Mean 67</p> <p>Sample Size in Hypothesis Testing 68</p> <p>Exercise 68</p> <p>Assignment 69</p> <p>Bibliography 70</p> <p><b>4 One-Way Repeated Measures Design 73</b></p> <p>Introduction to Design 73</p> <p>Advantage of One-Way Repeated Measures Design 74</p> <p>Weakness of Repeated Measures Design 74</p> <p>Application 74</p> <p>Layout Design 75</p> <p>Case I: When the Levels of Within-Subjects Variable are Different Treatments 75</p> <p>Case II: When the Levels of Within-Subjects Variable are Different Time Durations 76</p> <p>Steps in Solving One-Way Repeated Measures Design 77</p> <p>Illustration 77</p> <p>Testing Assumptions 77</p> <p>Layout Design 78</p> <p>Distribution of Variation and Degrees of Freedom 79</p> <p>Hypothesis Construction 80</p> <p>Level of Significance 80</p> <p>Solving One-Way Repeated Measures Design Using SPSS 81</p> <p>SPSS Output and Interpretation 83</p> <p>Descriptive Statistics 83</p> <p>Testing Sphericity 84</p> <p>Testing Significance of Within-Subjects Effect 86</p> <p>How to Report the Findings 88</p> <p>Inference 88</p> <p>Exercise 88</p> <p>Assignment 89</p> <p>Bibliography 90</p> <p><b>5 Two-Way Repeated Measures Design 91</b></p> <p>Introduction 91</p> <p>Advantages of Using Two-Way Repeated Measures Design 92</p> <p>Assumptions 92</p> <p>Layout Design 93</p> <p>Case I: When Levels of Within-Subjects Variable are Different Treatment 93</p> <p>Case II: When the Levels of the Within-Subjects Variable are Different Time Durations 94</p> <p>Application 94</p> <p>Steps in Solving Two-Way Repeated Measures Design 95</p> <p>Illustration 97</p> <p>Layout Design 97</p> <p>Distribution of Variation and Degrees of Freedom 98</p> <p>Research Questions 100</p> <p>Hypotheses Construction 100</p> <p>Level of Significance 101</p> <p>Solving Repeated Measures Design with Two-Way ANOVA Using SPSS 101</p> <p>SPSS Output and Interpretation 104</p> <p>Testing Assumptions 105</p> <p>Data Type 106</p> <p>Independence of Measurement 106</p> <p>Normality 106</p> <p>Sphericity 106</p> <p>Descriptive Statistics 106</p> <p>Testing Main Effect of Music (Within-Subjects) 106</p> <p>Pairwise Comparison of Marginal Means of Music Groups 108</p> <p>Means Plot of Music 108</p> <p>Testing Main Effect of Environment (Within-Subjects) 108</p> <p>Testing Significance of Interaction (Environment × Music) 108</p> <p>Type I Error for Simple Effect 110</p> <p>Simple Effect of Environment (Within-Subjects) 110</p> <p>Simple Effect of Music (Within-Subjects) 116</p> <p>How to Report the Findings 120</p> <p>Assumptions 120</p> <p>Testing Main Effects 120</p> <p>Testing Simple Effects 121</p> <p>Inference 121</p> <p>Exercise 122</p> <p>Assignment 122</p> <p>Bibliography 124</p> <p><b>6 Two-Way Mixed Design 125</b></p> <p>Introduction 125</p> <p>Advantages of Two-Way Mixed Design 127</p> <p>Assumptions 127</p> <p>Application 128</p> <p>Layout Design 129</p> <p>Case I: When Levels of the Within-Subjects Factor are Different Treatment 129</p> <p>Case II: When Levels of the Within-Subjects Factor are Different Time Durations 130</p> <p>Steps in Solving Mixed Design with Two-Way ANOVA 131</p> <p>Illustration 132</p> <p>Layout Design 132</p> <p>Distribution of Variation and Degrees of Freedom 134</p> <p>Research Questions 135</p> <p>Hypothesis Construction 136</p> <p>Level of Significance 136</p> <p>Solving Mixed Design with Two-Way ANOVA using SPSS 137</p> <p>SPSS Outputs and Interpretation 140</p> <p>Testing Assumptions 141</p> <p>Assumption of Normality 141</p> <p>Homogeneity of Variance Covariance Matrices 142</p> <p>Homogeneity of Variance 142</p> <p>Sphericity Assumption 142</p> <p>Descriptive Statistics 143</p> <p>Testing Main Effect of Movie (within-Subjects) 144</p> <p>Pair-Wise Comparison of Marginal Means of Movie Groups 144</p> <p>Means Plot of Movie 145</p> <p>Testing Main Effect of Age (between-Subjects) 145</p> <p>Pair-Wise Comparison of Marginal Means of Age Groups 146</p> <p>Means Plot of Age 146</p> <p>Testing Significance of Interaction (Movie × Age) 147</p> <p>Simple Effect of Movie (within-Subjects) 147</p> <p>Simple Effect of Age (between-Subjects) 151</p> <p>How to Report the Findings 155</p> <p>Assumptions 155</p> <p>Testing Main Effects 156</p> <p>Testing Simple Effects 156</p> <p>Inference 157</p> <p>Exercise 157</p> <p>Assignment 158</p> <p>Bibliography 159</p> <p><b>7 One-Way Repeated Measures MANOVA 161</b></p> <p>Introduction 161</p> <p>When to Use Repeated Measures MANOVA? 162</p> <p>Why to Use Repeated Measures MANOVA? 162</p> <p>Assumptions 163</p> <p>Application 164</p> <p>Layout Design 165</p> <p>Case I: When Levels of Within-Subjects Factor are Different Treatment 165</p> <p>Case II: When Levels of Within-Subjects Factor are Different Time Durations 166</p> <p>Steps in Solving One-Way Repeated Measures MANOVA 166</p> <p>Illustration 167</p> <p>Layout Design 167</p> <p>Research Questions 168</p> <p>Hypotheses Construction 168</p> <p>Level of Significance 170</p> <p>Solving One-Way Repeated Measures MANOVA Design with SPSS 170</p> <p>SPSS Output and Interpretation 173</p> <p>Descriptive Statistics 174</p> <p>Testing Assumptions 174</p> <p>Testing Correlation 174</p> <p>Testing Normality 176</p> <p>Testing Outliers 176</p> <p>Multivariate Testing 178</p> <p>Univariate Testing 181</p> <p>Testing Sphericity 181</p> <p>Pair-Wise Comparison of Marginal Means 181</p> <p>Means Plot of Maths 181</p> <p>Means Plot of English 182</p> <p>Means Plot of Reasoning 182</p> <p>How to Report the Findings 183</p> <p>Assumptions 183</p> <p>Testing Multivariate Effect 183</p> <p>Testing Univariate Effect 184</p> <p>Inference 184</p> <p>Exercise 184</p> <p>Assignment 185</p> <p>Bibliography 187</p> <p><b>8 Mixed Design with Two-Way MANOVA 189</b></p> <p>Introduction 189</p> <p>What Happens in MANOVA Experiment 190</p> <p>Assumptions 191</p> <p>Multivariate Analysis 191</p> <p>Univariate Analysis 192</p> <p>Layout Design 192</p> <p>Case I: When the Levels of Within-Subjects Factor are Different Treatment 192</p> <p>Case II: When the Levels of the Within-Subjects Factor are Different Time Durations 193</p> <p>Application 193</p> <p>Steps in Solving Mixed Design with Two-Way MANOVA 194</p> <p>Illustration 196</p> <p>Layout Design 196</p> <p>Research Questions 198</p> <p>Hypotheses Construction 198</p> <p>Level of Significance 200</p> <p>Solving Mixed Design with Two-Way MANOVA Using SPSS 200</p> <p>SPSS Output and Interpretation 204</p> <p>Multivariate Outcome 205</p> <p>Main Effect of Each Dependent Variable 205</p> <p>Simple Effect of Each Dependent Variable 205</p> <p>Testing Assumptions 205</p> <p>Data Type 205</p> <p>Testing Correlations 206</p> <p>Testing Normality 207</p> <p>Testing Outliers 210</p> <p>Homogeneity of Variances 211</p> <p>Homogeneity of Variance Covariance Matrices 211</p> <p>Sphericity Assumption for Within-Subjects Conditions 211</p> <p>Multivariate Testing 211</p> <p>Univariate Testing 213</p> <p>Main Effect of Between-Subjects Factor (Sex) 215</p> <p>Main Effect of Within-Subjects Factor (Chocolate) 215</p> <p>Level of Significance for Simple Effect 219</p> <p>Simple Effect on Taste 219</p> <p>Simple Effect on Crunchiness 226</p> <p>Simple Effect on Flavor 230</p> <p>Means Plots (Sex × Chocolate) 232</p> <p>How to Report Findings 234</p> <p>Assumptions 234</p> <p>Multivariate Effects 236</p> <p>Univariate Main Effects 236</p> <p>Univariate Simple Effects 237</p> <p>Inference 237</p> <p>Exercise 238</p> <p>Assignment 238</p> <p>Bibliography 240</p> <p>Appendix 243</p> <p>Index 255</p>

<p><b>J. P. Verma, PhD,</b> 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 <i>Statistics for Exercise Science and Health with Microsoft^® Office Excel^®</i>, also published by Wiley.

<p><b>Introduces the applications of repeated measures design processes with the popular IBM^® SPSS^® software</b> <p><i>Repeated Measures Design for Empirical Researchers</i> 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. <p>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 measuresdesigns, and testing assumptions. Along with an introduction to IBM SPSS software, <i>Repeated Measures Design for Empirical Researchers</i> includes: <ul> <li>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</li> <li>Coverage of sample size determination for the successful implementation of designing and analyzing a repeated measures study</li> <li>A step-by-step guide to analyzing the data obtained with real-world examples through out to illustrate the underlying advantages and assumptions</li> <li>A companion website with supplementary IBM SPSS data sets and programming solutions as well as additional case studies</li> </ul> <p><i>Repeated Measures Design for Empirical Researchers</i> 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.

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