{"product_id":"repeated-measures-design-for-empirical-researchers-isbn-9781119052715","title":"Repeated Measures Design for Empirical Researchers","description":"\u003cp\u003e\u003cb\u003eIntroduces the applications of repeated measures design processes with the popular IBM® SPSS® software\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eRepeated Measures Design for Empirical Researchers \u003c\/i\u003epresents 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.\u003c\/p\u003e \u003cp\u003eFeaturing 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, \u003ci\u003eRepeated Measures Design for Empirical Researchers \u003c\/i\u003eincludes:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eA 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\u003c\/li\u003e \u003cli\u003eCoverage of sample size determination for the successful implementation of designing and analyzing a repeated measures study\u003c\/li\u003e \u003cli\u003eA step-by-step guide to analyzing the data obtained with real-world examples throughout to illustrate the underlying advantages and assumptions\u003c\/li\u003e \u003cli\u003eA companion website with supplementary IBM SPSS data sets and programming solutions as well as additional case studies\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eRepeated Measures Design for Empirical Researchers \u003c\/i\u003eis 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.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eJ. P. Verma, PhD, \u003c\/b\u003eis 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 \u003ci\u003eStatistics for Exercise Science and Health with Microsoft® Office Excel®\u003c\/i\u003e, also published by Wiley.\u003c\/p\u003e \u003cp\u003ePreface xv\u003c\/p\u003e \u003cp\u003eIllustration Credits xix\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Foundations of Experimental Design 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 1\u003c\/p\u003e \u003cp\u003eWhat is Experimental Research? 2\u003c\/p\u003e \u003cp\u003eDesign of Experiment and its Principles 3\u003c\/p\u003e \u003cp\u003eRandomization 3\u003c\/p\u003e \u003cp\u003eReplication 4\u003c\/p\u003e \u003cp\u003eBlocking 4\u003c\/p\u003e \u003cp\u003eStatistical Designs 5\u003c\/p\u003e \u003cp\u003eCompletely Randomized Design 5\u003c\/p\u003e \u003cp\u003eRandomized Block Design 6\u003c\/p\u003e \u003cp\u003eMatched Pairs Design 8\u003c\/p\u003e \u003cp\u003eLatin Square designs 8\u003c\/p\u003e \u003cp\u003eFactorial Experiment 9\u003c\/p\u003e \u003cp\u003eTerminologies in Design of Experiment 10\u003c\/p\u003e \u003cp\u003eSubject 11\u003c\/p\u003e \u003cp\u003eExperimental Unit 11\u003c\/p\u003e \u003cp\u003eFactor and Treatment 11\u003c\/p\u003e \u003cp\u003eCriterion Variable 12\u003c\/p\u003e \u003cp\u003eVariation and Variance 12\u003c\/p\u003e \u003cp\u003eExperimental Error 12\u003c\/p\u003e \u003cp\u003eExternal Validity 13\u003c\/p\u003e \u003cp\u003eInternal Validity 13\u003c\/p\u003e \u003cp\u003eConsiderations in Designing an Experiment 13\u003c\/p\u003e \u003cp\u003eSystematic Variance 14\u003c\/p\u003e \u003cp\u003eExtraneous Variance 14\u003c\/p\u003e \u003cp\u003eRandomization Method 15\u003c\/p\u003e \u003cp\u003eElimination Method 15\u003c\/p\u003e \u003cp\u003eMatching Group Method 15\u003c\/p\u003e \u003cp\u003eAdding Additional Independent Variable 16\u003c\/p\u003e \u003cp\u003eStatistical Control 16\u003c\/p\u003e \u003cp\u003eError Variance 17\u003c\/p\u003e \u003cp\u003eExercise 17\u003c\/p\u003e \u003cp\u003eAssignment 18\u003c\/p\u003e \u003cp\u003eBibliography 18\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Analysis of Variance and Repeated Measures Design 21\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 21\u003c\/p\u003e \u003cp\u003eUnderstanding Variance and Sum of Squares 22\u003c\/p\u003e \u003cp\u003eOne Way Analysis of Variance for Independent Measures Design 24\u003c\/p\u003e \u003cp\u003eAssumptions 24\u003c\/p\u003e \u003cp\u003eIllustration I 25\u003c\/p\u003e \u003cp\u003ePartitioning of Total Variation in the Design 26\u003c\/p\u003e \u003cp\u003eComputation 26\u003c\/p\u003e \u003cp\u003eExplanation 27\u003c\/p\u003e \u003cp\u003ePartitioning of SS and Degrees of Freedom 27\u003c\/p\u003e \u003cp\u003eComputation 27\u003c\/p\u003e \u003cp\u003eResults 29\u003c\/p\u003e \u003cp\u003ePost-Hoc Analysis 29\u003c\/p\u003e \u003cp\u003eMeans Plot 31\u003c\/p\u003e \u003cp\u003eRepeated Measures Design 31\u003c\/p\u003e \u003cp\u003eWhen to Use Repeated Measures ANOVA 32\u003c\/p\u003e \u003cp\u003eAssumptions 32\u003c\/p\u003e \u003cp\u003eSolving Repeated Measures Design with One-Way ANOVA 33\u003c\/p\u003e \u003cp\u003eIllustration II 34\u003c\/p\u003e \u003cp\u003eHypothesis Construction 34\u003c\/p\u003e \u003cp\u003eLayout Design 35\u003c\/p\u003e \u003cp\u003eOne-Way Repeated Measures ANOVA Model 36\u003c\/p\u003e \u003cp\u003eComputation in Repeated Measures Design with One-Way ANOVA 36\u003c\/p\u003e \u003cp\u003eExplanation 37\u003c\/p\u003e \u003cp\u003eComputation 37\u003c\/p\u003e \u003cp\u003eTesting Sphericity Assumption 39\u003c\/p\u003e \u003cp\u003eCorrecting for Degrees of Freedom 41\u003c\/p\u003e \u003cp\u003eResults 43\u003c\/p\u003e \u003cp\u003ePair-Wise Comparison of Means 43\u003c\/p\u003e \u003cp\u003eBonferroni Correction 44\u003c\/p\u003e \u003cp\u003eEffect Size 45\u003c\/p\u003e \u003cp\u003eExercise 46\u003c\/p\u003e \u003cp\u003eAssignment 47\u003c\/p\u003e \u003cp\u003eBibliography 48\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Testing Assumptions in Repeated Measures Design Using SPSS 51\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 51\u003c\/p\u003e \u003cp\u003eFirst Step in Using SPSS 52\u003c\/p\u003e \u003cp\u003eAssumptions 54\u003c\/p\u003e \u003cp\u003eTesting Normality 54\u003c\/p\u003e \u003cp\u003eTest of Normality 57\u003c\/p\u003e \u003cp\u003eQ–Q Plot for Normality 57\u003c\/p\u003e \u003cp\u003eBox-plot for Identifying Outliers 57\u003c\/p\u003e \u003cp\u003eTesting Sphericity 59\u003c\/p\u003e \u003cp\u003eRemedial Measures When Assumption Fails 62\u003c\/p\u003e \u003cp\u003eTransforming Nonnormal Data into Normal 62\u003c\/p\u003e \u003cp\u003eChoice of Design and Sphericity 63\u003c\/p\u003e \u003cp\u003eSample Size Determination 64\u003c\/p\u003e \u003cp\u003eImportant Terms 64\u003c\/p\u003e \u003cp\u003eConfidence Interval 64\u003c\/p\u003e \u003cp\u003eConfidence Level 65\u003c\/p\u003e \u003cp\u003ePower of the Test 66\u003c\/p\u003e \u003cp\u003eSample Size Determination on the Basis of Cost 67\u003c\/p\u003e \u003cp\u003eSample Size Determination on the Basis of Accuracy Factor 67\u003c\/p\u003e \u003cp\u003eSample Size in Estimating Mean 67\u003c\/p\u003e \u003cp\u003eSample Size in Hypothesis Testing 68\u003c\/p\u003e \u003cp\u003eExercise 68\u003c\/p\u003e \u003cp\u003eAssignment 69\u003c\/p\u003e \u003cp\u003eBibliography 70\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 One-Way Repeated Measures Design 73\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction to Design 73\u003c\/p\u003e \u003cp\u003eAdvantage of One-Way Repeated Measures Design 74\u003c\/p\u003e \u003cp\u003eWeakness of Repeated Measures Design 74\u003c\/p\u003e \u003cp\u003eApplication 74\u003c\/p\u003e \u003cp\u003eLayout Design 75\u003c\/p\u003e \u003cp\u003eCase I: When the Levels of Within-Subjects Variable are Different Treatments 75\u003c\/p\u003e \u003cp\u003eCase II: When the Levels of Within-Subjects Variable are Different Time Durations 76\u003c\/p\u003e \u003cp\u003eSteps in Solving One-Way Repeated Measures Design 77\u003c\/p\u003e \u003cp\u003eIllustration 77\u003c\/p\u003e \u003cp\u003eTesting Assumptions 77\u003c\/p\u003e \u003cp\u003eLayout Design 78\u003c\/p\u003e \u003cp\u003eDistribution of Variation and Degrees of Freedom 79\u003c\/p\u003e \u003cp\u003eHypothesis Construction 80\u003c\/p\u003e \u003cp\u003eLevel of Significance 80\u003c\/p\u003e \u003cp\u003eSolving One-Way Repeated Measures Design Using SPSS 81\u003c\/p\u003e \u003cp\u003eSPSS Output and Interpretation 83\u003c\/p\u003e \u003cp\u003eDescriptive Statistics 83\u003c\/p\u003e \u003cp\u003eTesting Sphericity 84\u003c\/p\u003e \u003cp\u003eTesting Significance of Within-Subjects Effect 86\u003c\/p\u003e \u003cp\u003eHow to Report the Findings 88\u003c\/p\u003e \u003cp\u003eInference 88\u003c\/p\u003e \u003cp\u003eExercise 88\u003c\/p\u003e \u003cp\u003eAssignment 89\u003c\/p\u003e \u003cp\u003eBibliography 90\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Two-Way Repeated Measures Design 91\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 91\u003c\/p\u003e \u003cp\u003eAdvantages of Using Two-Way Repeated Measures Design 92\u003c\/p\u003e \u003cp\u003eAssumptions 92\u003c\/p\u003e \u003cp\u003eLayout Design 93\u003c\/p\u003e \u003cp\u003eCase I: When Levels of Within-Subjects Variable are Different Treatment 93\u003c\/p\u003e \u003cp\u003eCase II: When the Levels of the Within-Subjects Variable are Different Time Durations 94\u003c\/p\u003e \u003cp\u003eApplication 94\u003c\/p\u003e \u003cp\u003eSteps in Solving Two-Way Repeated Measures Design 95\u003c\/p\u003e \u003cp\u003eIllustration 97\u003c\/p\u003e \u003cp\u003eLayout Design 97\u003c\/p\u003e \u003cp\u003eDistribution of Variation and Degrees of Freedom 98\u003c\/p\u003e \u003cp\u003eResearch Questions 100\u003c\/p\u003e \u003cp\u003eHypotheses Construction 100\u003c\/p\u003e \u003cp\u003eLevel of Significance 101\u003c\/p\u003e \u003cp\u003eSolving Repeated Measures Design with Two-Way ANOVA Using SPSS 101\u003c\/p\u003e \u003cp\u003eSPSS Output and Interpretation 104\u003c\/p\u003e \u003cp\u003eTesting Assumptions 105\u003c\/p\u003e \u003cp\u003eData Type 106\u003c\/p\u003e \u003cp\u003eIndependence of Measurement 106\u003c\/p\u003e \u003cp\u003eNormality 106\u003c\/p\u003e \u003cp\u003eSphericity 106\u003c\/p\u003e \u003cp\u003eDescriptive Statistics 106\u003c\/p\u003e \u003cp\u003eTesting Main Effect of Music (Within-Subjects) 106\u003c\/p\u003e \u003cp\u003ePairwise Comparison of Marginal Means of Music Groups 108\u003c\/p\u003e \u003cp\u003eMeans Plot of Music 108\u003c\/p\u003e \u003cp\u003eTesting Main Effect of Environment (Within-Subjects) 108\u003c\/p\u003e \u003cp\u003eTesting Significance of Interaction (Environment × Music) 108\u003c\/p\u003e \u003cp\u003eType I Error for Simple Effect 110\u003c\/p\u003e \u003cp\u003eSimple Effect of Environment (Within-Subjects) 110\u003c\/p\u003e \u003cp\u003eSimple Effect of Music (Within-Subjects) 116\u003c\/p\u003e \u003cp\u003eHow to Report the Findings 120\u003c\/p\u003e \u003cp\u003eAssumptions 120\u003c\/p\u003e \u003cp\u003eTesting Main Effects 120\u003c\/p\u003e \u003cp\u003eTesting Simple Effects 121\u003c\/p\u003e \u003cp\u003eInference 121\u003c\/p\u003e \u003cp\u003eExercise 122\u003c\/p\u003e \u003cp\u003eAssignment 122\u003c\/p\u003e \u003cp\u003eBibliography 124\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Two-Way Mixed Design 125\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 125\u003c\/p\u003e \u003cp\u003eAdvantages of Two-Way Mixed Design 127\u003c\/p\u003e \u003cp\u003eAssumptions 127\u003c\/p\u003e \u003cp\u003eApplication 128\u003c\/p\u003e \u003cp\u003eLayout Design 129\u003c\/p\u003e \u003cp\u003eCase I: When Levels of the Within-Subjects Factor are Different Treatment 129\u003c\/p\u003e \u003cp\u003eCase II: When Levels of the Within-Subjects Factor are Different Time Durations 130\u003c\/p\u003e \u003cp\u003eSteps in Solving Mixed Design with Two-Way ANOVA 131\u003c\/p\u003e \u003cp\u003eIllustration 132\u003c\/p\u003e \u003cp\u003eLayout Design 132\u003c\/p\u003e \u003cp\u003eDistribution of Variation and Degrees of Freedom 134\u003c\/p\u003e \u003cp\u003eResearch Questions 135\u003c\/p\u003e \u003cp\u003eHypothesis Construction 136\u003c\/p\u003e \u003cp\u003eLevel of Significance 136\u003c\/p\u003e \u003cp\u003eSolving Mixed Design with Two-Way ANOVA using SPSS 137\u003c\/p\u003e \u003cp\u003eSPSS Outputs and Interpretation 140\u003c\/p\u003e \u003cp\u003eTesting Assumptions 141\u003c\/p\u003e \u003cp\u003eAssumption of Normality 141\u003c\/p\u003e \u003cp\u003eHomogeneity of Variance Covariance Matrices 142\u003c\/p\u003e \u003cp\u003eHomogeneity of Variance 142\u003c\/p\u003e \u003cp\u003eSphericity Assumption 142\u003c\/p\u003e \u003cp\u003eDescriptive Statistics 143\u003c\/p\u003e \u003cp\u003eTesting Main Effect of Movie (within-Subjects) 144\u003c\/p\u003e \u003cp\u003ePair-Wise Comparison of Marginal Means of Movie Groups 144\u003c\/p\u003e \u003cp\u003eMeans Plot of Movie 145\u003c\/p\u003e \u003cp\u003eTesting Main Effect of Age (between-Subjects) 145\u003c\/p\u003e \u003cp\u003ePair-Wise Comparison of Marginal Means of Age Groups 146\u003c\/p\u003e \u003cp\u003eMeans Plot of Age 146\u003c\/p\u003e \u003cp\u003eTesting Significance of Interaction (Movie × Age) 147\u003c\/p\u003e \u003cp\u003eSimple Effect of Movie (within-Subjects) 147\u003c\/p\u003e \u003cp\u003eSimple Effect of Age (between-Subjects) 151\u003c\/p\u003e \u003cp\u003eHow to Report the Findings 155\u003c\/p\u003e \u003cp\u003eAssumptions 155\u003c\/p\u003e \u003cp\u003eTesting Main Effects 156\u003c\/p\u003e \u003cp\u003eTesting Simple Effects 156\u003c\/p\u003e \u003cp\u003eInference 157\u003c\/p\u003e \u003cp\u003eExercise 157\u003c\/p\u003e \u003cp\u003eAssignment 158\u003c\/p\u003e \u003cp\u003eBibliography 159\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 One-Way Repeated Measures MANOVA 161\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 161\u003c\/p\u003e \u003cp\u003eWhen to Use Repeated Measures MANOVA? 162\u003c\/p\u003e \u003cp\u003eWhy to Use Repeated Measures MANOVA? 162\u003c\/p\u003e \u003cp\u003eAssumptions 163\u003c\/p\u003e \u003cp\u003eApplication 164\u003c\/p\u003e \u003cp\u003eLayout Design 165\u003c\/p\u003e \u003cp\u003eCase I: When Levels of Within-Subjects Factor are Different Treatment 165\u003c\/p\u003e \u003cp\u003eCase II: When Levels of Within-Subjects Factor are Different Time Durations 166\u003c\/p\u003e \u003cp\u003eSteps in Solving One-Way Repeated Measures MANOVA 166\u003c\/p\u003e \u003cp\u003eIllustration 167\u003c\/p\u003e \u003cp\u003eLayout Design 167\u003c\/p\u003e \u003cp\u003eResearch Questions 168\u003c\/p\u003e \u003cp\u003eHypotheses Construction 168\u003c\/p\u003e \u003cp\u003eLevel of Significance 170\u003c\/p\u003e \u003cp\u003eSolving One-Way Repeated Measures MANOVA Design with SPSS 170\u003c\/p\u003e \u003cp\u003eSPSS Output and Interpretation 173\u003c\/p\u003e \u003cp\u003eDescriptive Statistics 174\u003c\/p\u003e \u003cp\u003eTesting Assumptions 174\u003c\/p\u003e \u003cp\u003eTesting Correlation 174\u003c\/p\u003e \u003cp\u003eTesting Normality 176\u003c\/p\u003e \u003cp\u003eTesting Outliers 176\u003c\/p\u003e \u003cp\u003eMultivariate Testing 178\u003c\/p\u003e \u003cp\u003eUnivariate Testing 181\u003c\/p\u003e \u003cp\u003eTesting Sphericity 181\u003c\/p\u003e \u003cp\u003ePair-Wise Comparison of Marginal Means 181\u003c\/p\u003e \u003cp\u003eMeans Plot of Maths 181\u003c\/p\u003e \u003cp\u003eMeans Plot of English 182\u003c\/p\u003e \u003cp\u003eMeans Plot of Reasoning 182\u003c\/p\u003e \u003cp\u003eHow to Report the Findings 183\u003c\/p\u003e \u003cp\u003eAssumptions 183\u003c\/p\u003e \u003cp\u003eTesting Multivariate Effect 183\u003c\/p\u003e \u003cp\u003eTesting Univariate Effect 184\u003c\/p\u003e \u003cp\u003eInference 184\u003c\/p\u003e \u003cp\u003eExercise 184\u003c\/p\u003e \u003cp\u003eAssignment 185\u003c\/p\u003e \u003cp\u003eBibliography 187\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Mixed Design with Two-Way MANOVA 189\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 189\u003c\/p\u003e \u003cp\u003eWhat Happens in MANOVA Experiment 190\u003c\/p\u003e \u003cp\u003eAssumptions 191\u003c\/p\u003e \u003cp\u003eMultivariate Analysis 191\u003c\/p\u003e \u003cp\u003eUnivariate Analysis 192\u003c\/p\u003e \u003cp\u003eLayout Design 192\u003c\/p\u003e \u003cp\u003eCase I: When the Levels of Within-Subjects Factor are Different Treatment 192\u003c\/p\u003e \u003cp\u003eCase II: When the Levels of the Within-Subjects Factor are Different Time Durations 193\u003c\/p\u003e \u003cp\u003eApplication 193\u003c\/p\u003e \u003cp\u003eSteps in Solving Mixed Design with Two-Way MANOVA 194\u003c\/p\u003e \u003cp\u003eIllustration 196\u003c\/p\u003e \u003cp\u003eLayout Design 196\u003c\/p\u003e \u003cp\u003eResearch Questions 198\u003c\/p\u003e \u003cp\u003eHypotheses Construction 198\u003c\/p\u003e \u003cp\u003eLevel of Significance 200\u003c\/p\u003e \u003cp\u003eSolving Mixed Design with Two-Way MANOVA Using SPSS 200\u003c\/p\u003e \u003cp\u003eSPSS Output and Interpretation 204\u003c\/p\u003e \u003cp\u003eMultivariate Outcome 205\u003c\/p\u003e \u003cp\u003eMain Effect of Each Dependent Variable 205\u003c\/p\u003e \u003cp\u003eSimple Effect of Each Dependent Variable 205\u003c\/p\u003e \u003cp\u003eTesting Assumptions 205\u003c\/p\u003e \u003cp\u003eData Type 205\u003c\/p\u003e \u003cp\u003eTesting Correlations 206\u003c\/p\u003e \u003cp\u003eTesting Normality 207\u003c\/p\u003e \u003cp\u003eTesting Outliers 210\u003c\/p\u003e \u003cp\u003eHomogeneity of Variances 211\u003c\/p\u003e \u003cp\u003eHomogeneity of Variance Covariance Matrices 211\u003c\/p\u003e \u003cp\u003eSphericity Assumption for Within-Subjects Conditions 211\u003c\/p\u003e \u003cp\u003eMultivariate Testing 211\u003c\/p\u003e \u003cp\u003eUnivariate Testing 213\u003c\/p\u003e \u003cp\u003eMain Effect of Between-Subjects Factor (Sex) 215\u003c\/p\u003e \u003cp\u003eMain Effect of Within-Subjects Factor (Chocolate) 215\u003c\/p\u003e \u003cp\u003eLevel of Significance for Simple Effect 219\u003c\/p\u003e \u003cp\u003eSimple Effect on Taste 219\u003c\/p\u003e \u003cp\u003eSimple Effect on Crunchiness 226\u003c\/p\u003e \u003cp\u003eSimple Effect on Flavor 230\u003c\/p\u003e \u003cp\u003eMeans Plots (Sex × Chocolate) 232\u003c\/p\u003e \u003cp\u003eHow to Report Findings 234\u003c\/p\u003e \u003cp\u003eAssumptions 234\u003c\/p\u003e \u003cp\u003eMultivariate Effects 236\u003c\/p\u003e \u003cp\u003eUnivariate Main Effects 236\u003c\/p\u003e \u003cp\u003eUnivariate Simple Effects 237\u003c\/p\u003e \u003cp\u003eInference 237\u003c\/p\u003e \u003cp\u003eExercise 238\u003c\/p\u003e \u003cp\u003eAssignment 238\u003c\/p\u003e \u003cp\u003eBibliography 240\u003c\/p\u003e \u003cp\u003eAppendix 243\u003c\/p\u003e \u003cp\u003eIndex 255\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eJ. P. Verma, PhD,\u003c\/b\u003e 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 \u003ci\u003eStatistics for Exercise Science and Health with Microsoft\u003csup\u003e®\u003c\/sup\u003e Office Excel\u003csup\u003e®\u003c\/sup\u003e\u003c\/i\u003e, also published by Wiley.   \u003c\/p\u003e\u003cp\u003e\u003cb\u003eIntroduces the applications of repeated measures design processes with the popular IBM\u003csup\u003e®\u003c\/sup\u003e SPSS\u003csup\u003e®\u003c\/sup\u003e software\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003e\u003ci\u003eRepeated Measures Design for Empirical Researchers\u003c\/i\u003e 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. \u003c\/p\u003e\u003cp\u003eFeaturing 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, \u003ci\u003eRepeated Measures Design for Empirical Researchers\u003c\/i\u003e includes: \u003c\/p\u003e\u003cul\u003e \u003cli\u003eA 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\u003c\/li\u003e \u003cli\u003eCoverage of sample size determination for the successful implementation of designing    and analyzing a repeated measures study\u003c\/li\u003e \u003cli\u003eA step-by-step guide to analyzing the data obtained with real-world examples through   out to illustrate the underlying advantages and assumptions\u003c\/li\u003e \u003cli\u003eA companion website with supplementary IBM SPSS data sets and programming   solutions as well as additional case studies\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eRepeated Measures Design for Empirical Researchers\u003c\/i\u003e 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.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989944549605,"sku":"NP9781119052715","price":114.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119052715.jpg?v=1761785987","url":"https:\/\/k12savings.com\/products\/repeated-measures-design-for-empirical-researchers-isbn-9781119052715","provider":"K12savings","version":"1.0","type":"link"}