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.
Autorentext
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.
Inhalt
Preface xv
Illustration Credits xix
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 54
Testing Normality 54
Test of Normality 57
QQ Plot for Normality 57
Box-plot for Identifying Outliers 57
Testing Sphericity 59
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
...