Optimal Data Analysis: A Guidebook with Software for Windows (Book with CD-ROM)
by Yarnold, Paul R.Edition: 1st
Format: Paperback
Pub. Date: 2004-10-01
Publisher(s): AMERICAN PSYCHOLOGICAL ASSOCIATION
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Summary
Optimal Data Analysis: A Guidebook With Software for Windows offers the only statistical analysis paradigm that maximizes (weighted) predictive accuracy. This unique book fully explains this paradigm and includes simple-to-use software that empowers a universe of associated analyses. For any specific sample and exploratory or confirmatory hypothesis, optimal data analysis (ODA) identifies the statistical model that yields maximum predictive accuracy, assesses the exact Type I error rate, and evaluates potential cross-generalizability.
Table of Contents
| Preface | p. xi |
| Acknowledgments | p. xiii |
| Introduction to the ODA Paradigm | p. 3 |
| What Is ODA? | p. 3 |
| Why Is ODA Superior to Other Data Analysis Programs? | p. 5 |
| Who Is the Audience for This Book and Software? | p. 6 |
| How Should the Reader Use This Book? | p. 6 |
| Basic Steps and Key Concepts | p. 7 |
| Historical Perspective | p. 10 |
| Thirty Hypothetical Applications | p. 11 |
| Now It Is Time to Start Analyzing Data | p. 27 |
| Using the ODA Software | p. 29 |
| The ODA Commands | p. 29 |
| Running the ODA Software | p. 37 |
| Using ODA With PFE | p. 38 |
| Creating a Data Set for Analysis by ODA | p. 45 |
| Evaluating Classification Performance | p. 57 |
| How to Obtain an ODA Model | p. 61 |
| Selecting Among Multiple Optimal Models | p. 64 |
| Assessing Model Stability | p. 68 |
| Standardizing Transformations | p. 70 |
| Evaluating Statistical Significance | p. 73 |
| Analytic Methodology | p. 74 |
| Fisher's Randomization Methodology | p. 77 |
| Monte Carlo Methodology | p. 78 |
| Specifying the Type I Error Rate | p. 80 |
| A Priori Alpha Splitting | p. 83 |
| Two-Category Class Variables | p. 87 |
| Applications Involving Binary Attributes | p. 87 |
| Applications Involving Polychotomous Attributes | p. 91 |
| Applications Involving Ordinal Attributes | p. 93 |
| Applications Involving Continuous Attributes | p. 101 |
| Multicategory Class Variables | p. 107 |
| Applications Involving Binary Attributes | p. 108 |
| Applications Involving Polychotomous Attributes | p. 108 |
| Applications Involving Ordinal Attributes | p. 112 |
| Applications Involving Continuous Attributes | p. 115 |
| Reliability Analysis | p. 121 |
| Inter-Rater Reliability | p. 122 |
| Parallel Forms Reliability | p. 128 |
| Split-Half Reliability | p. 130 |
| Temporal Reliability | p. 132 |
| Nonlinear Reliability | p. 135 |
| Intraclass Correlation | p. 138 |
| Validity Analysis | p. 141 |
| Hold-Out (Cross-Generalizability) Validity | p. 142 |
| Construct Validity | p. 148 |
| Convergent and Discriminant Validity | p. 149 |
| Optimizing Suboptimal Multivariable Models | p. 155 |
| Optimizing Fisher's Linear Discriminant Analysis | p. 157 |
| Optimizing Logistic Regression Analysis | p. 160 |
| Optimizing Complex Models | p. 165 |
| Multiple Sample Analysis | p. 167 |
| Pooling Samples and Simpson's Paradox | p. 168 |
| The ODA Generalizability Algorithm | p. 170 |
| Evaluating Model Generalizability Across Samples | p. 172 |
| Analyzing Randomized Block Designs | p. 178 |
| Optimizing Multiple Suboptimal Multiattribute Models | p. 181 |
| Sequential Analyses | p. 187 |
| Identifying Structure in Markov Transition Tables | p. 187 |
| Analyzing Turnover Tables | p. 193 |
| Autocorrelation (Time Series) Analysis | p. 198 |
| Repeated Measures (Within-Subjects) Analysis | p. 203 |
| Single-Case (N-of-1) Analysis | p. 207 |
| Iterative Decomposition Analysis | p. 209 |
| Stopping Rules for Iterative Analyses | p. 212 |
| Structural Decomposition With Sequential Data | p. 214 |
| Reliability, Bias, and Random Error | p. 223 |
| Validity, Bias, and Random Error | p. 225 |
| Epilogue: The Future of ODA | p. 229 |
| General-Purpose MultiODA Models | p. 232 |
| Special-Purpose MultiODA Models | p. 233 |
| Nonlinear Classification Tree Analysis | p. 237 |
| Users of ODA | p. 239 |
| Dunn and Sidak Adjusted Per-Comparison p | p. 241 |
| Troubleshooting: Common Problems and Their Possible Solutions | p. 249 |
| References | p. 253 |
| Index | p. 275 |
| About the Authors | p. 287 |
| Table of Contents provided by Rittenhouse. All Rights Reserved. |
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