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| Preface | |
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| About the Author | |
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| Why Data Cleaning Is Important: Debunking the Myth of Robustness | |
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| Origins of Data Cleaning | |
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| Are Things Really That Bad? | |
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| Why Care About Testing Assumptions and Cleaning Data? | |
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| How Can This State of Affairs Be True? | |
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| The Best Practices Orientation of This Book | |
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| Data Cleaning Is a Simple Process; However… | |
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| One Path to Solving the Problem | |
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| For Further Enrichment | |
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| Best Practices as You Prepare for Data Collection | |
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| Power and Planning for Data Collection: Debunking the Myth of Adequate Power | |
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| Power and Best Practices in Statistical Analysis of Data | |
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| How Null-Hypothesis Statistical Testing Relates to Power | |
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| What Do Statistical Tests Tell Us? | |
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| How Does Power Relate to Error Rates? | |
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| Low Power and Type I Error Rates in a Literature | |
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| How to Calculate Power | |
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| The Effect of Power on the Replicability of Study Results | |
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| Can Data Cleaning Fix These Sampling Problems? | |
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| Conclusions | |
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| For Further Enrichment | |
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| Appendix | |
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| Being True to the Target Population: Debunking the Myth of Representativeness | |
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| Sampling Theory and Generalizability | |
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| Aggregation or Omission Errors | |
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| Including Irrelevant Groups | |
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| Nonresponse and Generalizability | |
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| Consent Procedures and Sampling Bias | |
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| Generalizability of Internet Surveys | |
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| Restriction of Range | |
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| Extreme Groups Analysis | |
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| Conclusion | |
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| For Further Enrichment | |
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| Using Large Data Sets With Probability Sampling Frameworks: Debunking the Myth of Equality | |
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| What Types of Studies Use Complex Sampling? | |
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| Why Does Complex Sampling Matter? | |
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| Best Practices in Accounting for Complex Sampling | |
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| Does It Really Make a Difference in the Results? | |
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| So What Does All This Mean? | |
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| For Further Enrichment | |
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| Best Practices in Data Cleaning and Screening | |
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| Screening Your Data for Potential Problems: Debunking the Myth of Perfect Data | |
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| The Language of Describing Distributions | |
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| Testing Whether Your Data Are Normally Distributed | |
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| Conclusions | |
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| For Further Enrichment | |
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| Appendix | |
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| Dealing With Missing or Incomplete Data: Debunking the Myth of Emptiness | |
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| What Is Missing or Incomplete Data? | |
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| Categories of Missingness | |
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| What Do We Do With Missing Data? | |
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| The Effects of Listwise Deletion | |
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| The Detrimental Effects of Mean Substitution | |
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| The Effects of Strong and Weak Imputation of Values | |
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| Multiple Imputation: A Modern Method of Missing Data Estimation | |
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| Missingness Can Be an Interesting Variable in and of Itself | |
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| Summing Up: What Are Best Practices? | |
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| For Further Enrichment | |
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| Appendixes | |
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| Extreme and Influential Data Points: Debunking the Myth of Equality | |
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| What Are Extreme Scores? | |
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| How Extreme Values Affect Statistical Analyses | |
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| What Causes Extreme Scores? | |
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| Extreme Scores as a Potential Focus of Inquiry | |
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| Identification of Extreme Scores | |
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| Why Remove Extreme Scores? | |
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| Effect of Extreme Scores on Inferential Statistics | |
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| Effect of Extreme Scores on Correlations and Regression | |
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| Effect of Extreme Scores on t-Tests and ANOVAs | |
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| To Remove or Not to Remove? | |
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| For Further Enrichment | |
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| Improving the Normality of Variables Through Box-Cox Transformation: Debunking the Myth of Distributional Irrelevance | |
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| Why Do We Need Data Transformations? | |
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| When a Variable Violates the Assumption of Normality | |
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| Traditional Data Transformations for Improving Normality | |
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| Application and Efficacy of Box-Cox Transformations | |
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| Reversing Transformations | |
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| Conclusion | |
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| For Further Enrichment | |
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| Appendix | |
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| Does Reliability Matter? Debunking the Myth of Perfect Measurement | |
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| What Is a Reasonable Level of Reliability? | |
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| Reliability and Simple Correlation or Regression | |
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| Reliability and Partial Correlations | |
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| Reliability and Multiple Regression | |
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| Reliability and Interactions in Multiple Regression | |
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| Protecting Against Overcorrecting During Disattenuation | |
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| Other Solutions to the Issue of Measurement Error | |
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| What If We Had Error-Free Measurement? | |
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| An Example From My Research | |
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| Does Reliability Influence Other Analyses? | |
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| The Argument That Poor Reliability Is Not That Important | |
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| Conclusions and Best Practices | |
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| For Further Enrichment | |
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| Advanced Topics in Data Cleaning | |
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| Random Responding, Motivated Misresponding, and Response Sets: Debunking the Myth of the Motivated Participant | |
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| What Is a Response Set? | |
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| Common Types of Response Sets | |
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| Is Random Responding Truly Random? | |
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| Detecting Random Responding in Your Research | |
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| Does Random Responding Cause Serious Problems With Research? | |
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| Example of the Effects of Random Responding | |
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| Are Random Responders Truly Random Responders? | |
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| Summary | |
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| Best Practices Regarding Random Responding | |
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| Magnitude of the Problem | |
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| For Further Enrichment | |
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| Why Dichotomizing Continuous Variables Is Rarely a Good Practice: Debunking the Myth of Categorization | |
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| What Is Dichotomization and Why Does It Exist? | |
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| How Widespread Is This Practice? | |
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| Why Do Researchers Use Dichotomization? | |
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| Are Analyses With Dichotomous Variables Easier to Interpret? | |
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| Are Analyses With Dichotomous Variables Easier to Compute? | |
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| Are Dichotomous Variables More Reliable? | |
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| Other Drawbacks of Dichotomization | |
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| For Further Enrichment | |
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| The Special Challenge of Cleaning Repeated Measures Data: Lots of Pits in Which to Fall | |
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| Treat All Time Points Equally | |
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| What to Do With Extreme Scores? | |
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| Missing Data | |
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| Summary | |
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| Now That the Myths Are Debunked …: Visions of Rational Quantitative Methodology for the 21st Century | |
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| Name Index | |
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| Subject Index | |