Best Practices in Data Cleaning: A Complete Guide to Everything You Need to Do Before and After Collecting Your Data

Abstract

Chapter 1. Why Data Cleaning is Important: Debunking the Myth of Robustness Part 1. Best Practices as you Prepare for Data Collection Chapter 2. Power and Planning for Data Collection: Debunking the Myth of Adequate Power Chapter 3. Being True to the Target Population: Debunking the Myth of Representativeness Chapter 4. Using Large Data Sets with Probability Sampling Frameworks: Debunking the Myth of Equality Part 2. Best Practices in Data Cleaning and Screening Chapter 5. Screening your Data for Potential Problems: Debunking the Myth of Perfect Data Chapter 6. Dealing with Missing or Incomplete Data: Debunking the Myth of Emptiness Chapter 7. Extreme and Influential Data Points: Debunking the Myth of Equality Chapter 8. Improving the Normality of Variables through Box-Cox Transformation: Debunking the Myth of Distributional Irrelevance Chapter 9. Does Reliability Matter? Debunking the Myth of Perfect Measurement Part 3. Advanced Topics in Data Cleaning Chapter 10. Random Responding, Motivated Mis-Responding, and Response Sets: Debunking the Myth of the Motivated Participant Chapter 11. Why Dichotomizing Continuous Variables is Rarely a Good Practice: Debunking the Myth of Categorization Chapter 12. The Special Challenge of Cleaning Repeated Measures Data: Lots of Pits to Fall into Chapter 13. Now that the Myths are Debunked... Visions of Rational Quantitative Methodology for the 21st Century

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