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

Many researchers jump straight from data collection to data analysis without realizing how analyses and hypothesis tests can go profoundly wrong without clean data. This book provides a clear, step-by-step process to examining and cleaning data in order to decrease error rates and increase both the power and replicability of results. Jason W. Osborne, author of Best Practices in Quantitative Methods (SAGE, 2008) provides easily-implemented suggestions that are research-based and will motivate change in practice by empirically demonstrating for each topic the benefits of following best practices and the potential consequences of not following these guidelines. If your goal is to do the best research you can do, draw conclusions that are most likely to be accurate representations of the population(s) you wish to speak about, and report results that are most likely to be replicated by other researchers, then this basic guidebook is indispensible.  

Table of Contents:
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