Best Practices in Data Cleaning

Jason W. Osborne is a thought leader and professor in higher education. His background in educational psychology, statistics and quantitative methods, along with that gleaned from high-level positions within Academia gives a unique perspective on the real-world data factors. In 2015, he was appointed Associate Provost and Dean of the Graduate School at Clemson University in Clemson, South Carolina. As well as Associate Provost, at Clemson University, Jason was a Professor of applied statistics at the School of Mathematical Sciences, with a secondary appointment in Public Health Science. In 2019, he took on the role of Provost and Executive VP for Academic Affairs at Miami University. As Provost, Jason implemented a transformative strategic plan to reposition the institution as one prepared for new challenges with a modern, compelling curriculum, a welcoming environment, and enhanced support for student faculty positions and staff. In 2021, he was named by Stanford University as one of the top 2% researchers in the world, underlining his commitment to world-class research methods across particular domains, ultimately influencing a generation of learners. Currently, Jason teaches and publishes on data analysis "best practices" in quantitative and applied research methods. He has served as evaluator or consultant on research projects and in public education (K-12), instructional technology, health care, medicine and business. He served as founding editor of Frontiers in Quantitative Psychology and Measurement and has been on the editorial boards of several other journals (such as Practical Assessment, Research, and Evaluation). Jason W Osborne also publishes on identification with academics and on issues related to social justice and diversity. He has written seven books covering topics to communicate logistic regression and linear modeling, exploratory factor analysis, best practices and modern research methods, data cleaning, and numerous other topics.

“This book provides the perfect bridge between the formal study of statistics and the practice of statistics. It fills the gap left by many of the traditional texts that focus either on the technical presentation or recipe-driven presentation of topics.”

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