Best Practices in Data Cleaning

A Complete Guide to Everything You Need to Do Before and After Collecting Your Data
1. Auflage 2012
ISBN: 978-1-4129-8801-8
Verlag: Sage Publications, Inc

Buch, Englisch, 296 Seiten, Format (B × H): 152 mm x 229 mm, Gewicht: 432 g

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 of 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 will be indispensible.

Autoren/Hrsg.

Osborne, Jason W.

Fachgebiete

• Sozialwissenschaften Soziologie | Soziale Arbeit Soziologie Allgemein Empirische Sozialforschung, Statistik
• Sozialwissenschaften Pädagogik Berufliche Bildung Wissenschaftliches Arbeiten, Studientechnik
• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
• Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Datenanalyse, Datenverarbeitung
• Interdisziplinäres Wissenschaften Wissenschaften: Allgemeines Wissenschaftliches Arbeiten, Studientechnik

Inhaltsverzeichnis

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

Über Autor(innen)

Osborne, Jason W.
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.

Jason W. Osborne is currently an Associate Professor of Educational Psychology and Research Methods at Old Dominion University. He teaches and publishes on best practices in quantitative and applied research methods. He has served as external evaluator or consultant on projects in public education (K–12), instructional technology, higher education, nursing and health care, medicine and medical training, epidemiology, business and marketing, and jury selection. He is Specialty Chief Editor of Frontiers in Quantitative Psychology and Measurement as well as being involved in several other journals. Jason also publishes on identification with academics (how a student’s self-concept impacts motivation to succeed in academics) and on issues related to social justice and diversity (such as stereotype threat). He is the very proud father of three and, along with his two sons, is currently a second degree black belt in American tae kwon do.