Best Practices in Data Cleaning

Jason W. Osborne

ISBN 10: 1412988012 / ISBN 13: 9781412988018

Published by SAGE Publications Inc, US, 2012

About this Item

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.

Seller Inventory # LU-9781412988018

Bibliographic Details

• Title: Best Practices in Data Cleaning
• Publisher: SAGE Publications Inc, US
• Publication Date: 2012
• Language: English
• ISBN 10: 1412988012
• ISBN 13: 9781412988018
• Binding: Paperback
• Condition: New

About this title

Synopsis

Many researchers jump from data collection directly into testing hypothesis without realizing these tests can go profoundly wrong without clean data. This book provides a clear, accessible, step-by-step process of important best practices in preparing for data collection, testing assumptions, and examining and cleaning data in order to decrease error rates and increase both the power and replicability of results.

Jason W. Osborne, author of the handbook Best Practices in Quantitative Methods (SAGE, 2008) provides easily-implemented suggestions that are evidence-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.

About the Author

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.