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

293 Pages·2012·9.26 MB·English

by  Jason W. Osborne

Best Practices in
Data
Cleaning
This book is dedicated to my parents, James and Susan Osborne. They have
always encouraged the delusions of grandeur that led me to write this book.
Through all the bumps life placed in my path, they have been the
constant support needed to persevere. Thank you, thank you, thank you.
I also dedicate this book to my children, Collin, Andrew, and Olivia,
who inspire me to be the best I can be, in the vague hope that at
some distant point in the future they will be proud of the work their
father has done. It is their future we are shaping through our research,
and I hope that in some small way I contribute to it being a bright one.
My wife deserves special mention in this dedication as
she is the one who patiently attempts to keep me grounded in the real
world while I immerse myself in whatever methodological esoterica
I am fascinated with at the moment. Thank you for everything.
Best Practices in
Data
Cleaning
This book is dedicated to my parents, James and Susan Osborne. They have
always encouraged the delusions of grandeur that led me to write this book.
Through all the bumps life placed in my path, they have been the
constant support needed to persevere. Thank you, thank you, thank you.
A Complete Guide to Everything
I also dedicate this book to my children, Collin, Andrew, and Olivia, You Need to Do Before and After
who inspire me to be the best I can be, in the vague hope that at
Collecting Your Data
some distant point in the future they will be proud of the work their
father has done. It is their future we are shaping through our research,
and I hope that in some small way I contribute to it being a bright one.
My wife deserves special mention in this dedication as
she is the one who patiently attempts to keep me grounded in the real
world while I immerse myself in whatever methodological esoterica
Jason W. Osborne
I am fascinated with at the moment. Thank you for everything.
Old Dominion University
FOR INFORMATION: Copyright © 2013 by SAGE Publications, Inc.
SAGE Publications, Inc.
All rights reserved. No part of this book may be
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Library of Congress Cataloging-in-Publication Data
SAGE Publications India Pvt. Ltd.
B 1/I 1 Mohan Cooperative Industrial Area Osborne, Jason W.
Mathura Road, New Delhi 110 044
Best practices in data cleaning : a complete guide to
India
everything you need to do before and after collecting
SAGE Publications Asia-Pacific Pte. Ltd. your data / Jason W. Osborne.
33 Pekin Street #02-01
p. cm.
Far East Square
Includes bibliographical references and index.
Singapore 048763
ISBN 978-1-4129-8801-8 (pbk.)
1. Quantitative research. 2. Social sciences—
Methodology. I. Title.
H62.O82 2013
001.4'2—dc23 2011045607
Acquisitions Editor: Vicki Knight
Associate Editor: Lauren Habib This book is printed on acid-free paper.
Editorial Assistant: Kalie Koscielak
Production Editor: Eric Garner
Copy Editor: Trey Thoelcke
Typesetter: C&M Digitals (P) Ltd.
Proofreader: Laura Webb
Indexer: Sheila Bodell
Cover Designer: Anupama Krishnan
Marketing Manager: Helen Salmon
Permissions Editor: Adele Hutchinson 12 13 14 15 16 10 9 8 7 6 5 4 3 2 1
CONTENTS
Preface xi
About the Author xv
Chapter 1 Why Data Cleaning Is Important:
Debunking the Myth of Robustness 1
Origins of Data Cleaning 2
Are Things Really That Bad? 5
Why Care About Testing Assumptions
and Cleaning Data? 8
How Can This State of Affairs Be True? 8
The Best Practices Orientation of This Book 10
Data Cleaning Is a Simple Process; However . . . 11
One Path to Solving the Problem 12
For Further Enrichment 13
SECTION I: BEST PRACTICES AS YOU
PREPARE FOR DATA COLLECTION 17
Chapter 2 Power and Planning for Data Collection:
Debunking the Myth of Adequate Power 19
Power and Best Practices in Statistical Analysis of Data 20
How Null-Hypothesis Statistical Testing Relates to Power 22
What Do Statistical Tests Tell Us? 23
How Does Power Relate to Error Rates? 26
Low Power and Type I Error Rates in a Literature 28
How to Calculate Power 29
The Effect of Power on the Replicability of Study Results 31
Can Data Cleaning Fix These Sampling Problems? 33
Conclusions 34
For Further Enrichment 35
Appendix 36
Chapter 3 Being True to the Target Population:
Debunking the Myth of Representativeness 43
Sampling Theory and Generalizability 45
Aggregation or Omission Errors 46
Including Irrelevant Groups 49
Nonresponse and Generalizability 52
Consent Procedures and Sampling Bias 54
Generalizability of Internet Surveys 56
Restriction of Range 58
Extreme Groups Analysis 62
Conclusion 65
For Further Enrichment 65
Chapter 4 Using Large Data Sets With Probability
Sampling Frameworks: Debunking the Myth of Equality 71
What Types of Studies Use Complex Sampling? 72
Why Does Complex Sampling Matter? 72
Best Practices in Accounting for Complex Sampling 74
Does It Really Make a Difference in the Results? 76
So What Does All This Mean? 80
For Further Enrichment 81
SECTION II: BEST PRACTICES IN
DATA CLEANING AND SCREENING 85
Chapter 5 Screening Your Data for Potential
Problems: Debunking the Myth of Perfect Data 87
The Language of Describing Distributions 90
Testing Whether Your Data Are Normally Distributed 93
Conclusions 100
For Further Enrichment 101
Appendix 101
Chapter 6 Dealing With Missing or Incomplete
Data: Debunking the Myth of Emptiness 105
What Is Missing or Incomplete Data? 106
Categories of Missingness 109
What Do We Do With Missing Data? 110
The Effects of Listwise Deletion 117
The Detrimental Effects of Mean Substitution 118
The Effects of Strong and Weak Imputation of Values 122
Multiple Imputation: A Modern Method
of Missing Data Estimation 125
Missingness Can Be an Interesting Variable in and of Itself 128
Summing Up: What Are Best Practices? 130
For Further Enrichment 131
Appendixes 132
Chapter 7 Extreme and Influential Data Points:
Debunking the Myth of Equality 139
What Are Extreme Scores? 140
How Extreme Values Affect Statistical Analyses 141
What Causes Extreme Scores? 142
Extreme Scores as a Potential Focus of Inquiry 149
Identification of Extreme Scores 152
Why Remove Extreme Scores? 153
Effect of Extreme Scores on Inferential Statistics 156
Effect of Extreme Scores on Correlations and Regression 156
Effect of Extreme Scores on t-Tests and ANOVAs 161
To Remove or Not to Remove? 165
For Further Enrichment 165
Chapter 8 Improving the Normality of Variables
Through Box-Cox Transformation: Debunking
the Myth of Distributional Irrelevance 169
Why Do We Need Data Transformations? 171
When a Variable Violates the Assumption of Normality 171
Traditional Data Transformations for Improving Normality 172
Application and Efficacy of Box-Cox Transformations 176
Reversing Transformations 181
Conclusion 184
For Further Enrichment 185
Appendix 185
Chapter 9 Does Reliability Matter? Debunking
the Myth of Perfect Measurement 191
What Is a Reasonable Level of Reliability? 192
Reliability and Simple Correlation or Regression 193
Reliability and Partial Correlations 195
Reliability and Multiple Regression 197
Reliability and Interactions in Multiple Regression 198
Protecting Against Overcorrecting During Disattenuation 199
Other Solutions to the Issue of Measurement Error 200
What If We Had Error-Free Measurement? 200
An Example From My Research 202
Does Reliability Influence Other Analyses? 205
The Argument That Poor Reliability Is Not That Important 206
Conclusions and Best Practices 207
For Further Enrichment 208
SECTION III: ADVANCED TOPICS IN DATA CLEANING 211
Chapter 10 Random Responding, Motivated Misresponding,
and Response Sets: Debunking the Myth of the
Motivated Participant 213
What Is a Response Set? 213
Common Types of Response Sets 214
Is Random Responding Truly Random? 216
Detecting Random Responding in Your Research 217
Does Random Responding Cause Serious
Problems With Research? 219
Example of the Effects of Random Responding 219
Are Random Responders Truly Random Responders? 224
Summary 224
Best Practices Regarding Random Responding 225
Magnitude of the Problem 226
For Further Enrichment 226
Chapter 11 Why Dichotomizing Continuous Variables Is
Rarely a Good Practice: Debunking the Myth of Categorization 231
What Is Dichotomization and Why Does It Exist? 233
How Widespread Is This Practice? 234
Why Do Researchers Use Dichotomization? 236
Are Analyses With Dichotomous
Variables Easier to Interpret? 236
Are Analyses With Dichotomous
Variables Easier to Compute? 237
Are Dichotomous Variables More Reliable? 238
Other Drawbacks of Dichotomization 246
For Further Enrichment 250
Chapter 12 The Special Challenge of Cleaning Repeated
Measures Data: Lots of Pits in Which to Fall 253
Treat All Time Points Equally 253
What to Do With Extreme Scores? 257
Missing Data 258
Summary 258
Chapter 13 Now That the Myths Are Debunked . . . : Visions
of Rational Quantitative Methodology for the 21st Century 261
Name Index 265
Subject Index 269