Best Practices in Data Cleaning : A Complete Guide to Everything You Need to Do Before and After Collecting Your Data - Jason W. Osborne

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Best Practices in Data Cleaning

A Complete Guide to Everything You Need to Do Before and After Collecting Your Data

By: Jason W. Osborne

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Paperback | 10 January 2012

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296 Pages

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23 x 15 x 2

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$138.60

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Few concepts in this book are new, many are the better part of a century old. So why should you read it? Because there are important steps every quantitative researcher needs to take prior to collecting their data to ensure the data meets their goals. Because after collecting data, and before conducting the critical analyses to test hypotheses, there are other important steps to be taken to ensure the ultimate high quality of the results of those analyses. All of these steps are referred to as ôdata cleaningö although in the strictest sense of the concept planning for data collection does not traditionally fall under that label. Yet careful planning for data collection is critically important to the overall success of the project. There are several critical steps that should be taken prior to testing hypotheses, and your research will benefit from doing these things. If your goal is to do the best research you can do, to draw conclusions that are most likely to be accurate representations of the population(s) you wish to speak about, to report results that are most likely to be replicated by other researchers, then this book is a basic guidebook to helping accomplish these goals.

Industry Reviews

"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." -- Elizabeth M. Flow-Delwiche
"The first comprehensive and generally accessible text in this area." -- J. Michael Hardin

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Non-Fiction Sociology & Anthropology Sociology Social Research & Statistics Science Science in General Reference, Information & Interdisciplinary Subjects Research & Information Research Methods Engineering & Technology Technology in General Engineering in General

Preface	p. xi
About the Author	p. xv
Why Data Cleaning Is Important: Debunking the Myth of Robustness	p. 1
Origins of Data Cleaning	p. 2
Are Things Really That Bad?	p. 5
Why Care About Testing Assumptions and Cleaning Data?	p. 8
How Can This State of Affairs Be True?	p. 8
The Best Practices Orientation of This Book	p. 10
Data Cleaning Is a Simple Process; However…	p. 11
One Path to Solving the Problem	p. 12
For Further Enrichment	p. 13
Best Practices as You Prepare for Data Collection	p. 17
Power and Planning for Data Collection: Debunking the Myth of Adequate Power	p. 19
Power and Best Practices in Statistical Analysis of Data	p. 20
How Null-Hypothesis Statistical Testing Relates to Power	p. 22
What Do Statistical Tests Tell Us?	p. 23
How Does Power Relate to Error Rates?	p. 26
Low Power and Type I Error Rates in a Literature	p. 28
How to Calculate Power	p. 29
The Effect of Power on the Replicability of Study Results	p. 31
Can Data Cleaning Fix These Sampling Problems?	p. 33
Conclusions	p. 34
For Further Enrichment	p. 35
Appendix	p. 36
Being True to the Target Population: Debunking the Myth of Representativeness	p. 43
Sampling Theory and Generalizability	p. 45
Aggregation or Omission Errors	p. 46
Including Irrelevant Groups	p. 49
Nonresponse and Generalizability	p. 52
Consent Procedures and Sampling Bias	p. 54
Generalizability of Internet Surveys	p. 56
Restriction of Range	p. 58
Extreme Groups Analysis	p. 62
Conclusion	p. 65
For Further Enrichment	p. 65
Using Large Data Sets With Probability Sampling Frameworks: Debunking the Myth of Equality	p. 71
What Types of Studies Use Complex Sampling?	p. 72
Why Does Complex Sampling Matter?	p. 72
Best Practices in Accounting for Complex Sampling	p. 74
Does It Really Make a Difference in the Results?	p. 76
So What Does All This Mean?	p. 80
For Further Enrichment	p. 81
Best Practices in Data Cleaning and Screening	p. 85
Screening Your Data for Potential Problems: Debunking the Myth of Perfect Data	p. 87
The Language of Describing Distributions	p. 90
Testing Whether Your Data Are Normally Distributed	p. 93
Conclusions	p. 100
For Further Enrichment	p. 101
Appendix	p. 101
Dealing With Missing or Incomplete Data: Debunking the Myth of Emptiness	p. 105
What Is Missing or Incomplete Data?	p. 106
Categories of Missingness	p. 109
What Do We Do With Missing Data?	p. 110
The Effects of Listwise Deletion	p. 117
The Detrimental Effects of Mean Substitution	p. 118
The Effects of Strong and Weak Imputation of Values	p. 122
Multiple Imputation: A Modern Method of Missing Data Estimation	p. 125
Missingness Can Be an Interesting Variable in and of Itself	p. 128
Summing Up: What Are Best Practices?	p. 130
For Further Enrichment	p. 131
Appendixes	p. 132
Extreme and Influential Data Points: Debunking the Myth of Equality	p. 139
What Are Extreme Scores?	p. 140
How Extreme Values Affect Statistical Analyses	p. 141
What Causes Extreme Scores?	p. 142
Extreme Scores as a Potential Focus of Inquiry	p. 149
Identification of Extreme Scores	p. 152
Why Remove Extreme Scores?	p. 153
Effect of Extreme Scores on Inferential Statistics	p. 156
Effect of Extreme Scores on Correlations and Regression	p. 156
Effect of Extreme Scores on t-Tests and ANOVAs	p. 161
To Remove or Not to Remove?	p. 165
For Further Enrichment	p. 165
Improving the Normality of Variables Through Box-Cox Transformation: Debunking the Myth of Distributional Irrelevance	p. 169
Why Do We Need Data Transformations?	p. 171
When a Variable Violates the Assumption of Normality	p. 171
Traditional Data Transformations for Improving Normality	p. 172
Application and Efficacy of Box-Cox Transformations	p. 176
Reversing Transformations	p. 181
Conclusion	p. 184
For Further Enrichment	p. 185
Appendix	p. 185
Does Reliability Matter? Debunking the Myth of Perfect Measurement	p. 191
What Is a Reasonable Level of Reliability?	p. 192
Reliability and Simple Correlation or Regression	p. 193
Reliability and Partial Correlations	p. 195
Reliability and Multiple Regression	p. 197
Reliability and Interactions in Multiple Regression	p. 198
Protecting Against Overcorrecting During Disattenuation	p. 199
Other Solutions to the Issue of Measurement Error	p. 200
What If We Had Error-Free Measurement?	p. 200
An Example From My Research	p. 202
Does Reliability Influence Other Analyses?	p. 205
The Argument That Poor Reliability Is Not That Important	p. 206
Conclusions and Best Practices	p. 207
For Further Enrichment	p. 208
Advanced Topics in Data Cleaning	p. 211
Random Responding, Motivated Misresponding, and Response Sets: Debunking the Myth of the Motivated Participant	p. 213
What Is a Response Set?	p. 213
Common Types of Response Sets	p. 214
Is Random Responding Truly Random?	p. 216
Detecting Random Responding in Your Research	p. 217
Does Random Responding Cause Serious Problems With Research?	p. 219
Example of the Effects of Random Responding	p. 219
Are Random Responders Truly Random Responders?	p. 224
Summary	p. 224
Best Practices Regarding Random Responding	p. 225
Magnitude of the Problem	p. 226
For Further Enrichment	p. 226
Why Dichotomizing Continuous Variables Is Rarely a Good Practice: Debunking the Myth of Categorization	p. 231
What Is Dichotomization and Why Does It Exist?	p. 233
How Widespread Is This Practice?	p. 234
Why Do Researchers Use Dichotomization?	p. 236
Are Analyses With Dichotomous Variables Easier to Interpret?	p. 236
Are Analyses With Dichotomous Variables Easier to Compute?	p. 237
Are Dichotomous Variables More Reliable?	p. 238
Other Drawbacks of Dichotomization	p. 246
For Further Enrichment	p. 250
The Special Challenge of Cleaning Repeated Measures Data: Lots of Pits in Which to Fall	p. 253
Treat All Time Points Equally	p. 253
What to Do With Extreme Scores?	p. 257
Missing Data	p. 258
Summary	p. 258
Now That the Myths Are Debunked …: Visions of Rational Quantitative Methodology for the 21st Century	p. 261
Name Index	p. 265
Subject Index	p. 269
Table of Contents provided by Ingram. All Rights Reserved.

Best Practices in Data Cleaning

A Complete Guide to Everything You Need to Do Before and After Collecting Your Data

At a Glance

Paperback

Industry Reviews

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How to return your order

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You Can Find This Book In

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