Best practices in data cleaning: a complete guide to everything you need to do before and after collecting your data
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Los Angeles [u.a.]
SAGE
2013
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis Klappentext |
| Item Description: | Nebentitel: Logistic regression Includes bibliographical references and indexes |
| Physical Description: | XV, 275 S. graph. Darst. 23 cm |
| ISBN: | 9781412988018 1412988012 |
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Record in the Search Index
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| adam_text | Titel: Best practices in data cleaning
Autor: Osborne, Jason W
Jahr: 2013
Preface xi
About the Author xv
Chapter 1 Why Data Cleaning Is Important:
Debunking the Myth of Robustness T
Origins of Data Cleaning 2
Are Things Reaily 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 ls 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 Teil 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 Sampüng Problems? 33
Conclusions 34
For Further Enrichment 35
Appendix 36
Chapter 3 Being True to the Target Population:
Debunking the Myth of Representativeness 43
SamplingTheory 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
WhatTypes 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 lncomplete
Data: Debunking the Myth of Emptiness 105
What Is Missing or lncomplete 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 21 st Century 261
Name Index 265
Subject Index 269
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 for examining and cleaning
data in order to decrease error rates and increase both the power and replicability of
results. Jason W. Osborne, author
oí
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 is indispensible.
FEATURES
&
BENEFITS
•
Clear guidance with a step-by-step process of examining and cleaning data
will decrease error rates and increase both the power and
replicability
of results.
Easily implementa
in practice.
.
will motivate change
The author demonstrates the benefits of following best practices through
examples of real research data.
Debunking ten common research myths helps the reader hone a more
accurate view of research and data gathering.
AKCILLARIES
;
www.saaepub.com/osborne includes relevant
and activities for both instructors and students to use to extend and reinforce their
understanding of the subject matter.
|
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| spelling | Osborne, Jason W. Verfasser (DE-588)1037994396 aut Best practices in data cleaning a complete guide to everything you need to do before and after collecting your data Jason W. Osborne Logistic regression Los Angeles [u.a.] SAGE 2013 XV, 275 S. graph. Darst. 23 cm txt rdacontent n rdamedia nc rdacarrier Nebentitel: Logistic regression Includes bibliographical references and indexes Sozialwissenschaften Quantitative research Social sciences Methodology Empirische Forschung (DE-588)4300400-3 gnd rswk-swf Datenanalyse (DE-588)4123037-1 gnd rswk-swf Datenanalyse (DE-588)4123037-1 s Empirische Forschung (DE-588)4300400-3 s DE-604 HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025498461&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Augsburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025498461&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext |
| spellingShingle | Osborne, Jason W. Best practices in data cleaning a complete guide to everything you need to do before and after collecting your data Sozialwissenschaften Quantitative research Social sciences Methodology Empirische Forschung (DE-588)4300400-3 gnd Datenanalyse (DE-588)4123037-1 gnd |
| subject_GND | (DE-588)4300400-3 (DE-588)4123037-1 |
| title | Best practices in data cleaning a complete guide to everything you need to do before and after collecting your data |
| title_alt | Logistic regression |
| title_auth | Best practices in data cleaning a complete guide to everything you need to do before and after collecting your data |
| title_exact_search | Best practices in data cleaning a complete guide to everything you need to do before and after collecting your data |
| title_full | Best practices in data cleaning a complete guide to everything you need to do before and after collecting your data Jason W. Osborne |
| title_fullStr | Best practices in data cleaning a complete guide to everything you need to do before and after collecting your data Jason W. Osborne |
| title_full_unstemmed | Best practices in data cleaning a complete guide to everything you need to do before and after collecting your data Jason W. Osborne |
| title_short | Best practices in data cleaning |
| title_sort | best practices in data cleaning a complete guide to everything you need to do before and after collecting your data |
| title_sub | a complete guide to everything you need to do before and after collecting your data |
| topic | Sozialwissenschaften Quantitative research Social sciences Methodology Empirische Forschung (DE-588)4300400-3 gnd Datenanalyse (DE-588)4123037-1 gnd |
| topic_facet | Sozialwissenschaften Quantitative research Social sciences Methodology Empirische Forschung Datenanalyse |
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