| Preface |
|
xi | |
| About the Author |
|
xv | |
|
Chapter 1 Why Data Cleaning Is Important: Debunking the Myth of Robustness |
|
|
1 | (16) |
|
|
|
2 | (3) |
|
Are Things Really That Bad? |
|
|
5 | (3) |
|
Why Care About Testing Assumptions and Cleaning Data? |
|
|
8 | (1) |
|
How Can This State of Affairs Be True? |
|
|
8 | (2) |
|
The Best Practices Orientation of This Book |
|
|
10 | (1) |
|
Data Cleaning Is a Simple Process; However... |
|
|
11 | (1) |
|
One Path to Solving the Problem |
|
|
12 | (1) |
|
|
|
13 | (4) |
|
SECTION I BEST PRACTICES AS YOU PREPARE FOR DATA COLLECTION |
|
|
17 | (68) |
|
Chapter 2 Power and Planning for Data Collection: Debunking the Myth of Adequate Power |
|
|
19 | (24) |
|
Power and Best Practices in Statistical Analysis of Data |
|
|
20 | (2) |
|
How Null-Hypothesis Statistical Testing Relates to Power |
|
|
22 | (1) |
|
What Do Statistical Tests Tell Us? |
|
|
23 | (3) |
|
How Does Power Relate to Error Rates? |
|
|
26 | (2) |
|
Low Power and Type I Error Rates in a Literature |
|
|
28 | (1) |
|
|
|
29 | (2) |
|
The Effect of Power on the Replicability of Study Results |
|
|
31 | (2) |
|
Can Data Cleaning Fix These Sampling Problems? |
|
|
33 | (1) |
|
|
|
34 | (1) |
|
|
|
35 | (1) |
|
|
|
36 | (7) |
|
Chapter 3 Being True to the Target Population: Debunking the Myth of Representativeness |
|
|
43 | (28) |
|
Sampling Theory and Generalizability |
|
|
45 | (1) |
|
Aggregation or Omission Errors |
|
|
46 | (3) |
|
Including Irrelevant Groups |
|
|
49 | (3) |
|
Nonresponse and Generalizability |
|
|
52 | (2) |
|
Consent Procedures and Sampling Bias |
|
|
54 | (2) |
|
Generalizability of Internet Surveys |
|
|
56 | (2) |
|
|
|
58 | (4) |
|
|
|
62 | (3) |
|
|
|
65 | (1) |
|
|
|
65 | (6) |
|
Chapter 4 Using Large Data Sets With Probability Sampling Frameworks: Debunking the Myth of Equality |
|
|
71 | (14) |
|
What Types of Studies Use Complex Sampling? |
|
|
72 | (1) |
|
Why Does Complex Sampling Matter? |
|
|
72 | (2) |
|
Best Practices in Accounting for Complex Sampling |
|
|
74 | (2) |
|
Does It Really Make a Difference in the Results? |
|
|
76 | (4) |
|
So What Does All This Mean? |
|
|
80 | (1) |
|
|
|
81 | (4) |
|
SECTION II BEST PRACTICES IN DATA CLEANING AND SCREENING |
|
|
85 | (126) |
|
Chapter 5 Screening Your Data for Potential Problems: Debunking the Myth of Perfect Data |
|
|
87 | (18) |
|
The Language of Describing Distributions |
|
|
90 | (3) |
|
Testing Whether Your Data Are Normally Distributed |
|
|
93 | (7) |
|
|
|
100 | (1) |
|
|
|
101 | (1) |
|
|
|
101 | (4) |
|
Chapter 6 Dealing With Missing or Incomplete Data: Debunking the Myth of Emptiness |
|
|
105 | (34) |
|
What Is Missing or Incomplete Data? |
|
|
106 | (3) |
|
Categories of Missingness |
|
|
109 | (1) |
|
What Do We Do With Missing Data? |
|
|
110 | (7) |
|
The Effects of Listwise Deletion |
|
|
117 | (1) |
|
The Detrimental Effects of Mean Substitution |
|
|
118 | (4) |
|
The Effects of Strong and Weak Imputation of Values |
|
|
122 | (3) |
|
Multiple Imputation: A Modern Method of Missing Data Estimation |
|
|
125 | (3) |
|
Missingness Can Be an Interesting Variable in and of Itself |
|
|
128 | (2) |
|
Summing Up: What Are Best Practices? |
|
|
130 | (1) |
|
|
|
131 | (1) |
|
|
|
132 | (7) |
|
Chapter 7 Extreme and Influential Data Points: Debunking the Myth of Equality |
|
|
139 | (30) |
|
|
|
140 | (1) |
|
How Extreme Values Affect Statistical Analyses |
|
|
141 | (1) |
|
What Causes Extreme Scores? |
|
|
142 | (7) |
|
Extreme Scores as a Potential Focus of Inquiry |
|
|
149 | (3) |
|
Identification of Extreme Scores |
|
|
152 | (1) |
|
Why Remove Extreme Scores? |
|
|
153 | (3) |
|
Effect of Extreme Scores on Inferential Statistics |
|
|
156 | (1) |
|
Effect of Extreme Scores on Correlations and Regression |
|
|
156 | (5) |
|
Effect of Extreme Scores on t-Tests and ANOVAs |
|
|
161 | (4) |
|
To Remove or Not to Remove? |
|
|
165 | (1) |
|
|
|
165 | (4) |
|
Chapter 8 Improving the Normality of Variables Through Box-Cox Transformation: Debunking the Myth of Distributional Irrelevance |
|
|
169 | (22) |
|
Why Do We Need Data Transformations? |
|
|
171 | (1) |
|
When a Variable Violates the Assumption of Normality |
|
|
171 | (1) |
|
Traditional Data Transformations for Improving Normality |
|
|
172 | (4) |
|
Application and Efficacy of Box-Cox Transformations |
|
|
176 | (5) |
|
Reversing Transformations |
|
|
181 | (3) |
|
|
|
184 | (1) |
|
|
|
185 | (1) |
|
|
|
185 | (6) |
|
Chapter 9 Does Reliability Matter? Debunking the Myth of Perfect Measurement |
|
|
191 | (20) |
|
What Is a Reasonable Level of Reliability? |
|
|
192 | (1) |
|
Reliability and Simple Correlation or Regression |
|
|
193 | (2) |
|
Reliability and Partial Correlations |
|
|
195 | (2) |
|
Reliability and Multiple Regression |
|
|
197 | (1) |
|
Reliability and Interactions in Multiple Regression |
|
|
198 | (1) |
|
Protecting Against Overcorrecting During Disattenuation |
|
|
199 | (1) |
|
Other Solutions to the Issue of Measurement Error |
|
|
200 | (1) |
|
What If We Had Error-Free Measurement? |
|
|
200 | (2) |
|
An Example From My Research |
|
|
202 | (3) |
|
Does Reliability Influence Other Analyses? |
|
|
205 | (1) |
|
The Argument That Poor Reliability Is Not That Important |
|
|
206 | (1) |
|
Conclusions and Best Practices |
|
|
207 | (1) |
|
|
|
208 | (3) |
|
SECTION III ADVANCED TOPICS IN DATA CLEANING |
|
|
211 | (54) |
|
Chapter 10 Random Responding, Motivated Misresponding, and Response Sets: Debunking the Myth of the Motivated Participant |
|
|
213 | (18) |
|
|
|
213 | (1) |
|
Common Types of Response Sets |
|
|
214 | (2) |
|
Is Random Responding Truly Random? |
|
|
216 | (1) |
|
Detecting Random Responding in Your Research |
|
|
217 | (2) |
|
Does Random Responding Cause Serious Problems With Research? |
|
|
219 | (1) |
|
Example of the Effects of Random Responding |
|
|
219 | (5) |
|
Are Random Responders Truly Random Responders? |
|
|
224 | (1) |
|
|
|
224 | (1) |
|
Best Practices Regarding Random Responding |
|
|
225 | (1) |
|
|
|
226 | (1) |
|
|
|
226 | (5) |
|
Chapter 11 Why Dichotomizing Continuous Variables Is Rarely a Good Practice: Debunking the Myth of Categorization |
|
|
231 | (22) |
|
What Is Dichotomization and Why Does It Exist? |
|
|
233 | (1) |
|
How Widespread Is This Practice? |
|
|
234 | (2) |
|
Why Do Researchers Use Dichotomization? |
|
|
236 | (1) |
|
Are Analyses With Dichotomous Variables Easier to Interpret? |
|
|
236 | (1) |
|
Are Analyses With Dichotomous Variables Easier to Compute? |
|
|
237 | (1) |
|
Are Dichotomous Variables More Reliable? |
|
|
238 | (8) |
|
Other Drawbacks of Dichotomization |
|
|
246 | (4) |
|
|
|
250 | (3) |
|
Chapter 12 The Special Challenge of Cleaning Repeated Measures Data: Lots of Pits in Which to Fall |
|
|
253 | (8) |
|
Treat All Time Points Equally |
|
|
253 | (4) |
|
What to Do With Extreme Scores? |
|
|
257 | (1) |
|
|
|
258 | (1) |
|
|
|
258 | (3) |
|
Chapter 13 Now That the Myths Are Debunked...: Visions of Rational Quantitative Methodology for the 21st Century |
|
|
261 | (4) |
| Name Index |
|
265 | (4) |
| Subject Index |
|
269 | |
