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Behavioral Research Data Analysis with R |
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Preface |
5 |
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Contents |
9 |
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Chapter 1 Introduction |
13 |
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1.1 An Example R Session |
13 |
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1.2 A Few Useful Concepts and Commands |
15 |
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1.2.1 Concepts |
15 |
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1.2.2 Commands |
16 |
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1.2.2.1 Working Directory |
16 |
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1.2.2.2 Getting Help |
17 |
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1.2.2.3 Installing Packages |
18 |
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1.2.2.4 Assignment, Logic, and Arithmetic |
18 |
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1.2.2.5 Loading and Saving |
20 |
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1.2.2.6 Dealing with Objects |
21 |
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1.3 Data Objects and Data Types |
21 |
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1.3.1 Vectors of Character Strings |
22 |
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1.3.2 Matrices, Lists, and Data Frames |
24 |
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1.3.2.1 Summaries and Calculations by Row, Column, or Group |
26 |
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1.4 Functions and Debugging |
27 |
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Chapter 2 Reading and Transforming Data Format |
30 |
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2.1 Reading and Transforming Data |
30 |
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2.1.1 Data Layout |
30 |
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2.1.2 A Simple Questionnaire Example |
30 |
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2.1.2.1 Extracting Subsets of Data |
31 |
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2.1.2.2 Finding Means (or Other Things) of Sets of Variables |
32 |
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2.1.2.3 One Row Per Observation |
32 |
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2.1.3 Other Ways to Read in Data |
36 |
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2.1.4 Other Ways to Transform Variables |
37 |
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2.1.4.1 Contrasts |
37 |
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2.1.4.2 Averaging Items in a Within-Subject Design |
38 |
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2.1.4.3 Selecting Cases or Variables |
39 |
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2.1.4.4 Recoding and Replacing Data |
39 |
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2.1.4.5 Replacing Characters with Numbers |
41 |
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2.1.5 Using R to Compute Course Grades |
41 |
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2.2 Reshape and Merge Data Frames |
42 |
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2.3 Data Management with a SQL Database |
44 |
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2.4 SQL Database Considerations |
46 |
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Chapter 3 Statistics for Comparing Means and Proportions |
49 |
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3.1 Comparing Means of Continuous Variables |
49 |
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3.2 More on Manual Checking of Data |
52 |
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3.3 Comparing Sample Proportions |
53 |
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3.4 Moderating Effect in loglin() |
55 |
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3.5 Assessing Change of Correlated Proportions |
59 |
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3.5.1 McNemar Test Across Two Samples |
60 |
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Chapter 4 R Graphics and Trellis Plots |
65 |
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4.1 Default Behavior of Basic Commands |
65 |
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4.2 Other Graphics |
66 |
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4.3 Saving Graphics |
66 |
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4.4 Multiple Figures on One Screen |
67 |
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4.5 Other Graphics Tricks |
67 |
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4.6 Examples of Simple Graphs in Publications |
68 |
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4.6.1 http://journal.sjdm.org/8827/jdm8827.pdf |
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4.6.2 http://journal.sjdm.org/8814/jdm8814.pdf |
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4.6.3 http://journal.sjdm.org/8801/jdm8801.pdf |
74 |
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4.6.4 http://journal.sjdm.org/8319/jdm8319.pdf |
75 |
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4.6.5 http://journal.sjdm.org/8221/jdm8221.pdf |
76 |
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4.6.6 http://journal.sjdm.org/8210/jdm8210.pdf |
78 |
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4.7 Shaded Areas Under a Curve |
79 |
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4.7.1 Vectors in polygon() |
81 |
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4.8 Lattice Graphics |
82 |
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4.8.0.1 Mathematics Achievement and Socioeconomic Status |
82 |
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Chapter 5 Analysis of Variance: Repeated-Measures |
88 |
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5.1 Example 1: Two Within-Subject Factors |
88 |
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5.1.1 Unbalanced Designs |
92 |
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5.2 Example 2: Maxwell and Delaney |
94 |
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5.3 Example 3: More Than Two Within-Subject Factors |
97 |
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5.4 Example 4: A Simpler Design with Only One Within-Subject Variable |
98 |
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5.5 Example 5: One Between, Two Within |
98 |
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5.6 Other Useful Functions for ANOVA |
100 |
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5.7 Graphics with Error Bars |
102 |
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5.8 Another Way to do Error Bars Using plotCI() |
104 |
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5.8.1 Use Error() for Repeated-Measure ANOVA |
105 |
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5.8.1.1 Basic ANOVA Table with aov() |
106 |
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5.8.1.2 Using Error() Within aov() |
107 |
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5.8.1.3 The Appropriate Error Terms |
107 |
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5.8.1.4 Sources of the Appropriate Error Terms |
108 |
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5.8.1.5 Verify the Calculations Manually |
110 |
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5.8.2 Sphericity |
111 |
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5.8.2.1 Why Is Sphericity Important? |
111 |
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5.9 How to Estimate the Greenhouse–Geisser Epsilon? |
112 |
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5.9.1 Huynh–Feldt Correction |
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Chapter 6 Linear and Logistic Regression |
117 |
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6.1 Linear Regression |
117 |
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6.2 An Application of Linear Regression on Diamond Pricing |
118 |
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6.2.1 Plotting Data Before Model Fitting |
119 |
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6.2.2 Checking Model Distributional Assumptions |
122 |
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6.2.3 Assessing Model Fit |
123 |
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6.3 Logistic Regression |
126 |
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6.4 Log–Linear Models |
127 |
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6.5 Regression in Vector–Matrix Notation |
128 |
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6.6 Caution on Model Overfit and Classification Errors |
130 |
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Chapter 7 Statistical Power and Sample Size Considerations |
136 |
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7.1 A Simple Example |
136 |
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7.2 Basic Concepts on Statistical Power Estimation |
137 |
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7.3 t-Test with Unequal Sample Sizes |
138 |
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7.4 Binomial Proportions |
139 |
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7.5 Power to Declare a Study Feasible |
140 |
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7.6 Repeated-Measures ANOVA |
140 |
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7.7 Cluster-Randomized Study Design |
142 |
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Chapter 8 Item Response Theory |
145 |
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8.1 Overview |
145 |
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8.2 Rasch Model for Dichotomous Item Responses |
145 |
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8.2.1 Fitting a rasch() Model |
146 |
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8.2.2 Graphing Item Characteristics and Item Information |
149 |
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8.2.3 Scoring New Item Response Data |
151 |
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8.2.4 Person Fit and Item Fit Statistics |
151 |
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8.3 Generalized Partial Credit Model for Polytomous ItemResponses |
152 |
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8.3.1 Neuroticism Data |
153 |
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8.3.2 Category Response Curves and Item InformationCurves |
153 |
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8.4 Bayesian Methods for Fitting IRT Models |
155 |
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8.4.1 GPCM |
155 |
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8.4.2 Explanatory IRT |
158 |
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Chapter 9 Imputation of Missing Data |
166 |
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9.1 Missing Data in Smoking Cessation Study |
166 |
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9.2 Multiple Imputation with aregImpute() |
168 |
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9.2.1 Imputed Data |
170 |
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9.2.2 Pooling Results Over Imputed Datasets |
171 |
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9.3 Multiple Imputation with the mi Package |
173 |
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9.4 Multiple Imputation with the Amelia and Zelig Packages |
176 |
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9.5 Further Reading |
178 |
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Chapter 10 Linear Mixed-Effects Models in Analyzing Repeated-Measures Data |
181 |
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10.1 The "Language-as-Fixed-Effect Fallacy' |
181 |
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10.2 Recall Scores Example: One Between and One Within Factor |
184 |
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10.2.1 Data Preparations |
184 |
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10.2.2 Data Visualizations |
185 |
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10.2.3 Initial Modeling |
186 |
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10.2.4 Model Interpretation |
186 |
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10.2.4.1 Fixed Effects |
186 |
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10.2.4.2 Random Effects |
189 |
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10.2.5 Alternative Models |
190 |
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10.2.6 Checking Model Fit Visually |
193 |
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10.2.7 Modeling Dependence |
194 |
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10.3 Generalized Least Squares Using gls() |
199 |
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10.4 Example on Random and Nested Effects |
202 |
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10.4.1 Treatment by Therapist Interaction |
204 |
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Chapter 11 Linear Mixed-Effects Models in Cluster-Randomized Studies |
209 |
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11.1 The Television, School, and Family Smoking Prevention and Cessation Project |
209 |
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11.2 Data Import and Preparations |
210 |
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11.2.1 Exploratory Analyses |
211 |
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11.3 Testing Intervention Efficacy with Linear Mixed-Effects Models |
214 |
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11.4 Model Equation |
217 |
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11.5 Multiple-Level Model Equations |
219 |
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11.6 Model Equation in Matrix Notations |
220 |
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11.7 Intraclass Correlation Coefficients |
224 |
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11.8 ICCs from a Mixed-Effects Model |
225 |
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11.9 Statistical Power Considerationsfor a Group-Randomized Design |
227 |
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11.9.1 Calculate Statistical Power by Simulation |
227 |
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Appendix A Data Management with a Database |
232 |
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A.1 Create Database and Database Tables |
232 |
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A.2 Enter Data |
233 |
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A.3 Using RODBC to Import Data from an ACCESS Database |
235 |
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A.3.1 Step 1: Adding an ODBC Data Source Name |
236 |
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A.3.2 Step 2: ODBC Data Source Name Points to the ACCESS File |
236 |
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A.3.3 Step 3: Run RODBC to Import Data |
237 |
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References |
239 |
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Index |
244 |
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