Preface |
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xiii | |
Symbols and Acronyms |
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xxi | |
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1 | (30) |
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1.1 Guidance from Samples |
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1 | (2) |
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1.2 Populations and Representative Samples |
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3 | (3) |
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6 | (4) |
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1.3.1 Convenience Samples |
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6 | (1) |
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1.3.2 Purposive or Judgment Samples |
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6 | (1) |
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1.3.3 Self-Selected Samples |
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6 | (2) |
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8 | (1) |
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8 | (1) |
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9 | (1) |
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1.3.7 What Good Are Samples with Selection Bias? |
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9 | (1) |
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10 | (3) |
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13 | (4) |
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1.6 Sampling and Nonsampling Errors |
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17 | (1) |
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18 | (2) |
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1.7.1 Advantages of Taking a Census |
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19 | (1) |
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1.7.2 Advantages of Taking a Sample Instead of a Census |
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19 | (1) |
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20 | (2) |
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22 | (9) |
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2 Simple Probability Samples |
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31 | (48) |
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2.1 Types of Probability Samples |
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32 | (2) |
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2.2 Framework for Probability Sampling |
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34 | (5) |
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2.3 Simple Random Sampling |
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39 | (5) |
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44 | (2) |
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46 | (4) |
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2.6 Using Statistical Software to Analyze Survey Data |
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50 | (1) |
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2.7 Determining the Sample Size |
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50 | (5) |
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55 | (1) |
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2.9 Randomization Theory for Simple Random Sampling* |
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56 | (2) |
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2.10 Model-Based Theory for Simple Random Sampling* |
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58 | (4) |
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2.11 When Should a Simple Random Sample Be Used? |
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62 | (1) |
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63 | (3) |
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66 | (13) |
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79 | (42) |
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3.1 What Is Stratified Sampling? |
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79 | (4) |
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3.2 Theory of Stratified Sampling |
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83 | (4) |
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3.3 Sampling Weights in Stratified Random Sampling |
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87 | (2) |
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3.4 Allocating Observations to Strata |
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89 | (7) |
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3.4.1 Proportional Allocation |
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89 | (2) |
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91 | (2) |
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3.4.3 Allocation for Specified Precision within Strata |
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93 | (1) |
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3.4.4 Which Allocation to Use? |
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94 | (2) |
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3.4.5 Determining the Total Sample Size |
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96 | (1) |
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96 | (3) |
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3.6 Model-Based Theory for Stratified Sampling* |
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99 | (1) |
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100 | (1) |
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101 | (20) |
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4 Ratio and Regression Estimation |
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121 | (46) |
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4.1 Ratio Estimation in Simple Random Sampling |
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121 | (14) |
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4.1.1 Why Use Ratio Estimation? |
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122 | (3) |
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4.1.2 Bias and Mean Squared Error of Ratio Estimators |
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125 | (7) |
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4.1.3 Ratio Estimation with Proportions |
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132 | (2) |
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4.1.4 Ratio Estimation Using Weight Adjustments |
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134 | (1) |
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4.1.5 Advantages of Ratio Estimation |
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135 | (1) |
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4.2 Regression Estimation in Simple Random Sampling |
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135 | (4) |
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4.3 Estimation in Domains |
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139 | (3) |
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142 | (3) |
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4.5 Ratio Estimation with Stratified Sampling |
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145 | (2) |
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4.6 Model-Based Theory for Ratio and Regression Estimation* |
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147 | (7) |
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4.6.1 A Model for Ratio Estimation |
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148 | (3) |
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4.6.2 A Model for Regression Estimation |
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151 | (1) |
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4.6.3 Differences between Model-Based and Design-Based Estimators |
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152 | (2) |
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154 | (1) |
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155 | (12) |
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5 Cluster Sampling with Equal Probabilities |
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167 | (52) |
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5.1 Notation for Cluster Sampling |
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171 | (1) |
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5.2 One-Stage Cluster Sampling |
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172 | (10) |
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5.2.1 Clusters of Equal Sizes: Estimation |
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172 | (2) |
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5.2.2 Clusters of Equal Sizes: Theory |
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174 | (5) |
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5.2.3 Clusters of Unequal Sizes |
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179 | (3) |
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5.3 Two-Stage Cluster Sampling |
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182 | (10) |
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5.4 Designing a Cluster Sample |
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192 | (5) |
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5.4.1 Choosing the psu Size |
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193 | (1) |
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5.4.2 Choosing Subsampling Sizes |
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194 | (2) |
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5.4.3 Choosing the Sample Size (Number of psus) |
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196 | (1) |
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197 | (3) |
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5.6 Model-Based Theory for Cluster Sampling* |
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200 | (5) |
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5.6.1 Estimation Using Models |
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202 | (3) |
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5.6.2 Design Using Models |
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205 | (1) |
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205 | (2) |
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207 | (12) |
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6 Sampling with Unequal Probabilities |
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219 | (54) |
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6.1 Sampling One Primary Sampling Unit |
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221 | (3) |
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6.2 One-Stage Sampling with Replacement |
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224 | (6) |
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6.2.1 Selecting Primary Sampling Units |
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224 | (2) |
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6.2.2 Theory of Estimation |
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226 | (3) |
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6.2.3 Designing the Selection Probabilities |
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229 | (1) |
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6.2.4 Weights in Unequal-Probability Sampling with Replacement |
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230 | (1) |
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6.3 Two-Stage Sampling with Replacement |
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230 | (3) |
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6.4 Unequal-Probability Sampling without Replacement |
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233 | (10) |
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6.4.1 The Horvitz-Thompson Estimator for One-Stage Sampling |
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235 | (4) |
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239 | (1) |
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6.4.3 The Horvitz-Thompson Estimator for Two-Stage Sampling |
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239 | (1) |
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6.4.4 Weights in Unequal-Probability Samples |
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240 | (3) |
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6.5 Examples of Unequal-Probability Samples |
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243 | (4) |
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6.6 Randomization Theory Results and Proofs* |
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247 | (7) |
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6.7 Model-Based Inference with Unequal-Probability Samples* |
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254 | (2) |
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256 | (2) |
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258 | (15) |
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273 | (38) |
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7.1 Assembling Design Components |
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273 | (3) |
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7.1.1 Building Blocks for Surveys |
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273 | (2) |
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7.1.2 Ratio Estimation in Complex Surveys |
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275 | (1) |
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7.1.3 Simplicity in Survey Design |
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276 | (1) |
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276 | (4) |
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7.2.1 Constructing Sampling Weights |
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276 | (3) |
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7.2.2 Self-Weighting and Non-Self-Weighting Samples |
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279 | (1) |
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7.3 Estimating Distribution Functions and Quantiles |
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280 | (6) |
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286 | (2) |
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7.5 The National Health and Nutrition Examination Survey |
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288 | (3) |
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7.6 Graphing Data from a Complex Survey |
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291 | (10) |
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292 | (3) |
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295 | (6) |
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301 | (2) |
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303 | (8) |
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311 | (48) |
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8.1 Effects of Ignoring Nonresponse |
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312 | (2) |
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8.2 Designing Surveys to Reduce Nonresponse |
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314 | (5) |
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319 | (1) |
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8.4 Response Propensities and Mechanisms for Nonresponse |
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320 | (3) |
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8.4.1 Auxiliary Information for Treating Nonresponse |
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320 | (1) |
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8.4.2 Methods to Adjust for Nonresponse |
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320 | (1) |
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8.4.3 Response Propensities |
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321 | (1) |
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8.4.4 Types of Missing Data |
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321 | (2) |
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8.5 Adjusting Weights for Nonresponse |
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323 | (6) |
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8.5.1 Weighting Class Adjustments |
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324 | (4) |
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8.5.2 Regression Models for Response Propensities |
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328 | (1) |
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329 | (6) |
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8.6.1 Poststratification Using Weights |
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330 | (1) |
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331 | (2) |
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8.6.3 Steps for Constructing Final Survey Weights |
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333 | (1) |
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8.6.4 Advantages and Disadvantages of Weighting Adjustments |
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334 | (1) |
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335 | (5) |
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8.7.1 Deductive Imputation |
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335 | (1) |
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8.7.2 Cell Mean Imputation |
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336 | (1) |
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8.7.3 Hot-Deck Imputation |
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337 | (1) |
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8.7.4 Regression Imputation and Chained Equations |
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338 | (1) |
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8.7.5 Imputation from Another Data Source |
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338 | (1) |
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8.7.6 Multiple Imputation |
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339 | (1) |
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8.7.7 Advantages and Disadvantages of Imputation |
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339 | (1) |
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8.8 Response Rates and Nonresponse Bias Assessments |
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340 | (6) |
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8.8.1 Calculating and Reporting Response Rates |
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340 | (2) |
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8.8.2 What Is an Acceptable Response Rate? |
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342 | (1) |
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8.8.3 Nonresponse Bias Assessments |
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343 | (3) |
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346 | (2) |
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348 | (11) |
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9 Variance Estimation in Complex Surveys |
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359 | (36) |
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9.1 Linearization (Taylor Series) Methods |
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359 | (4) |
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363 | (4) |
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9.2.1 Replicating the Survey Design |
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363 | (2) |
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9.2.2 Dividing the Sample into Random Groups |
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365 | (2) |
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9.3 Resampling and Replication Methods |
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367 | (12) |
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9.3.1 Balanced Repeated Replication (BRR) |
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367 | (6) |
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373 | (2) |
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375 | (2) |
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9.3.4 Creating and Using Replicate Weights |
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377 | (2) |
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9.4 Generalized Variance Functions |
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379 | (2) |
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381 | (3) |
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9.5.1 Confidence Intervals for Smooth Functions of Population Totals |
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381 | (1) |
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9.5.2 Confidence Intervals for Population Quantiles |
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382 | (2) |
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384 | (2) |
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386 | (9) |
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10 Categorical Data Analysis in Complex Surveys |
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395 | (24) |
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10.1 Chi-Square Tests with Multinomial Sampling |
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395 | (4) |
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10.1.1 Testing Independence of Factors |
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397 | (1) |
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10.1.2 Testing Homogeneity of Proportions |
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398 | (1) |
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10.1.3 Testing Goodness of Fit |
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398 | (1) |
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10.2 Effects of Survey Design on Chi-Square Tests |
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399 | (4) |
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10.2.1 Contingency Tables for Data from Complex Surveys |
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400 | (1) |
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10.2.2 Effects on Hypothesis Tests and Confidence Intervals |
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401 | (2) |
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10.3 Corrections to Chi-Square Tests |
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403 | (5) |
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403 | (2) |
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405 | (2) |
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10.3.3 Model-Based Methods for Chi-Square Tests |
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407 | (1) |
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408 | (3) |
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10.4.1 Loglinear Models with Multinomial Sampling |
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409 | (1) |
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10.4.2 Loglinear Models in a Complex Survey |
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410 | (1) |
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411 | (1) |
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412 | (7) |
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11 Regression with Complex Survey Data |
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419 | (38) |
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11.1 Model-Based Regression in Simple Random Samples |
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420 | (3) |
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11.2 Regression with Complex Survey Data |
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423 | (10) |
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424 | (3) |
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427 | (3) |
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11.2.3 Multiple Regression |
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430 | (2) |
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11.2.4 Regression Using Weights versus Weighted Least Squares |
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432 | (1) |
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11.3 Using Regression to Compare Domain Means |
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433 | (2) |
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11.4 Interpreting Regression Coefficients from Survey Data |
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435 | (5) |
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11.4.1 Purposes of Regression Analyses |
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435 | (1) |
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11.4.2 Model-Based and Design-Based Inference |
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436 | (1) |
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11.4.3 Survey Weights and Regression |
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437 | (1) |
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11.4.4 Survey Design and Standard Errors |
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438 | (1) |
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11.4.5 Mixed Models for Cluster Samples |
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439 | (1) |
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440 | (2) |
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11.6 Calibration to Population Totals |
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442 | (4) |
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446 | (2) |
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448 | (9) |
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457 | (26) |
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12.1 Theory for Two-Phase Sampling |
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459 | (2) |
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12.2 Two-Phase Sampling with Stratification |
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461 | (3) |
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12.3 Ratio and Regression Estimation in Two-Phase Samples |
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464 | (3) |
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12.3.1 Two-Phase Sampling with Ratio Estimation |
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464 | (2) |
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12.3.2 Generalized Regression Estimation in Two-Phase Sampling |
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466 | (1) |
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12.4 Jackknife Variance Estimation for Two-Phase Sampling |
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467 | (2) |
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12.5 Designing a Two-Phase Sample |
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469 | (2) |
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12.5.1 Two-Phase Sampling with Stratification |
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469 | (2) |
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12.5.2 Optimal Allocation for Ratio Estimation |
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471 | (1) |
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471 | (1) |
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472 | (11) |
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13 Estimating the Size of a Population |
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483 | (16) |
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13.1 Capture-Recapture Estimation |
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483 | (5) |
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13.1.1 Contingency Tables for Capture-Recapture Experiments |
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484 | (1) |
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13.1.2 Confidence Intervals for AT |
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485 | (1) |
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13.1.3 Using Capture-Recapture on Lists |
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486 | (2) |
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13.2 Multiple Recapture Estimation |
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488 | (3) |
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491 | (1) |
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492 | (7) |
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14 Rare Populations and Small Area Estimation |
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499 | (18) |
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14.1 Sampling Rare Populations |
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500 | (6) |
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14.1.1 Stratified Sampling with Disproportional Allocation |
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500 | (1) |
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14.1.2 Two-Phase Sampling |
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501 | (1) |
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14.1.3 Unequal-Probability Sampling |
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501 | (1) |
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14.1.4 Multiple Frame Surveys |
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502 | (2) |
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14.1.5 Network or Multiplicity Sampling |
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504 | (1) |
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505 | (1) |
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14.1.7 Sequential Sampling |
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506 | (1) |
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14.2 Small Area Estimation |
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506 | (4) |
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507 | (1) |
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14.2.2 Synthetic and Composite Estimators |
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508 | (1) |
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14.2.3 Model-Based Estimators |
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509 | (1) |
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510 | (2) |
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512 | (5) |
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15 Nonprobability Samples |
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517 | (40) |
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15.1 Types of Nonprobability Samples |
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518 | (6) |
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15.1.1 Administrative Records |
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518 | (1) |
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519 | (3) |
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522 | (1) |
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15.1.4 Convenience Samples |
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523 | (1) |
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15.2 Selection Bias and Mean Squared Error |
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524 | (7) |
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15.2.1 Random Variables Describing Participation in a Sample |
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525 | (3) |
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15.2.2 Bias and Mean Squared Error of a Sample Mean |
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528 | (3) |
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15.3 Reducing Bias of Estimates from Nonprobability Samples |
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531 | (8) |
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531 | (5) |
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15.3.2 Estimate the Values of the Missing Units |
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536 | (1) |
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15.3.3 Measures of Uncertainty for Nonprobability Samples |
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537 | (2) |
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15.4 Nonprobability versus Low-Response Probability Samples |
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539 | (3) |
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542 | (2) |
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544 | (13) |
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557 | (22) |
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559 | (3) |
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16.1.1 Measuring Coverage and Coverage Bias |
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559 | (1) |
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16.1.2 Coverage and Survey Mode |
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560 | (2) |
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16.1.3 Improving Coverage |
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562 | (1) |
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562 | (2) |
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564 | (6) |
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16.3.1 Measuring and Modeling Measurement Error |
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565 | (2) |
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16.3.2 Reducing Measurement Error |
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567 | (1) |
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16.3.3 Sensitive Questions |
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568 | (1) |
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16.3.4 Randomized Response |
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568 | (2) |
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570 | (1) |
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16.5 Total Survey Quality |
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571 | (2) |
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573 | (2) |
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575 | (4) |
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A Probability Concepts Used in Sampling |
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579 | (14) |
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579 | (3) |
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A.1.1 Simple Random Sampling with Replacement |
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580 | (1) |
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A.1.2 Simple Random Sampling without Replacement |
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581 | (1) |
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A.2 Random Variables and Expected Value |
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582 | (3) |
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A.3 Conditional Probability |
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585 | (2) |
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A.4 Conditional Expectation |
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587 | (4) |
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591 | (2) |
Bibliography |
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593 | (48) |
Index |
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641 | |