Preface |
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xv | |
Authors |
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xxi | |
1 Applied Survey Data Analysis: An Overview |
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1 | (14) |
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1 | (1) |
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1.2 A Brief History of Applied Survey Data Analysis |
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2 | (3) |
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1.2.1 Key Theoretical Developments |
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2 | (2) |
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1.2.2 Key Software Developments |
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4 | (1) |
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1.3 Example Data Sets and Exercises |
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5 | (4) |
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1.4 Steps in Applied Survey Data Analysis |
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9 | (6) |
2 Getting to Know the Complex Sample Design |
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15 | (40) |
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15 | (1) |
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2.1.1 Technical Documentation and Supplemental Literature Review |
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15 | (1) |
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2.2 Classification of Sample Designs |
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16 | (4) |
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17 | (1) |
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2.2.2 Other Types of Study Designs Involving Probability Sampling |
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18 | (1) |
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2.2.3 Inference from Survey Data |
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19 | (1) |
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2.3 Target Populations and Survey Populations |
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20 | (1) |
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2.4 Simple Random Sampling: A Simple Model for Design- Based Inference |
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21 | (5) |
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2.4.1 Relevance of SRS to Complex Sample Survey Data Analysis |
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21 | (1) |
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2.4.2 SRS Fundamentals: A Framework for Design-Based Inference |
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22 | (2) |
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2.4.3 Example of Design-Based Inference under SRS |
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24 | (2) |
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2.5 Complex Sample Design Effects |
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26 | (4) |
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2.5.1 Design Effect Ratio |
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26 | (2) |
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2.5.2 Generalized Design Effects and Effective Sample Sizes |
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28 | (2) |
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2.6 Complex Samples: Cluster Sampling and Stratification |
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30 | (8) |
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2.6.1 Cluster Sampling Plans |
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31 | (3) |
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34 | (3) |
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2.6.3 Joint Effects of Sample Stratification and Cluster Sampling |
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37 | (1) |
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2.7 Weighting in Analysis of Survey Data |
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38 | (11) |
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2.7.1 Introduction to Weighted Analysis of Survey Data |
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38 | (2) |
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2.7.2 Weighting for Probabilities of Selection (wsel) |
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40 | (2) |
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2.7.3 Nonresponse Adjustment Weights (wnr) |
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42 | (3) |
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2.7.3.1 Weighting Class Approach (wnr,wc) |
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42 | (1) |
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2.7.3.2 Propensity Cell Adjustment Approach (wnrprop) |
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43 | (2) |
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2.7.4 Poststratification Weight Factors (wps) |
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45 | (2) |
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2.7.5 Design Effects Due to Weighted Analysis |
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47 | (2) |
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2.8 Multistage Area Probability Sample Designs |
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49 | (4) |
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2.8.1 Primary Stage Sampling |
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50 | (1) |
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2.8.2 Secondary Stage Sampling |
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51 | (1) |
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2.8.3 Third-and Fourth-Stage Sampling of HUs and Eligible Respondents |
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52 | (1) |
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2.9 Special Types of Sampling Plans Encountered in Surveys |
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53 | (2) |
3 Foundations and Techniques for Design-Based Estimation and Inference |
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55 | (42) |
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55 | (1) |
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3.2 Finite Populations and Superpopulation Models |
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56 | (2) |
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3.3 CIs for Population Parameters |
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58 | (1) |
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3.4 Weighted Estimation of Population Parameters |
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59 | (3) |
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3.5 Probability Distributions and Design-Based Inference |
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62 | (5) |
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3.5.1 Sampling Distributions of Survey Estimates |
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62 | (3) |
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3.5.2 Degrees of Freedom for t under Complex Sample Designs |
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65 | (2) |
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67 | (22) |
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3.6.1 Simplifying Assumptions Employed in Complex Sample Variance Estimation |
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69 | (1) |
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70 | (5) |
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3.6.3 Replication Methods for Variance Estimation |
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75 | (11) |
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3.6.3.1 Jackknife Repeated Replication |
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76 | (4) |
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3.6.3.2 Balanced Repeated Replication |
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80 | (3) |
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83 | (1) |
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3.6.3.4 Bootstrap (Rao-Wu Rescaling Bootstrap) |
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84 | (1) |
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3.6.3.5 Construction of Replicate Weights for Replicated Variance Estimation |
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85 | (1) |
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3.6.4 Example Comparing Results from the TSL, JRR, BRR, and Bootstrap Methods |
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86 | (3) |
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3.7 Hypothesis Testing in Survey Data Analysis |
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89 | (2) |
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3.8 TSE and Its Impact on Survey Estimation and Inference |
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91 | (6) |
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91 | (1) |
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3.8.2 Biases in Survey Data |
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92 | (5) |
4 Preparation for Complex Sample Survey Data Analysis |
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97 | (28) |
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97 | (1) |
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4.2 Final Survey Weights: Review by the Data User |
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98 | (6) |
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4.2.1 Identification of the Correct Weight Variable(s) for the Analysis |
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99 | (1) |
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4.2.2 Determining the Distribution and Scaling of the Weight Variable(s) |
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100 | (2) |
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4.2.3 Weighting Applications: Sensitivity of Survey Estimates to the Weights |
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102 | (2) |
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4.3 Understanding and Checking the Sampling Error Calculation Model |
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104 | (9) |
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4.3.1 Stratum and Cluster Codes in Complex Sample Survey Data Sets |
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105 | (2) |
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4.3.2 Building the NCS-R Sampling Error Calculation Model |
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107 | (2) |
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4.3.3 Combining Strata, Randomly Grouping PSUs, and Collapsing Strata |
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109 | (2) |
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4.3.4 Checking the Sampling Error Calculation Model for the Survey Data Set |
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111 | (2) |
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4.4 Addressing Item Missing Data in Analysis Variables |
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113 | (3) |
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4.4.1 Potential Bias due to Ignoring Missing Data |
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114 | (1) |
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4.4.2 Exploring Rates and Patterns of Missing Data Prior to Analysis |
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114 | (2) |
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4.5 Preparing to Analyze Data for Sample Subpopulations |
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116 | (5) |
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4.5.1 Subpopulation Distributions across Sample Design Units |
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118 | (1) |
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4.5.2 Unconditional Approach for Subclass Analysis |
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119 | (2) |
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4.5.3 Preparation for Subclass Analyses |
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121 | (1) |
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4.6 Final Checklist for Data Users |
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121 | (4) |
5 Descriptive Analysis for Continuous Variables |
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125 | (34) |
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125 | (1) |
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5.2 Special Considerations in Descriptive Analysis of Complex Sample Survey Data |
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126 | (2) |
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5.2.1 Weighted Estimation |
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126 | (1) |
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5.2.2 Design Effects for Descriptive Statistics |
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127 | (1) |
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5.2.3 Matching the Method to the Variable Type |
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128 | (1) |
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5.3 Simple Statistics for Univariate Continuous Distributions |
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128 | (17) |
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5.3.1 Graphical Tools for Descriptive Analysis of Survey Data |
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129 | (2) |
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5.3.2 Estimation of Population Totals |
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131 | (5) |
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5.3.3 Means of Continuous, Binary, or Interval Scale Data |
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136 | (3) |
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5.3.4 Standard Deviations of Continuous Variables |
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139 | (1) |
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5.3.5 Estimation of Percentiles, Medians, and Measures of Inequality in Population Distributions for Continuous Variables |
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140 | (5) |
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5.3.5.1 Estimation of Distribution Quantiles |
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140 | (2) |
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5.3.5.2 Estimation of Measures of Inequality in Population Distributions |
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142 | (3) |
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5.4 Bivariate Relationships between Two Continuous Variables |
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145 | (4) |
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145 | (1) |
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5.4.2 Product Moment Correlation Statistic (r) |
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146 | (2) |
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5.4.3 Ratios of Two Continuous Variables |
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148 | (1) |
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5.5 Descriptive Statistics for Subpopulations |
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149 | (2) |
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5.6 Linear Functions of Descriptive Estimates and Differences of Means |
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151 | (8) |
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5.6.1 Differences of Means for Two Subpopulations |
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152 | (3) |
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5.6.2 Comparing Means over Time |
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155 | (4) |
6 Categorical Data Analysis |
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159 | (36) |
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159 | (1) |
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6.2 Framework for Analysis of Categorical Survey Data |
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160 | (2) |
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6.2.1 Incorporating the Complex Design and Pseudo Maximum Likelihood |
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160 | (1) |
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6.2.2 Proportions and Percentages |
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160 | (1) |
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6.2.3 Crosstabulations, Contingency Tables, and Weighted Frequencies |
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161 | (1) |
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6.3 Univariate Analysis of Categorical Data |
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162 | (10) |
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6.3.1 Estimation of Proportions for Binary Variables |
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162 | (4) |
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6.3.2 Estimation of Category Proportions for Multinomial Variables |
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166 | (3) |
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6.3.3 Testing Hypotheses Concerning a Vector of Population Proportions |
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169 | (1) |
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6.3.4 Graphical Display for a Single Categorical Variable |
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170 | (2) |
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6.4 Bivariate Analysis of Categorical Data |
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172 | (13) |
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6.4.1 Response and Factor Variables |
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172 | (1) |
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6.4.2 Estimation of Total, Row, and Column Proportions for Two-Way Tables |
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172 | (2) |
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6.4.3 Estimating and Testing Differences in Subpopulation Proportions |
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174 | (1) |
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6.4.4 x2 Tests of Independence of Rows and Columns |
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175 | (6) |
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6.4.5 Odds Ratios and Relative Risks |
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181 | (2) |
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6.4.6 Simple Logistic Regression to Estimate the Odds Ratio |
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183 | (1) |
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6.4.7 Bivariate Graphical Analysis |
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184 | (1) |
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6.5 Analysis of Multivariate Categorical Data |
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185 | (6) |
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6.5.1 Cochran-Mantel-Haenszel Test |
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186 | (2) |
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6.5.2 Log-Linear Models for Contingency Tables |
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188 | (3) |
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191 | (4) |
7 Linear Regression Models |
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195 | (62) |
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195 | (2) |
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7.2 Linear Regression Model |
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197 | (5) |
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7.2.1 Standard Linear Regression Model |
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199 | (1) |
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7.2.2 Survey Treatment of the Regression Model |
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200 | (2) |
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7.3 Four Steps in Linear Regression Analysis |
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202 | (21) |
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7.3.1 Step 1: Specifying and Refining the Model |
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202 | (1) |
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7.3.2 Step 2: Estimation of Model Parameters |
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203 | (7) |
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7.3.2.1 Estimation for the Standard Linear Regression Model |
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203 | (2) |
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7.3.2.2 Linear Regression Estimation for Complex Sample Survey Data |
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205 | (5) |
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7.3.3 Step 3: Model Evaluation |
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210 | (7) |
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217 | (6) |
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7.3.4.1 Inference Concerning Model Parameters |
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218 | (3) |
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7.3.4.2 Prediction Intervals |
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221 | (2) |
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7.4 Some Practical Considerations and Tools |
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223 | (8) |
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7.4.1 Distribution of the Dependent Variable |
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223 | (1) |
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7.4.2 Parameterization and Scaling for Independent Variables |
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224 | (3) |
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7.4.3 Standardization of the Dependent and Independent Variables |
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227 | (1) |
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7.4.4 Specification and Interpretation of Interactions and Nonlinear Relationships |
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227 | (3) |
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7.4.5 Model-Building Strategies |
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230 | (1) |
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7.5 Application: Modeling Diastolic Blood Pressure with the 2011-2012 NHANES Data |
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231 | (26) |
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7.5.1 Exploring the Bivariate Relationships |
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232 | (3) |
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7.5.2 Naive Analysis: Ignoring Sample Design Features |
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235 | (1) |
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7.5.3 Weighted Regression Analysis |
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236 | (2) |
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7.5.4 Appropriate Analysis: Incorporating All Sample Design Features |
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238 | (19) |
8 Logistic Regression and Generalized Linear Models for Binary Survey Variables |
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257 | (42) |
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257 | (1) |
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8.2 GLMs for Binary Survey Responses |
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258 | (4) |
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8.2.1 Logistic Regression Model |
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260 | (1) |
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8.2.2 Probit Regression Model |
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261 | (1) |
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8.2.3 Complementary-Log-Log Model |
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262 | (1) |
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8.3 Building the Logistic Regression Model: Stage 1-Model Specification |
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262 | (1) |
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8.4 Building the Logistic Regression Model: Stage 2-Estimation of Model Parameters and Standard Errors |
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263 | (5) |
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8.5 Building the Logistic Regression Model: Stage 3-Evaluation of the Fitted Model |
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268 | (4) |
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8.5.1 Wald Tests of Model Parameters |
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268 | (2) |
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8.5.2 GOF and Logistic Regression Diagnostics |
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270 | (2) |
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8.6 Building the Logistic Regression Model: Stage 4- Interpretation and Inference |
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272 | (11) |
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283 | (10) |
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8.7.1 Stage 1: Model Specification |
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283 | (2) |
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8.7.2 Stage 2: Model Estimation |
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285 | (1) |
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8.7.3 Stage 3: Model Evaluation |
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286 | (2) |
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8.7.4 Stage 4: Model Interpretation/Inference |
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288 | (5) |
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8.8 Comparing the Logistic, Probit, and C-L-L GLMs for Binary Dependent Variables |
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293 | (6) |
9 Generalized Linear Models for Multinomial, Ordinal, and Count Variables |
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299 | (40) |
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299 | (1) |
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9.2 Analyzing Survey Data Using Multinomial Logit Regression Models |
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299 | (14) |
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9.2.1 Multinomial Logit Regression Model |
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299 | (2) |
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9.2.2 Multinomial Logit Regression Model: Specification Stage |
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301 | (1) |
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9.2.3 Multinomial Logit Regression Model: Estimation Stage |
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302 | (2) |
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9.2.4 Multinomial Logit Regression Model: Evaluation Stage |
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304 | (1) |
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9.2.5 Multinomial Logit Regression Model: Interpretation Stage |
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304 | (1) |
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9.2.6 Example: Fitting a Multinomial Logit Regression Model to Complex Sample Survey Data |
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305 | (8) |
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9.3 Logistic Regression Models for Ordinal Survey Data |
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313 | (10) |
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9.3.1 Cumulative Logit Regression Model |
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314 | (1) |
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9.3.2 Cumulative Logit Regression Model: Specification Stage |
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315 | (1) |
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9.3.3 Cumulative Logit Regression Model: Estimation Stage |
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315 | (1) |
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9.3.4 Cumulative Logit Regression Model: Evaluation Stage |
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316 | (1) |
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9.3.5 Cumulative Logit Regression Model: Interpretation Stage |
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317 | (2) |
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9.3.6 Example: Fitting a Cumulative Logit Regression Model to Complex Sample Survey Data |
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319 | (4) |
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9.4 Regression Models for Count Outcomes |
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323 | (16) |
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9.4.1 Survey Count Variables and Regression Modeling Alternatives |
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323 | (3) |
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9.4.2 Generalized Linear Models for Count Variables |
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326 | (3) |
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9.4.2.1 Poisson Regression Model |
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326 | (1) |
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9.4.2.2 Negative Binomial Regression Model |
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327 | (1) |
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Part Models: Zero-Inflated Poisson and Negative Binomial Regression Models |
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327 | (2) |
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9.4.3 Regression Models for Count Data: Specification Stage |
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329 | (1) |
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9.4.4 Regression Models for Count Data: Estimation Stage |
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329 | (1) |
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9.4.5 Regression Models for Count Data: Evaluation Stage |
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330 | (1) |
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9.4.6 Regression Models for Count Data: Interpretation Stage |
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330 | (1) |
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9.4.7 Example: Fitting Poisson and Negative Binomial Regression Models to Complex Sample Survey Data |
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331 | (8) |
10 Survival Analysis of Event History Survey Data |
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339 | (32) |
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339 | (1) |
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10.2 Basic Theory of Survival Analysis |
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339 | (5) |
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10.2.1 Survey Measurement of Event History Data |
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339 | (2) |
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10.2.2 Data for Event History Models |
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341 | (1) |
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10.2.3 Important Notation and Definitions |
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342 | (1) |
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10.2.4 Models for Survival Analysis |
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343 | (1) |
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10.3 (Nonparametric) K-M Estimation of the Survivor Function |
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344 | (7) |
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10.3.1 K-M Model Specification and Estimation |
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345 | (2) |
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10.3.2 K-M Estimator: Evaluation and Interpretation |
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347 | (1) |
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10.3.3 K-M Survival Analysis Example |
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347 | (4) |
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10.4 The Cox Proportional Hazards (CPH) Model |
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351 | (8) |
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10.4.1 CPH Model: Specification |
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351 | (1) |
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10.4.2 CPH Model: Estimation Stage |
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352 | (2) |
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10.4.3 CPH Model: Evaluation and Diagnostics |
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354 | (1) |
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10.4.4 CPH Model: Interpretation and Presentation of Results |
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354 | (1) |
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10.4.5 Example: Fitting a CPH Model to Complex Sample Survey Data |
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355 | (4) |
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10.5 Discrete Time Survival Models |
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359 | (12) |
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10.5.1 Discrete Time Logistic Model |
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359 | (1) |
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10.5.2 Data Preparation for Discrete Time Survival Models |
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360 | (3) |
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10.5.3 Discrete Time Models: Estimation Stage |
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363 | (1) |
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10.5.4 Discrete Time Models: Evaluation and Interpretation |
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364 | (1) |
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10.5.5 Fitting a Discrete Time Model to Complex Sample Survey Data |
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365 | (6) |
11 Analysis of Longitudinal Complex Sample Survey Data |
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371 | (56) |
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371 | (1) |
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11.2 Alternative Analytic Objectives with Longitudinal Survey Data |
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372 | (17) |
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11.2.1 Objective 1: Descriptive Estimation at a Single Time Point |
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372 | (2) |
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11.2.2 Objective 2: Estimation of Change across Two Waves |
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374 | (1) |
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11.2.3 Objective 3: Trajectory Estimation Based on Three or More Waves |
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375 | (14) |
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11.2.3.1 Approach 1: Weighted Multilevel Modeling |
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375 | (7) |
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11.2.3.2 Approach 2: Covariance Structure Modeling |
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382 | (2) |
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11.2.3.3 Approach 3: Weighted GEE Estimation |
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384 | (3) |
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11.2.3.4 Approach 4: Multiple Imputation Analysis |
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387 | (1) |
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11.2.3.5 Approach 5: Calibration Adjustment for Respondents with Complete Data |
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388 | (1) |
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11.3 Alternative Longitudinal Analyses of the HRS Data |
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389 | (33) |
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11.3.1 Example: Descriptive Estimation at a Single Wave |
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390 | (5) |
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11.3.2 Example: Change across Two Waves |
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395 | (7) |
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11.3.2.1 Accounting for Refreshment Samples When Estimating Mean Change |
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401 | (1) |
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11.3.3 Example: Weighted Multilevel Modeling |
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402 | (14) |
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11.3.3.1 Example: Veiga et al. (2014) |
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410 | (6) |
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11.3.4 Example: Weighted GEE Analysis |
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416 | (6) |
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422 | (5) |
12 Imputation of Missing Data: Practical Methods and Applications for Survey Analysts |
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427 | (46) |
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427 | (2) |
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12.2 Important Missing Data Concepts |
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429 | (7) |
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12.2.1 Sources and Types of Missing Data |
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429 | (1) |
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12.2.2 Patterns of Item Missing Data in Surveys |
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430 | (1) |
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12.2.3 Item Missing Data Mechanisms |
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431 | (2) |
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12.2.4 Review of Strategies to Address Item Missing Data in Surveys |
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433 | (3) |
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12.3 Factors to Consider in Choosing an Imputation Method |
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436 | (3) |
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439 | (12) |
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12.4.1 Overview of MI and MI Phases |
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439 | (1) |
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12.4.2 Models for Multiply Imputing Missing Data |
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440 | (4) |
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12.4.2.1 Choosing the Variables to Include in the Imputation Model |
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441 | (3) |
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12.4.2.2 Distributional Assumptions for the Imputation Model |
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444 | (1) |
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444 | (4) |
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12.4.3.1 Transforming the Imputation Problem to Monotonic Missing Data |
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445 | (1) |
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12.4.3.2 Specifying an Explicit Multivariate Model and Applying Exact Bayesian Posterior Simulation Methods |
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445 | (1) |
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12.4.3.3 SR or "Chained Regressions" |
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446 | (2) |
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12.4.4 Estimation and Inference for Multiply Imputed Data |
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448 | (3) |
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12.4.4.1 Estimators for Population Parameters and Associated Variance Estimators |
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448 | (1) |
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12.4.4.2 Model Evaluation and Inference |
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449 | (2) |
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12.5 Fractional Imputation |
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451 | (5) |
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451 | (1) |
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452 | (2) |
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12.5.3 Estimation and Inference with Fractionally Imputed Data |
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454 | (1) |
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455 | (1) |
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12.6 Application of MI and FI Methods to the NHANES 2011-2012 Data |
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456 | (17) |
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12.6.1 Problem Definition |
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456 | (1) |
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12.6.2 Imputation Models for the NHANES DBP Example |
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457 | (2) |
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12.6.3 Imputation of the Item Missing Data |
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459 | (5) |
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12.6.3.1 Multiple Imputation |
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459 | (2) |
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12.6.3.2 FEFI: Hot Deck Method |
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461 | (3) |
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12.6.4 Estimation and Inference |
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464 | (4) |
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12.6.4.1 Multiple Imputation |
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464 | (3) |
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12.6.4.2 FI Estimation and Inference |
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467 | (1) |
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12.6.5 Comparison of Example Results from Complete Case Analysis, MI, and FEFI |
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468 | (5) |
13 Advanced Topics in the Analysis of Survey Data |
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473 | (28) |
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473 | (1) |
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13.2 Bayesian Analysis of Complex Sample Survey Data |
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474 | (4) |
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13.3 GLMMs in Survey Data Analysis |
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478 | (11) |
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478 | (3) |
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13.3.2 GLMMs and Complex Sample Survey Data |
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481 | (4) |
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13.3.3 Alternative Approaches to Fitting GLMMs to Survey Data: The PISA Example |
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485 | (4) |
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13.4 Fitting Structural Equation Models to Complex Sample Survey Data |
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489 | (9) |
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13.4.1 SEM Example: Analysis of ESS Data from Belgium |
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491 | (7) |
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13.5 Small Area Estimation and Complex Sample Survey Data |
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498 | (1) |
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13.6 Nonparametric Methods for Complex Sample Survey Data |
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499 | (2) |
References |
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501 | (18) |
Appendix A: Software Overview |
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519 | (30) |
Index |
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549 | |