http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Note continued: 9.4.1. Survey Count Variables and Regression Modeling Alternatives -- 9.4.2. Generalized Linear Models for Count Variables -- 9.4.2.1. Poisson Regression Model -- 9.4.2.2. Negative Binomial Regression Model -- 9.4.2.3. Two- -- Part Models: Zero-Inflated Poisson and Negative Binomial Regression Models -- 9.4.3. Regression Models for Count Data: Specification Stage -- 9.4.4. Regression Models for Count Data: Estimation Stage -- 9.4.5. Regression Models for Count Data: Evaluation Stage -- 9.4.6. Regression Models for Count Data: Interpretation Stage -- 9.4.7. Example: Fitting Poisson and Negative Binomial Regression Models to Complex Sample Survey Data -- 10.1. Introduction -- 10.2. Basic Theory of Survival Analysis -- 10.2.1. Survey Measurement of Event History Data -- 10.2.2. Data for Event History Models -- 10.2.3. Important Notation and Definitions -- 10.2.4. Models for Survival Analysis -- 10.3. (Nonparametric) K-M Estimation of the Survivor Function -- 10.3.1. K-M Model Specification and Estimation -- 10.3.2. K-M Estimator: Evaluation and Interpretation -- 10.3.3. K-M Survival Analysis Example -- 10.4. The Cox Proportional Hazards (CPH) Model -- 10.4.1. CPH Model: Specification -- 10.4.2. CPH Model: Estimation Stage -- 10.4.3. CPH Model: Evaluation and Diagnostics -- 10.4.4. CPH Model: Interpretation and Presentation of Results -- 10.4.5. Example: Fitting a CPH Model to Complex Sample Survey Data -- 10.5. Discrete Time Survival Models -- 10.5.1. Discrete Time Logistic Model -- 10.5.2. Data Preparation for Discrete Time Survival Models -- 10.5.3. Discrete Time Models: Estimation Stage -- 10.5.4. Discrete Time Models: Evaluation and Interpretation -- 10.5.5. Fitting a Discrete Time Model to Complex Sample Survey Data -- 11.1. Introduction -- 11.2. Alternative Analytic Objectives with Longitudinal Survey Data -- 11.2.1. Objective 1: Descriptive Estimation at a Single Time Point -- 11.2.2. Objective 2: Estimation of Change across Two Waves -- 11.2.3. Objective 3: Trajectory Estimation Based on Three or More Waves -- 11.2.3.1. Approach 1: Weighted Multilevel Modeling -- 11.2.3.2. Approach 2: Covariance Structure Modeling -- 11.2.3.3. Approach 3: Weighted GEE Estimation -- 11.2.3.4. Approach 4: Multiple Imputation Analysis -- 11.2.3.5. Approach 5: Calibration Adjustment for Respondents with Complete Data -- 11.3. Alternative Longitudinal Analyses of the HRS Data -- 11.3.1. Example: Descriptive Estimation at a Single Wave -- 11.3.2. Example: Change across Two Waves -- 11.3.2.1. Accounting for Refreshment Samples When Estimating Mean Change -- 11.3.3. Example: Weighted Multilevel Modeling -- 11.3.3.1. Example: Veiga et al. (2014) -- 11.3.4. Example: Weighted GEE Analysis -- 11.4. Concluding Remarks -- 12.1. Introduction -- 12.2. Important Missing Data Concepts -- 12.2.1. Sources and Types of Missing Data -- 12.2.2. Patterns of Item Missing Data in Surveys -- 12.2.3. Item Missing Data Mechanisms -- 12.2.4. Review of Strategies to Address Item Missing Data in Surveys -- 12.3. Factors to Consider in Choosing an Imputation Method -- 12.4. Multiple Imputation -- 12.4.1. Overview of MI and MI Phases -- 12.4.2. Models for Multiply Imputing Missing Data -- 12.4.2.1. Choosing the Variables to Include in the Imputation Model -- 12.4.2.2. Distributional Assumptions for the Imputation Model -- 12.4.3. Creating the MIs -- 12.4.3.1. Transforming the Imputation Problem to Monotonic Missing Data -- 12.4.3.2. Specifying an Explicit Multivariate Model and Applying Exact Bayesian Posterior Simulation Methods -- 12.4.3.3. SR or "Chained Regressions" -- 12.4.4. Estimation and Inference for Multiply Imputed Data -- 12.4.4.1. Estimators for Population Parameters and Associated Variance Estimators -- 12.4.4.2. Model Evaluation and Inference -- 12.5. Fractional Imputation -- 12.5.1. Background -- 12.5.2. Creating the FIs -- 12.5.3. Estimation and Inference with Fractionally Imputed Data -- 12.5.4. FI Software -- 12.6. Application of MI and FI Methods to the NHANES 2011-2012 Data -- 12.6.1. Problem Definition -- 12.6.2. Imputation Models for the NHANES DBP Example -- 12.6.3. Imputation of the Item Missing Data -- 12.6.3.1. Multiple Imputation -- 12.6.3.2. FEFI: Hot Deck Method -- 12.6.4. Estimation and Inference -- 12.6.4.1. Multiple Imputation -- 12.6.4.2. FI Estimation and Inference -- 12.6.5. Comparison of Example Results from Complete Case Analysis, MI, and FEFI -- 13.1. Introduction -- 13.2. Bayesian Analysis of Complex Sample Survey Data -- 13.3. GLMMs in Survey Data Analysis -- 13.3.1. Overview of GLMMs -- 13.3.2. GLMMs and Complex Sample Survey Data -- 13.3.3. Alternative Approaches to Fitting GLMMs to Survey Data: The PISA Example -- 13.4. Fitting Structural Equation Models to Complex Sample Survey Data -- 13.4.1. SEM Example: Analysis of ESS Data from Belgium -- 13.5. Small Area Estimation and Complex Sample Survey Data -- 13.6. Nonparametric Methods for Complex Sample Survey Data.
Machine generated contents note: 1.1. Introduction -- 1.2. A Brief History of Applied Survey Data Analysis -- 1.2.1. Key Theoretical Developments -- 1.2.2. Key Software Developments -- 1.3. Example Data Sets and Exercises -- 1.4. Steps in Applied Survey Data Analysis -- 2.1. Introduction -- 2.1.1. Technical Documentation and Supplemental Literature Review -- 2.2. Classification of Sample Designs -- 2.2.1. Sampling Plans -- 2.2.2. Other Types of Study Designs Involving Probability Sampling -- 2.2.3. Inference from Survey Data -- 2.3. Target Populations and Survey Populations -- 2.4. Simple Random Sampling: A Simple Model for Design- Based Inference -- 2.4.1. Relevance of SRS to Complex Sample Survey Data Analysis -- 2.4.2. SRS Fundamentals: A Framework for Design-Based Inference -- 2.4.3. Example of Design-Based Inference under SRS -- 2.5. Complex Sample Design Effects -- 2.5.1. Design Effect Ratio -- 2.5.2. Generalized Design Effects and Effective Sample Sizes -- 2.6. Complex Samples: Cluster Sampling and Stratification -- 2.6.1. Cluster Sampling Plans -- 2.6.2. Stratification -- 2.6.3. Joint Effects of Sample Stratification and Cluster Sampling -- 2.7. Weighting in Analysis of Survey Data -- 2.7.1. Introduction to Weighted Analysis of Survey Data -- 2.7.2. Weighting for Probabilities of Selection (wsel) -- 2.7.3. Nonresponse Adjustment Weights (wnr) -- 2.7.3.1. Weighting Class Approach (wnr, wc) -- 2.7.3.2. Propensity Cell Adjustment Approach (wnrprop) -- 2.7.4. Poststratification Weight Factors (wps) -- 2.7.5. Design Effects Due to Weighted Analysis -- 2.8. Multistage Area Probability Sample Designs -- 2.8.1. Primary Stage Sampling -- 2.8.2. Secondary Stage Sampling -- 2.8.3. Third-and Fourth-Stage Sampling of HUs and Eligible Respondents -- 2.9. Special Types of Sampling Plans Encountered in Surveys -- 3.1. Introduction -- 3.2. Finite Populations and Superpopulation Models -- 3.3. CIs for Population Parameters -- 3.4. Weighted Estimation of Population Parameters -- 3.5. Probability Distributions and Design-Based Inference -- 3.5.1. Sampling Distributions of Survey Estimates -- 3.5.2. Degrees of Freedom for t under Complex Sample Designs -- 3.6. Variance Estimation -- 3.6.1. Simplifying Assumptions Employed in Complex Sample Variance Estimation -- 3.6.2. TSL Method -- 3.6.3. Replication Methods for Variance Estimation -- 3.6.3.1. Jackknife Repeated Replication -- 3.6.3.2. Balanced Repeated Replication -- 3.6.3.3. Fay's BRR Method -- 3.6.3.4. Bootstrap (Rao-Wu Rescaling Bootstrap) -- 3.6.3.5. Construction of Replicate Weights for Replicated Variance Estimation -- 3.6.4. Example Comparing Results from the TSL, JRR, BRR, and Bootstrap Methods -- 3.7. Hypothesis Testing in Survey Data Analysis -- 3.8. TSE and Its Impact on Survey Estimation and Inference -- 3.8.1. Variable Errors -- 3.8.2. Biases in Survey Data -- 4.1. Introduction -- 4.2. Final Survey Weights: Review by the Data User -- 4.2.1. Identification of the Correct Weight Variable(s) for the Analysis -- 4.2.2. Determining the Distribution and Scaling of the Weight Variable(s) -- 4.2.3. Weighting Applications: Sensitivity of Survey Estimates to the Weights -- 4.3. Understanding and Checking the Sampling Error Calculation Model -- 4.3.1. Stratum and Cluster Codes in Complex Sample Survey Data Sets -- 4.3.2. Building the NCS-R Sampling Error Calculation Model -- 4.3.3. Combining Strata, Randomly Grouping PSUs, and Collapsing Strata -- 4.3.4. Checking the Sampling Error Calculation Model for the Survey Data Set -- 4.4. Addressing Item Missing Data in Analysis Variables -- 4.4.1. Potential Bias due to Ignoring Missing Data -- 4.4.2. Exploring Rates and Patterns of Missing Data Prior to Analysis -- 4.5. Preparing to Analyze Data for Sample Subpopulations -- 4.5.1. Subpopulation Distributions across Sample Design Units -- 4.5.2. Unconditional Approach for Subclass Analysis -- 4.5.3. Preparation for Subclass Analyses -- 4.6. Final Checklist for Data Users -- 5.1. Introduction -- 5.2. Special Considerations in Descriptive Analysis of Complex Sample Survey Data -- 5.2.1. Weighted Estimation -- 5.2.2. Design Effects for Descriptive Statistics -- 5.2.3. Matching the Method to the Variable Type -- 5.3. Simple Statistics for Univariate Continuous Distributions -- 5.3.1. Graphical Tools for Descriptive Analysis of Survey Data -- 5.3.2. Estimation of Population Totals -- 5.3.3. Means of Continuous, Binary, or Interval Scale Data -- 5.3.4. Standard Deviations of Continuous Variables -- 5.3.5. Estimation of Percentiles, Medians, and Measures of Inequality in Population Distributions for Continuous Variables -- 5.3.5.1. Estimation of Distribution Quantiles -- 5.3.5.2. Estimation of Measures of Inequality in Population Distributions -- 5.4. Bivariate Relationships between Two Continuous Variables -- 5.4.1. X-Y Scatter Plots -- 5.4.2. Product Moment Correlation Statistic (r) -- 5.4.3. Ratios of Two Continuous Variables -- 5.5. Descriptive Statistics for Subpopulations -- 5.6. Linear Functions of Descriptive Estimates and Differences of Means -- 5.6.1. Differences of Means for Two Subpopulations -- 5.6.2. Comparing Means over Time -- 6.1. Introduction -- 6.2. Framework for Analysis of Categorical Survey Data -- 6.2.1. Incorporating the Complex Design and Pseudo Maximum Likelihood -- 6.2.2. Proportions and Percentages -- 6.2.3. Crosstabulations, Contingency Tables, and Weighted Frequencies -- 6.3. Univariate Analysis of Categorical Data -- 6.3.1. Estimation of Proportions for Binary Variables -- 6.3.2. Estimation of Category Proportions for Multinomial Variables -- 6.3.3. Testing Hypotheses Concerning a Vector of Population Proportions -- 6.3.4. Graphical Display for a Single Categorical Variable -- 6.4. Bivariate Analysis of Categorical Data -- 6.4.1. Response and Factor Variables -- 6.4.2. Estimation of Total, Row, and Column Proportions for Two-Way Tables -- 6.4.3. Estimating and Testing Differences in Subpopulation Proportions -- 6.4.4. x2 Tests of Independence of Rows and Columns -- 6.4.5. Odds Ratios and Relative Risks -- 6.4.6. Simple Logistic Regression to Estimate the Odds Ratio -- 6.4.7. Bivariate Graphical Analysis -- 6.5. Analysis of Multivariate Categorical Data -- 6.5.1. Cochran-Mantel-Haenszel Test -- 6.5.2. Log-Linear Models for Contingency Tables -- 6.6. Summary -- 7.1. Introduction -- 7.2. Linear Regression Model -- 7.2.1. Standard Linear Regression Model -- 7.2.2. Survey Treatment of the Regression Model -- 7.3. Four Steps in Linear Regression Analysis -- 7.3.1. Step 1: Specifying and Refining the Model -- 7.3.2. Step 2: Estimation of Model Parameters -- 7.3.2.1. Estimation for the Standard Linear Regression Model -- 7.3.2.2. Linear Regression Estimation for Complex Sample Survey Data -- 7.3.3. Step 3: Model Evaluation -- 7.3.4. Step 4: Inference -- 7.3.4.1. Inference Concerning Model Parameters -- 7.3.4.2. Prediction Intervals -- 7.4. Some Practical Considerations and Tools -- 7.4.1. Distribution of the Dependent Variable -- 7.4.2. Parameterization and Scaling for Independent Variables -- 7.4.3. Standardization of the Dependent and Independent Variables -- 7.4.4. Specification and Interpretation of Interactions and Nonlinear Relationships -- 7.4.5. Model-Building Strategies -- 7.5. Application: Modeling Diastolic Blood Pressure with the 2011-2012 NHANES Data -- 7.5.1. Exploring the Bivariate Relationships -- 7.5.2. Naive Analysis: Ignoring Sample Design Features -- 7.5.3. Weighted Regression Analysis -- 7.5.4. Appropriate Analysis: Incorporating All Sample Design Features -- 8.1. Introduction -- 8.2. GLMs for Binary Survey Responses -- 8.2.1. Logistic Regression Model -- 8.2.2. Probit Regression Model -- 8.2.3. Complementary-Log-Log Model -- 8.3. Building the Logistic Regression Model: Stage 1-Model Specification -- 8.4. Building the Logistic Regression Model: Stage 2-Estimation of Model Parameters and Standard Errors -- 8.5. Building the Logistic Regression Model: Stage 3-Evaluation of the Fitted Model -- 8.5.1. Wald Tests of Model Parameters -- 8.5.2. GOF and Logistic Regression Diagnostics -- 8.6. Building the Logistic Regression Model: Stage 4- Interpretation and
Inference -- 8.7. Analysis Application -- 8.7.1. Stage 1: Model Specification -- 8.7.2. Stage 2: Model Estimation -- 8.7.3. Stage 3: Model Evaluation -- 8.7.4. Stage 4: Model Interpretation/Inference -- 8.8. Comparing the Logistic, Probit, and C-L-L GLMs for Binary Dependent Variables -- 9.1. Introduction -- 9.2. Analyzing Survey Data Using Multinomial Logit Regression Models -- 9.2.1. Multinomial Logit Regression Model -- 9.2.2. Multinomial Logit Regression Model: Specification Stage -- 9.2.3. Multinomial Logit Regression Model: Estimation Stage -- 9.2.4. Multinomial Logit Regression Model: Evaluation Stage -- 9.2.5. Multinomial Logit Regression Model: Interpretation Stage -- 9.2.6. Example: Fitting a Multinomial Logit Regression Model to Complex Sample Survey Data -- 9.3. Logistic Regression Models for Ordinal Survey Data -- 9.3.1. Cumulative Logit Regression Model -- 9.3.2. Cumulative Logit Regression Model: Specification Stage -- 9.3.3. Cumulative Logit Regression Model: Estimation Stage -- 9.3.4. Cumulative Logit Regression Model: Evaluation Stage -- 9.3.5. Cumulative Logit Regression Model: Interpretation Stage -- 9.3.6. Example: Fitting a Cumulative Logit Regression Model to Complex Sample Survey Data -- 9.4. Regression Models for Count Outcomes.
Steven G. Heeringa, Brady T. West, and Patricia A. Berglund.
Includes bibliographical references (pages 501-518) and index.
Statistics in the social and behavioral sciences series