ECU Libraries Catalog

Applied survey data analysis / Steven G. Heeringa, Brady T. West, and Patricia A. Berglund.

Author/creator Heeringa, Steven, 1953- author.
Other author/creatorWest, Brady T., author.
Other author/creatorBerglund, Patricia A., author.
Format Tactile Material, Book, and Print
EditionSecond edition.
Publication Info Boca Raton, FL : CRC Press, Taylor & Francis Group, [2017]
Descriptionxxii, 568 pages : illustrations ; 24 cm.
Subject(s)
Series Chapman & Hall/CRC statistics in the social and behavioral sciences series
Statistics in the social and behavioral sciences series. ^A696398
Contents 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.
Contents 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.
Abstract Highly recommended by the Journal of Official Statistics, The American Statistician, and other journals, Applied Survey Data Analysis, Second Edition provides an up-to-date overview of state-of-the-art approaches to the analysis of complex sample survey data. Building on the wealth of material on practical approaches to descriptive analysis and regression modeling from the first edition, this second edition expands the topics covered and presents more step-by-step examples of modern approaches to the analysis of survey data using the newest statistical software. Designed for readers working in a wide array of disciplines who use survey data in their work, this book continues to provide a useful framework for integrating more in-depth studies of the theory and methods of survey data analysis. An example-driven guide to the applied statistical analysis and interpretation of survey data, the second edition contains many new examples and practical exercises based on recent versions of real-world survey data sets. Although the authors continue to use Stata for most examples in the text, they also continue to offer SAS, SPSS, SUDAAN, R, WesVar, IVEware, and Mplus software code for replicating the examples on the book's updated website.-- Provided by Publisher.
Bibliography noteIncludes bibliographical references (pages 501-518) and index.
Genre/formStatistics.
LCCN 2016050459
ISBN9781498761604 (hardback cover)
ISBN1498761607 (hardback cover)
ISBN(e-book)

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