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Taking a practical approach that draws on the authors' extensive teaching, consulting, and research experiences, Applied Survey Data Analysis provides an intermediate-level statistical overview of the analysis of complex sample survey data. It emphasizes methods and worked examples using available software procedures while reinforcing the principles and theory that underlie those methods.After introducing a step-by-step process for approaching a survey analysis problem, the book presents the fundamental features of complex sample designs and shows how to integrate design characteristics into the statistical methods and software for survey estimation and inference. The authors then focus on the methods and models used in analyzing continuous, categorical, and count-dependent variables; event history; and missing data problems. Some of the techniques discussed include univariate descriptive and simple bivariate analyses, the linear regression model, generalized linear regression modeling methods, the Cox proportional hazards model, discrete time models, and the multiple imputation analysis method. The final chapter covers new developments in survey applications of advanced statistical techniques, including model-based analysis approaches.Designed for readers working in a wide array of disciplines who use survey data in their work, this book also provides a useful framework for integrating more in-depth studies of the theory and methods of survey data analysis. A guide to the applied statistical analysis and interpretation of survey data, it contains many examples and practical exercises based on major real-world survey data sets. Although the authors use Stata for most examples in the text, they offer SAS, SPSS, SUDAAN, R, WesVar, IVEware, and Mplus software code for replicating the examples on the book's website: http://www.isr.umich.edu/src/smp/asda/
Contents
Applied Survey Data AnalysisIntroductionA Brief History of Applied Survey Data AnalysisExample Data Sets and ExercisesGetting to Know the Complex Sample DesignIntroductionClassification of Sample DesignsTarget Populations and Survey PopulationsSimple Random Sampling: A Simple Model for Design-Based InferenceComplex Sample Design EffectsComplex Samples: Clustering and StratificationWeighting in Analysis of Survey DataMultistage Area Probability Sample DesignsSpecial Types of Sampling Plans Encountered in SurveysFoundations and Techniques for Design-Based Estimation and InferenceIntroductionFinite Populations and Superpopulation ModelsConfidence Intervals for Population ParametersWeighted Estimation of Population ParametersProbability Distributions and Design-Based InferenceVariance EstimationHypothesis Testing in Survey Data AnalysisTotal Survey Error and Its Impact on Survey Estimation and InferencePreparation for Complex Sample Survey Data AnalysisIntroductionAnalysis Weights: Review by the Data UserUnderstanding and Checking the Sampling Error Calculation ModelAddressing Item Missing Data in Analysis VariablesPreparing to Analyze Data for Sample SubpopulationsA Final Checklist for Data UsersDescriptive Analysis for Continuous VariablesIntroductionSpecial Considerations in Descriptive Analysis of Complex Sample Survey DataSimple Statistics for Univariate Continuous DistributionsBivariate Relationships between Two Continuous VariablesDescriptive Statistics for SubpopulationsLinear Functions of Descriptive Estimates and Differences of MeansExercisesCategorical Data AnalysisIntroductionA Framework for Analysis of Categorical Survey DataUnivariate Analysis of Categorical DataBivariate Analysis of Categorical DataAnalysis of Multivariate Categorical DataExercisesLinear Regression ModelsIntroductionThe Linear Regression ModelFour Steps in Linear Regression AnalysisSome Practical Considerations and ToolsApplication: Modeling Diastolic Blood Pressure with the NHANES DataExercisesLogistic Regression and Generalized Linear Models (GLMs) for Binary Survey VariablesIntroductionGLMs for Binary Survey ResponsesBuilding the Logistic Regression Model: Stage 1, Model SpecificationBuilding the Logistic Regression Model: Stage 2, Estimation of Model Parameters and Standard ErrorsBuilding the Logistic Regression Model: Stage 3, Evaluation of the Fitted ModelBuilding the Logistic Regression Model: Stage 4, Interpretation and InferenceAnalysis ApplicationComparing the Logistic, Probit, and Complementary Log-Log GLMs for Binary Dependent VariablesExercisesGLMs for Multinomial, Ordinal, and Count VariablesIntroductionAnalyzing Survey Data Using Multinomial LogitRegression ModelsLogistic Regression Models for Ordinal Survey DataRegression Models for Count OutcomesExercisesSurvival Analysis of Event History Survey DataIntroductionBasic Theory of Survival Analysis(Nonparametric) Kaplan-Meier Estimation of the Survivor FunctionCox Proportional Hazards ModelDiscrete Time Survival ModelsExercisesMultiple Imputation: Methods and Applications for Survey AnalystsIntroductionImportant Missing Data ConceptsAn Introduction to Imputation and the Multiple Imputation MethodModels for Multiply Imputing Missing DataCreating the ImputationsEstimation and Inference for Multiply Imputed DataApplications to Survey DataExercisesAdvanced Topics in the Analysis of Survey DataIntroductionBayesian Analysis of Complex Sample Survey DataGeneralized Linear Mixed Models (GLMMs) in Survey Data AnalysisFitting Structural Equation Models to Complex Sample Survey DataSmall Area Estimation and Complex Sample Survey DataNonparametric Methods for Complex Sample Survey DataReferencesAppendix: Software Overview
