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/
Steve G. Heeringa is a research scientist in the Survey Methodology Program, the director of the Statistical and Research Design Group in the Survey Research Center, and the director of the Summer Institute in Survey Research Techniques at the University of Michigan's Institute for Social Research. Brady T. West is a doctoral student and research assistant in the Survey Research Center at the University of Michigan's Institute for Social Research. He is also a statistical consultant in the Center for Statistical Consultation and Research. Patricia A. Berglund is a senior research associate in the Youth and Social Indicators Program and Survey Methodology Program in the Survey Research Center at the University of Michigan's Institute for Social Research.
Applied Survey Data Analysis: Overview Introduction A Brief History of Applied Survey Data Analysis Example Data Sets and Exercises Getting to Know the Complex Sample Design Introduction Classification of Sample Designs Target Populations and Survey Populations Simple Random Sampling: A Simple Model for Design-Based Inference Complex Sample Design Effects Complex Samples: Clustering and Stratification Weighting in Analysis of Survey Data Multistage Area Probability Sample Designs Special Types of Sampling Plans Encountered in Surveys Foundations and Techniques for Design-Based Estimation and Inference Introduction Finite Populations and Superpopulation Models Confidence Intervals for Population Parameters Weighted Estimation of Population Parameters Probability Distributions and Design-Based Inference Variance Estimation Hypothesis Testing in Survey Data Analysis Total Survey Error and Its Impact on Survey Estimation and Inference Preparation for Complex Sample Survey Data Analysis Introduction Analysis Weights: Review by the Data User Understanding and Checking the Sampling Error Calculation Model Addressing Item Missing Data in Analysis Variables Preparing to Analyze Data for Sample Subpopulations A Final Checklist for Data Users Descriptive Analysis for Continuous Variables Introduction Special Considerations in Descriptive Analysis of Complex Sample Survey Data Simple Statistics for Univariate Continuous Distributions Bivariate Relationships between Two Continuous Variables Descriptive Statistics for Subpopulations Linear Functions of Descriptive Estimates and Differences of Means Exercises Categorical Data Analysis Introduction A Framework for Analysis of Categorical Survey Data Univariate Analysis of Categorical Data Bivariate Analysis of Categorical Data Analysis of Multivariate Categorical Data Exercises Linear Regression Models Introduction The Linear Regression Model Four Steps in Linear Regression Analysis Some Practical Considerations and Tools Application: Modeling Diastolic Blood Pressure with the NHANES Data Exercises Logistic Regression and Generalized Linear Models (GLMs) for Binary Survey Variables Introduction GLMs for Binary Survey Responses Building the Logistic Regression Model: Stage 1, Model Specification Building the Logistic Regression Model: Stage 2, Estimation of Model Parameters and Standard Errors Building the Logistic Regression Model: Stage 3, Evaluation of the Fitted Model Building the Logistic Regression Model: Stage 4, Interpretation and Inference Analysis Application Comparing the Logistic, Probit, and Complementary Log-Log GLMs for Binary Dependent Variables Exercises GLMs for Multinomial, Ordinal, and Count Variables Introduction Analyzing Survey Data Using Multinomial Logit Regression Models Logistic Regression Models for Ordinal Survey Data Regression Models for Count Outcomes Exercises Survival Analysis of Event History Survey Data Introduction Basic Theory of Survival Analysis (Nonparametric) Kaplan-Meier Estimation of the Survivor Function Cox Proportional Hazards Model Discrete Time Survival Models Exercises Multiple Imputation: Methods and Applications for Survey Analysts Introduction Important Missing Data Concepts An Introduction to Imputation and the Multiple Imputation Method Models for Multiply Imputing Missing Data Creating the Imputations Estimation and Inference for Multiply Imputed Data Applications to Survey Data Exercises Advanced Topics in the Analysis of Survey Data Introduction Bayesian Analysis of Complex Sample Survey Data Generalized Linear Mixed Models (GLMMs) in Survey Data Analysis Fitting Structural Equation Models to Complex Sample Survey Data Small Area Estimation and Complex Sample Survey Data Nonparametric Methods for Complex Sample Survey Data References Appendix: Software Overview
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