Applied Survey Data Analysis

By Brady T. West, S. Heeringa, P. Berglund. 2010

1,087 citations recorded by the source corpus.

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

Subfields: Sociology and Political Science

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Citations in the corpus

Multilevel and Single-Level Models for Measured and Latent Variables When Data Are Clustered (2016, background): “2Readers are encouraged to review the accessible overviews provided by Heeringa, West, and Berglund (2010) and McNeish, Stapleton, and Silverman (2016). can specify the sampling design (e.g., the components of nesting) and then conduct analyses that will provide linearized cluster robust standard…”
Impact of participation in extra-curricular activities during college on graduate employability: an empirical study of graduates of Taiwanese business schools (2014, background): “Hence, our study examined one facet of employability, individual competence (Hillage and Pollard 1998; Schaeper 2009). Indeed, Saunders and Zuzel (2010) reported a strong correlation between employer and graduate perceptions of employability.”
Handbook of International Large-Scale Assessment : Background, Technical Issues, and Methods of Data Analysis (2013, background): “If this is the case, taking into account sampling weights results in a loss of efficiency and less precise estimates (Heeringa et al., 2010).”
Using SPSS to Analyze Complex Survey Data: A Primer (2020, methodology): “…complex sampling and its related analytical techniques have been discussed since the 1930s (e.g., Groves, 2011; Valliant et al., 2018; Lewis, 2017; Heeringa et al., 2017; Lee & Forthofer, 2005; Lumley, 2010; Chambers & Skinner, 2003; Biemer & Lyberg, 2003), much of the often very mathematical…”