Analysis of Survey Data from Complex Sample Designs

Brief Description:

This course teaches you how to estimate variances for complex surveys, and also how to model the results using linear and logistic regression, and other generalized linear models.

Instructor(s):
Level: Advanced/Intermediate

Who Should Take This Course:

Anyone designing surveys or analyzing survey data.

Dates:
All courses have already commenced.
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Registration:
Please read the syllabus tab, noting the prerequisites, text and software requirements.

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Analysis of Survey Data from Complex Sample Designs

Aim of Course:

In order to extract maximum information at minimum cost, sample designs are typically more complex than simple random samples. Cluster sampling and stratified designs are common. But how do you analyze the resulting data - in particular, how do you determine margins of error? This course teaches you how to estimate variances when analyzing survey data from complex samples, and also how to fit linear and logistic regression models to complex sample survey data.

This course is a core requirement or elective in the following Program(s) in Advanced Statistical Studies (PASS):

• Business Analytics

• Statistics for Social Sciences

Prerequisite(s):

Students should also have completed the Survey Design and Survey Analysis courses at statistics.com, or their equivalents. Students should also have some familiarity with generalized linear models (GLM), in specific, logistic regression. These topics are covered as part of the Categorical Data Analysis course, and in greater depth in the GLM and Logistic Regression courses.

Course Program:

SESSION 1: Overview

Applied Survey Data Analysis: An Overview

• Important terms, concepts, and notation
• Software Overview

Getting to Know the Complex Sample Design

• Classification of Sample Designs
• Target Populations and Survey Populations
• Simple Random Sampling
• Complex Sample Design Effects
• Complex Samples: Clustering and Stratification
• Weighting in Analysis of Survey Data
• Multi-stage Area Probability Sample Designs

SESSION 2: Overview continued

Foundations and Techniques for Design-based Estimation and Inference

• 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

Preparation for Complex Sample Survey Data Analysis

• 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 from Sample Subclasses
• A Final Checklist for Data Users

SESSION 3: Descriptive Statistics

Descriptive Analysis for Continuous Variables

• Special Considerations in Descriptive Analysis of Complex Sample Survey Data
• Simple Statistics for Univariate Continuous Distruibutions
• Bivariate Relationships between Two Continuous Variables
• Descriptive Statistics for Subpopulations
• Linear Functions of Descriptive Estimates and Differences of Means

Categorical Data Analysis

• A Framework for Analysis of Categorical Survey Data
• Univariate Analysis of Categorical Data
• Bivariate Analysis of Categorical Data
• Analysis of Multivariate Categorical Data

SESSION 4: Regression Models

Linear Regression Models

• The Linear Regression Model
  • Fitting linear regression models to survey data
• Four Steps in Linear Regression Analysis
• Some Practical Considerations and Tools
• Application: Modeling Diastolic Blood Pressure with the NHANES Data

Logistic Regression and Generalized Linear Models for Binary Survey Variables

• Generalized Linear Models (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 (C-L-L) GLMs for Binary Dependent Variables

Organization of the Course:

This course takes place over the internet, at statistics.com for 4 weeks. During each course week, you participate at times of your own choosing - there are no set times when you must be online. Course participants will be given access to a private discussion board. In class discussions led by the instructor, you can post questions, seek clarification, and interact with your fellow students and the instructor.

The course typically requires 15 hours per week. At the beginning of each week, you receive the relevant material, in addition to answers to exercises from the previous session. During the week, you are expected to go over the course materials, work through exercises, and submit answers. Discussion among participants is encouraged. The instructor will provide answers and comments, and you will receive individual feedback on your homework answers.

Credit:
Students come to The Institute for a variety of reasons:

1. You may be interested only in learning the material presented, and not be concerned with grades or a record of completion.
2. You may be enrolled in PASS (Program in Advanced Statistical Studies) that requires demonstration of proficiency in the subject, in which case your work will be assessed for a grade.
3. You may require a "Record of Course Completion," along with professional development credit in the form of Continuing Education Units (CEU's).

As you begin the class, you will be asked to specify your category.

This course offers continuing education units (CEU's). For those successfully completing the course (generally this means marks of 50% or better on the homework), 5.0 CEU's and a record of course completion will be issued by Statistics.com, upon request.

Course Text:

Applied Survey Data Analysis (April 2010) by Steve Heeringa, Brady West, and Pat Berglund, by Chapman and Hall. You may order it here. CRC Press offers a discount on the text but you must use this form to receive the discount.

Software:

The course will be driven by learning how to use specialized software procedures for the analysis of complex sample survey data, using real data sets, and exercises will be selected from the book chapters. Participants could use R, WesVar, or IVEware (free packages) or SAS, Stata, SUDAAN, or SPSS (commercial packages, with SPSS users required to purchase the Complex Samples Module).

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Analysis of Survey Data from Complex Sample Designs

Instructor(s):
Dates:
All courses have already commenced.
Course Fee: $499
Academic Discounted Rate: $399

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