Analysis of Survey Data from Complex Sample Designs
This course will teach you how to estimate descriptive quantities and sampling variances from complex surveys, and also how to fit linear and logistic regression models to complex sample survey data.
Overview
Complex sample designs such as stratified cluster sampling make it possible to extract maximum information at minimum cost, but they are typically harder to work with than simple random samples. 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.
- Intermediate, Advanced
- 4 Weeks
- Expert Instructor
- Tuiton-Back Guarantee
- 100% Online
- TA Support
Learning Outcomes
Students who complete this course will gain a basic understanding of applied survey data analysis and complex sample design. The course covers foundational statistics for finite populations and superpopulation models, descriptive statistics and a variety of regression models.
- Fit regression models to complex sample survey data
- Describe and classify different sample designs
- Explain design-based estimation and inference
- Analyze both continuous and categorical data from complex sample surveys
Who Should Take This Course
Anyone designing surveys or analyzing survey data.
Our Instructors
Dr. Brady T. West
Dr. Brady T. West is a Research Professor in the Survey Research Center at the University of Michigan Institute for Social Research. His current research interests include survey nonresponse, interviewer variance, responsive and adaptive survey design, the analysis of complex sample survey data, and multilevel regression models for clustered and longitudinal data. He teaches several yearly short courses on statistical methodology and software, and is a lead or co-author on numerous publications presenting analyses of complex sample survey data.
Course Syllabus
Week 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
Week 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
Week 3
Descriptive Statistics
- Descriptive Analysis for Continuous Variables
- 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
- 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
Week 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
Class Dates
2025
Instructors: Dr. Brady T. West
Prerequisites
You should have some familiarity with logistic regression, which is covered in several Institute courses (Logistic Regression, GLM, Categorical Data Analysis). You should also be familiar with basic survey design and analysis, as covered in these courses:
Survey Analysis
- Topic: Test Topic
- Skill: Intermediate, Advanced
- Credit Options: CEU
- Class Start Dates: June 23, 2023, June 21, 2023
Survey Design and Sampling Procedures
- Topic: Test Topic
- Skill: Intermediate, Advanced
- Credit Options: CEU
- Class Start Dates: June 23, 2023, June 21, 2023
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Analysis of Survey Data from Complex Sample Designs
Additional Information
Organization of Course
This course takes place online at The Institute 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.
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 at the end of the week, you will receive individual feedback on your homework answers.
Time Requirements
This is a 4-week course requiring 10-15 hours per week of review and study, at times of your choosing.
Homework
The homework in this course consists of short answer questions to test concepts, guided exercises in writing code and guided data analysis problems using software.
In addition to assigned readings, this course also has example software codes, supplemental readings available online, and an end of course data modeling project.
Course Text
The course text is Applied Survey Data Analysis 2nd Edition, by Steve Heeringa, Brady West, and Pat Berglund.
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). If you plan to use other software, check to be sure that it can analyze data from complex survey designs (clustered, stratified, multistage, etc.).
Course Fee & Information
Enrollment
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This course is eligible for the following credit and recognition options:
No Credit
You may take this course without pursuing credit or a record of completion.
Mastery or Certificate Program Credit
If you are enrolled in mastery or certificate program that requires demonstration of proficiency in this subject, your course work may be assessed for a grade.
CEUs and Proof of Completion
If you require a “Record of Course Completion” along with professional development credit in the form of Continuing Education Units (CEU’s), upon successfully completing the course, CEU’s and a record of course completion will be issued by The Institute upon your request.
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