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Analysis of Survey Data from Complex Sample Designs
Instructor(s): Dates:October 11, 2013 to November 08, 2013 October 10, 2014 to November 07, 2014
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Analysis of Survey Data from Complex Sample Designs
taught by Brady West and Patricia Berglund
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 Analytics and Statistical Studies (PASS):
SESSION 1: Overview
- Important terms, concepts, and notation
- Software Overview
- 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
- 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
- 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
- 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
- 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
- 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
- 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
Analysis of Survey Data from Complex Sample Designs
Instructor(s):
Dates:October 11, 2013 to November 08, 2013 October 10, 2014 to November 07, 2014
Course Fee: $499
Add $50 service fee if you require a prior invoice, or if you need to submit a purchase order or voucher, pay by wire transfer or EFT, or refund and reprocess a prior payment. Please use this printed registration form, for these and other special orders.
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Have you reviewed the REQUIREMENTS for this course?Analysis of Survey Data from Complex Sample Designs
taught by Brady West and Patricia Berglund
Who Should Take This Course:Anyone designing surveys or analyzing survey data.
Level:Advanced/Intermediate
Prerequisite:
Organization of the Course: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.
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.
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 at the end of the week, you will receive individual feedback on your homework answers.
Credit:
Students come to the Institute for a variety of reasons. As you begin the course, you will be asked to specify your category:
- You may be interested only in learning the material presented, and not be concerned with grades or a record of completion.
- You may be enrolled in PASS (Programs in Analytics and Statistical Studies) that requires demonstration of proficiency in the subject, in which case your work will be assessed for a grade.
- You may require a "Record of Course Completion," along with professional development credit in the form of Continuing Education Units (CEU's). For those successfully completing the course, 5.0 CEU's and a record of course completion will be issued by The Institute, upon request.
The course text is Applied Survey Data Analysis by Steve Heeringa, Brady West, and Pat Berglund, which you can order from CRC Press, or by using this form. CRC Press typically gives students a generous discount when students order the text using the above form (not by ordering the text online).
PLEASE ORDER YOUR COPY IN TIME FOR THE COURSE STARTING DATE.
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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