When you first took statistics, surveys such
as presidential opinion polls were probably prominent in learning inference for
proportions. Unfortunately, that "simple random sample" from
your textbook is more a creature of myth than an actual reality. Most
surveys nowadays are complex, with stratification, multi-stage sampling,
cluster sampling, etc. Analysis via a simple "confidence interval
for a proportion" is rarely suitable.
In "Analysis of Survey Data from Complex Sample Designs," you'll learn how to estimate variances for complex surveys, and also how to model the results using linear and logistic regression, and other generalized linear models with Dr. Brady T. West and Ms. Patricia Berglund at Statistics.com. For more details please visit at http://www.statistics.com/surveycomplex.
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).
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.
Who Should Take
This Course:
Anyone designing surveys or analyzing survey
data.
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
The instructors are Dr. Brady West (Lead
Statistician, Center for Statistical Consultation and Research, Univ. of
Michigan) and Ms. Patricia Bergland (Senior Research Associate in the Youth and
Social Indicators Program and the Survey Methodology Program at the University
of Michigan-Institute for Social Research). Brady West is the lead author
of "Linear Mixed Models: A Practical Guide using Statistical
Software" (Chapman Hall/CRC) and a co-author of "Applied Survey Data
Analysis" (Chapman Hall/CRC).
You will be able to ask questions and exchange comments with Dr. Brady West and Ms. Patricia Bergland via a private discussion board throughout the course. The courses take place online at statistics.com in a series of 4 weekly lessons and assignments, and require about 15 hours/week. Participate at your own convenience; there are no set times when you must be online. You have the flexibility to work a bit every day, if that is your preference, or concentrate your work in just a couple of days.
For Indian
participants statistics.com accepts registration for its courses at special
prices in Indian Rupees through its partner, the Center for eLearning and
Training (C-eLT), Pune.
Call: 020 66009116
Websites:
