Event Phone: 1-610-715-0115

Cancellation Policy: If you cancel your registration at least two weeks before the course is scheduled to begin, you are entitled to a full refund (minus a processing fee of $50).

In the unlikely event that Statistical Horizons LLC must cancel a seminar, we will do our best to inform you as soon as possible of the cancellation. You would then have the option of receiving a full refund of the seminar fee or a credit towards another seminar. In no event shall Statistical Horizons LLC be liable for any incidental or consequential damages that you may incur because of the cancellation.

A 3-Day Livestream Seminar Taught by Brady West, Ph.D.

Standard courses on statistical analysis assume that survey data are collected from a simple random sample of the target population. Little attention is given to design features of the survey, including unequal probabilities of observation and stratified multistage sampling.

Most standard statistical programs commonly used for data analysis (e.g., SAS, SPSS, and Stata) do not allow the analyst to take these properties of survey data into account. Failure to do so can have an important impact on estimation and population inference for all types of analyses, ranging from simple descriptive statistics to multivariable regression models.

This seminar provides an introduction to statistical methods for the analysis of complex sample survey data. Such data typically include weights that adjust for differences in probability of selection, differences in subgroup response rates, stratification, and clustering, often in multiple stages.

The course will introduce variance estimation techniques that take into account the weighting, stratification, and cluster sampling that are properties of the multistage sampling designs used by most major survey organizations. Initially, we’ll focus on the estimation of sampling variances for descriptive statistics (means, proportions and quantiles of distributions), and then we’ll turn to variance estimation for subpopulations and multivariable modeling.

There will be a strong practical focus on available software procedures for commonly used analyses, including testing for between-group differences in means and proportions, linear regression analysis for continuous dependent variables, contingency table analysis for categorical data, logistic regression for categorical responses, and multilevel modeling. Numerous examples of these types of analyses will be presented “live” using statistical software.