Before you analyze your data, it is very important that you **
check the distribution and normality** of the data and identify
outliers for continuous variables with a univariate analysis. If the
distribution is **highly skewed**, you can do a data
transformation to make the distribution of the data closer to normal
(the underlying assumption in most statistical analyses is that the
distribution of the data is normal). The common types of
transformation are LOGIT, LOG, LOG10, SQRT, INVERSE, or ARCSIN.
Transforming data should be covered in any basic biostatistics text
and will not be covered in detail in this tutorial.

After checking the distribution and normality of the data, plot the survey
weight against the variable to determine which of the extreme values identified
in the univariate analysis are outliers. You must also determine if the outliers
represent **valid values** and, if so, also carry **extremely large survey
weights**. Outliers with extremely large weights could have an influential
impact on your estimates. Then you have to decide whether to keep these
influential outliers in your analysis or not. It is up to the **analysts to
make that decision**. Please consult the
Analytical Guidelines for more information on this topic.