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.