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.