  This paper provides a user's guide to the general theory of approximate
randomization tests developed in Canay, Romano, and Shaikh (2017) when
specialized to linear regressions with clustered data. Such regressions include
settings in which the data is naturally grouped into clusters, such as villages
or repeated observations over time on individual units, as well as settings
with weak temporal dependence, in which pseudo-clusters may be formed using
blocks of consecutive observations. An important feature of the methodology is
that it applies to settings in which the number of clusters is small -- even as
small as five. We provide a step-by-step algorithmic description of how to
implement the test and construct confidence intervals for the quantity of
interest. We additionally articulate the main requirements underlying the test,
emphasizing in particular common pitfalls that researchers may encounter.
Finally, we illustrate the use of the methodology with two applications that
further elucidate these points. The required software to replicate these
empirical exercises and to aid researchers wishing to employ the methods
elsewhere is provided in both {\tt R} and {\tt Stata}.
