Rarely, if ever, are we
able to get responses from all of the people that we would like to survey.
Usually, we only get information from a subset, or sample, of the
people or organizations that we are interested in. One of the most
important goals of survey research is ensuring that the sample we get
information from is representative of the group (the population) that we
wish to make statements about. If the sample is large enough and if it
represents the population well, then we can make accurate statements about the
opinions, preferences or intentions of the group as a whole or of subgroups that
interest us -- even when we are not able to get information from everyone.
When the group that we are
interested in is not unmanageably large and we are able to identify all
members of the group, then we can approach sampling from the perspective of
trying to get information from everyone in the group. We refer to this as
a census sample. Often, surveying all of your employees,
your business-to-business customers, your dealers, wholesalers, or other
interested parties is manageable using Web-based surveying.
At other times and
depending on the survey goals, we may not need to or be able to survey
everyone we are interested in. In these cases, we draw a random subset or
subsets of the population and invite them to participate in the survey.
Randomness is important in this context - all of the statistics that we use are
based on the assumption that the sample we have obtained is a random subset of
the population that we are interested in.
procedures have been developed to help us sample from within subgroups of the
population that we are interested in. There are also procedures we can use
to make adjustments in the data we receive to bring it into line with what
we know about the characteristics of the original population we sampled from (sample
weighting) and there are statistical techniques that we can use to estimate
the accuracy of the findings we obtain. These include computing
margins of error or confidence intervals.