Contemporary survey research requires a sophisticated approach to sampling.
The basic intuition behind sampling for survey research has long rested on the idea of simple random sampling (SRS). The process behind SRS is straightforward: the researcher has a complete listing of the population, and then using some type of randomization device selects a subset of observations from that population list for the survey sample. If the list is accurate and complete, and the researcher has has sufficient resources to contact and interview every observation on that list, then this methodology will typically produce reliable survey results.
But in today’s world, many complications arise that render SRS impossible or overly costly to implement. For example, many of the customer lists that businesses and organizations maintain are incomplete: a list might only have telephone numbers or email addresses for a fraction of those customers on the list because many may not be willing to provide their contact information. If the customers who do not give their phone numbers or email addresses are indeed different from those who do (they are older, or wealthier, for example) that means that the list with contact information is biased.
One way to resolve the biased list problem is to send that list to a firm that specializes in the augmentation or appending of contact information; while that may produce a complete list, such an approach can be costly and may not be as accurate as needed for the subsequent survey problem.
Our team has developed sampling approaches that can be used for situations like these. We also have expertise dealing with problems that can arise during sampling or survey implementation, like significant compliance or non-response problems. The Pivotal Targeting team has substantial experience working with complex survey designs, especially those that include experimental treatments embedded within the survey.