“Hope for the best, prepare for the worst”: salvaging the client list

You’ve probably heard the story before. It begins, “The study started with a client list….”

I can’t tell you how many times I had a client call and tell me that. The stories follow a pattern. The client says it’s a great list and you should be able to easily complete the study with it. Sounds great, right?

Here comes the plot twist. They forgot to tell you the list is 4 years old and hasn’t been touched since. Oh, and by the way, only 30% of the records have a phone or email address. Suddenly, easy street is filled with potholes.

This isn’t the end of the story, and it can have a happy ending. A sub-standard client list can be rescued with these investigative approaches and performance enhancements:

• Flag any cell phone numbers so they can be separated out and dialed manually, which also ensures TCPA compliance.

• Ask yourself: what is most important on their list? What is the key sampling element? Is it the individual (contact name)? If so, the file can be run against the National Change of Address (NCOA) database to see if the person has moved. If the person has moved, a search can be run for the new address. The next step is to identify the landline and (or) cellular telephone numbers associated with that individual at the new address.

• If location/address is the key element, check for the most up-to-date telephone numbers (either landline or cellular) and name associated with that address.

• Send the call list to a sample provider for verification. Does the information in your list match the sample provider’s database?

• If information doesn’t match, can you append on a new phone number or email address?

• Do you still have open quotas? See if you can append demographics to target for open quotas.

• When you’ve exhausted all options on the client list and the study still isn’t completed, order an additional custom sample that meets the ultimate client’s specifications (or at least comes close). Then you should dedupe the client list from any custom sample orders.

With the help of a good sample provider, even a subpar client list can be salvaged and the study brought to completion on time.

Split-Frame Sampling

Oftentimes, researchers are faced with the challenging task of targeting rare domains in a population while maintaining the probability-based nature of the employed sample.  For instance, in a national RDD sample it might be necessary to oversample households with small children or those with even less prevalent attributes.  While an epsem sampling design, whereby all numbers have the same chance of selection, will provide the most efficient sample with respect to the precision of survey estimates, from a cost perspective such a design can be completely prohibitive due to the required level of screening for reaching eligible households.  This is where a cleverly designed stratified sampling alternative that employs disproportional allocation can prove highly valuable.

In practice, an optimal sample allocation scheme takes into account the unit cost per interview in each sampling stratum.  As such, a stratum with a high incidence of reaching members of the target population will receive a higher allocation as compared to other strata.  This disproportionate sample allocation should be exercised while providing a non-zero chance of selection for all telephone numbers to ensure a probability-based sample.

The objective of this stratification is to provide a means for over sampling the target populations by segregating higher incidence households into distinct sampling strata.  This is done by matching all numbers against commercial databases, which contain household and individual level demographic data, and identifying the numbers that meet the specified target.

With access to all the top commercial databases Marketing Systems Group can provide cost-effective solutions for sample surveys that aim to target rare domains.  By placing such telephone numbers in the “top” or high incidence stratum and the remaining telephone numbers covering the geography of interest in another, you can create a complete sampling frame.  Subsequently, using an optimization procedure a higher sampling fraction will be determined for the top stratum cognizant of the design effect that will result from a disproportional sample allocation and will need to be adjusted for when weighting.