When you run a survey, the people who respond to your survey are called your sample because they are a sample of people from the larger population you are studying, such as adults who live in the U.S. A sampling frame is a list of information that will allow you to contact potential respondents – your sample – from a population. Ultimately, it’s the sampling frame that allows you to draw a sample from the larger population. For a mail-based survey, it’s a list of addresses in the geographic area in which your population is located; for an online panel survey, it’s the people in the panel; for a telephone survey, it’s a list of phone numbers. Thinking through how to design your sample to best match the population of study can help you run a more accurate survey that will require fewer adjustments afterwards to match the population.
One approach is to use multiple sampling frames; for example, in a phone survey, you can combine a sampling frame of people with cell phones and a sampling frame of people with landlines (or both), which is now considered a best practice for phone surveys.
Surveys can be either probability-based or nonprobability-based. For decades, probability samples, often used for telephone surveys, were the gold standard for public opinion polling. In these types of samples, there is a frame that covers all or almost all the population of interest, such as a list of all the phone numbers in the U.S. or all the residential addresses, and individuals are selected using random methods to complete the survey. More recently, nonprobability samples and online surveys have gained popularity due to the rising cost of conducting probability-based surveys. A survey conducted online can use probability samples, such as those recruited using residential addresses, or can use nonprobability samples, such as “opt-in” online panels or participants recruited, through social media or personal networks. Analyzing and reporting nonprobability-based survey results often require using special statistical techniques and taking great care to ensure transparency about the methodology.