units have a differing probability of selection that is unaccounted for by the researcher. For example, some households have multiple phone numbers making them more likely to be selected in a telephone survey than households with only one phone number. This selection bias would be corrected by applying a survey weight equal to [1/(# of phone numbers)] to each household. Self-selection bias: A type of bias in which individuals voluntarily select themselves into a group, thereby potentially biasing the response of that group. Participation bias: Bias that arises due to the characteristics of those who choose to participate in a survey or poll. Coverage bias: Coverage bias can occur when population members do not appear in the sample frame (undercoverage). Coverage bias occurs when the observed value deviates from the population parameter due to differences between covered and non-covered units. Telephone surveys suffer from a well known source of coverage bias because they cannot include households without telephones. Non-probability sampling Many surveys are not based on probability samples, but rather on finding a suitable collection of respondents to complete the survey. Some common examples of non-probability sampling are: Judgement Samples: A researcher decides which population members to include in the sample based on his or her judgement. The researcher may provide some alternative justification for the representativeness of the sample. The underlying assumption is that the investigator will select units that are characteristic of the population. This method can be subjected to researcher's biases and perception. Snowball Samples: Often used when a target population is rare. Members of the target population recruit other members of the population for the survey. Quota Samples: The sample is designed to include a designated number of people with certain specified characteristics. For example, 100 coffee drinkers. This type of sampling is common in non-probability market research surveys. Convenience Samples: The sample is composed of whatever persons can be most easily accessed to fill out the survey. In non-probability samples the relationship between the target population and the survey sample is immeasurable and potential bias is unknowable. Sophisticated users of non-probability survey samples tend to view the survey as an experimental condition, rather than a tool for population measurement, and examine the results for internally consistent relationships. See also Sample size determination Sampling (statistics) Total survey error References Further reading The textbook by Groves et alia provides an overview of survey methodology, including recent literature