Identify a bias or error that might occur in a study where researchers used random digit dialing to ask 1003 US adults their plans on working during retirement.
What about those who do not have phones?
Could it be that you don't know if you are calling adults? Or maybe the adults should all be the same age?
In this study where random digit dialing is used to gather information about the retirement plans of US adults, one possible bias or error that might occur is selection bias.
Selection bias refers to the distortion of research results caused by the way participants are selected for inclusion in the study. In this case, using random digit dialing may lead to biases because it relies solely on phone numbers that are randomly generated. As a result, certain groups of people may be under or overrepresented in the sample, leading to a biased sample that does not accurately represent the broader population of US adults.
For example, individuals without landlines or those who rely solely on cell phones may be excluded from the sample. This could lead to an underrepresentation of younger demographics, who are more likely to use mobile phones as their primary means of communication. Similarly, individuals without access to telephones or those who do not answer unfamiliar numbers may also be excluded, which could introduce additional biases.
To reduce selection bias, researchers using random digit dialing should ensure that they use proper sampling techniques to ensure a more representative sample. For example, they may need to account for the exclusion of certain demographics and adjust their analysis accordingly. Additionally, researchers should consider using multiple modes of data collection, such as online surveys or in-person interviews, to reach a wider range of participants and mitigate the limitations associated with random digit dialing.