Which type of sampling method can result in a biased sample due to self-selection?

Volunteer sampling can lead to a biased sample due to the inherent nature of self-selection. In this method, participants choose to join the study based on their own preferences or motivations, which can mean that those who opt in may share similar characteristics or experiences. This self-selection process can skew the results, as it may attract individuals who have a particular interest or strong feelings about the subject being studied, thereby not representing the broader population accurately.

For instance, if a survey about mental health is open for volunteers, individuals who are experiencing mental health issues or are more aware of them may be more likely to participate than those who are not affected or are unaware. This can lead to an overrepresentation of those with particular views or experiences, thus reducing the generalizability of the findings.

In contrast, methods such as stratified, random, and systematic sampling aim to minimize bias by ensuring that participants are selected in a way that better represents the overall population. These methods typically involve systematic processes that do not rely on individuals’ choice to participate, which helps mitigate the risk of skewed data that volunteer sampling can introduce.