Studies of human health use samples to obtain information on the whole population, the aims being for the sample to represent the population of interest accurately. Selection should be done in such a manner that the population remains representative of the whole population. When the sample consists of volunteers, the risk is that they are not representative of the general population.
Volunteer bias can occur at all stages of the trial from recruitment, retention through to follow-up. Differences between volunteers and the target population are not restricted to socio-demographic factors but can include attitudes towards the trial and institutions involved.
Volunteer bias may also relate to the diseases or conditions being studied, as volunteers might be less likely to put themselves forward for studies of diseases or behaviours that are regarded as less socially acceptable, such as drug abuse-related conditions.
A study exploring volunteer bias using data from a trial of probiotic supplementation for childhood atopy (Jordan et al. 2013) found that as the trial progressed, representation of the most deprived decreased. These participants were more likely to be lost to follow-up at six months, and two years, and consent to infant blood sample donation. They also found that mothers interested in probiotics were more likely to attend research clinics and consent to skin-prick testing. Mothers participating to help their children were also more likely to consent to blood sample donation.
In another study of volunteer bias in sexuality research, the authors reported that volunteers reported a more positive attitude towards sexuality, less sexual guilt, and more sexual experience than non-volunteers (Strassberg et al. 1995). These differences were independent of subject gender, and the authors concluded that these findings have “sobering implications of these findings for the generalizability of sexuality research results.”
It is difficult to estimate the impact of volunteer bias and the direction of its effect. Volunteers tend to be more educated, come from high social class and more approval motivated. One report found that females are more likely to volunteer than males and are healthier and adhere to treatment more often.
The likelihood of volunteer bias increases as the refusal rate to volunteer increases. Therefore, any technique that increases volunteer numbers is likely to reduce bias. Ensuring anonymity and confidentiality of volunteers are essential to increase participation in studies and decreasing volunteer bias.