Without the willingness of people to voluntarily participate in studies, research on health and disease is not possible. However, a sufficiently high number of study participants is not the only dimension of great importance for science. Study participants should also reflect the diversity of society or of the individuals about whom it wishes to make statements. This is referred to as the representativeness of the study sample. Representativeness is a quality feature of quantitative studies. It is therefore important to know whether the study sample represents the target population in all relevant aspects. For example, people with a low socioeconomic status are often underrepresented in health research. Furthermore, study participation differs depending on differential categories such as sex/gender, socioeconomic status or ethnic origin. Therefore, an improved understanding of study participation is of great importance for research. 

An intersectional perspective does not understand study participation as the result of an exclusively individual decision. Instead, social processes such as discrimination are taken into account as explanatory approaches. In the case of underrepresented groups, there is a lack of reliable statements about their health and therefore also of measures to reduce health inequities. Here is an example: 

What are the reasons people do not participate in studies? In fact, it is a challenge to obtain sufficient information about this. One possibility is to survey people who have declined the offer to participate in a study in so-called non-responder surveys. However, these questionnaires are very short and, accordingly, contain only a small selection of questions. Therefore, the reasons for a decision against participation can often only be understood to a limited extent. Here, qualitative studies are suitable as a way to gain a deeper understanding of study participation and non-participation.  

What are the characteristics that identify groups with a high or low probability of participation? A clear identification of these characteristics is another challenge. From an intersectional perspective, the question arises whether social categories such as sex/gender, ethnicity, age or income are suitable as predictors of study participation. This information can support the development of appropriate recruitment strategies for future research projects. 

Here you will find options for an intersectionality-informed description of study participation in population-based health studies. An intersectional perspective can help identify over- or underrepresented social groups more precisely. When developing questions for a non-responder survey, the possibilities for describing study participation could be expanded by considering relevant social categories. Finally, quantitative methods are available that allow for an intersectionality-informed analysis of the representation of all intersections in a study population.