Sex/gender-sensitive data analysis can contribute to reducing health inequities by identifying the causes of good and ill health, as well as starting points for prevention. The chosen analysis methods should always take into account the social and biological dimensions of sex/gender. Sex/gender-theoretical concepts bring focus to those aspects of sex/gender that are relevant for the respective question.
Sex/gender cannot be analysed independently of other categories - and vice versa. This gives rise to interdependencies that must be adequately considered and transparently communicated. According to the theoretical concept of intersectionality, social categories are mutually dependent and interwoven. Below is an example showing the importance of an intersectionality-informed perspective for data analysis in health research:
Often, the available analytical methods do not meet the theoretical requirements, for example because relevant variables are not available. Which aspects of the data analysis are consistent with the theoretical concepts? Which ones deviate from them? These points should always be critically discussed and documented in order to correctly present an intersectionality-informed analysis.
Intersections of several social categories must be made visible. However, established statistical methods such as stratification or interaction terms in regression analyses quickly reach their limits. For example, when forming combinations of many social categories, the number of observations for some subgroups can become very small and limit the applicability of these methods. Innovative methods are needed to adequately implement intersectionality-informed perspectives in data analysis.
To implement sex/gender-sensitive and intersectionality-informed data analysis, AdvanceGender has developed new methods or drawn on existing ones. Here you will find exemplary frames of reference for variable selection as well as options for sex/gender-sensitive and intersectionality-informed data analysis.