An intersectional analysis gives an insight into how a combination of socio-demographic characteristics might relate to specific forms of disadvantage. There is an increasing awareness that taking an intersectional approach to research, policy making and operational decisions is important, as intersectionality can give insight into the experiences of different groups in society, and how particular characteristics can combine to impact on an individual's experiences. However, there is not always disaggregated data available to support such an approach. This is likely to mean that the information on which important decisions are made is not fully representative of the population it is intending to measure.
For example, data on demographic characteristics may be collected by a public body, but not disaggregated in an intersectional way due to issues around sample size and risk of disclosing an individual's identity. In these cases, organisations should not risk disclosing information about a person's sex, or trans status. The Government Statistical Service guidance mentioned in section 7 above can help here.
Where sample size and quality allows, data should be disaggregated, including by sex, gender reassignment, race, religion or belief, age, disability, and sexual orientation, where combinations of these factors can result in discrimination, disadvantage and inequality. Being able to identify cases where combinations of factors are resulting in disadvantage enables policies to be developed and action taken to address these issues.
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