Data collection and publication - disability: guidance

Analysis and presentation of results

When producing disability breakdowns it is recommended to include the following categories, as these are designed to closely match the 2010 Act disability definition:

Disabled: Q1a = 'Yes'; AND Q2 = 'Yes, a lot or 'Yes, a little'

Non-disabled: Q1a = 'No'; OR Q2 = 'Not at all'

It may be useful to include a clarification of what is meant by disability, i.e. a "long-term limiting physical or mental health condition" in a table footnote or in the publication/chapter introduction. Footnotes should be printed in the same font size as the main text for accessibility purposes.

It is the data collector's decision whether to include the optional question 1b and the additional question regarding duration of activity restriction, depending on the needs of your users.

Intersectionality

An intersectional analysis gives an insight into how a combination of socio-demographic characteristics might relate to specific forms of disadvantage. For example disadvantage for a young, minority ethnic, disabled woman may be different to an older, white, disabled man.

There is an increasing awareness that taking an intersectional approach to research, policy making and operational decisions is important. This is because 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 an individual's disability status or their disabilities.

Where sample size and data 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.