Using intersectionality in policymaking and analysis: summary findings

How can intersectionality be integrated throughout the analytical process?

This section outlines how intersectionality can be integrated throughout the analytical process. More detail on analytical techniques is provided in the main report.

Addressing the power imbalance in research

Reflexivity

Reflexivity is an important part of conducting intersectional research: a researcher should ask themselves questions about their own social positions, values, assumptions, interests and experiences and how these can shape the research process, as well as putting the research into context.^[9]

Public involvement in research

Public involvement refers to the active contribution from people with lived experience in research projects and in research organisations.^[10] This is distinct from more traditional research approaches in which people take part in a research study as 'participants' or 'data subjects' (e.g. being interviews or answering questions about their experiences) and where information gathered through research is disseminated to people rather than with them.

Ensuring marginalised groups are reached

Adopting an intersectional approach to analysis requires that people with different intersecting identities, particularly from multiple marginalised groups, be included in research so that their voices are heard.^[11] This means that analysts need to consider and take practical steps to aid diverse research recruitment.

Sampling techniques and reporting

The sampling techniques used in research are important considerations when taking an intersectional approach. A completely random sampling approach or use of a sampling frame designed to be representative of a whole population may lead to problems with low sample size for lower-frequency groups in the population or among those who may be more reluctant to take part in or contribute to research. If this is the case, the validity and applicability of other sampling techniques should be considered to assist coverage. The extent to which data can be utilised for intersectional analysis should also be a priority.

Analysts should carry out intersectional data analysis where possible. A range of statistical techniques, and their respective advantages and disadvantages, are discussed in the main report including regression, multi-level models and moderated mediation. Analysts should consider a range of options for carrying out intersectional data analysis ahead of data collection. In addition, an assessment of the sampling approach and likely sample size could help determine which technique is most appropriate.