Skills Development Workshop

Day 1: Thursday, 12 September

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How to develop composite indicators for social science research - the example of the UK Gender Equality Index

Session convener: Caitlin Schmid, Global Institute for Women's Leadership, King's College London

Composite indicators are valuable instruments for the social sciences to illustrate complex social, economic, or environmental phenomena. Providing simplified country, regional, or local comparisons of performances, composite indicators are becoming increasingly influential in policy analysis and the public communication of current trends. Yet, the informative value of composite indicators rests on the conceptual and methodological soundness of their construction. In this Skills Development Workshop, we guide participants through the ten methodological steps in the construction of composite indicators developed by the OECD and the European Commission's Joint Research Centre: conceptual development, indicator selection, data treatment, normalisation, weighting, aggregation, correlation analysis, robustness testing, data analysis, and visualisation of scores. The demonstration uses the example of the UK Gender Equality Index (UKGEI), developed by the Global Institute for Women's Leadership at King's College London, comparing women's and men's socio-economic outcomes across the 374 local authorities of the UK. The UKGEI combines multiple indicators related to the domains of Paid Work, Unpaid Work, Money, Education, Health, and Power & Participation to evaluate the extent and variation of gender equality across the four nations.Given the proliferation of and increasing reliance on composite indicators in policy-making and public discourse, social scientists should be aware of the benefits and risks associated with their development and interpretation. By the end of the workshop, participants will understand the conceptual and methodological decisions involved in the construction process and will have gained the skills necessary to assess the validity and robustness of composite indicators and their knowledge claims.