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Integrating administrative data from multiple sources offers important statistical insights that can expedite the knowledge-to-policy development cycle. Yet, administrative data lack contextual complexity because they are designed to measure
service contact and not service experience. Put differently, they tell us about people’s movements through systems of intervention but not about the people using services. Reliance on administrative data alone therefore risks omitting critical
dimensions of experience and perspective which, when interrogated, have the potential to inform programme and policy design.
This poster demonstrates how a sequential (explanatory) mixed methods design will be operationalised in a study that examines the temporal dynamics of family homelessness in Dublin, Ireland.
Over the course of the research, the Pathway Accommodation and Support System (PASS) and Local Authority housing list will be linked to create a rich dataset of the subpopulation of homeless families which will be supplemented with primary
data generated by in-depth interviews with families experiencing particular trajectories through homelessness. A core goal is to illustrate how data integration will occur with the aim of: 1) contextualising administrative (quantitative) data with
narrative (qualitative) findings; and 2) examining experiential dimensions of family homelessness that cannot be captured by the study’s administrative datasets.
It is argued that ‘mixing’ quantitative and qualitative techniques can contribute to fuller understanding of the circumstances that facilitate or block families’ paths to housing stability and advance knowledge of the type(s) of policy and housing
interventions needed to ensure that families successfully exit homelessness and remain housed.
The development and implementation of a mixed methods approach has the potential to produce an explanatory framework by integrating the reach and rigour of administrative data with the depth and nuance of qualitative inquiry. This, in turn, will yield more robust understanding of effective and appropriate policy responses.
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