The Value of Data F or Informing Policy and Practice

1. The Value of DataFor Informing Policy and Practice Dr Shereen Hussein Principal Research Fellow (Chair) King’s College London, UK Melbourne, Australia, August 2017

2. Administrative Data in Social Services • Collected for organisational, regulatory and planning purposes • Provide considerable opportunities to understand and improve the workforce and service outcomes • Through ‘secondary data analysis’, researchers can address ‘new research questions’ • Have many advantages and some limitations • With careful planning are likely to be hugely cost-effective sources of evidence to inform policy and practice and guide future research agenda Future Social Service Institute & RMIT

3. Advantages of Administrative Data • Readily available • A potentially rich source of information about a large number of people with high coverage of a certain group • Are often available over a number of years • Allow examining trends over time • Are potentially linkable to other administrative and governmental data and indicators • Less demanding than planning, funding and executing long-term experiential studies Future Social Service Institute & RMIT

4. Challenges of Using Administrative data • They are often compiled for a different purpose than the specific research • Posing challenges when adapting a research question and when choosing suitable statistical methodologies • Missing values • Might need some imputation • Missing variables • Possible change over time • Some data (re)coding might be required prior to analysis • Free-text/comment boxes often include valuable information Future Social Service Institute & RMIT

5. Examples of Administrative Data Usage to Inform Policy and Practice Future Social Service Institute & RMIT

6. Longitudinal Social Work Students’ Records • Data collected by the regulatory body for social work in England • Detailed information on social work students, their courses and progression • Allowed examining differentials in ‘completion on time’ • Identified the separate effects at the individual and institutional level characteristics Future Social Service Institute & RMIT

7. Adult Protection: Referrals of Abuse Records • Data already collected when referrals are made for safeguarding concerns • Recorded detail characteristics of workers involved, nature of abuse, dates, details of outcomes • Allowed various analyses • Discussed issues of human rights, safeguarding policies and implications on the workforce • Free text fields were consistently recoded and used in the analysis Future Social Service Institute & RMIT

8. Combining New & Prior Knowledge • Estimates used by the HMRC and other governmental bodies • Further investigations by HMRC confirmed findings • Recommendations and case-studies put on the HMRC website based on findings • Public awareness (panorama & press)

9. Linkage of Workforce Records to Other Data Sources • Workforce data linked to Indices of Multiple Deprivation at small local areas • Enabled understanding patterns of volunteering in social care settings • Some significant associations were found between volunteering, personal characteristics and level of rurality of an area.

10. Addressing the ‘nesting’ structure of effects

11. Mixed-Methods: Quantitative and Qualitative Data • Used administrative data to set the context and establish the magnitude of a research question • Quantitative analysis identified initial themes related to the interplay between gender, migration and employment characteristics • Qualitative interviews provided in-depth understanding of job seeking processes

12. Data Collected by Successive Regulators • Examining trends in the number of overseas qualified social workers registered to work in England between 2003-2015 • A sharp decline observed since 2010 • Coincided with change of regulator from GSCC to HCPC • But also wider policy changes Hussein, S. (2018) In search of better opportunity Trend could be related to how data were collected OR represents real change due to increased supply of UK- qualified SWs (fast-track training) and changing immigration policies Future Social Service Institute & RMIT

13. Impact of Administrative Data • Maximize the benefits of currently available and historical data • Can be easily used to provide evidence on sub-groups • Enabling effective targeted and preventative strategies (e.g. identifying higher risk groups) • Effective use of resources • Compliance with the NMW regulation • Groups at higher risk of abuse or failure • Findings from large sector-specific samples enable evidence-based decision making and effective lobbying for policy change • Investing in setting data collection tools is cost effective and rewarding for future analysis and establishing evidence Future Social Service Institute & RMIT

14. References Hussein, S. (2018) In search of better opportunity: Transnational social workers in the United Kingdom navigating the maze of global and social mobility. In L. Beddoe and A. Bartley (eds.) Transnational Social Work: Opportunities and challenges of a global profession, Policy Press. Hussein, S. (2017) ‘We don’t do it for the money’...  The scale and reasons of poverty-pay among frontline long term care workers in England. Health and Social Care in the Community. 12th June 2017, doi:10.1111/hsc.12455. Hussein, S. and Christensen, K. (2017) Migration, gender and low-paid work: on migrant men’s entry dynamics into the feminised social care work in the UK. Journal of Ethnic and Migration Studies. 43(5): 749-765. Hussein, S. and Manthorpe, J. (2014) Volunteers supporting older people in formal care settings in England: personal and local factors influencing prevalence and type of participation. Journal of Applied Gerontology, 33(8): 923-941. Hussein, S. and Manthorpe J. (2014) Structural marginalisation among the long-term care workforce in England: evidence from mixed-effect models of national pay data. Ageing and Society. 34(1): 21-41. Hussein S. (2011) The use of ‘large scale datasets’ in UK social care research, Method Review 5, National Institute for Health Research, School of Social Care Research, the London School of Economics and Political Science. Hussein S. (2009) Multilevel modelling of results at first attempt of progression among undergraduate social work students in England using longitudinal routinely collected data, Royal Statistic Society Conference, Edinburgh. Hussein S.et al. (2009) Banned from working in social care: secondary analysis of staff characteristics and reasons for their referrals to the POVA List in England and Wales, Health and Social Care in the Community, 17(5): 423-433. Future Social Service Institute & RMIT

15. Shereen.hussein@kcl.ac.uk @DrShereeHussein 00 44 2078481669 This presentation draws on a number of studies, most are funded by the Department of Health, Policy Research Programme. Future Social Service Institute & RMIT