Introduction to using administrative data for social and health research

Date:

02/12/2019 - 05/12/2019

Organised by:

Scottish Centre for Administrative Data Research

Presenter:

Various

Level:

Intermediate (some prior knowledge)

Contact:

scadr@ed.ac.uk

Map:

View in Google Maps  (EH1 1SU)

Venue:

Edinburgh Training and Conference Venue, 16 St Mary’s Street, Edinburgh

Description:

Interested in using linked administrative data in your research but don’t know what data is out there, how to access it, or if you have the skills?

If you are a social or health researcher with experience of analysing survey data, or if you already work with administrative data but want to analyse multiple administrative datasets linked together, then this course is for you.

This course (run jointly by the Scottish Centre for Administrative Data Research and the Scottish Longitudinal Study Development and Support Unit) will give an introduction to administrative data, describing what it is, some of the particular problems in working with this type of data and how to deal with them.

Theoretical sessions will be backed up by hands-on practical sessions, using R or Stata to write syntax to tidy, clean and recode data; link datasets; manipulate data; conduct data visualisation; identify data quality issues; and fit regression models. Scottish Longitudinal Study (SLS) synthetic data will be used in the practical exercise.

There will be sessions on:

  • Indexing, linking and joining datasets;
  • Working with dates and times;
  • Descriptive and inferential statistics for administrative data;
  • Information on how to apply for access to linked data, and secure data access within a safe setting, as well as the ethical, confidentiality and disclosure issues around using this type of data. A data showcase session will give a flavour of the type of data that is available. Current researchers will highlight their research using linked administrative data and describe the advantages of this approach, as well as the problems they have encountered and the lessons learned.

By the end of this course participants will have the skills to understand, access and prepare linked administrative data for analysis.

Course participants who expect to access data shortly can also elect to attend free ONS Safe Researcher Training on 6th December. Undertaking this training and passing the test is required in order to access data in the safe haven. This part of the training will:

  • Describe the research community and your role in it
  • Introduce a framework for data access (the five safes)
  • Describe and help you manage the risks involved
  • Explain the theory behind statistical disclosure control (SDC) rules and give examples of rules you may need to follow to get data released

By the end of the course you should understand the factors that affect your data access, the importance of attitudes and engagement, specific statistical issues and how to work efficiently and effectively.

Requirements:

Demand for this course is expected to be high, and places will be given to those who submit applications who are most likely to benefit from the course, and use it to contribute to the goals of the partners involved. Applicants should have prior experience of quantative analysis, experience with packages such as R or Stata, and set out why they are interested in attending.

Cost: £350 for students; £500 for others.

To apply to attend, complete the form at https://www.scadr.ac.uk/news-and-events/training-four-day-introduction-using-administrative-data-social-and-health-research by 6 November 2019, and for further information, email scadr@ed.ac.uk.

Please note that the data you submit in your application will only be used to administer your application to attend this course. It will not be shared with any third party (beyond Google Forms tool used to collect the data). For more information see our privacy policy.

Cost:

Cost: £350 for students; £500 for others.

Website and registration:

Region:

Scotland

Keywords:

Quantitative Data Handling and Data Analysis, Mixed Methods Data Handling and Data Analysis, Research Skills, Communication and Dissemination, Administrative data , Linked data

Related publications and presentations:

Quantitative Data Handling and Data Analysis
Mixed Methods Data Handling and Data Analysis
Research Skills, Communication and Dissemination

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