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ADRCE

ADRCE

Combining data from multiple administrative and survey sources for statistical purposes

Combining Data from Multiple Admin and Survey Sources for Statistical Purposes

Course Summary

Day one provides a general introduction to combining multiple administrative and survey datasets for statistical purposes. A total-error framework is presented for integrated statistical data, which provides a systematic overview of the origin and nature of the various potential errors. The most typical data configurations are illustrated and the relevant statistical methods reviewed.

Day two covers a handful of selected statistical methods. Training will be given on the techniques of data fusion, or statistical matching, by which joint statistical data is created from separate marginal observations. The participants will be introduced to several imputation or adjustment techniques, in the presence of constraints arising from overlapping data sources.

Target Audience:

This course is ideal for social and medical researchers with interests in combining data from multiple sources or analysing data from different sources; staff at National Statistical Institutes (or similar organisations) who are involved in the design, management and quality assurance of statistical processes based on data from multiple sources including censuses, administrative data and sample surveys.

 Pre-requisites:

Understanding of the following are required: central concepts of statistical uncertainty (such as bias, variance, confidence interval) and distribution, basic knowledge of data cleaning and imputation, basic experience/skill of R for statistical computing. Methodological training, knowledge and experience will be helpful.

Further course details can be found here.

Podcast for some of our previous courses can be found here

StartEndPlaces LeftCourse Fee 
07/11/201708/11/2017[Read More]
ADRCE

Confident Spatial Analysis and Statistics in R & GeoDa

We are pleased to offer you this short course jointly organised by the Administrative Data Research Centre for England (ADRC-E) and the Consumer Data Research Centre (CDRC).

Please note this course can be taken as a one-day course, or can also be taken in conjunction with other two one-day courses on 15 January and 16 January 2018.

In this course we will cover how to prepare and analyse spatial data in RStudio & GeoDa. We will use RStudio to perform spatial overlay techniques (such as union, intersection and buffers) to combine different spatial data layers to support a spatial analysis decision. We will also use RStudio and GeoDa to explore a range of different spatial analyses including Moran’s I and clustering. By the end of the course you will understand how RStudio manages spatial data and be able to use RStudio for a range of spatial analysis.

Target Audience

This course is ideal for anyone who wishes to use spatial data in their role. If you are not already familiar with the basic elements of GIS or R, you may wish to attend the one-day course ‘Introduction to Spatial Data & Using R as a GIS’ prior to this course on 16 January where these skills are covered.

 

Further course details can be found here.

 

More information regarding our courses can be found here.

 

Podcast for some of our previous courses can be found here.

StartEndPlaces LeftCourse Fee 
17/01/201817/01/2018[Read More]
ADRC

Handling missing data in administrative studies: multiple imputation & inverse probability weighting

Course Summary

This ADRC-E course will consider the issues raised by missing data (both item and unit non-response) in studies using routinely collected data, for example electronic health records. Following a review of the issues raised by missing data, we will focus on two methods of analysis: multiple imputation and inverse probability weighting. We will also discuss how they can be used together. The concepts will be illustrated with medical and social data examples.

Target Audience

The course is aimed at quantitative researchers, who have an interest or experience in analysing administrative data. PhD students are also welcome. Detailed technical arguments will not be presented; instead the focus will be on concepts and examples, with participants encouraged to bring their own data for discussion.

This course includes computer workshops, using the statistical software package Stata. Full details of all commands will be given, so no previous experience with Stata is necessary, though it will inevitably be an advantage.

Pre-requisites

Practical experience using regression modelling (including survival data modelling) and preferably multilevel modelling.

Further course details can be found here.

More information regarding our courses can be found here

Podcast for some of our previous courses can be found here.

StartEndPlaces LeftCourse Fee 
09/11/201710/11/2017[Read More]
ADRC

Introduction to Hospital Episode Statistics - 2018

Course Summary

This course will provide participants with an understanding of how Hospital Episode Statistics (HES) data are collected and coded, their structure, and how to clean and analyse HES data. A key focus will be on developing an understanding of the strengths and weaknesses of HES data, how inconsistencies arise, and approaches to deal with these. Participants will also learn how to ensure individuals’ anonymity and confidentiality when analysing and publishing using HES. The course consists of a mixture of lectures and practicals for which participants will use Stata software to clean and analyse HES data.

Target Audience

Researchers at all levels in academia, government and private sector at all levels who are using/planning to use Hospital Episode Statistics in their work.

Pre-requisites

Participants will write and execute programmes in Stata during the practical sessions. Previous experience of programming in Stata, R or SAS will therefore be helpful, but Stata code and instructions will be provided to all participants. There are no pre-requisites for the lectures.

Further course details can be found here.

More information regarding our courses can be found here.

Podcast for some of our previous courses can be found here.

StartEndPlaces LeftCourse Fee 
05/03/201806/03/2018[Read More]
ADRCE logos

Introduction to QGIS: Understanding and Presenting Spatial Data

We are pleased to offer you this short course jointly organised by the Administrative Data Research Centre for England (ADRC-E) and Consumer Data Research Centre (CDRC).

Please note this course can be taken as a one-day course, or can also be taken in conjunction with other two one-day courses on 16 January and 17 January 2018.

This course will introduce spatial data and show you how to import and display spatial data with the free open source GIS program QGIS. We will also show you how to create choropleth maps and explain appropriate methods of visualising spatial data. We will also cover some basic spatial data analysis (e.g. calculating rates).

 

This course is ideal for anyone who wishes to use spatial data in their role. This includes academics, commercial users, government & other public sector researchers who have data with some spatial information (e.g. address, postcode, etc.) which they wish to show on a map. This course is also suitable for those who wish to have an overview of what spatial data can be used for. No previous experience of spatial data is required.

 

No previous experience of GIS or QGIS is required, but some experience of using spatial data will be beneficial.

 

Further course details can be found here.

 

More information regarding our courses can be found here.

 

Podcast for some of our previous courses can be found here.

StartEndPlaces LeftCourse Fee 
15/01/201815/01/2018[Read More]
ADRCE

Introduction to Spatial Data & Using R as a GIS

We are pleased to offer you this short course jointly organised by the Administrative Data Research Centre for England (ADRC-E) and the Consumer Data Research Centre (CDRC).

Please note this course can be taken as a one-day course, or can also be taken in conjunction with other two one-day courses on 15 January and 17 January 2018.

The course will cover an introduction to R, how to load and manage spatial data and how to create maps using R and RStudio. We will show you appropriate ways of using classifications for choropleth maps, using loops in R to create multiple maps and some basic spatial analysis.

We will be using RStudio to work with the R environment. By the end of the course you will be able to load data into R, represent it effectively and be able to prepare an output quality map.

 

This course is ideal for anyone who wishes to use spatial data in their role. This includes academics, commercial users, government & other public sector researchers who have data with some spatial information (e.g. address, postcode, etc.) which they wish to show on a map. This course is also suitable for those who wish to have an overview of what spatial data can be used for. No previous experience of spatial data is required.

 

No experience of spatial data, GIS or scripting required. Some basic experience of using Google Earth / Maps would be beneficial.

 

Further course details can be found here.

 

More information regarding our courses can be found here.

 

Podcast for some of our previous courses can be found here.

StartEndPlaces LeftCourse Fee 
16/01/201816/01/2018[Read More]
adrce

SQL Database Management Software Edinburgh

Course Summary

Database systems are increasingly being used for working with medical data and enable the rapid querying of complex data in health and social care.

This short course will introduce the theory behind the relational data model and enable participants to gain an understanding on how data can be modelled and stored in a relational database system and what different data types are used.

Through a series of practical-driven sessions using real-life data, students will learn how to load existing data in a contemporary relational database management system and how to craft simple and complex queries for analysing the data. By the end of the course, students will be able to load, format and export data in a format suitable for analysis by common statistical packages.

Target Audience:

Course suitable for epidemiologists, medical statisticians and other researchers working with electronic health records data.

Further course details can be found here.

 

More information regarding our courses can be found here.

 

Podcast for some of our previous courses can be found here.

StartEndPlaces LeftCourse Fee 
30/01/201830/01/2018[Read More]

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