Multiple Imputation of Missing Data

RADIANCE courses are targeted to the broad community of UK data scientists working in public health. They include epidemiologists, clinicians, data engineers/informaticians, statisticians, as well as quantitative researchers from other disciplines (e.g. psychology, social sciences, health economics).They may be from academia, charities, government departments and non-profitable organisations.

RADIANCE

This online course is for anyone needing to address the issue of missing information in their quantitative data.

RADIANCE courses are targeted to the broad community of UK data scientists working in public health. They include epidemiologists, clinicians, data engineers/informaticians, statisticians, as well as quantitative researchers from other disciplines (e.g. psychology, social sciences, health economics).They may be from academia, charities, government departments and non-profitable organisations.Lecturers Paola Zaninotto, Andrea Aparicio Castro and Tim MorrisCourse descriptionThis online course is for anyone needing to address the issue of missing information in their quantitative data. It covers the most important principles of missing data analysis and how to effectively address the issues in analyses.Learning objectivesThe aim is to develop skills in conducting multiple imputation analysis for cross-sectional data.By the end of this course you will be able to:<ul><li>Identify different mechanisms of missing data</li></ul><ul><li>Use a multiple imputation method for dealing with missing data in cross-sectional studies</li></ul><ul><li>Specify perform and select models</li></ul>Course StructureThere will be three pre-recorded lectures sessions and two 1.5-hour live computer practical sessions in R or Stata.N﻿ote: To attend the live sessions on the 26th and 27th September, you must have watched all pre-recorded lectures.

Pre-requisiteAn understanding of regression models, quantitative data structures and types of variables.FeeFreeMore informationThis is a UKRI funded project offering rigorous training in longitudinal data science. Please note that this training is NOT available to undergraduate or masters students.

Multiple Imputation of Missing Data

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Multiple Imputation of Missing Data

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