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Longitudinal Data Analysis

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Course Information

NCRM

This course starts by reviewing the advantages of collecting and analysing longitudinal data. After discussing the various types of longitudinal data, we focus on panel data containing repeated measures. Topics will include: methods for exploring longitudinal data; alternative approaches for modelling repeated measures data for continuous and categorical responses with particular attention to population average and subject-specific models; and methods for handling complex survey designs, weights and non-response.

 

This course will include the following topics:

  • Issues when analysing longitudinal survey data
  • Overview of approaches to analysing longitudinal survey data
  • Population average (marginal) models
  • Random effects models
  • Fixed effects models
  • Methods for categorical responses
  • Handling complex survey designs, weights and non-response

The methods will be illustrated and compared using analyses of a variety of socio-economic, attitudinal and health outcomes collected in the British Household Panel Survey and in Understanding Society. The course will have a strong practical emphasis, with regular computer sessions using STATA enabling participants to work through examples.

Learning outcomes:

  • To provide an introduction to various approaches for analysing longitudinal survey data, including methods for handling complex surveys, weights and non-response.
  • To enable participants to identify the important issues when analysing longitudinal survey data.

Course Code

Longitudinal Data Analysis

Course Dates

3rd July 2017 – 5th July 2017

Course Leader

Professor Peter Smith, Professor Ann Berrington and Dr Marcel Vieira
Course Description

Day 1: Introduction to Longitudinal Data, Introduction to STATA, Exploring Repeated Measures Data. Approaches to Modelling Repeated Measures Data.

 

Day 2: Population Average (Marginal) Models. Random Effects Models. Fixed Effects Models. Exploring Repeated Binary Measures Data.

 

Day 3: Logistic Regression Models for Repeated Measures Data. Handling Non-response, Weighting and Complex Survey Design in Longitudinal Data.

 

Full details to follow.

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