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MSc Data Science

Become an agile, skilled data scientist, and be prepared for the challenges and rewards of interdisciplinary teamwork.

MSc Data Science / Course details

Year of entry: 2019

Course unit details:
Longitudinal Data Analysis

Unit code SOST70022
Credit rating 15
Unit level FHEQ level 7 – master's degree or fourth year of an integrated master's degree
Teaching period(s) Semester 2
Offered by Social Statistics
Available as a free choice unit? Yes

Overview

The UK is fortunate in having a rich and growing store of longitudinal studies for researchers to analyse. The course will introduce students to the methodological and statistical skills that will enable them to address questions about the measurement and explanation of change.
 

Aims

To provide students with an understanding of different longitudinal designs and the skills needed to conduct appropriate analyses using longitudinal data. Methods covered include the multilevel model for change, and models for investigating event occurrence over time.

Learning outcomes

• To gain facility in the concepts, designs and terms of longitudinal research;
• To be able to apply a range of different methods of longitudinal data analysis;
• To have a general understanding of how each method represents different kinds of longitudinal processes;
• To be able to choose a design, a plausible model and an appropriate method of analysis for a range of research questions.
 

Teaching and learning methods

The course will comprise 5 days of teaching and learning spread over one month. The days of intensive training will be made up of lectures and computer-lab examples and exercises implemented with appropriate statistical software, focusing on the use of R for longitudinal data analysis.

Assessment methods

The module will be assets based on an essay of 3000 words that uses longitudinal data analysis methods to answer a substantive question
 

Recommended reading

  • Singer, J., & Willett, J. (2003). Applied longitudinal data analysis: modeling change and event occurrence. Oxford University Press. (available online)
  • Long, J. D. (2011). Longitudinal Data Analysis for the Behavioral Sciences Using R. Thousand Oaks, Calif: SAGE Publications, Inc.

Newsom, J. T. (2015). Longitudinal Structural Equation Modeling: A Comprehensive Introduction. Routledge.

Study hours

Scheduled activity hours
Lectures 25
Independent study hours
Independent study 125

Teaching staff

Staff member Role
Alexandru Cernat Unit coordinator

Additional notes

Optional for SRMS

Part time students must take    ISM as a pre-requisite and MLM prior to or in the same semester as LDA  

Timetable Semester 2 - 10.00 am - 4.00pm - Sackville Street G11 cluster

 

Wednesday 27 February

Wednesday 6 March

Wednesday 13 March

Wednesday 20 March

Wednesday 27 March

Wednesday 3 April

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