Programmes

Statistical Methods for Multivariate Data in Social Science Research

  • Summer schools
  • Department of Statistics
  • Application code SS-ME303
  • Starting 2020
  • Short course: Closed
  • Location: Houghton Street, London

UPDATE: Due to the global COVID-19 pandemic we will no longer be offering this course in summer 2020. Please check our latest news on this situation here.

This course is an introduction to multivariate methods and their applications in the social sciences. The course will provide an overview of multivariate methods and then focus on latent variable models and structural equation models for continuous and categorical observed variables, and their use in measurement and in modelling complex substantive hypothesis in the social sciences.

This course is suitable for advanced undergraduates, as well as postgraduate and academic staff in applied statistics, medicine, and in social and behavioral sciences as well as government employees and people working in marketing, management, public health and banking. It provides participants with introductions to (1) modern statistical methodology for analyzing multivariate continuous and categorical data, (2) the use of latent variables for measuring unobserved constructs such as attitudes, beliefs, health state, etc. through observed indicators, and (3) the use of structural equation models for formulating and testing research hypothesis among latent variables and observed covariates. The course is largely self-contained and reviews the necessary mathematical concepts. No previous knowledge of latent variable analysis, structural equation modelling or of any particular software are required


Session: One
Dates: 22 June – 10 July 2020
Lecturers: Dr Yunxiao Chen and Professor Irini Moustaki


 

Programme details

Key facts

Level: 300 level. Read more information on levels in our FAQs

Fees:  Please see Fees and payments

Lectures: 36 hours

Classes: 18 hours

Assessment*: A midsession exam during the second week of the course (worth 40%) and a final exam on the Friday of the third week (worth 60%).

Typical credit**: 3-4 credits (US) 7.5 ECTS points (EU)


*Assessment is optional

**You will need to check with your home institution

For more information on exams and credit, read Teaching and assessment

Prerequisites

Elementary Statistical Theory and Applied Regression.

Programme structure

Specific topics of this course include

  • cluster analysis
  • multidimensional scaling
  • principal component analysis
  • exploratory factor analysis
  • confirmatory factor analysis
  • path analysis and structural equation models
  • latent trait models
  • latent class models
  • multi-group analysis (cross-national survey data) 
  • models for longitudinal data.

Course outcomes

After taking this course, students are expected to be able to:

  • use multivariate methods for the exploratory analysis of multivariate data in social sciences (e.g., dimension reduction, data visualization).
  • use path models to represent complex relationships among latent variables and covariates.
  • model relationships among latent constructs and observed covariates and providing the ability to measure direct and indirect (mediation) effects.
  • model cross-sectional data including the treatment of longitudinal data and multi-group data such as cross-national data.
  • use STATA and MPlus software to run real data examples. Participants will have the opportunity to run the analysis and interpret the output.

Teaching

Department of Statistics at LSE has a distinguished history. Its roots can be traced back to the appointment of Sir Arthur Lyon Bowley, an alumnus of the University of Cambridge, at LSE in 1895. He was appointed Chair in Statistics in 1919, probably the first appointment of its kind in Britain. The Department of Statistics was submitted jointly to REF 2014 with LSE's Department of Mathematics: 84% of the research outputs of the two departments were classed as either world-leading or internationally excellent in terms of originality, significance and rigour.

The department has an international reputation for development of statistical methodology that has grown from its long history of active contributions to research and teaching in statistics for the social sciences.

On this three week intensive programme, you will engage with and learn from full-time lecturers from the LSE’s statistics faculty.

Reading materials

Main text

D J Bartholomew, F Steele, I Moustaki; J I Galbraith (2008) Analysis of Multivariate Social Science Data (2nd edition), Chapman and Hall.

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How to Apply

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