Statistical Methods for Multivariate Data in Social Science Research

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

Please note: This course will not be running as part of the 2021 programme. However, you may be interested in our confirmed courses.

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: TBC
Dates: TBC
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


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.


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.

Sign up for updates

Sign up for updates

  • Please enter a valid email address. We will send you relevant material regarding the LSE Summer School programme.
  • Which course subject area(s) would you like to know more about?
  • Your privacy
    The details you give on this form will be stored on a secure database. LSE Summer School will use your data to send you relevant information about the School and to find out about your experiences of applying to LSE. The data on the form will also be used for monitoring purposes and to track future applications. LSE will not give or sell your details to any other third party organisation. Your data is subject to the LSE website terms and conditions and our Data Protection Policy. You can withdraw from our lists at any time by using the 'unsubscribe/manage email preferences' link that can be found in the footer of each email, or by contacting

How to Apply

Related Programmes

Applied Econometrics and Big Data

Code(s) SS-EC320

Real Analysis

Code(s) SS-ME306

Introduction to Data Science and Machine Learning

Code(s) SS-ME314

Request a prospectus

  • Name
  • Address

Register your interest

  • Name

Speak to Admissions

Content to be supplied