CMIST Short Course :: Latent Factor Analysis

Description:

Outline

This short course covers latent variables and factor analysis at an introductory and intermediate level. A latent variable is something invisible (such as a concept, an attitude, or an illness) that cannot be measured directly that has been measured using a set of related observed indicators.

Factor analysis is one way to derive a single factor from a set of variables, and is thus called a data reduction method. Other data reduction methods include principal components analysis, which is very closely related to factor analysis, and multiple correspondence analysis.

We will focus on confirmatory factor analysis, but talk a bit about the differences with exploratory factor analysis. The course is suitable both for primary-data collection researchers (who may need to write a suitable questionnaire), and for those who want to analyse secondary data sets.

Objectives

• Show what kinds of models would lead to an adequate confirmatory factor model
• Distinguish a one- from a two-factor model (or more factors) 
• Help participants become familiar with examples from social science research where latent variables are useful 
• See how categorical variables can be transformed into a continuous latent factor
• Introduce the concept of a test of goodness of fit.

Software used 

The course is not strongly dependent on any one computer package, but participants are shown the STATA and MPLUS methods of running confirmatory factor analysis.

(The examples offered are likely to include attitude scales, a human capabilities index or an index of well-being, and models of the division of labour by gender.)

Prerequisites

A basic knowledge of syntax (commands) in a statistical package such as STATA or MPLUS, and a previous exposure to regression analysis, are both required. The course aims to present the results of factor analysis using STATA and MPLUS software.

Note: The course does not cover latent class analysis. In latent class analysis, the results would give discrete classes which optimally separate the cases into groups according to the values of the manifest variables. Once MPLUS is used, it is relatively easy to move from latent factor analysis to latent class analysis. Both techniques can then be used in more advanced contexts.

Recommended reading

Brown, T. (2012) Confirmatory Factor Analysis for Applied Research. NY: Guildford Press.
Bollen, K. (1989) Structural equations with latent variables. NY: Wiley

Pettigrew, T.F. & Meertens, R.W. (1995) 'Subtle and blatant prejudice in western Europe'. European Journal of Social Psychology, 25(1) p57-75.

Cost:

£195 (£140 for those from educational and charitable institutions). The Cathie Marsh Institute (CMIST) offers 5 free places to research staff and students within the Faculty of Humanities at The University of Manchester and the North West Doctoral Training Centre.

Website and registration:

http://www.cmist.manchester.ac.uk/study/courses/short/booking/

Region:

North West

Keywords:

Latent Variable Models

Related publications and presentations:

Latent Variable Models

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