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Data Analysis: Factor Analysis using SPSS

This introductory course will explain Factor Analysis as an exploratory data analysis technique often used as a data reduction method. The reduced data is then feed into segmentation or cluster analysis, logistic regression and discriminant analysis. You will learn when to use Factor Analysis, how to use Factor Analysis and how to describe the resulting components in the context of your data.

Aimed at

Researchers (early stage, mid stage, final stage)

Pre-requisites

​Factor Analysis will be run through SPSS, but no previous knowledge of SPSS is required.

Joining instructions

Time will be set aside to examine each participant's work and provide suggestions and corrections.

Key learning outcomes

​By the end of this session participants will:

  • Understand the principles of factor analysis.
  • Examine Principal Component Analysis – a popular reduction method use in Factor Analysis to identify components and their significant variable(s) in each component. 
  • Learn how to select representative set of variables to be used in further analysis such as cluster (segmentation) analysis, logistic regression, and discriminant analysis.
  • Perform a cluster analysis on representative set of variables from each component.
  • Observe the benefit of using Factor Analysis prior to logistic or discriminant analysis, to remove highly correlated predictor variables (multicollinearity) which can yield unsuitable solutions.
  • Observe the benefit of using Factor Analysis prior to cluster analysis to remove over-weight or over-influence variables which result from multicollinearity variables on the cluster model.
  • Observe the benefit of Factor Analysis prior to cluster or segmentation analysis. A key benefit is the conceptual clarity and simplicity of working with themes instead of individual large numbers of variables which become difficult to see and describe.
  • Distinguish between Factor Analysis as Exploratory data technique from AMOS which is confirmatory factor method.

Specific skills focused on in this session

Research and information literacy, critical thinking skills

Health and Safety and general notice

Participants will be viewing presentations projected onto a SMART Board screen. Participants will be required to use a desktop PC or laptop during the workshop. 

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Brunel 3D Development Tool

Develops the following:

Skills needed to do your research and career

Researcher Development Framework (RDF) Competencies

Develops skills relevant to the following domains of the RDF:

This workshop is provided by

Brunel Graduate School Logo

Email address graduateschool@brunel.ac.uk Telephone number 01895 265935