Making Sense Of Multivariate Data: Principle Component Analysis, Factor Analysis And Clustering Technique


Date and Venue

April 17, 2023 - April 21, 2023
Crowne Plaza Hotel, Dubai Deira. UAE.
July 3, 2023 - July 7, 2023
Sheraton Memphis Downtown Hotel, USA.
Sept. 11, 2023 - Sept. 15, 2023
Kigali Serena Hotel, KN 3 Ave, Kigali, Rwanda
Nov. 20, 2023 - Nov. 24, 2023
University of Ghana, Legon, Accra Ghana



This workshop is designed to provide participants experience using statistical methods that can help them make sense of data when there are a large number of variables and/or cases. The workshop will first cover the basic principles of constructing and testing multivariate statistical models. Next, participants will be introduced to three fundamental multivariate methods: principal component analysis, factor analysis, and cluster analysis. Besides having practical utility, the three methods provide an essential background for learning other multivariate techniques in the future. Students will gain experience applying each of the three methods on real datasets with SAS statistical software.

Prerequisites: Participants should have some knowledge of introductory statistics, including variance, correlation, regression, and hypothesis testing.


Middle and Senior Level Officers of Planning, Research and Statistics Departement that are interested in:

  1. Learning about basic principles of multivariate data analysis to apply in their own data analysis efforts as well as to foster their learning other techniques.
  2. Acquiring experience in analyzing and interpreting selected multivariate datasets using the following fundamental multivariate techniques: principal component analysis, factor analysis, and cluster analysis.
  3. Learning or reviewing how to use SAS for data analysis.
  4. Hands-on experience using SAS to carry out multivariate analyses


  1. Strategies for analyzing multivariate datasets
  2. SAS basics and exercises
  3. Overview of statistical methods covered in the course
  4. Introduction to Principal Component Analysis (PCA)
  5. Overview of PCA
  6. Algebra of PCA
  7. Geometric representation of PCA
  8. PCA exercises using SAS

 MODULE 2: (Brief daily outline or expectations.)

  1. Overview of Factor Analysis
  2. Exploratory Factor Analysis
  3.  Factor extraction techniques
  4.  Factor rotation techniques
  5. Exploratory factor analysis exercises using SAS


Topics in Exploratory Factor Analysis

  1. Estimating factor scores
  2. Visualizing factor analysis
  3. Confirmatory factor analysis
  4. Confirmatory factor analysis exercises using SAS

 MODULE 4: (Brief daily outline or expectations.)

  1. Cluster analysis overview
  2. Issues in cluster analyses
  3. Partitioning approaches
  4. k-means cluster analysis exercise using SAS
  5. Hierarchical approaches
  6. hierarchical cluster analysis exercise using SAS