Teaching factor analysis in terms of variable space and subject space using multimedia visualization

Chong Ho Yu, Sandra Andrews, David Winogard, Angel Jannasch-Pennell, Samuel DiGangi

Research output: Contribution to journalArticle

3 Scopus citations

Abstract

There are many common misconceptions regarding factor analysis. For example, students do not know that vectors representing latent factors rotate in subject space, rather than in variable space. Consequently, eigenvectors are misunderstood as regression lines, and data points representing variables are misperceived as data points depicting observations. The topic of subject space is omitted by many statistics textbooks, and indeed it is a very difficult concept to illustrate. An animated tutorial was developed in an attempt to alleviate this problem. Since the target audience is intermediate statistics students who are familiar with regression, regression in variable space is used as an analogy to lead learners into factor analysis in the subject space. At the end we apply the Gabriel biplot to combine the two spaces. Findings from a textbook review, a survey, and a "think aloud" protocol were taken into account during the program development and are discussed here.

Original languageEnglish (US)
JournalJournal of Statistics Education
Volume10
Issue number1
StatePublished - Mar 1 2002

Keywords

  • Biplot
  • Eigenvector
  • Hypermedia
  • Vector space

ASJC Scopus subject areas

  • Statistics and Probability
  • Education
  • Statistics, Probability and Uncertainty

Fingerprint Dive into the research topics of 'Teaching factor analysis in terms of variable space and subject space using multimedia visualization'. Together they form a unique fingerprint.

  • Cite this