Some dimension reduction strategies for the analysis of survey data

Jiaying Weng, Derek S. Young

Research output: Contribution to journalArticlepeer-review

20 Scopus citations


In the era of big data, researchers interested in developing statistical models are challenged with how to achieve parsimony. Usually, some sort of dimension reduction strategy is employed. Classic strategies are often in the form of traditional inference procedures, such as hypothesis testing; however, the increase in computing capabilities has led to the development of more sophisticated methods. In particular, sufficient dimension reduction has emerged as an area of broad and current interest. While these types of dimension reduction strategies have been employed for numerous data problems, they are scantly discussed in the context of analyzing survey data. This paper provides an overview of some classic and modern dimension reduction methods, followed by a discussion of how to use the transformed variables in the context of analyzing survey data. We highlight some of these methods with an analysis of health insurance coverage using the US Census Bureau’s 2015 Planning Database.

Original languageEnglish
Article number43
JournalJournal of Big Data
Issue number1
StatePublished - Dec 1 2017

Bibliographical note

Publisher Copyright:
© 2017, The Author(s).


  • Big data
  • Central mean subspace
  • Flexible models
  • Official statistics
  • Principal component analysis
  • Sufficient dimension reduction

ASJC Scopus subject areas

  • Information Systems
  • Hardware and Architecture
  • Computer Networks and Communications
  • Information Systems and Management


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