This paper is in the following e-collection/theme issue:

Statistical Methods for Surveillance and Population Health (158) Electronic/Mobile Data Capture, Internet-based Survey & Research Methodology (851) New Methods (964) Data Science (278)

Published on in Vol 10 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/48186, first published .
Improving the Efficiency of Inferences From Hybrid Samples for Effective Health Surveillance Surveys: Comprehensive Review of Quantitative Methods

Improving the Efficiency of Inferences From Hybrid Samples for Effective Health Surveillance Surveys: Comprehensive Review of Quantitative Methods

Improving the Efficiency of Inferences From Hybrid Samples for Effective Health Surveillance Surveys: Comprehensive Review of Quantitative Methods

Authors of this article:

Mansour Fahimi1 Author Orcid Image ;   Elizabeth C Hair2 Author Orcid Image ;   Elizabeth K Do2 Author Orcid Image ;   Jennifer M Kreslake2 Author Orcid Image ;   Xiaolu Yan3 Author Orcid Image ;   Elisa Chan3 Author Orcid Image ;   Frances M Barlas3 Author Orcid Image ;   Abigail Giles3 Author Orcid Image ;   Larry Osborn3 Author Orcid Image
Article Authors Cited by Tweetations (3) Metrics Author Video

Mansour Fahimi   1 , PhD ;   Elizabeth C Hair   2 , PhD ;   Elizabeth K Do   2 , MPH, PhD ;   Jennifer M Kreslake   2 , MPH, PhD ;   Xiaolu Yan   3 , MS ;   Elisa Chan   3 , MS ;   Frances M Barlas   3 , PhD ;   Abigail Giles   3 , MS ;   Larry Osborn   3 , MPH

1 Marketing Systems Group, Horsham, PA, United States

2 Truth Initiative Schroeder Institute, Washington, DC, United States

3 Ipsos USA, San Francisco, CA, United States

Corresponding Author:

  • Mansour Fahimi, PhD
  • Marketing Systems Group
  • 755 Business Center Drive
  • Suite 200
  • Horsham, PA, 19044
  • United States
  • Phone: 1 2156202880
  • Email: mfahimi@m-s-g.com