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Article
Detecting bias in meta-analyses of distance education research: big pictures we can rely on
Distance Education
  • Robert M. Bernard, Concordia University
  • Eugene Borokhovski, Concordia University
  • Rana M. Tamim, Zayed University
Document Type
Article
Publication Date
1-1-2014
Abstract

© 2014, © 2014 Open and Distance Learning Association of Australia, Inc. This article has two interrelated purposes. The first is to explain how various forms of bias, if introduced during any stage of a meta-analysis, can provide the consumer with a misimpression of the state of a research literature. Five of the most important bias-producing aspects of a meta-analysis are presented and discussed. Second, armed with this information, we examine 15 meta-analyses of the literatures of distance education (DE), online learning (OL), and blended learning (BL), conducted from 2000 to 2014, with the intention of assessing potential sources of bias in each. All of these meta-analyses address the question: “How do students taking courses through DE, OL, and BL compare to students engaged in pure classroom instruction in terms of learning achievement outcomes?” We argue that questions asked by primary researchers must change to reflect issues that will drive improvements in designing and implementing DE, OL, and BL courses.

Publisher
Routledge
Disciplines
Keywords
  • blended and online learning,
  • distance education,
  • meta-analysis,
  • quality of research synthesis
Scopus ID
84908612528
Indexed in Scopus
Yes
Open Access
No
https://doi.org/10.1080/01587919.2015.957433
Citation Information
Robert M. Bernard, Eugene Borokhovski and Rana M. Tamim. "Detecting bias in meta-analyses of distance education research: big pictures we can rely on" Distance Education Vol. 35 Iss. 3 (2014) p. 271 - 293 ISSN: <a href="https://v2.sherpa.ac.uk/id/publication/issn/0158-7919" target="_blank">0158-7919</a>
Available at: http://works.bepress.com/rana-tamim/11/