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Issues involving the use of significant sameness in testing replications and generating knowledge

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  • Ball, A. Dwayne
  • Sawyer, Alan G.

Abstract

Hubbard and Lindsay's article in this issue proposes a framework for generating knowledge based upon significant sameness of replications rather than significant differences. This critique agrees with their basic premise, but points out weaknesses in their proposal to use overlapping confidence intervals around effect sizes in close replications as the criterion for knowledge.

Suggested Citation

  • Ball, A. Dwayne & Sawyer, Alan G., 2013. "Issues involving the use of significant sameness in testing replications and generating knowledge," Journal of Business Research, Elsevier, vol. 66(9), pages 1389-1392.
    • Handle: RePEc:eee:jbrese:v:66:y:2013:i:9:p:1389-1392
    DOI: 10.1016/j.jbusres.2012.05.003
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    References listed on IDEAS

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    1. John U. Farley & Donald R. Lehmann & Alan Sawyer, 1995. "Empirical Marketing Generalization Using Meta-Analysis," Marketing Science, INFORMS, vol. 14(3_supplem), pages 36-46.
    2. Brinberg, David & Lynch, John G, Jr & Sawyer, Alan G, 1992. "Hypothesized and Confounded Explanations in Theory Tests: A Bayesian Analysis," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 19(2), pages 139-154, September.
    3. Kellaris, James J & Cox, Anthony D, 1989. "The Effects of Background Music in Advertising: A Reassessment," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 16(1), pages 113-118, June.
    4. Frank M. Bass & Jerry Wind, 1995. "Introduction to the Special Issue: Empirical Generalizations in Marketing," Marketing Science, INFORMS, vol. 14(3_supplem), pages 1-5.
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    Cited by:

    1. Florence Jeannot & Eline Jongmans & Maud Dampérat, 2022. "Visual design and online shopping experiences: When expertise allows consumers to refocus on website attractiveness," Post-Print halshs-04159592, HAL.
    2. Hubbard, Raymond & Lindsay, R. Murray, 2013. "The significant difference paradigm promotes bad science," Journal of Business Research, Elsevier, vol. 66(9), pages 1393-1397.

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