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An Evaluation of Error Variance Bias in Spatial Designs

Author

Listed:
  • Emlyn R. Williams

    (Australian National University)

  • Hans-Peter Piepho

    (University of Hohenheim)

Abstract

Spatial design and analysis are widely used, particularly in field experimentation. However, it is often the case that spatial analysis does not significantly enhance more traditional approaches such as row–column analysis. It is then of interest to gauge the degree of error variance bias that accrues when a spatially designed experiment is analysed as a row–column design. This paper uses uniformity data to study error variance bias in $$7\times 12$$ 7 × 12 spatial designs for 21 treatments.

Suggested Citation

  • Emlyn R. Williams & Hans-Peter Piepho, 2018. "An Evaluation of Error Variance Bias in Spatial Designs," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 23(1), pages 83-91, March.
    • Handle: RePEc:spr:jagbes:v:23:y:2018:i:1:d:10.1007_s13253-017-0309-2
    DOI: 10.1007/s13253-017-0309-2
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    References listed on IDEAS

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    1. D. R. Cox, 2009. "Randomization in the Design of Experiments," International Statistical Review, International Statistical Institute, vol. 77(3), pages 415-429, December.
    2. E. R. Williams & J. A. John & D. Whitaker, 2006. "Construction of Resolvable Spatial Row–Column Designs," Biometrics, The International Biometric Society, vol. 62(1), pages 103-108, March.
    3. Hans-Peter Piepho & Emlyn R. Williams & Volker Michel, 2016. "Nonresolvable Row–Column Designs with an Even Distribution of Treatment Replications," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 21(2), pages 227-242, June.
    4. Johannes Forkman, 2016. "A Comparison of Super-Valid Restricted and Row–Column Randomization," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 21(2), pages 243-260, June.
    Full references (including those not matched with items on IDEAS)

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