在實驗設計中，經常因為成本上的考量無法執行完全因子設計，而必須選擇實驗次數較少的部分因子設計。
與完全因子設計相比，部分因子設計中會產生效應混淆的情形。在傳統實驗設計中常使用的固定效應模型下，效應混淆的性質已有完整且有系統的討論與發展。然而在電腦實驗設計中，
經常使用的模型是隨機域模型，而在隨機域模型下，該如何發展及討論效應混淆的情形，在文獻上則較少有深入的探討。Wang(2009)論文中討論了在隨機域模型下，二水準的正規設計中的效應混淆情形，
在本論文中我們將推廣至更一般的部分因子設計。
我們將在本論文中討論多水準因子在隨機域模型下各種效應的重要性，並對任意的部分因子設計提出方法來觀察隨機域模型下的效應混淆程度。

Cantoni, A. and Butler, P. (1976), "Eigenvalues and eigenvectors of symmetric centrosymmetric matrices", Linear Algebra and its Applications, 13, 275-288.

Chipman, H., Hamada, M., and Wu, C. F. J. (1997), "A Bayesian variable selection approach for analyzing designed experiments with complex aliasing", Technometrics, 39, 372-381.

Sacks, J., Welch, W. J., and Mitchell, T. J. (1989), "Design and analysis of computer experiments", Statistical Science, 4(4),409-423.

Santner, T. J., Williams, B.J., and Notz, W. I. (2003), The Design and Analysis of Computer Experiments, Springer.

Steinberg, D. M. and Bursztyn, D. (2004), "Data analytic tools for understanding random field regression models", Technometrics, 46(4), 411-420.

Wang, W. T. (2009),"Effect aliasing in random field moedl", Master's thesis, National Tsing Hua University.

Wu, C. F. J. and Hamada, M. S. (2009), Experiments: Planning , Analysis, and Optimization, 2nd Edition. Wiley Series.

Ye, K. Q. (1998), "Orthogonal column Latin hypercubes and their application in computer experiments", Journal of the American Statistical Association, 93, 1430-1439.