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Planning of Experiments, by D. R. Cox
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Offers a comprehensive nonmathematical treatment regarding the design and analysis of experiments, focusing on basic concepts rather than calculation of technical details. Much of the discussion is in terms of examples drawn from numerous fields of applications. Subjects include the justification and practical difficulties of randomization, various factors occurring in factorial experiments, selecting the size of an experiments, different purposes for which observations may be made and much more.
- Sales Rank: #2797073 in Books
- Published on: 1992-05-07
- Ingredients: Example Ingredients
- Original language: English
- Number of items: 1
- Dimensions: 9.67" h x .70" w x 6.00" l, .96 pounds
- Binding: Paperback
- 320 pages
About the Author
D. R. Cox is one of the world's pre-eminent statisticians. Author or co-author of numerous books and papers, his work on the proportional hazards regression model is one of the most-cited and most influential papers in modern statistics. In 2010 he won the Copley Medal of the Royal Society 'for his seminal contributions to the theory and application of statistics'.
Most helpful customer reviews
7 of 7 people found the following review helpful.
Still one of the most cogent and clear primers available on the topic
By Stuart Hurlbert
In any technical discipline, it is unusual that a textbook written more than half a century ago would still be of much use let alone stand near the head of its class. Yet such is exactly the case with D.R. Cox's Planning of Experiments. This was published in 1958 and never revised. It was written when Cox was in his early thirties and evidently too na�ve to understand the importance to maximization of long term sales of including enough errors, illogical argument and other nonsense to justify the putting out of 2d, 3d, and 4th editions later on.
This remains one of the best concept-focused primers on experimental design available. It was designed by its author "to give a comprehensive nonmathematical course on the design and analysis of experiments...in conjunction with a textbook on statistical analysis [and].... to avoid statistical and mathematical technicalities and to concentrate on a treatment that will be intuitively acceptable to the experimental worker, for whom the book is primarily intended." Neophytes starting experimental work in any discipline - biology, psychology, industrial processes, medicine, etc. - would be well advised to digest early the content of this slim treatise. For students, if your instructor has assigned another statistics or design textbook, reading this one first may be very helpful and give you a head start. Until Roger Mead's Design of Experiments came out in 1988, I used Planning of Experiments, with many supplementary readings, as the text for my own course on experimental design for biology graduate students.
To their detriment many recent books on experimental design ignore or misrepresent the classical conceptual and terminological frameworks of experimental design. Cox, in contrast, presents these with great clarity. Especially valuable is his careful attention to the core (and widely misunderstood) concept of the experimental unit (pp. 2, 13, 19-21, 155, 191-196), a term completely abandoned by many more recent, more mathematically oriented books on experimental design. Treatment of other core concepts such as randomization, blocking, concomitant observations, crossover designs, and split-unit designs are equally clear and cogent.
Shortcomings of the book as a primer are few and reflect only the state of the pre-1958 experimental design literature, not errors on the part of Cox. The tripartite nature of an experimental design as consisting of a treatment structure, design structure and response structure (Urquhart 1981, Hurlbert & Lombardi 2004, Hinkelmann & Kempthorne 2008, Hurlbert 2013), had been presented, without those labels, by Finney (1955), but Cox, like most modern design texts, did not (p.4) explicitly acknowledge response structure as distinct from the other two components. Repeated measures response structures, one of the commonest types in many experimental fields, are very superficially discussed in a single paragraph late in the book (p. 277). That at least makes clear to the reader that for basic instruction on that topic recourse must be had to some modern text that treats that subject matter well (e.g. Mead 1988, Mead et al. 2003, Hinkelmann & Kempthorne 2008).
A final quibble is with Cox's recommendation (pp. 92-93) (1) that variables such as species, variety, breed, sex, etc. cannot be considered treatment factors because they represent "an intrinsic property of the unit and not something assigned to it by the experimenter," and (2) that they should be labeled "classification factors." This would seem a needless complication of matters. It is only necessary to acknowledge that experimental units have an "ontogeny", undergo changes, during the course of an experiment. The material of prime interests (e.g. an animal or a crop yield) may not even be present at the time decisions are made as to which treatment (placement of an animal or sowing of a seed set) each experimental unit (cage or plot) will receive. So one cannot randomly assign the property "variety" to a set of "generic" seeds, but one can randomly assign a set of seeds of a given variety to a plot. In Example 6.4 (p. 93), Cox further elaborates on his idea of classification factors. But in that example his classification factor ("center") is better regarded as a classification blocking factor (in contrast to the more typical nuisance blocking factor), not a treatment factor.
Buy this classic soon! As of this writing, you can still get a good used copy of the original hardcover edition for $5, whereas used copies of the paper cover reprint (unrevised) edition start at $40.
LITERATURE CITED
Finney, D.J. (1955) Experimental Design and Its Statistical Basis. Univ. Chicago Press, Chicago.
Hinkelman, K., & Kempthorne, O. (2008) Design and Analysis of Experiments, vol. 1, Introduction to Experimental Design, 3d edn. Wiley-Interscience, New York.
Hurlbert, S.H. (2013) Affirmation of the classical terminology for experimental design via a critique of Casella (2008). Agronomy Journal (in press).
Hurlbert, S.H., & Lombardi, C.M. (2004) Research methodology: experimental design, sampling design, statistical analysis. Encyclopedia of Animal Behavior (ed M.M. Bekoff), pp. 755-762. Greenwood Press, London.
Mead, R. (1988) The Design of Experiments. Cambridge University Press, Cambridge.
Mead, R., Curnow, R.N. & Hasted, A.M. (2003) Statistical Methods in Agriculture and Experimental Biology, 3d edn. Chapman & Hall, New York.
Urquhart, N.S. (1981) The anatomy of a study. HortScience, 16, 100-116.
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