- Hardcover: 600 pages
- Publisher: W. H. Freeman; 1st edition (January 19, 2000)
- Language: English
- ISBN-10: 0716735105
- ISBN-13: 978-0716735106
- Product Dimensions: 7.6 x 1.8 x 9.3 inches
- Shipping Weight: 3 pounds
- Average Customer Review: 14 customer reviews
- Amazon Best Sellers Rank: #662,443 in Books (See Top 100 in Books)
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A First Course in Design and Analysis of Experiments 1st Edition
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Oehlert takes design seriously, not just the analysis of experiments. He is obviously an experienced statistician with deep knowledge of both practice and theory. As a bonus, he writes well and uses excellent real examples.
Aspects of the text that stand out as different from other texts include:
1. A detailed explanation of different error rates for multiple comparisons. More detail is present than is useful for a beginning student, but the exposition is excellent as a reference for those who need it.
2. An unusually well-informed (and practical) discussion of assumptions.
3. Discussion of SAS Type II errors. Common practice is not always the most sensible, and the author's advocacy of the Type II approach for many problems is compelling.
4. A good discussion of mixed model assumptions (restricted and unrestricted models). I have not seen a comparable exposition of this potentially confusing issue.
5. The use of Hasse diagrams for mixed models. I had not worked with Hasse diagrams before I used this text, but find them to be useful tools for analysis of complex designs.
My only quibble is that some items in chapter 13 could be introduced earlier....although probably not fully covered. In particular, RCBDs without interactions could appear (with appropriate caveats) along with factorial designs. I confess some ambivalence on this issue, noting that I only quarrel because I am starting to get rushed for time by chapter 13.
If you want a cookbook, go elsewhere. If you want a highly mathematical approach, this is also not for you. For a serious treatment of real statistical issues, however, both analysis and design, I doubt if you can do much better.
After the myriad of complaints, I spent Spring Break searching for a better book. A colleague recommended Oehlert's text. Chapter 2 on randomization methods was a perfect foray into the Normal-theoretic ANOVA, which is incredibly well-developed in Chapter 3. Theoretical arguments are juxtaposed with solid examples in what can best be described as an expository triumph. Chapter 6 on checking assumptions should technically come next (and I cover next it in my course), followed by contrasts in Chapter 4 and multiple comparisons in Chapter 5. The author does a very good job avoiding the "cookbook" approach to multiple comparisons so often seen in other texts. Chapter 7 on power analysis and sample size determination is one of the best I have read, even though most instructors omit this topic. A stylistic split comes thereafter: go to blocking designs or factorial treatment structures? I followed the author's lead, but one could go either way. Chapter 11 introduces random-effect models, with Chapter 12 building off of that with mixed-effects models and nesting. One of this book's strongest features is in Chapter 12: Hasse diagrams for deriving expected mean squares. Chapter 13 introduces variance reduction through blocking, though more detailed examples are needed. This is as far as I was able to get in one semester. I had planned on covering Chapter 14 on incomplete block designs, which upon my initial reading also seemed to lack detailed examples.
In terms of software emphasis, the author clearly prefers a package from his university. It is freely downloadable, but requires the learning of new syntax. For my class, I offered both R and SAS demonstrations as a supplement to the text, with researchers preferring the former and practitioners preferring the latter. Some students relied on SPSS.
After Spring Break, I referred my class to this book. My summer course then used this book as the required text, and it seemed to be very well-received. As far as I know, it is still being used now.
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