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frankderuyck
frankderuyck
Level VI

"Animals" Split Plot DOE

Created: Sep 19, 2022 10:48 AM  |  Last Modified: Jun 8, 2023 05:11 PM (1923 views)

Hi, in JMP Sample Data there is a small data set "Animals" for illustrating a split plot design, see attachment.

I understand that nesting subject into species generates six whole plots for estimating random error. However it is strange to see that the 4 levels of the factor Season, to my opinion a "hard to change" factor is treated as a subplot effect? How is this Animals DOE created using Custom design? I can do this when using Species and Subject both as hard to change factors and Season as easy to change? Technically this works but I have problems with the time effect "Season" easy to change..

Animals.jmp
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21 REPLIES 21
frankderuyck
frankderuyck
Level VI

Re: "Animals" Split Plot DOE

Sep 22, 2022 01:21 AM (286 views)  |  Posted in reply to message from ih 09-21-2022

Thanks a lot for this very useful info! The Animal example does not really represent a split plot setup  approach starting from hard & easy to change effects; in the table there is no whole plot column..(?) like you mention it is a nice example for specification & analysis of a nested random effect. 

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Phil_Kay
Staff Phil_Kay
Staff
Solution

Re: "Animals" Split Plot DOE

Sep 22, 2022 04:20 AM (1004 views)  |  Posted in reply to message from frankderuyck 09-22-2022

As other contributors have said, the Animals example is not a split plot. It is not an example for learning how to design a split plot design. And the questions about which factors in this example are hard- or easy-to-change make no sense.

 

I think it is maybe a bit confusing that it is listed in the JMP help documentation under "Simple Split Plot or Repeated Measures Model." Although I don't think that the documentation actually says anywhere that this is a split plot design. For any issues with the help documentation you can contact JMP Technical Support (support@jmp.com).

 

If you want to learn about split plot experiments, there are many better resources, some of which have already been mentioned. I would also recommend Optimal Design of Experiments: A Case Study approach by Goos and Jones.

 

If you want to learn more about mixed models there is also an excellent book from my JMP colleagues.

 

Alternatively, if you have an actual experiment in mind that includes hard-to-change factors, I suggest that you start a new discussion about how to use Custom Design to create the experiment that you need.

 

I hope this helps.

Phil