"Animals" Split Plot DOE

[#1]

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..

[#2]

My take: Consider the relative difficulty of changing each column.  I presume this was developed with some sort of tracking device attached to each of three foxes and three coyotes.  Adding another tracking device on another animal is probably more expensive than just waiting a year to get information from all six animals, and adding a tracking device to another species of animal seems harder still.  Here 6 total animals were involved.  Waiting a year to capture data from those six subjects seems easy compared to capturing new subjects each season.

[#3]

I also assume that animals are tracked. Season levels can't be changed from run to run so this is a hard to change factor and consequently each season is a separate plot which is not the case in this DOE; looks like all three factors here are hard to change, right?

[#4]

I think season is easy to change in this case, just wait 3 months and it is changes.  Perhaps the month the tracker was installed could be assigned, but that might mean bringing the team together multiples times to tag animals which could be cost prohibitive.  

It is possible this design was not created with the DOE platform at all.  The goal might have been to estimate variation which they could then be used in the power analysis of a future DOE, or it might have been a budget-constrained experiment.

I believe this mixed model is the same technique you would use to analyze a split plot design, but the same technique can be used other places where an individual or unit is tested with more than 2 treatments.

[#5]

My take on the data set.  IMHO, this isn't an experiment.  This is a sampling plan that includes subjects nested in species and systematic sampling over seasonal time periods.  Experimentation is the purposeful manipulation of factors to see their effect on response variables.  I don't see any manipulation.  Instead, this is directed sampling of the variation that occurs in nature.  Also, I believe the numbering is wrong in the data table.  Subjects should be 1-6.  The same subject can't be both a fox and a coyote.

Split-plots are the family of experiments and are appropriate anytime there is a restriction on randomization in experimentation.  There can be multiple splits and therefore multiple sub-plots.

[#6]

OK Thanks a lot for these interesting comments, yes I believe this is an illstrating samling plan. Suppose we want to create an experimenta plan on this case, how to do this with Custom DOE?

[#7]

Sorry, the question is non-sensical to me?  Are you asking how do we create a data table to match how the data was collected?  I would draw a sampling tree where the layers represent to components of variation and the numbering depicts the relationship(s) between the layers (e.g., nested, systematic or crossed).  Then I would create the data table to match the tree.

[#8]

In the JMP DOE tutorial the animal case is to illustrate a split-plot DOE, I understand that the data set rather is a sampling plan and does not really represent a split plot scheme. So there are three factors that influence the miles response variation (1) Season, (2) Species and (3) Subject. My question is how to generate a correct split-plot DOE using Custom design; what are the hard and easy to change factors? Goal is to get an interaction model with these 3 factors.

[#9]

Sorry, I'm not trying to be difficult and just an FYI, I have no affiliation with JMP other than as a user.  It will be impossible to get a crossed study with subject and species.  These are hierarchical (nested).  You can possibly get an estimate of the interaction between season and species.

Y= Season + Species + Season*Species + Subject[Species]

[#10]

This is a very interesting discussion on a special case that is to my opinion not the usual way to set-up a spit-plot DOE. A split plot usually kicks off by specifying hard & easy to change factors and whole & sub plots; it is still not clear to me why Season is easy? One can't change a time effect level from run to run; wait 3 monthst to change? On the other hand one can change from fox to coyote in each run so rather easy to change? Confusing.. 

This case is one that shows up frequently in industrial studies i.e. where in treatments different product or supplier batches are used that can't be changed from run to run. Also in medical studies with groups of patients and healthy people, in each group there are different participants ("subjects"). Usually these studies take a long time where time blocking is necessary.

So how to tackle these studies with Custom design that can generate any DOE? Row - Column experiments with covariates?

Sincerely thanks for input, I appreciate!