DOE with attribute response

We has 2 options to do :
1. Try to convert the response to be discrete or continuous response. You have to define your response related the attribute resonse such as size of the defect, amount of defect, severity or etc. If none are applicable, most of the time we still have likert scale as the last choice.
2. Use attribute response. But it requires resource a lot.
My experience, I always use choice No. 1.
Jaran S.

Hi,
          You have to take a transformations on the response data if it is of attribute type, depending on the distribution. May be in your case it may follows poission distribution. Take a transformation do the similar analysis as you will for continuous response.
      You can look into minitab for this. Are you can go through this website which have some questions and answers on DOE.
http://www.minitab.com/support/answers/index.asp?topic=DOE
hope this helps you
sridhar
 

I think that the suggestions given above are all good, but the technically correct tool to use here is logistic regression.  Minitab does a pretty good job of simplifying this tool and explaining how to use it.
The problem with a transformation approach is that you lose the power of the sample size.
 

can anyone help me,
is there any basic theory about DOE Attribute?
 
thank you very much

Where can I get a copy of the article “Analysis of Factorial Experiments with defects or Defects as the Response” by S. Gisgaard and H. T. Fuller?

I’ve been trying to search for that on the web with no luck.  Is there a specific website you can give me to get the article? 

(1) Copy of the article can be obtained from
http://www.dekker.com
(2) Transformation from attribute data, e.g. defective rate (i.e. binomial variate) and defect count (i.e. poisson variate) to continuous variable, see BH2, Statistics for Experimenters by Box, Hunter and Hunter
(3) Quality Progress, March 2004, page 66 “3.4 per million” has another example of Attribute DOE
(4) Based on my experience, unless the range of the untransformed data (Y) is very large, (e.g. max Y >> min Y), conclusion from the transformed data is often the same as the conclusion from the untransformed data.
 
Aldous Wong  
  
 

Dear Tony ,
Sorry for delay in response. I was on tour etc. Anyway , the way you can transform the data I ‘ve explained in my original post on the subject above. So now I’m not explaining that & rather I’ll answer specific to your question.. Say, in a DOE you have the response as a defect count in invoices.  Now for each set of run (you have to the choose the facors & level setting ) say you are taking 10 Invoices each & counting the no of defects as response. Now while dealing with attribute data , the assumtions of DOE are generally violated & hence you have to transform this data. Now in this example your data will follow poisson distribution. Hence you can use the transformation formulae what I have already posted in my earlier post on this subject.
Thanks
 

Hi ,everybody
what’s likert scale? where will i find the relative content ..
Thanks for you again .
 

Hi There
If you can call me at 954-394-0773, I have some questions in regards of design review.
I am looking forward.
Best regards.Mirza