Re: "fixing" a control
- From: Art Kendall <Arthur.Kendall@xxxxxxxxxxx>
- Date: Wed, 19 Oct 2005 02:36:14 GMT
Were cases randomly assigned to test and control?
How were the segments chosen? Are they exhaustive of the strata of the pop of interest? A random set of values, e.g., places?
If the outcome is a continuous variable, might have a two way ANOVA. # of segments by test/control?
If the outcome is a dichotomy, you might have a contingency table?
Art Art@xxxxxxxxxxxxx Social Research Consultants Inside the Washington, DC beltway.
b_branford@xxxxxxxxx wrote:
Hi,
Thanks for your response. The context is direct marketing campaigns. We send out promotional offers to a "test" population and then compare their behaviors to the "control" population to see if we have a lift.
Actually, the problem is in the way the mail plan was designed it is difficult to have an overall "test" and overall "control".
Step 1: Segment 1 was selected based on certain criteria and then broken up into "Test" and "Control".
Step 2: Segment 2 was created (after suppressing segment 1) and then broken up into "test" and "control".
.....etc.
Obviously, if you combine all the test cells and all the control cells together, they will not look similar (comparison of means, etc). However, for analytical purposes it would be useful to have an overall "test" population and an overall "control" population which look similar. Yes, I see your point regarding how do we compare. In fact, that is another question I had -- is there a multivariate way of validating that a test population looks similar to a control or, if not, is there a way to do it at the backend?
Thoughts ?
Thanks.
brad
Data Matter wrote:
The bad news for you is you can't "fix" the control cell. I presume the experiment has been completed so there is no way to re-run history.
The most important thing for you to find out is why did the control cell look different. Was it a problem with the randomization procedure? Is it a systematic problem? Were your sample sizes small?
What do you mean by "not looking alike"? Are you comparing means? variances? are you comparing the response variable or other variable?
Any post-hoc "fix" would involve a multitude of assumptions, which will likely invalidate your conclusion anyway. Without further details, it is impossible for us to provide useful advice.
b_branford@xxxxxxxxx wrote:
hi,
I have a control cell which does not look like the test cell. Any
thoughts on how I can "fix" the control so that it looks like the test
population.
thx.
brad
.
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