**Date:** Tue, 6 Jul 2010 02:01:42 +0000
**Reply-To:** DorraJ Oet <love_u_endlessly@hotmail.com>
**Sender:** "SPSSX(r) Discussion" <SPSSX-L@LISTSERV.UGA.EDU>
**From:** DorraJ Oet <love_u_endlessly@hotmail.com>
**Subject:** Re: Require urgent help on ANOVA
**In-Reply-To:** <COL120-W32A793E8C52F61F1BEF66FA5B10@phx.gbl>
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Hi,

Perhaps you can also consider C5, a data mining model in the family of decision tree.

It gives you the output of the important attributes in ranking order, the prediction and also it gives you the profile of those purchasers and non-purchasers. It will state for example, a purchaser is someone who is 18 yrs old, earning 1200/mth and single as one of the rule.

C5 can be found in PASW Modeler.

I hope this helps.

Regards

Dorraj Oet

Date: Mon, 5 Jul 2010 14:12:14 +0100
From: mike_g50@hotmail.co.uk
Subject: Re: Require urgent help on ANOVA
To: SPSSX-L@LISTSERV.UGA.EDU

This certainly depends on what the questioner wants to do. Ryan's solution would be best if the questioner wants, as Ryan says, to look at each attribute taking into account the others. On the other hand, if John wants to consider each question independently, he would be better off with separate analyses.

A regression analysis (including logistic regression) will answer sophisticated questions, such as whether the answer to a given question adds more information on top of the answers to the other questions. For example, suppose that two of the questions were "How much do you like the appearance?" and "How much do you like the colour?". If those, and only those, questions are put into the analysis at the same time, and the "appearance" question is significant, it means that there are aspects of the appearance that add significantly to predicting whether the customer will buy the product, on top of that which is already predicted by knowing their opinion of the colour. (I am ignoring further complications, such as suppression.) Regression is also a good way of estimating the total predictability of all the questions, taken as a whole. And it would be possible to examine interactions, if one wanted to and if there were enough power.

If the questioner just wants to take each question at face value, however, I remain of the opinion that he should do a separate analysis for each question and compare the effect sizes. He could do this with a series of logistic regressions, but surely a series of Anovas (details as in my earlier post) would be easier to interpret.

I am happy to be challenged or enlarged on - that is what discussion groups are all about!

Mike Griffiths

Date: Sun, 4 Jul 2010 21:54:48 -0400
From: ryan.andrew.black@gmail.com
Subject: Re: Require urgent help on ANOVA
To: SPSSX-L@LISTSERV.UGA.EDU

John,

As another poster suggested, I think you should consider fitting a binary logistic regression model with "purchased product A" (yes/no) as the dependent variable and each of the attributes as an independent variable. This would tell you which of the attributes, if any, is significantly predicting the purchase of product A while taking into account the other attributes. You would also obtain the relative predictive strength of each attribute. Before fitting this type of model, you should confirm that you have met the assumptions. There's so much more that could be said on this topic, but without more information I will stop for now.

Ryan

On Fri, Jul 2, 2010 at 2:55 PM, John Watson <johnwatson_1981@yahoo.com> wrote:

Hi All,

I need urgent help on the following situation. Would really appreciate your help.
I need to use ANOVA on the following variables:
1. Whether purchased product A - Yes or No (consider this factor variable)
2. Series of ratings on attribute importance statements

Objective: I need to run ANOVA to find out which attributes' means are significantly different between those purchased vs. non-purchasers. Once I find the attributes that have higher mean on those who purchased, I need to find out which attribute is more predictive or influencing in likely to purchase the product. In nutshell, I need to rank order the attributes by their ability to impact purchasers vs. non-purchasers.

Help: Can ANOVA analysis help to accomplish aforementioned objective? If yes, how?

I am totally lost and I need to get this out asap. I would really really appreciate any help you can provide.

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