Nicki,

I've often wondered about this myself. How to interpret the interaction between a factor and a continuous covariate? Recently I've been exposed to Cluster Analysis. If you have a number of variables that are related in some theoretical way to your covariate you could run a Cluster Analysis to create profiles of individuals based on the covariate and the other variables that it is theoretically associated with and then see if those profiles differ as a function of your factor. Basically what you will have done is divided up your sample into much more meaningful groupings than a median split would do. You'd have to interpret what each cluster represents as you would do in a factor analysis. You would then look at the interaction between cluster membership and the factor. If you get an interaction it is then more interpretable because you have all of the other variables (the theoretically associated variables that you used to help create your clusters) that characterize each cluster. Furthermore, now that your sample is divided up into the theoretically meaningful clusters (of which you can have more than 2) it seems to me that you're preserving more info about your data than with a median split.

Of course, it would rely on your having other variables that logically relate to you covariate.

I'd be curious to know what others think about this. It seems to me like it gets rid of some of the messiness of interpreting an interaction between a factor and a continuous covariate.

I've not done much of this, but this has been my impression.

Thanks, Matt

-----Original Message----- From: SPSSX(r) Discussion [mailto:SPSSX-L@LISTSERV.UGA.EDU] On Behalf Of Kersting, Nicole Sent: Thursday, December 14, 2006 10:48 AM To: SPSSX-L@LISTSERV.UGA.EDU Subject: Re: Statistical methods to investigate interactions between factors and continuous covariates

Hi all,

I ran an ANCOVA model which yielded a significant interaction between a fixed factor and a continuous covariate. I am interested in investigating the interaction further but I ran into the following problem: I created a median split in the continuous covariate, which in combination with the factor gave me four means for pairwise comparisons. While I realize all the issues attached to median splits, I have the additional problem that the pairwise comparisons weren;t significant, indicating that the interactions is not represented well by the median split.

So I am wondering if there are any other statistical methods to investigate an interaction between a continuous covariate and a factor or if I am doomed to fish around for the appropriate split because for reporting purposes I will need the pairwise comparisons. What do people do in general in those cases. Given that we didn't expect the interaction (not part of the design) it's hard to come up with a theoretical rationale on how to split the data for pairwise comparisons and graphs.

Many thanks in advance, Nicki

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