```Date: Wed, 20 Mar 1996 16:51:02 GMT Reply-To: Richard F Ulrich Sender: "SPSSX(r) Discussion" From: Richard F Ulrich Organization: University of Pittsburgh Subject: Re: Interactions in regression equations. Bisson Jocelyn (bisson@ERE.UMONTREAL.CA) wrote: : From my understanding of analytical strategies concerning regression : analysis, when an interaction term turns out to be significant (using : the r2 change after simple effects have been entered), it is indicated : to perfom separate analyses, one for each level of the interaction term. -- Well, you CAN do it that way, and it might be handy in taking a CLOSE look at your data.... : For instance, if gender (X1) interacts with husbands' drinking behaviors : (X2) in explaining wives' drinking patterns (Y), one makes a separate : regression for each gender. -- ... especially when, say, the regression of a husband-variable explaining a wife-variable depends on *gender*. (Is that the gender of the husband, or of the wife?) -- In answer to your later question: As David Nichols says, you properly need to look at 2x3 cells, for gender and age, *if* there is a higher interaction including THEM; in your example, a 3-way interaction. Else, you can look at two groups, and three groups. Rich Ulrich, biostatistician wpilib+@pitt.edu Western Psychiatric Inst. and Clinic Univ. of Pittsburgh ============remainder of original note : My question is: What should be done when there are two or more separate : significant interaction terms ? For example, if on top of the previous : interaction, wives' age (3 groups) interacts with husbands' drinking : behaviors in explaining wives' drinking patterns. : Should one necessarily present in this case 6 (2 x 3) different regression : equations ? One for each combinations of levels of the interacting : variables ? : We are using data from a population survey, and for that matter the : analysis design is nonexperimental and nonorthogonal. ```

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