```========================================================================= Date: Wed, 26 Jul 2006 18:24:23 +0200 Reply-To: "Peters Gj (PSYCHOLOGY)" Sender: "SPSSX(r) Discussion" From: "Peters Gj (PSYCHOLOGY)" Subject: Addition of covariates in forward regression analyses Content-Type: text/plain; charset="us-ascii" Dear list, [if this question is inappropriate (as it discusses a topic not limited to SPSS) please tell me; I could not find rules prohibiting this online] In forward selection multiple linear regression, which of these factors influence whether a covariate is added to the model? - the size of the regression weight the covariate would get - the standard error of that regression weight - the complete sample size I suspect that both the size & standard error of the regression weight are of influence, and that the sample size influences the standard error of the regression weight. If you don't want to know why I'm asking this, you can stop reading now :-) In any case thanks in advance :-) Why I want to know this: I am conducting several very exploratory regression analyses, regressing the same covariates on the same criterion in a number of different subsamples (persons with a different value on a certain variable; in this case for example ecstasy use status (non-users, users & ex-users)). I use the forward method to probe which covariates yield a significant addition to the model. The covariates are placed in six blocks (on the basis of theoretical proximity to the criterion; the idea is that more distal covariates only enter the model if they explain a significant portion of the criterion variance over and above the more proximal covariates already in the model). P to enter is .05. (peripheral question: am I correct in assuming that this is the p-value associated with the t-value of the beta of the relevant covariate?) The sample sizes of the samples are unequal (e.g., ranging from 200 to 500). I get the strong impression that the number of covariates in the final model depends on the sample size. This would imply that covariates with less 'impact' would be added to the model when the model is developed with a larger sample (e.g., with equal standard errors of the parameter weight, when a covariate increases 1 standard deviation, an increase of the criterion of 0.2 * Y's standard deviation could suffice (lead to inclusion) with n=500, but not with n=200). If this correct? And if so, is there a way to 'correct' the p-to-enter for sample size, so that all final models comprise covariates with roughly equal relevance? (except for selecting sub-subsamples from all subsamples of the size of the smallest subsample) My goal in the end is to cursorily compare the models in the different subsamples (no, sorry, I'm not going to use SEM; given the amount of potential predictors, the sample sizes are too small). This is not very 'fair' if the model in one subsample has lower thresholds for 'inclusion' than the model in another. If what I'm trying is completely insade/stupid/otherwise unadvisable, I'm of course eager to learn :-) Thanks a lot in advance (if nothing else, for reading this far :-)), Gjalt-Jorn _____________________________________ Gjalt-Jorn Ygram Peters Phd. Student Department of Experimental Psychology Faculty of Psychology University of Maastricht ```

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