Wonderful suggestion, "dowhatnow"! I never would have thought of that, but it is a great idea. Thanks!

First of all, great memory Satchi! I"m glad my advice was useful! Thanks for your great advice about pursuing a business consultant. I think it is a good idea.

Walminski, thank-you also for your thoughts, as they are very helpful. I do already have a facebook and linkedin page setup and I"m also associated with a company that do I contract for occasionally. I haven't tried your idea of contacting an "ask the expert" site though, so thanks for that suggestion! Thank again everyone for your wonderful insights and thoughts. Best.

Jeremy

Hey Everyone,

I am a PhD candidate that does some side work helping other graduate students with their graduate research stats analysis. Do you guys have any suggestions about how to get the word out about my services? I'm not going to post my link because I don't want to get banned or kick-off the board, but I'm curious if you all have any suggestions.

Thanks!

Jeremy

Thanks Satchi, I'm glad it was helpful!

If the Stats/SPSS books don't prove to be sufficient, please consider letting me know as I offer a consulting service that offers help with your analysis, as well as understanding how both the stats and the program works. You can get more info at www.varyyourstats.com or email me at [email protected]

Satchi (and others),

Suppression is when the strength of an association between two variables changes (statistically, i.e. coefficient change) when an additional variable is added to the model to predict the DV. Most commonly, the coefficient actually increases in the presence of the "suppressor" (the third variable), because the third variable accounts for a portion of the "irrelevant variability" (variability not associated with the original IV) of the DV. In its simplest conceptualization, a suppressor effect may be present when a regression coefficient between an IV and DV is stronger in the presence of a third variable, than the original correlation indicated (when the the third variable was not included).

As an example, lets say that we are examining a potential link between stress and depression (purely hypothetical), but our trusty correlation matrix surprisingly tells us that the two are not related. Surprised and determined to find an explanation, we decide to test for differences in gender on our outcome (depression), since the literature suggests that we might expect to see them. Sure enough, males and females differ on depression in our sample and when we include gender as a control (suppressing the variability of depression that was related to gender, but not stress), we are now able to see that stress and depression are significantly and positively related. In this example variability of depression based on gender was obscuring between person differences based on stress, so the effects of gender needed to be suppressed to reveal the effect of stress. I hope this is helpful! If you have additional questions, please feel free to visit my site www.varyyourstats.com or email me at [email protected]. Best of luck!