This is my first post. I am a newly-begun career-researcher and while I have come here now for help, I intend to continue to use this forum and offer my assistance to others where I can in the future.

Here is the issue I'm having right now. I am analyzing a study on Down Syndrome (DS) children, looking at counting scores. The study has a 2x2 design; group (DS v TD (typically developing) and age (young v old). Two independent-samples t-tests, one conducted for each group, show that within the DS group older Ps perform significantly better than younger (p<.001), while in the TD group there is no difference between ages (p=.222).

What I now want is the cleanest way to demonstrate that there is a significant difference of-the-effect-of-age-on-counting between groups. Now, you might tell me, just add group, age, and group*age into a regression with counting as independent variable, and read off the significance of the interactive variable.

My issue with this is that this will actually tell me whether the interactive variable is a significant unique predictor

*over and above*age and group by themselves. This is not what I want to know. For theoretical reasons I just want to know whether there is an interactive effect all by itself.

My initial answer to this was that I would run a regression with just one independent variable, the interaction itself. This lumps DS young with TD old, and DS old with TD young. However, I have been told by an old lecturer who has enough time to tell me I'm wrong but not enough to tell me why that this is incorrect. He says that you must include all simple variables that went into producing an interaction in a regression in order to be able to justifiably interpret the interaction variable itself.

Is he correct? If so, why am I wrong? And if I am wrong, is there some other, legitimate way of checking the interaction without it being

*over and above*the effects of the simple variables?

Thank you very much for reading and/or replying,

Stephen