Dear all,
Anyone can explain how to conduct simple slope for hierarchical moderated regression analysis through SPSS?
I would greatly appreciate it.
The classic text for this issue is Aiken, L. S., & West, S. G. (1991). Multiple regression: Testing and interpreting interactions. Newbury Park, London, Sage. It's a short and remarkably readable book.
Jeremy Dawson has produced an Excel macro that allows almost any type of moderated effect to be probed - see http://www.jeremydawson.co.uk/slopes.htm
There have been some exciting applications of 'regions of significance' (e.g http://www.yourpersonality.net/interaction/)
Moderated regression is identical to testing for interaction effects. By "hierarchical" I assume you mean multiples steps in the entry of the variables (rather than hierarchical linear models or HLM). In this case, step one includes all the variable(s) and the dummy variable for the moderator. the next step adds interaction effect(s) between the dummy variable and each of the variables.
If you cannot follow the description I just gave, I suggest you look at something the Sage little green book on dummy variables by Hardy, which covers this topic in some detail.
The classic text for this issue is Aiken, L. S., & West, S. G. (1991). Multiple regression: Testing and interpreting interactions. Newbury Park, London, Sage. It's a short and remarkably readable book.
Jeremy Dawson has produced an Excel macro that allows almost any type of moderated effect to be probed - see http://www.jeremydawson.co.uk/slopes.htm
There have been some exciting applications of 'regions of significance' (e.g http://www.yourpersonality.net/interaction/)
Dear Amin Ansary
Please see this video tutorial. I think it may help you:
https://www.youtube.com/watch?v=0vcUtzPDGrw
Dear Prof. Mark Shevlin,
Thank you so much for introducing useful links.
Dear Prof. Hafidha S. Al-Barashdi,
Many thanks for introducing the tutorial video link.
I think this video will help you
Kindly, go through the (1-4) parts
https://www.youtube.com/watch?v=aVV7KnAr-qY
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