Dear all
I'm testing the parsimonious Technology Acceptance Model in the context of new retail technologies. As some studies found signficant influence of variables such as age, gender and education I thought about adding them into my regression model together with my main independant variables "perceived Usefulness" and "perceived ease of use".
I saw on Research gate that it is possible to just add the control variables together with the independant variables into the multiple regression model in SPSS? But for me it doesn't make sense, because these variables are not controlled and just treated as an independant variable.
Is that correct? Or do I need to do a hierarchical regression?
Here is the discussion that talks about putting it together with the independant variables:
https://www.researchgate.net/post/How_control_variable_be_treated_in_SPSS_I_have_Independent_variable_Attitude_Dependent_variable_Knowledgewant_control_for_teachers_training
Thank you very much!
Hi Linda,
I think it is alright to put them into independent variables, even if they were controlled for. No need to do a hierarchical regression.
Best regards,
Assem Al Refaei Hi Assem
Thank you for your answer! So I didn’t really control them when collecting the data. For example, I didn’t just collect data from one age group or educational background.
So I guess my multiple regression now shows if these other variables (age, gender, education) have an influence on the relation aswell right? ( in case they are significant).
Thank you in advance.
Linda H. Since you didn't control for them, then they are independent variables definitely. It will show if they have influence yes.
You're welcome.
What are your research questions? This will affect whether it is appropriate to include them. I would not use the word "control" though, and they are not controlling anything. It is worth reading the following, who gives good arguments for not including covariates like these. https://meehl.umn.edu/sites/meehl.umn.edu/files/files/084nuisancevariables.pdf
First thing is identify what are categorical and what are continuous one.For categorical one you have to make dummy variables as an example a categorical variable with 5 levels you have to make 4 dummy variables.
I think if you can control the demographic variables to show more appropriate results, you can also use ANCOVA analysis by following the assumptions.
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