Hello,
Please I need to perform a logistic regression analysis using 2 independent variables, each has multiple indicators using SPSS. For example, the independent variable perceived behavioral control (PBC) is measured using two indicators, which are self-efficacy and easy-to-start (each is binary). The other independent variable is the subjective norm, measured by 2 indicators (respect and motivation), each of which is also binary.
My question is: how to deal with the multiple indicators for one independent variable when performing the analysis?
In case that I want the outcome to appear like in the attached table, in which it includes only the independent variables (not each indicator individually). I assume that I need to compute each variable by summing its indicators but I am not sure if this is correct. So, I need the assistance of experts.
I hope that I am able to communicate my inquiry properly.
Thank you.
If I follow, you are asking how to handle categorical explanatory variables that have more than 2 categories. If so, and if you are using the LOGISTIC REGRESSION command, use the /CATEGORICAL sub-command. You can find an example on this UCLA page, where rank is a categorical variable with 4 levels:
https://stats.oarc.ucla.edu/spss/dae/logit-regression/
If you are using the GENLIN command, categorical explanatory variables follow the key word BY, whereas quantitative variables follow the key word WITH. In the GUI, the former go into the Fixed Factors box, the latter into the Covariates box. HTH.
How do you know that your multiple indicators really measure the same construct?
Your binary variables make me fear that you dichotomised a measurement scale score. Not a great idea – imagine taking a black and white picture and replacing each pixel with either white or black, depending on whether it was above or below the median brightness. Your intuition is correct. you can lose over a third of the information content of your variable as a result.
So my advice would be go with Bruce, but check your assumptions first.
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