I have only ever used similarly scaled items in my PLS models. Logically, I can see that would be possible to rescale items with more categories to smaller scales, but with some loss of information - say to convert items in a 5 point Likert having Strongly Disagree, Disagree, Neutral, Agree, Strongly Agree to a 3 point Likert scale of Disagree, Neutral, Agree. I am less convinced that one could go the other direction without adding information that isn't there, or that one could standardize dichotomous variables alongside likert scale variables - but I am not a mathematical statistician. Hence, my question is about mixing indicators in the model...
Suppose we want to create a latent reflective construct using 4 items from a recently administered survey, as follows:
Item 1 - Y or N answers
Item 2 - 3 point likert answers
Item 3 - 5 point likert answers
Item 4 - Continuous variable that can be binned into several categories
Is this advisable or is it ill advised? Please include your rationale.
It is not advisable. Different scale have different variance (standard deviations), thus would affect the construct weight.
Hi,
I think you can, recently I have conducted research and used the survey instrument with the following measuring scale:
7 = Completely Agree,
6=Mostly Agree,
5=Somewhat Agree,
4=Neither Agree nor Disagree,
3=Somewhat Disagree,
2=Mostly Disagree,
1=Completely Disagree.
Best Regards
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