# Solved – Is it reasonable to treat a three point Likert dependent variable as a continuous variable

I have a three point Likert scale question:

``How happy are you?     1= low levels of happiness      2= medium levels      3= high levels  ``

I want to do multiple linear regression on the variable. I am making the assumption that that it has the same difference between low and medium and medium and high. I want to treat it as a continuos dependent variable.

I know some people would see this as inappropriate, and there is the age old issue about whether to treat survey data as continuous.

• But are there any further problems applicable to my dependent variable because it is only on a 3 point scale as opposed to 4 or 5?
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What I like to do in situations like this is run ordinal logistic, multinomial logistic and linear regression and compare results. I compare results by looking at predicted values from the different models. This is easy for multinomial vs. ordinal, because both yield probabilities. To compare with linear, I sometimes see what the highest probability response is from logistic, and how close it is to the predicted value from linear.

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# Solved – Is it reasonable to treat a three point Likert dependent variable as a continuous variable

I have a three point Likert scale question:

``How happy are you?     1= low levels of happiness      2= medium levels      3= high levels  ``

I want to do multiple linear regression on the variable. I am making the assumption that that it has the same difference between low and medium and medium and high. I want to treat it as a continuos dependent variable.

I know some people would see this as inappropriate, and there is the age old issue about whether to treat survey data as continuous.

• But are there any further problems applicable to my dependent variable because it is only on a 3 point scale as opposed to 4 or 5?

What I like to do in situations like this is run ordinal logistic, multinomial logistic and linear regression and compare results. I compare results by looking at predicted values from the different models. This is easy for multinomial vs. ordinal, because both yield probabilities. To compare with linear, I sometimes see what the highest probability response is from logistic, and how close it is to the predicted value from linear.

Rate this post