I was speaking to a statistician recently who suggested that using dummy variables rather than one variable with several levels reduced the constraints on models, particular reducing the assumption of linearity. I didn't understand the explanation and was wondering if someone could make it clear?
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Perhaps he is saying that treating an ordinal variable as continuous (which is reasonably common) means more assumptions in the relationship to the response variable than if you treat it properly as a categorical factor (nominal or ordinal). If you treat an ordinal variable as though it is continuous you are assuming that the differences between different adjacent levels of the scale are in some sense constant, as well as that this variable is linearly related to the response (assuming you have a linear model). |
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