# Are the variable types here considered correct?

If we want to determine the variable types, will it be as follows for the below variables?

Age ---> quantitative, discrete (we can count)

Fitness ---> If the values that we will enter here are 0 and 1 only, will the type of this variable be qualitative, nominal?

Thanks a lot.

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How would such a determination help you with your analysis? One good answer, found in many places on this site, is not at all. Indeed, this (Stevens') classification of variables has been criticized (justifiably, IMHO) for unnecessarily limiting the analyst's options. This suggests you might get more value from this site by forgetting about variable type altogether and asking the questions that I suspect may be lurking beneath the surface here, questions concerning how to understand and analyze your data. –  whuber Apr 3 '12 at 16:06
BTW, there are important distinctions between counted data and other types of data. The issue is not that of discrete versus continuous, but of expectations (and theoretical limitations) concerning their statistical behavior. Although age is definitely not a count, it can be considered discrete or continuous depending on one's analytical objectives and capabilities. –  whuber Apr 3 '12 at 16:08
If it is not too late, you might collect date of birth and then calculate age. The variable would then be continuous. Also, people remember their date of birth, but sometimes not their age. –  Joel W. Aug 1 '12 at 21:08
@whuber, would you please provide a link to one or more of the pages on this site on the topic of Steven's levels of measurement? Thanks. –  Joel W. Aug 1 '12 at 21:11
@Joel Search the site for "Stevens". One that turns up is Does it ever make sense to treat categorical data as continuous?. It also uncovers my partial criticism. –  whuber Aug 1 '12 at 21:16

It depends on requirements of software and specific procedure you use for the analysis. In general, I personally like to register binary variables (1=present vs 0=absent) as ordinal level, not nominal level, such as sex (1=male, 2=female). But is matter of taste and custom...

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Questions like this expose the problems with Stevens' typology of variables. Some variables just aren't any of his categories; or are hard to categorize. Age is ratio level data in the sense that someone who is (say) 40 years old is twice as old as someone who is 20 years old. You just haven't got it measured precisely. But so? What difference does that make?

Dichotomies - here it really doesn't matter HOW you treat them for most purposes. E.g. in regression, you can consider them to be nominal, ordinal or interval and you will get the same results.

I wrote about other problems with this typology on my blog

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