# How to test for increases in categorical variable values measured over time?

attached is a (German...) bar chart which shows the five possible values of the one variable "Culture": Nix, BV, Candida, Mix, Unspecific.
The variable is measured at three different times, 1 (blue), 2 (green), 3 (something).

I am looking for some kind of test to determine that the number of measured individual values increase, or decrease. For example, I'd like to show that over time (i.e. from time=1 to time=3), the value "Nix" is measured more often, whereas "Candida" and possibly "Mix" go down.

I am a bit confused because we have three time points and five possible values, this makes it a bit unclear for me.

• There is probably some information missing in your explanation: it seems unlikely that the absolute counts make sense over the 3 measurement times. Don't you want to compare (over the measurement times) the ratio of each value (i.e. the count divided by the number of items counted on that measurement time). In any case there are bound to be relations between the values that you don't specify. If not, you'll have to make grave assumptions to "assert" going up per value if you only have 3 numbers. Aug 24, 2011 at 11:02
• Do I understand the data right in that you have 3 variables (columns time1, time2, time3) with a common list of 5 possible categorical values (Nix, BV, etc). Each row of the data is an observer; at time1 he observes, say, Nix, at time2, say, BV, at time3, say, Nix again. If that is the case than why does amount of observations differ for 3 times (50 in time1, 44 in time2, 41 in time3)? Is this because of missing data? Or your data is another structure? Aug 24, 2011 at 11:10
• ttnphns: Yes, there are some patients who apparently went missing while the study was executed. Aug 24, 2011 at 11:50
• Nick Sabbe: Wait, you are right. There are more missing patients as time goes on. I should use ratios. What exactly do you mean with "relations between the values"? Aug 24, 2011 at 11:58