Can a dependent variable be calculated or does it have to be directly obtained as raw data?

Reply Thu 16 Aug, 2018 01:58 am
I have a quick question regarding the experimental design and the dependent variable.

I am to investigate the amount of RDS (Rapidly Digestible Starch) in relation to the retrogradation temperature but what I will measure is the amount of glucose after chemical digestion and I will calculate the amount of RDS by subtracting the amount of free glucose.

In this case, can I say that the RDS is the dependent variable of the experiment because I am looking at the relationship with that?

I was not sure if the dependent variable has to be the thing measured in the experiment or it can be the calculated value from the experiment.
Thank you so much for your help!
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Reply Thu 16 Aug, 2018 02:49 am
By definition the dependent variable is observed i.e measured as a result of manipulation/obsergation of the independent variable. But 'observation' can include all levels of measurement from 'nominal' (naming or counting) through 'ordinal' and 'interval' to 'ratio'. It may be that the higher levels of measurement require some 'calculation' within the observed data set.

In the above, if you are not manipulating either variable it may be that you are looking for a 'correlation', in which case the labels 'dependent' and 'independent' are inappropriate.
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Reply Thu 16 Aug, 2018 03:08 am
Excuse typo ...observation NOT obsergation
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Reply Thu 16 Aug, 2018 06:51 am
Kind of a tough one without fully understanding your process. People often measure one attribute by measuring something different and using a correlation. For example, when you measure temperature with an electrical temperature probe, you are not really measuring temperature, you are measuring the change in current flowing through a metal whose conductivity changes with temperature. Do you have to say you are measuring electrical current? No, because we know that the relationship between current and temperature is extremely good, so we just call it temperature. If you are using a standard, widely accepted way of computing RDS, then I see no problem with calling that the dependent variable. If you are computing a new way of calculating RDS, then I think you need worry about how noise in your model correlation interacts with your experiment. In that case, I would call the amount of glucose after chemical digestion the dependent variable and then do the correlation between that and RDS by using an approved technique. Either that or use the approved technique in the first place to directly measure RDS.

Does that help?
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