Hi all,
I have a problem where I want to prove that the data belonging to invidivuals of one group has a higher variance than the other.
The experiment involves a parameter of movement that changed in one group of organisms, but without a clear trend. The parameter remains stable in individuals belonging the control group, whereas it either fluctuated in the test group both up and down depending on the individual. I want to show that it changes, but it is hard to test as I am measuring the variances rather than a trend (where regression could be used).
There is a significant effect there. In a crude (and weak) analysis I computed the variances of the individual organisms, and then compared the variences between the control and test group in a simple independant samples ttest using SPSS. The data was "normally" distributed and with unequal variances (although in this case the variance refers to the variance of the variances... quite confusing), and the ttest showed a significant difference... just. The results of this ttest comparison are attached.
A few things bothered me about this analysis:
The data for each individual organism is compressed into a single value, weakening the comparison.
It is a twostage test. A stastical test of stastical outputs: not very beautiful.
The ttest used is quite basic and antiquated. A more robust test would be preferable.
Would it be possible to compare the variances of two populations when the data is stored as individuals.*
*unfortunately pooling the data prior to calculating variance isn't possible
Alternatively, would there be some kind of sideways manner to attack this problem such as converting the data somehow?
I have a problem where I want to prove that the data belonging to invidivuals of one group has a higher variance than the other.
The experiment involves a parameter of movement that changed in one group of organisms, but without a clear trend. The parameter remains stable in individuals belonging the control group, whereas it either fluctuated in the test group both up and down depending on the individual. I want to show that it changes, but it is hard to test as I am measuring the variances rather than a trend (where regression could be used).
There is a significant effect there. In a crude (and weak) analysis I computed the variances of the individual organisms, and then compared the variences between the control and test group in a simple independant samples ttest using SPSS. The data was "normally" distributed and with unequal variances (although in this case the variance refers to the variance of the variances... quite confusing), and the ttest showed a significant difference... just. The results of this ttest comparison are attached.
A few things bothered me about this analysis:
The data for each individual organism is compressed into a single value, weakening the comparison.
It is a twostage test. A stastical test of stastical outputs: not very beautiful.
The ttest used is quite basic and antiquated. A more robust test would be preferable.
Would it be possible to compare the variances of two populations when the data is stored as individuals.*
*unfortunately pooling the data prior to calculating variance isn't possible
Alternatively, would there be some kind of sideways manner to attack this problem such as converting the data somehow?
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