Hi there,
I am designing a study that compares three conditions (differences in glass shape) on one main DV (drinking rate) (as well as some secondary DVs).
I want an adequate sample size to be powerful enough to test the primary hypothesis, that there will be a difference between the three glasses (IVs) in drinking rate (DV), tested using a one way anova.
One previous study has found a difference in drinking rate between two of the glasses (similar design at least), with an effect size of d = .92, using alcohol. However, there are two issues: they did not find a difference (d = .09) when using a soft drink (which I will be doing), and also the third glass shape hasn't ever been tested in its impact on drinking rate.
Does anyone know how I should power my study to detect differences in drinking rate between these conditions? .. does it matter that I have a third condition that hasn't been tested before?
Sorry if it seems simple, but I would be grateful for some advice. Thank you in advance!
I am designing a study that compares three conditions (differences in glass shape) on one main DV (drinking rate) (as well as some secondary DVs).
I want an adequate sample size to be powerful enough to test the primary hypothesis, that there will be a difference between the three glasses (IVs) in drinking rate (DV), tested using a one way anova.
One previous study has found a difference in drinking rate between two of the glasses (similar design at least), with an effect size of d = .92, using alcohol. However, there are two issues: they did not find a difference (d = .09) when using a soft drink (which I will be doing), and also the third glass shape hasn't ever been tested in its impact on drinking rate.
Does anyone know how I should power my study to detect differences in drinking rate between these conditions? .. does it matter that I have a third condition that hasn't been tested before?
Sorry if it seems simple, but I would be grateful for some advice. Thank you in advance!
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