The point I was trying to communicate here was simply that our design was able to find a pattern of differences between the control and treatment groups which is interpretable (i.e. in terms of different ages and career stage). I think this provides some validation of the design, in that if large enough differences exist then our measures pick up these differences and we can statistically measure them. We don’t, for instance, see an unintelligable mess of results that would cast doubt on the validity of our measures or the design itself. Of course, if as you point out the effect size for attending the conference is smaller then we won’t be able to detect that given our sample size. For most of our measures this was around 15-20%. But given we were able to measure sufficiently large effects using this design, I think it provides justification for thinking that a large enough sample size using a similar study design would be able to detect smaller effects, if they existed. Hope that clarifies a bit.
Hi David,
The point I was trying to communicate here was simply that our design was able to find a pattern of differences between the control and treatment groups which is interpretable (i.e. in terms of different ages and career stage). I think this provides some validation of the design, in that if large enough differences exist then our measures pick up these differences and we can statistically measure them. We don’t, for instance, see an unintelligable mess of results that would cast doubt on the validity of our measures or the design itself. Of course, if as you point out the effect size for attending the conference is smaller then we won’t be able to detect that given our sample size. For most of our measures this was around 15-20%. But given we were able to measure sufficiently large effects using this design, I think it provides justification for thinking that a large enough sample size using a similar study design would be able to detect smaller effects, if they existed. Hope that clarifies a bit.