Thank you for your quick response. I've made edits in line with your suggestions. We have also provided an explanation to allay your concerns regarding Table 6.
34 -> “Only 2% of the individuals considered to be ‘eminent’ in science, before 1950”
137 These sentences are confusing if the reader is not fully aware that you’re referring to the sex of the actual board members specifically – if it refers to academics in general, the conclusions would be the opposite. So to avoid confusion you might want to be overexplicit, like “Thus if women on boards have a higher academic output, despite their lower variance in IQ, we can be confident that there is anti-female bias for admission to the board. We can also say that the larger the sex difference in favour of men on boards, the lower the likelihood of anti-female bias and the higher the likelihood of anti-male bias for admission to the board. So if men on editorial boards have a higher academic output than women we can be confident that there is no anti-female bias for admitting board members.
I’ve edited this section now to be more explicit that I’m referring to editorial board members:
“It must be noted that a sex difference in the academic output of editorial board members can only be an indicator, not proof of sex bias. As mentioned, the variance in intelligence is higher amongst males, and their average also seems to be somewhat higher. This would cause men, on editorial boards, to have a higher academic output even if there was no bias. Thus if women have a higher academic output, despite their lower variance in IQ, we can be confident that there is anti-female bias. We can also say that the larger the sex difference in favour of men, the lower the likelihood of anti-female bias and the higher the likelihood of anti-male bias. So if men have a higher academic output than women we can be confident that there is no extreme anti-female bias.”
272 -> “scaled into standard deviation units as Z-scores, according to” or even better
271 -> “were first log10 transformed and then Z-transformed into standard deviation units within each academic discipline”
423 Very confusing to look at a graph with a distribution mean of 0 and the caption reads “Distributions of Log10 Transformed h-Index”. Please add that the data are z-transformed.
New caption: “Distributions of Log10 then Z-Transformed h-Index of female and male editorial board members"
Table 6. Still not clear to me what the function of the numbers 1-12 is, before I thought it was to identify the same the horizontal indicies. The F values still seem much too high. I ran several MRA with similar data, and got for example R2 = 0.048, F = 4.66, p = .0013 and R2 = 0.086, F = 5.016, p = .0017. So regardless of higher or lower R2, F is always much lower than your values. I’m not saying you’re wrong, but please make sure you’re not.
The row is labelled model numbers and then there are numbers above each model, which help us refer to our results in the text. I hope you consider this reasonable.
In the models with low R^2s (eg. 0.03) we only have 1 variable (K =1) and sample sizes of around 1000. Using the formula below F = (0.03/1)/(0.97/998) = 31. The R squared is reasonable (sex alone shouldn’t explain much of the variation), sample size is right, formula is right so the reasonably high F values are also as we should expect.
Let’s take the even higher F values we get from our models with two variables. R^2 is around 0.5 (sex and years publishing should explain something like 50% of the variation, this makes sense). F = (0.5/2)/(0.5/997) = 498.5 .
Given our sample size is high, our R^2s are right and reasonable and the formula is right, our high F values are expected. This calibration exercise shows our high F values are reasonable.
F = (R^2/K)/((1-R^2)/(n-k-1))
600 One cannot see the x-axis in the PDF – obscured by the “Note”
600 I assume you mean “For questions regarding age and sex preference, lower scores indicate”
But that is the opposite of “we labelled the right end of responses “They should favor females above their academic accomplishments” and the left the same but for males”, no?
Yep, the caption was the wrong way around. This is fixed.
735 “uncertain about the reasons for this, but suggest that (1) older scholars have had more time..” Mustn’t this explanation also include sex somehow?
Changed to “In regression results, we found that controlling for years publishing reduces the male advantage in research output, implying men in our sample have been publishing for longer. We are uncertain about the reasons for this, but suggest that (1) older scholars have had more time to publish papers, (2) younger cohorts of scholars are worse than older ones and (3) journals could have a pro-old age bias.”