How do you objectively quantify something like sexual assault and harassment statistics? I see this sentiment a lot and it's so baffling to me. Everything's got to be a hard data point to be legitimate, they say, but I think we should be more willing to accept what people say about their experiences instead of going on and on about how everything is conjecture until we get the facts. These are the facts. You're not gonna get any better data about this than people telling you it happens.
I think you misunderstand me. I don't doubt the experience of any of the respondents, and my heart goes out to them. What I doubt is that this survey represents a good picture of the state of sexual violence in the field. To get that picture a much more scientific survey would be needed. My guess is that this survey overestimates the number of women who are victimized, because of reporting bias, not inaccurate accounts on an individual basis.
I guess I am unsure what else you would need to tell you how bad it is. The other side certainly isn't going to condemn themselves by saying anything about what they do. If you ask them, nothing happens at all! This being the case, how do you get more in-depth analyses than surveying the only participating parties, both of which may be "inaccurate"?
What you would want is a survey where the sample is randomly drawn from a cross section of professionals in the field. This was an online self-reporting survey where anyone was invited to respond. In that case, it is my conjecture that women who have been harassed or assaulted will be more likely to reply, because they will want to get their experience known to the world, whereas someone who has never had an issue may think taking a survey is a waste of time. I'm not a statistician, so I can't really say how to design a survey with a low margin of error, but I know enough to say that this isn't a way to do it. Anyway, I'm glad when issues of abuse are addressed, but when we overestimate the problem, I think it's easy to start to characterize a problem as an epidemic, and then to demonize scientists in the field, when really we don't know what the numbers are.