Thanks, David! My intuition a bit different—most people I talked to about how got they got into EA mentioned multiple factors, so I was curious whether there would be general patterns/strong clusters of e.g. “offline EA (e.g. local groups + personal contact)” vs “Online EA” or clusters based on type of activity/contribution, e.g. “donation cluster (Givewell + GWWC + TLYCS + ACE)” vs “direct work cluster (80K + EA global)” vs “producing/reading research (books/articles + SSC + LW)”. I see it doesn’t seem to be the case for the whole sample base don your analysis.
Regarding the more engaged subgroup—while I perceive analysing of “where people first heard about EA” as interesting, the question of “which factors actually got people into EA” seems to me as more important. The more that most people I talked to mentioned that first point of contact with EA didn’t cause them to “get into it” right away, but only after encountering some other factors they finally made a step to “dive into it” or “get involved”. Therefore, looking whether there are some patterns of these factors for more vs less involved people seems to me potentially promising, I agree that “how long people have been in EA” will probably add a lot of noise to this as these patterns could have changed over time. Not sure whether the sample size would be big enough to analyse each year separately or whether there are some other ways to control for it.
Using cluster analysis for the group membership seems to me like a good example of such approach.
Thanks David. I agree the online vs offline idea is pretty intuitive. Arguably you can vaguely see some signs of this in the data, but not to a great extent. As the membership post shows, there’s quite a lot of overlap between online (EA Facebook) and offline (Local Group):
looking whether there are some patterns of these factors [how people report getting more involved] for more vs less involved people seems to me potentially promising
Yes, this is what the analysis in the previous comment aims to do. And yes, you are correct that controlling for time in EA is very difficult because of the very small numbers in the earlier years of EA, hence in the analysis above we just display results splitting by those joining before/after 2016.
Thanks, David! My intuition a bit different—most people I talked to about how got they got into EA mentioned multiple factors, so I was curious whether there would be general patterns/strong clusters of e.g. “offline EA (e.g. local groups + personal contact)” vs “Online EA” or clusters based on type of activity/contribution, e.g. “donation cluster (Givewell + GWWC + TLYCS + ACE)” vs “direct work cluster (80K + EA global)” vs “producing/reading research (books/articles + SSC + LW)”. I see it doesn’t seem to be the case for the whole sample base don your analysis.
Regarding the more engaged subgroup—while I perceive analysing of “where people first heard about EA” as interesting, the question of “which factors actually got people into EA” seems to me as more important. The more that most people I talked to mentioned that first point of contact with EA didn’t cause them to “get into it” right away, but only after encountering some other factors they finally made a step to “dive into it” or “get involved”. Therefore, looking whether there are some patterns of these factors for more vs less involved people seems to me potentially promising, I agree that “how long people have been in EA” will probably add a lot of noise to this as these patterns could have changed over time. Not sure whether the sample size would be big enough to analyse each year separately or whether there are some other ways to control for it.
Using cluster analysis for the group membership seems to me like a good example of such approach.
Thanks David. I agree the online vs offline idea is pretty intuitive. Arguably you can vaguely see some signs of this in the data, but not to a great extent. As the membership post shows, there’s quite a lot of overlap between online (EA Facebook) and offline (Local Group):
Yes, this is what the analysis in the previous comment aims to do. And yes, you are correct that controlling for time in EA is very difficult because of the very small numbers in the earlier years of EA, hence in the analysis above we just display results splitting by those joining before/after 2016.