Thanks a lot for looking at this! I’m also fascinated by possible links between Big 5 and other personality/psychometric measures, and EA related judgements, and think this kind of analysis could be quite informative about EA thinking, and perhaps prioritisation in particular. To echo another commenter, I also like the graphs.
Unfortunately, we had to drop all of our psychometric measures last year due to space constraints, but I hope that we may be able to reintroduce them at some point.
I recall that when I looked into possible relationships between our Big 5 measures and cause prioritisation, treating the response categories as an ordinal scale, and found mostly null results. One reason might have been low reliability of some of the measures: we used an established short form measure of the Big 5, the Ten Item Personality Inventory, which often seems to face low reliability issues.
It’s interesting that you found such strong results looking at the difference between people who indicated that the cause should receive no resourcess versus those who indicated that it is the top priority. One thing worth bearing in mind is that these are very small proportions of the responses overall. Unfortunately the Forum now formats our 2018 post weirdly, with all the images really small, but you can see the distribution here:
It seems possible to me that ‘extreme responders’ indicating that a cause should receive no resources vs or it’s the top priority might be a slightly unusual group overall (a priori, I might have expected ‘no resources’ respondents to be less agreeable and less conscientious). The results might also be influenced a little by the minority of respondents who selected multiple causes as “top cause”, since these would disproportionately appear in your ‘top cause’ category.
It also seems likely that there might be confounders due to demographic differences which are associated with differences in Big 5. For example, there are big gender differences, which vary across the lifespan (see here), i.e. women are higher in Conscientiousness, Agreeableness, Openness, and Extraversion, although this varies at the aspect level (you can see the age x gender population norms for the TIPI specifically here and here), and we know that there were dramatic gender differences in cause prioritisation.
For example, women were much more supportive of Climate Change than men in our survey, so if women are more Conscientious, this would plausibly show up as a difference in Conscientiousness scores between supporters and opponents of Climate Change as a cause. So presumably you’d want to control for other differences in order to determine whether the difference in personality traits seems to predict the difference in cause prioritisation. Of course there could be other differences which are relevant, for example, I seem to recall that I found differences in the personality traits of LessWrong members vs non-LessWrong members (which could also be confounded by gender).
I agree that pitting the “top priority” group against the “no resources” group in a correlation is an odd way of approaching the data. I was trained not to create dichotomous variables unless advanced statistics tell us that the data is, indeed, dichotomous. Doing so gets rid of variance that may well be useful. The current analysis goes even further by excluding large numbers of people, as you noted.
I would be interested in seeing non-parametric correlations that can deal with ordinal data (e.g, Spearman’s) used on the full sample. I’m guessing the story may be different.
I was trained not to create dichotomous variables unless advanced statistics tell us that the data is, indeed, dichotomous.
I had not heard this before your comment, probably due to being early in my coursework, but it makes perfect sense to me. I will remember this guideline in the future. Thank you for the feedback.
I’m happy that this comment is the highest upvoted since it provides important context. Thank you for writing it. I’ve directed first-time readers’ attention to your comment in an edit at the top of my post.
Thanks again for writing it! It’s nudged me to go back and look at our data again when I have some time. I expect that we’ll probably replicate at least some of your broad findings.
Thanks a lot for looking at this! I’m also fascinated by possible links between Big 5 and other personality/psychometric measures, and EA related judgements, and think this kind of analysis could be quite informative about EA thinking, and perhaps prioritisation in particular. To echo another commenter, I also like the graphs.
Unfortunately, we had to drop all of our psychometric measures last year due to space constraints, but I hope that we may be able to reintroduce them at some point.
I recall that when I looked into possible relationships between our Big 5 measures and cause prioritisation, treating the response categories as an ordinal scale, and found mostly null results. One reason might have been low reliability of some of the measures: we used an established short form measure of the Big 5, the Ten Item Personality Inventory, which often seems to face low reliability issues.
It’s interesting that you found such strong results looking at the difference between people who indicated that the cause should receive no resourcess versus those who indicated that it is the top priority. One thing worth bearing in mind is that these are very small proportions of the responses overall. Unfortunately the Forum now formats our 2018 post weirdly, with all the images really small, but you can see the distribution here:
It seems possible to me that ‘extreme responders’ indicating that a cause should receive no resources vs or it’s the top priority might be a slightly unusual group overall (a priori, I might have expected ‘no resources’ respondents to be less agreeable and less conscientious). The results might also be influenced a little by the minority of respondents who selected multiple causes as “top cause”, since these would disproportionately appear in your ‘top cause’ category.
It also seems likely that there might be confounders due to demographic differences which are associated with differences in Big 5. For example, there are big gender differences, which vary across the lifespan (see here), i.e. women are higher in Conscientiousness, Agreeableness, Openness, and Extraversion, although this varies at the aspect level (you can see the age x gender population norms for the TIPI specifically here and here), and we know that there were dramatic gender differences in cause prioritisation.
For example, women were much more supportive of Climate Change than men in our survey, so if women are more Conscientious, this would plausibly show up as a difference in Conscientiousness scores between supporters and opponents of Climate Change as a cause. So presumably you’d want to control for other differences in order to determine whether the difference in personality traits seems to predict the difference in cause prioritisation. Of course there could be other differences which are relevant, for example, I seem to recall that I found differences in the personality traits of LessWrong members vs non-LessWrong members (which could also be confounded by gender).
Hi David,
I agree that pitting the “top priority” group against the “no resources” group in a correlation is an odd way of approaching the data. I was trained not to create dichotomous variables unless advanced statistics tell us that the data is, indeed, dichotomous. Doing so gets rid of variance that may well be useful. The current analysis goes even further by excluding large numbers of people, as you noted.
I would be interested in seeing non-parametric correlations that can deal with ordinal data (e.g, Spearman’s) used on the full sample. I’m guessing the story may be different.
I had not heard this before your comment, probably due to being early in my coursework, but it makes perfect sense to me. I will remember this guideline in the future. Thank you for the feedback.
No problem, best of luck! :)
Hi David,
I’m happy that this comment is the highest upvoted since it provides important context. Thank you for writing it. I’ve directed first-time readers’ attention to your comment in an edit at the top of my post.
Thanks again for writing it! It’s nudged me to go back and look at our data again when I have some time. I expect that we’ll probably replicate at least some of your broad findings.
I believe you can edit the image size of images on old posts by dragging their bottom border down when in edit mode
Thanks!