A snapshot of the "white male effect" -- i.e., "white male hierarch individualist effect" -- on climate change
Been a while since I posted on this so ...
The "white male effect," as every school child knows!, refers to the tendency of white males to be less concerned with a large variety of societal risks than are women and minorities. It is one of the classic findings from the study of public risk perceptions.
One thing that engagement with this phenomenon has revealed, however, is that the "white male effect" is really a "white hierarchical and individualist male effect": the extreme risk skepticism of white males with these cultural outlooks is so great that it suggests white males generally are less concerned, when in fact the gender and race divides largely disappear among people with alternative cultural outlooks.
In a CCP study, we linked the interaction between gender, race, and worldviews to identity protective cognition, finding that white hierarchical and individualistic males tend to discount evidence that activities essential to their status within their cultural communities are sources of danger.
The way to test explanations like this one for the "white male effect" is usually to construct an appropriate regression model -- one that combines race and gender with other indicators of risk dispositions in a manner that simultaneously enables any sort of interaction of this sort to be observed and avoids modeling the influence of such characeristics in a manner that doesn't fit the sorts of packages that they come in in the real world (a disturbingly common defect in public opinion analyeses that use "overspecified" regression models).
But once one constructs such a model, one still wants to be able to graphically display the model results. This is invariably necessary b/c multivariate regression outputs (typically reported in tables of regression coefficients and associated precision measures such as t-statistics, standard errors, and stupefying "p-values") invariably defy meaningful interpretation by even stats-sophisticated readers.
The last time I reported some results on the white male effect, I supplied various graphic illustrations that helped to show the size (and precision) of the "white male hierarch individualist" effect.
But I didn't supply a look at the raw data. One should do this too! Generally speaking, statistical models discipline and vouch for the inferences one wants to draw from data; but what they are disciplining and vouching for should be observable. Effects that can be coaxed into showing themselves only via statistical manipulation usually aren't genuine but rather a product of interpreter artifice.
A thoughtful reader called me on that, quite appropriately! He or she wanted to see the model effects that I was illustrating in the raw data--to be sure I wasn't constructing it out of nothing.
There are various ways to do this & the one I chose (quite some time ago; I posted the link in a response to his or her comment but I have no idea whether this person ever saw it!) involved density plots that illustrate the distribution of climate change risk perceptions of "white males," "white females" & "nonwhites," respectively (among survey respondents from an N = 2000 nationally represenative sample recruited in April/May) with varying cultural worldviews.
The cultural worldview scales are continuous, and should be used as continuous variables when testing study hypotheses, both to maximize statistical power and to avoid spurious findings of differences that can occur when one arbitrarily divides a larger data set into smaller parts in relation to a continuous variable.
But for exploratory or illustrative purposes, it's fine to resort to this device to make effects visible in the raw data so long as one then performs the sort of statistical modeling--here w/ continuous versions of the worldview scales--that disciplines & vouches for the inferences one is drawing from what one "sees" in the raw data. These points about looking at raw data to vouch for the model and looking at an appropriately constructed model to vouch for what one sees in the raw data are reciprocal!
Here -- in the Figure at the top -- what we see are that white males are decidedly more "skeptical" about climate change risks only among "hierarch individualists." There is no meaningful difference between whte males and others for "egalitarian individualists" and "egalitarian communitarians."
There is some difference for "hierarch communitarians" -- but there really isn't a consistent effect for members of that or any other subsample of respondents with those values; "hierarch communitarians" don't have a particularly cohesive view of climate change risks, these data suggest.
Hierarch individualists and egalitarian communitarians clearly do -- the former being skpetical, and the latter being very concerned. Moreover, while the effects are present for women and nonwhite hierarch individualis (how many of the latter are there? this way of displaying the raw data doesn't allow you to see that and creates the potentially misleading impression that there are many...), they aren't as strong as for white males with that cultural outlook.
Egalitarian individualists seem to be pretty risk concerned, too. The effect is a bit less sharp--there's more dispersion-- as it is for egalitarian communitarians. But they are closer to being of "one mind" than their counterparts in the hierarch communitarian group. The "EI vs. HC" diagonal is the one that usually displays sharpest divisions for "public health" (e.g., abortion risks for women) and "deviancy risks" (legalizing marijuana or prostitution).
Anyway, just thought other people might enjoy seeing this picture, too, and better still be moved to offer their own views on the role of graphic display of raw and modeled data in general and the techniques I've chosen to use here.