Communicating Science: Things to keep in mind when thinking about police shootings . . .

Things to keep in mind when thinking about police shootings:

Police show considerable race-weapon stereotyping, and race-aggression stereotyping on the social-psychologist designed “shooter task” – other people show even more (Correll and colleagues’ work).

Everyone has to resolve ambiguity – that’s when stereotypes can creep in. Is that a gun or a wallet? Is that person aggressive or scared? It could even lead to sensory distortions under high stress conditions. Anyone who has ever been really nervous knows to watch out for this (as opposed to blindly acting upon it). For a more everyday example of a sensory distortion, ever misread something while copy editing? Your brain “filled in the gap” with a coherent story, and the typo remained, unseen.

Police departments are not known for cultivating good mental health – but someone with a gun acting out poor mental health is a problem across the board – whether they’re killing themselves or killing someone else.

Citizens have less experience coping wth spikes of fear – of adrenaline and cortisol – than police, who have been through training, do.

White citizens are more likely to call cops on black citizens doing “ambiguously criminal” behaviors. Cops, then, are more likely to be monitoring for “suspicious black people.”

Studies of police-driver interactions at traffic stops often see the the citizen’s reactions – perfectly at ease versus even politely defensive – leading to more controlling attitudes from police. It would be hard for citizens who have been targeted to ever be perfectly at ease. Heck, even I’ve been harassed by customs agents and police for “looking nervous”.

Mentally ill people are particularly likely to become targets – because any not perfectly “safe and predictable” behavior is interpreted as a threat. This is why some departments call in specialists who are better able to assess the situation when mental illness is suspected.

Police, like the rest of us, like their stories – even ones that are more a matter of faith than fact. You can also imagine that departments would vary by how often they actually deal wth threat. On the low end, they may, on average, be looking for an opportunity to “suit up,” at the high end the may live, on average, in a state of chronic stress and fear.

Statistically – we can control for a lot of things – including actual-race-based difference in weapons charges and other signs of real versus imagined racial differences in dangerousness. Stereotypes are likely still relevant, even after those things are controlled for. This makes sense, empirically. We apply schema – ideas about the world – to resolve ambiguity all the time (the typo example). The solution would seem to be better schema and better methods for gathering information in the moment (both better schema and better attempts at evaluating the situation are part of the training that specialized mental health responders have).

However, statisticians, particularly ones relying on observational data, can cherry pick which measures they include, and which they exclude. They are also often trying to “start a conversation” with others in their field, such that national attention may be secondary. Sometimes “starting a conversation” means generating controversy. Even academics engage in PR, albeit for a limited audience.

So always ask yourself “Would I expect to see a race-based difference if the neighborhoods, suspects, or the officers were matched on a different set of characteristics? Statistics don’t provide the final answer, only pieces of the puzzle. To know they fit together, you have to look at them closely.In the end, however, most statistics are asking:

“Is there an average difference in y as we go up one unit on x, constant across (controlling for) levels of these other variables?” Y, for example, could be likelihood of getting shot, a one unit change in x could be going from “white, coded as 0, to black, coded as 1.” That would be a categorical variable. It could be that when people are matched on income, education, etc, a racial difference disappears, remains, or even increases. This is called “controlling for” or “adjusting for” those variables.

Often, the statistician (and you) could ask, does the odds of getting shot when black versus white depend upon another of those other variables, so that if that variable, W, let’s say, is high, the difference in Y (odds of getting shot) as X goes from 0, white, to 1, black, is bigger (or smaller)?” So if “W” is median income – it could be that the odds of getting shot while black versus white is lower in areas with high median income, controlling for (holding constant) the percentage of black people living in that area. So many questions can be asked (and partially answered) with statistical models!
The mechanics may seem complex, even scary, but understanding what a model is trying to evaluate, and how it works, is not rocket science. Like a jigsaw puzzle, it requires patience, but you can get it done!If you want to try your hand, think about the following and try to break it down into primary relationships (outcome variables versus focal predictor variables, control variables, and moderators (if any):

Police shootings involve a rich if terrible tapestry of factors.
However, if you’re talking to any every day person, and they are feeling a sense of concrete personal danger, why not tend and befriend first, discuss and debate second?

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