The Intern's Bailiwick

Following the theater shooting in Aurora, Colorado, I posted this graph from The Atlantic with a critique on the poor data analysis employed (my original post was titled: “Where to Go Wrong with the Causes of Gun Violence, Or, Why the Above Graph is Less Useful Than it Looks”). I concluded the post with:

It would be problematic to conclude from these data, for instance, that mental illness has no significant correlation with gun violence - in fact, it may well be that while there is an elevated risk of gun violence among people with serious mental illnesses, the United States has such an uncommonly high rate of gun deaths that the severity of numerous other factors obscure mental illness as a strong cause here. The point is there are far more accurate and less misleading ways of investigating these factors on the individual level, and in a way that can actually establish causal relationships. This isn’t it.

In the wake of tragedies like the Aurora and Tuscon shootings, we should of course always strive for reasoned discourse about how we can try to prevent future gun violence. But sensitivity and careful deliberation are of utmost importance in these kinds of policy debates: both out of respect to the victims and their families, and to avoid allowing emotions to cloud our judgement and evaluation of rhetoric and data.

Details are still emerging from today’s mass shooting at Sandy Hook Elementary School. But as we enter into reflection and policy debate, we must strive to be informed and discriminating producers and consumers of information. That can be a difficult task in today’s information environment.

Where to Go Wrong with the Causes of Gun Violence,
Or, Why the Above Graph is Less Useful Than it Looks

In the wake of last week’s horrific shooting in Aurora, Colorado, The Atlantic made reference to an article they published in 2011 following the shooting of Rep. Gabrielle Giffords and 18 others in Tuscon, Arizona. Both discussed state-level causes of gun violence. While I don’t necessarily disagree with the conclusions, the data analysis they used to get to them was careless to the point of potential tendentiousness due to a common mistake (or shortcut) in statistics: ecological fallacy.

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motherjones:

The GOP’s war on voting, in charts.

Good aggregation of statistics on generally partisan disenfranchisement efforts, and why the voter fraud argument is, as we’ve always known, specious at best.

Although I try to keep well-informed about voting restrictions, I hadn’t heard how high the percentage of Asian Americans not voting due to ID and registration issues were relative to other racial groups. I can imagine many reasons for why this could be true, but be careful - the sample size of Asian Americans who are eligible to vote but did not is likely very small (consider that only about 5% of the US population, both voting citizens and not, is Asian American). Perhaps even more significantly, much higher proportions of Asian respondents cited almost every possible reason in the survey as being a factor for not voting compared with other racial groups: 43% of Asian respondents gave “illness” as a reason for not voting, 72% “out of town,” 61% “forgot,” 61% “did not receive absentee ballot,” 81% “too busy,” 78% “transportation,” 67% “didn’t like choices,” 78% “weather,” 61% “line too long,” 78% “did not receive ballot/not on time.”

This seems to be a methodological issue more than anything else. It is worth noting, however, than Asian Americans as a voting bloc are largely ignored and voter mobilization and registration efforts targeting this community are relatively limited.

(via jrnels)