The Ingrham, writing for The Washington Post, used data to investigate the claim that undocumented immigrants are a large source of crime. You may hit a paywall when you try to access this piece, FYI.
Ingraham provides two pieces of evidence that suggest that undocumented immigrants are NOT a large source of crime. He draws on a policy brief from the Cato Institute and a research study by Light and Miller for his arguments.
The Cato Institute policy brief about illegal immigration and crime is actually part of a much larger study. It provides a nice conceptual example of a 3 (citizenship status: Native born, Undocumented Immigrant, Legal Immigrant) x 3 (Crime Type: All crimes, homicide, larceny) ANOVA. I also like that this data shows criminal conviction rates per 100K people, thus eliminating any base rate issues when comparing groups.
The second piece of evidence come from research by Light and Miller. This provides an example of a linear relationship being used to provide evidence. Here, each dot on the scatter plot represents a single state during a single year. Y-axis shows the violent crime rate why the x-axis shows the portion of the population that is Undocumented. Added bonus: The data has all been standardized, giving you a good example of why we create z-scores for data.
How to use in class:
-Different analysis with different data sets can inform the same problem. Cato used data from the Texas Department of Public Safety. Mean while, Light and Miller used a variety of different data sources, listed below:
-Linear relationships example
-That linear relationship used standardized data, so an example of a situation when they needed to standardize data.
-3x3 ANOVA example
-How could this data be challenged? An argument could be made that any crime at all committed by an undocumented immigrant could be eliminated if the U.S. eliminated all illegal immigrants. Or that more information is required for different types of crimes.
-The Light and Miller research provides a great example of co-variates without ever saying co-variates. And I am inclined to examples from the popular press that don't use any stats jargon. Read on...
"That's just a simple correlation, of course, and it's well documented that many factors beyond immigration can affect the crime rate. So Light and Miller ran a number of statistical analyses to more clearly isolate the effects of illegal immigration from those other factors. Among other things, they find that the relationship between high levels of illegal immigration and low levels of crime persists even after controlling for various economic and demographic factors such as age, urbanization, labor market conditions and incarceration rates." -Ingraham
Ingraham provides two pieces of evidence that suggest that undocumented immigrants are NOT a large source of crime. He draws on a policy brief from the Cato Institute and a research study by Light and Miller for his arguments.
The Cato Institute policy brief about illegal immigration and crime is actually part of a much larger study. It provides a nice conceptual example of a 3 (citizenship status: Native born, Undocumented Immigrant, Legal Immigrant) x 3 (Crime Type: All crimes, homicide, larceny) ANOVA. I also like that this data shows criminal conviction rates per 100K people, thus eliminating any base rate issues when comparing groups.
 |
| From: https://www.washingtonpost.com/amphtml/news/wonk/wp/2018/06/19/two-charts-demolish-the-notion-that-immigrants-here-illegally-commit-more-crime/ |
The second piece of evidence come from research by Light and Miller. This provides an example of a linear relationship being used to provide evidence. Here, each dot on the scatter plot represents a single state during a single year. Y-axis shows the violent crime rate why the x-axis shows the portion of the population that is Undocumented. Added bonus: The data has all been standardized, giving you a good example of why we create z-scores for data.
 |
| From: https://www.washingtonpost.com/amphtml/news/wonk/wp/2018/06/19/two-charts-demolish-the-notion-that-immigrants-here-illegally-commit-more-crime/ |
How to use in class:
-Different analysis with different data sets can inform the same problem. Cato used data from the Texas Department of Public Safety. Mean while, Light and Miller used a variety of different data sources, listed below:
 |
| https://onlinelibrary.wiley.com/doi/full/10.1111/1745-9125.12175 |
-Linear relationships example
-That linear relationship used standardized data, so an example of a situation when they needed to standardize data.
-3x3 ANOVA example
-How could this data be challenged? An argument could be made that any crime at all committed by an undocumented immigrant could be eliminated if the U.S. eliminated all illegal immigrants. Or that more information is required for different types of crimes.
-The Light and Miller research provides a great example of co-variates without ever saying co-variates. And I am inclined to examples from the popular press that don't use any stats jargon. Read on...
"That's just a simple correlation, of course, and it's well documented that many factors beyond immigration can affect the crime rate. So Light and Miller ran a number of statistical analyses to more clearly isolate the effects of illegal immigration from those other factors. Among other things, they find that the relationship between high levels of illegal immigration and low levels of crime persists even after controlling for various economic and demographic factors such as age, urbanization, labor market conditions and incarceration rates." -Ingraham
Comments
Post a Comment