Skip to main content

Rich, Cox, and Bloch's "Money, Race and Success: How Your School District Compares"

If you are familiar with financial and racial disparities that exist in the US, you can probably guess where this article is going based on its title. Kids in wealthy school districts do better in school than poor kids. Within each school district, white kids do better than African American and Latino kids.

How did they get to this conclusion? For every school district in the US, the researchers used the Stanford Educational Data Archive to figure out 1) the median household income within each school district and 2) the grade level at which the students in each school district perform (based on federal test performance).

This piece also provides multiple examples for use within the statistics classroom. Highly sensitive examples, but good examples none the less.

-Most obviously, this data provides an easy-to-follow example of linear relationships and correlations. The SES:school performance relationship is fairly intuitive and easy to follow (see below)

From the New York Times: Positive linear relationship between parental SES and performance on standardized tests

-The data is provided in an interactive format. You can type in the name of a given school district so that you can see where that school district falls in the scatter plot. This makes this example interactive and more applicable to your students' lives and experiences. Below, I have highlighted the school district in the city where I teach.



-The data provides a good example for explaining between-group and within-group differences. As discussed, between school districts, high SES students outperform low SES. However, within school districts, white students outperform black and Hispanic students (see below: Here, the data is divided by school district as well as white, Hispanic, and black students within each district).

From the New York Times: SES x test performance x race


So, there is a lot to unpack here. A lot of sensitive stuff to unpack. However...it is all illustrated with interactive scatter plots that beautifully illustrate correlation and linear relationships.

I think caution should be used with this example. You can also delve into issues of race. The data demonstrate, time and time again, that if you break up data by ethnicity, regardless of SES, white students perform better than Latino and African American students. There are many historical/SES issues related to underperformance among African-American and Latino students. If you are going to share the data related to these issues, I think that it is worth the time to address these so that racial stereotypes aren't used to explain this data (the authors of the NYTs piece do a good job of doing so).

Comments

  1. Thank you, very informative and I am sharing with my networks

    ReplyDelete

Post a Comment

Popular posts from this blog

Ways to use funny meme scales in your stats classes

Have you ever heard of the theory that there are multiple people worldwide thinking about the same novel thing at the same time? It is the multiple discovery hypothesis of invention . Like, multiple great minds around the world were working on calculus at the same time. Well, I think a bunch of super-duper psychology professors were all thinking about scale memes and pedagogy at the same time. Clearly, this is just as impressive as calculus. Who were some of these great minds? 1) Dr.  Molly Metz maintains a curated list of hilarious "How you doing?" scales.  2) Dr. Esther Lindenström posted about using these scales as student check-ins. 3) I was working on a blog post about using such scales to teach the basics of variables.  So, I decided to create a post about three ways to use these scales in your stats classes:  1) Teaching the basics of variables. 2) Nominal vs. ordinal scales.  3) Daily check-in with your students.  1. Teach your students the basics...

Using pulse rates to determine the scariest of scary movies

  The Science of Scare project, conducted by MoneySuperMarket.com, recorded heart rates in participants watching fifty horror movies to determine the scariest of scary movies. Below is a screenshot of the original variables and data for 12 of the 50 movies provided by MoneySuperMarket.com: https://www.moneysupermarket.com/broadband/features/science-of-scare/ https://www.moneysupermarket.com/broadband/features/science-of-scare/ Here is my version of the data in Excel format . It includes the original data plus four additional columns (so you can run more analyses on the data): -Year of Release -Rotten Tomato rating -Does this movie have a sequel (yes or no)? -Is this movie a sequel (yes or no)? Here are some ways you could use this in class: 1. Correlation : Rotten Tomato rating does not correlate with the overall scare score ( r = 0.13, p = 0.36).   2. Within-subject research design : Baseline, average, and maximum heart rates are reported for each film.   3. ...

If your students get the joke, they get statistics.

Gleaned from multiple sources (FB, Pinterest, Twitter, none of these belong to me, etc.). Remember, if your students can explain why a stats funny is funny, they are demonstrating statistical knowledge. I like to ask students to explain the humor in such examples for extra credit points (see below for an example from my FA14 final exam). Using xkcd.com for bonus points/assessing if students understand that correlation =/= causation What are the numerical thresholds for probability?  How does this refer to alpha? What type of error is being described, Type I or Type II? What measure of central tendency is being described? Dilbert: http://search.dilbert.com/comic/Kill%20Anyone Sampling, CLT http://foulmouthedbaker.com/2013/10/03/graphs-belong-on-cakes/ Because control vs. sample, standard deviations, normal curves. Also,"skewed" pun. If you go to the original website , the story behind this cakes has to do w...