asa section memberships by race

The American Sociological Association just released section membership data for 2017. In addition to the usual breakdown by gender, ASA has also released section membership data by race (Excel file here). These data come from ASA’s racial demographic question, that looks as follows:

ASA racial demographic question as of September 10, 2018.

ASA translates this question into 8 mutually exclusive categories: Black or African American, Asian or Asian American, Hispanic/Latino(a), Native, White, Other, Multiple, Missing.

For Section memberships, ASA also does the following: “when the number of members in a category is less than five, they were counted as “missing.”” Presumably this is some kind of privacy measure, though I’m not entirely sure. Whatever the rationale, the result ends up being that almost every section has a listed “0” for Native American membership, and some smaller sections have zeroes for other categories as well. These are not necessarily “true” zeroes, and they also tend to inflate the “Missing” category. Below, I present super rough graphs of the section membership breakdowns by race for White, Black or African American, Hispanic/Latino(a), and Asian or Asian American. There are lots of ways one could usefully visualize these data depending on the questions you were asking of them; I encourage you to download the spreadsheet and tool around yourself. The section names are as given in the ASA spreadsheet (plus “Total ASA Membership” which refers to all ASA members).

EDIT: I added a vertical line at the value for ASA overall, per David Brady’s suggestion on twitter.














Author: Dan Hirschman

I am a sociologist interested in the use of numbers in organizations, markets, and policy. For more info, see here.

6 thoughts on “asa section memberships by race”

  1. Thanks, Dan.

    It’s tricky because people can belong to more than one section. So every section could have below/above average minority representation. Also means you can’t calculate segregation scores (though I probably did with the old gender data, just thought of this).


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