Monday, September 26, 2016

AMS/ASL Meetings, 1995-2016

Today I'm going to try to answer the same question for the AMS/ASL meetings that I answered on Saturday for the Annual Meetings: how has the representation of women among speakers changed over time? I'm considering the same factors:
  • Time span: The AMS/ASL meeting has only been an annual occurrence since 1995, so I'll take that as my starting year.
  • Types of talks: The only talks I know of at AMS/ASL meetings since 1995 are plenaries, so that's easy! Again, I'm including speakers who were invited to give a talk, accepted the invitation, and then couldn't attend since the goal is to study who is invited, not who is invited and not prevented from attending.
  • Numbers v. proportions: Once again, I'm going to argue for studying the proportion rather than the number. The 2 female speakers out of 10 in 2004 and the 2 female speakers out of 6 in 2009 don't demonstrate the same level of representation.
Here's my first stab at a model: a simple linear regression. Take a look at the scatterplot of the proportions of female speakers at a meeting versus the year of the meeting with the regression line added.
Note that I'm representing this one with a graph where the axis for proportions goes from 0 to 1 instead of 0 to 0.5 like I did last time: I want to remind everyone that the proportions never get close to their possible maximum! The equation of the regression line is
proportion = 0.004814(year)-9.501405.
I'm not going to mention any predictions one can make from this equation: the R2 values are dismally low. In fact, the adjusted R2 is negative (-0.002544). When I looked at the residuals, the first two flagged years were 1995 and 1996—the first two years I considered, in which 28.6% of the speakers were women (no other year had a percentage that high until 2007). The other two were 2012 and 2013. 2012 was the year with the highest-ever level of representation (42.9%), and there were no female speakers in 2013. These percentages occurring in consecutive years does not make a good linear model likely!

Looking at a LOWESS (locally weighted scatterplot smoothing) plot helps a lot in making sense of this data: representation was not horrible in the first two years, it tapered off to almost nothing for about a decade, and since then, there have been some better years and some very bad ones.

Summary: I'm not going to comment on the linear regression because that doesn't seem to be a reasonable model at all. Representation of women fell and then stayed extremely low for about a decade before beginning to increase again, but even the improvement since 2007 isn't stable: in six of those years, representation has been at least 28.6%, but it has dipped down to 0% in three rather regularly-spaced years.

Next up: the APA/ASL meetings!

Saturday, September 24, 2016

ASL Annual Meetings, 1989-2016

The question I've been asked most since my first post is whether women have become better represented among ASL speakers over time. I'll try to answer it today for the Annual Meetings. Here are the factors I'm taking into account:
  • Time span: 1989 begins the era in which the Annual Meetings occur independently instead of in conjunction with either the APA or AMS, so I'm starting then.
  • Types of talks: In that time span, the Annual Meetings have had plenaries, tutorials, Gödel lectures, retiring Presidential addresses, symposia, and panels. I'm restricting this analysis to plenaries because those occur every year (unlike tutorials, symposia, and panels) and because there is more than one of them each year (unlike Gödel lectures and retiring Presidential addresses). I'm also leaving off speakers in special sessions for now: I'd like to analyze speakers invited by a "universal" program committee separately from speakers invited by other members of their subfield.

    There are a few instances of speakers who were unable to deliver their talks. Since the goal is to study who is invited, I'm including all speakers who accepted an invitation. 

  • Numbers v. proportions: It makes the most sense to me to consider proportions. In 1994, 2 women and 11 men spoke, and in 2015, 2 women and 5 men spoke. Although the number of female speakers is the same, women were certainly not represented equally well.
My first step was to construct a basic regression. Here's a scatterplot of the proportions of female speakers at a meeting versus the year of the meeting complete with regression line.
The equation of the regression line is
proportion = .009357(year) - 18.617671.
This predicts that -0.7% of the speakers in 1989 and that 24.6% in 2016 were female: low on both counts, but the model isn't an obvious bad fit for the data. So how good is it?

The R2 isn't too shabby but isn't great either: it's about 0.40. When I examined the residuals, they mostly supported a linear regression, but a few years were flagged as unusual: 2016, the only year in which we've had parity, 2009, the year with the second-highest proportion (33.33%), and 1999, the only year before 2009 in which the percentage of female speakers went over 20% (25%). In short, the unusual years are the years with a relatively high proportion of female speakers.

My next step was to apply a locally weighted scatterplot smoothing (LOWESS). Here's the result:

It seems like there are two different eras: 1989–1998, when the percentage of female speakers was often 0 and below 20% even when it was positive, and 1999–2016, when the percentage of female speakers was generally at least 10% and ranged up to 50%. I have no idea what brought about the change—Richard Zach informs me that the ASL statement on women in logic wasn't adopted until 2012.

Summary: The basic linear regression tells us that the proportion of female speakers is increasing by just under 1% each year. The LOWESS model suggests it's increasing faster than that now, but the trouble with LOWESS is that it lets you see trends more clearly but doesn't let you quantify them easily. It looks to me like there are a few very good years with relatively high proportions of women improving the predictions while the rest have much lower proportions (and very consistent lower proportions, too—look at those flat sections in the scatterplot!).

Next up: the AMS/ASL meetings! Let me know what other analyses you'd like to see.

Friday, September 16, 2016

Gender ratios of speakers at ASL meetings

Hello, everyone! I'm Johanna Franklin, an assistant professor in the math department at Hofstra UniversityThank you, Valeria, for inviting me to write a guest post.

I've put together a fairly complete spreadsheet of people who have been invited to speak at three of the four varieties of ASL meetings: the Annual Meetings, the AMS/ASL meetings, and the APA/ASL meetings. This spreadsheet contains the name, sex, and role of every invited speaker for

  • the Annual Meetings back to 1989 and
  • the AMS/ASL meetings back to 1988 with the exceptions of 1989, 1991, 1992, and 1994.

It's difficult to tell what data I'm missing for the APA/ASL meetings, since the timing of these meetings has historically not been as consistent: it seems like there were both winter and spring meetings in some years but not others, and I don't know which years were which. 

My next post will be a more detailed analysis of the data I have, but here are some basic statistics for the plenaries given at the Annual Meetings and AMS/ASL meetings.

Annual Meetings

  • Out of 268 plenaries in 28 years, 238 (89%) were given by men and 30 (11%) by women.
  • The smallest number of male speakers was 3 (in 2016, the only time the ratio has ever been 1:1); the largest was 13 (in 1997, when there were no plenaries by women).
  • The smallest number of female speakers was 0 (in 9 different years and as recently as 2003); the largest was 3 (which has happened twice: 2016 and 2009).
So the highest number of plenaries given by women in a year matches the lowest number given by men.

AMS/ASL meetings

  • Out of 180 plenaries in 25 years, 155 (86%) were given by men and 25 (14%) by women.
  • The smallest number of male speakers was 3 (in 1990, when there were only 3 plenaries!); the largest was 12 (in 2000, when there were no plenaries by women).
  • The smallest number of female speakers was 0 (in 11 different years and as recently as 2013); the largest was 3 (which has happened once: in 2012, when the most even male:female ratio was achieved).

Once again, the highest number of plenaries given by women in a year matches the lowest number given by men.

Please help me fill in the missing information and let me know what kind of statistical analysis you'd like to see!