Additional (older) #SAchat data: Participation, Geography, and Gender

In a comment to my previous post sharing some of my thoughts about #sachat in advance of their “State of #SAchat” discussion tomorrow, Gary Honickel asked about demographics of #sachat participants.  In our forthcoming chapter (I’m not trying to advertise it – honest! Just trying to explain why I have all of this information. I’m a researcher, not a stalker!), Laura Pasquini and I analyze #sachat and we include some information about the participants.  We didn’t include the specific information Gary asked about: gender and geographic location of participants.  But I did collect that data and although it’s for three sessions that occurred last year maybe this is still useful or helpful.  My sense is that these things haven’t changed much in the past year.

Keep in mind that these data come from three 2011 chat sessions:

The thing that jumps out at me in the table above are the average number of messages per participant and the standard deviation of that number.  There is immense variance in the number of messages posted by each participant and that makes me wonder about the pattern(s) of participation for each session.  The histogram below showing how many people posted a particular number of messages in each chat helps us understand these numbers (click on it to view a larger version).

This histogram is a classic “long tail” distribution, showing us that most participants in these three #sachat sessions posted very few messages and only a handful of participants posted many messages; the participant with the most messages is, of course, the moderator.  This is a very typical situation and an unsurprising finding.

This gives us a broad understanding of #sachat participation but let’s look a bit deeper and explore two different ways of classifying participants: gender and geography. First, a few words of caution: these data were inferred from the Twitter profiles and messages posted by these participants.  Geography was the easier datum to capture for each participant as most participants associated themselves with a particular college or university, either in their profile or in their introduction during one or more #sachat sessions.  Gender was much more difficult and I present these data with trepidation because there was a significant amount of guesswork involved in classifying participants as male or female.  If this were anything more than a one-off blog post or if gender were a central concern for this or any other analysis, I wouldn’t even share or use these data because inferring gender from name and photo obviously lacks rigor.

This chart shows the geographic locations of the participants in these three #sachat sessions (I used the U.S. Census geographic regions to aggregate the data).  Nothing surprising here.  #SAchat is indeed U.S.-dominated but even that isn’t a surprise.  Nothing particularly interesting is discovered if you look at the number of messages posted by participants from each region; the numbers get very small very quickly when slicing the data this many ways so it’s not worth trying to display.

What about gender?  For at least these three sessions, the gender breakdown seems to be about even.  Like geographic region, nothing terribly interesting happens if you slice these numbers in different ways.

So what do we make of all of this?  I think it shows that – for these three sessions – there was considerable diversity among #SAchat participants, at least in two ways we can measure. Of course, these are coarse (and in the case of gender, potentially problematic) measures and there are many other ways in which we might examine the makeup and diversity of this population.  Functional area and role (student, entry-level professional, faculty, etc.) are two measures that jump to mind as interesting and useful.  (Incidentally, I tried to classify participants using those two measures in a previous study; it was difficult, time-consuming, and very incomplete since those data are not spontaneously volunteered by all participants.)

Are #sachat participants diverse enough?  I don’t know.  How do we define “diverse enough?”  Should we be concerned about how well the #sachat population matches the larger student affairs population?  A quick glance shows some alignment between these populations but I have not done any definitive work in this area, partially because it’s very hard to obtain data about the larger student affairs population.

Of course, all of this does not and can not include anything about lurkers.  I agree that there is value in #sachat even for those who do not directly or visibly participate but we’d have to make a concerted effort to identify those people if we want to know anything about them.

I hope this is helpful or interesting!  I wish I had more up-to-date data but I don’t.  I’m job searching, working, and trying to finish a dissertation so I don’t have time or plans to gather additional data right now.  This is data that I had at hand and I am happy to share it in the hopes that it’s useful for someone.