Social Contact Survey II

Continuing from the previous post, I performed some more analyses of the data to better understand this network analysis business.

First, we need an exploration of basic demographics. I was able to get 63 people to participate in the survey. All but 7 of the people included in the survey were female. Most everyone was from the School of Public Health, which was not surprising since nearly all recruitment occurred through the SPH open student email group. Some recruitment was done through the Rackham Merit Fellowship email group. There were 19 people who were not from the School of Public Health. The largest group represented was Epidemiology with 21 people, 9 from HBHE, 7 from EHS and 7 from Biostat. The other 19 were from other departments.  A department/gender breakdown is as follows:

Respondents were overwhelmingly female. The SPH is also overwhelmingly female implying that is participation was a random act, then we would expect to have more women than men by chance alone. However, I am inclined to think that men are less likely to participate at all if for nothing else, men appeared to have fewer social contacts than women over all. The mean number of contacts for women was 20.34, whereas the mean number of contacts for men was a sad 11.7. Women had a max of 86 contacts. Men scored a paltry 26. The following graphic illustrates the sad social state of masculinity at the SPH:

So basically, if you want to spread some information or perhaps a fun infectious disease, you would have better success to target women rather than men. Specifially, you would want to choose the person who had 86 contacts. According to reliable information, this individual was at a professional conference, and had a reportedly inordinate number of contacts on that particular day.

Using UCINET 6, we were able to calculate the density of the network. 86% of possible connections between people are present within the data. There were 2200 possible connections between individuals within this particular group of people, suggesting that this is a tightly knit social group. Again, this is hardly surprising since participants were recruited out of an already established institutional context. All members of the network were reachable by any other member of the network, indicating that there were no isolates in the entire group. Every one of these 63 people were connected with one another through at least one person in the network.

An analysis of geodesic connections between individuals indicated that the mean shortest path for everyone on the list was 1, that is, it only takes (on average) a single step to get from person A to person B. However, even though average distances may be short, there may be a number of paths to get from person A to person B. If person A has a small number of possible connections to person B, then we would assume that person A and person B are somewhat separated from one another in terms of the level of social connectedness. If person A has many shortest possible connections to person B, then we would assume that person A and person B are highly connected within the social space. Here we see a break down of the mean number of  possible distances between individuals by department:

Overall, the mean number of possible paths between individuals was 1.58. However, we can see that some departments are more connected than others within the group, namely Epid and Biostat, whereas other groups have low number of possible connections, indicating a level of seperateness from the individuals within the survey group. These results were not surprising in that I maintain strong connections to individuals within Biostat and Epidemiology. While I’d like to make some great conclusions about this measure, I can only conclude that my having done recruiting in groups that I am already connected with biases the results somewhat.

This measure is perhaps the most important of all the measures considered in this project. The number of possible paths between people indicate the number of possible paths that an infection may pass through to get from one person to another. Although this group appears to be fairly tightly knit, infections may only have a limited number of possible paths to get from one infected host to one noninfected host.

more later, back to important stuff….

About Pete Larson

Researcher at the University of Michigan Institute for Social Research. Lecturer in the University of Michigan School of Public Health and at the University of Massachusetts Amherst. I do epidemiology, public health, GIS, health disparities and environmental justice. I also do music and weird stuff.

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