Max Forte is absolutely right that using gini coefficients this way would require that the researcher make sensible judgments about attribution and content value. To really know your subjects and their relationship a researcher probably would need deep experience with their interactions. However, the graph could be composed even if the researcher had no qualitative experience with the group, using number of people on one axis and number of words, number of lines, number of sentences, number of posts (as Hendricksen does). These things easy to count no need for qualitative judgments. Probably more interesting would be to graph number of people on one axis and ‘good ideas generated’, ‘smart repartee’ or ‘meaningful engagement’ on the other axis. These things would require deep knowledge of the group and a qualitative researcher’s instinct for how to define, measure and attribute a good contribution. A qualitative researcher’s insight would also be needed to figure out any causal connection there might be between being a member of a social elite, writing a lot of text, and generating good ideas. The coding would probably have to be done by hand. I interpreted the original post to be a query about how to measure any deviance from a perfectly egalitarian group, where all the respondents participate equally, generating the same amount of text, ideas, or hot air. Mathematically we can know what that perfect distribution is, so my gini idea is just a way of calculating deviation from that ideal. I absolutely would not argue that ginis can be meaningful alone, for the reasons Forte offers. I like the qualitative approach that then provides some quantitative context to show how qualitative findings are transportable. And anyway multi method triangulation is always best. I originally suggested that the number would be most useful in a comparative context. Lakhani agrees, and even if we did ginis for AIR it would be interesting to compare ginis on different topics or variation over time. He also points out that there would be noise inany database of content, but there must be a way of doing somekind of confidence interval, error term on the datapoints, or significance tests on the gini itself. My math skills end here, so if anyone can develop a significance test for a quantitative measure of deviation from a theoretically perfect distribution of content/ideas in a group of people please post it! But please don’t do it to the AIR list too inside baseball I think. p. Philip N. Howard Assistant Professor Department of Communication University of Washington http://faculty.washington.edu/pnhoward/