***** To join INSNA, visit http://www.insna.org ***** Hi, I collected a matrix of 86 fashion stores in Berlin. Each has a number of attributes, of which I extract only three (quarter, style and price) First, I am able to determine possible correlations between the attributes (quarter/style, quarter/price, style/price). As a matter of fact, these correlations are hard to find in my data (and in the city itself as well, I find). My goal is to determine what I would call "similarity hierarchies", showing which stores are alike (or "Mainstream, if you will) regarding the three qualities and which are rather "different" (an adjective used quite often in Berlin, especially regarding the "vane" field from which the data stem). Of course the data structure is simple, but maybe useful to make some points (also to those who are relatively new in the field like myself). What I do is this: I treat the matrix as 2-Mode data (86 stores as actors; 10 quarters, 6 styles, 6 price range as events). For instance: Store A, Berlin-Kreuzberg, Casual, affordable Store B, Berlin-Charlottenburg, Classical, high priced. Deriving a 1-mode network allows me find correlational data between the 86 stores. I apply the valued core function, in this way taking into account not only the number of arcs but also the their values. Now there´s the bias question. If I picked only 2 stores in Charlottenburg (out of available 15) with affordable prices, Store B holds a lower valued core measures and might be labelled "less mainstream" than others. In order to normalize the data, I calculate a factor: The inversed sum of (Stores in the quarter + Stores with this particular style + Stores in this particular price range). Multiplying the valued core measure by the normalization factor I obtain a measure of the "similarity" or "uniqueness" of the fashion stores. Any thoughts on this approach are appreciated. I am willing to provide a summary of the answers to the list, if you prefer to answer to me directly. Thanks in advance, Steffen __________________________________________________ Do You Yahoo!? Tired of spam? Yahoo! Mail has the best spam protection around http://mail.yahoo.com _____________________________________________________________________ SOCNET is a service of INSNA, the professional association for social network researchers (http://www.insna.org). To unsubscribe, send an email message to [log in to unmask] containing the line UNSUBSCRIBE SOCNET in the body of the message.