***** To join INSNA, visit http://www.insna.org ***** Generator Microsoft Word 11 (filtered medium) Dear Social Networkers, I am a social statistician based in the Centre for Census and Survey Research (CCSR), School of Social Sciences, University of Manchester, UK. CCSR has four fully funded PhD studentships available for a September 2007 start. Funding is from the Economic and Social Research Council (ESRC). I would be particularly interested in supervising PhDs that involve some aspect of Social Network Analysis or 2. Multilevel modelling. I have listed some possible social network analysis topics below, with references. The multilevel modelling topics can be found via the CCSR website. Please contact me here: [log in to unmask] if you are interested in these possibilities or visit www.ccsr.ac.uk for more information. PhD topics in social statistics - Autumn 2007 start Mark Tranmer, School of Social Sciences, University of Manchester. Email: [log in to unmask] I am interested in hearing from you if you are interested in pursuing a PhD that involves some aspect of social statistics - design or analysis. Two themes of particular interest to me - 1. Social Network Analysis and 2. Multilevel Modelling - are briefly described below. Within each theme I have specified research topics I would be especially interested in supervising. I have provided useful references for these. Social Network Analysis If you are interested in pursuing PhD research that involves any aspect of Social Network Analysis, I am interested to hear from you. Useful references on Social Network Analysis include: Scott (2000) ' Social Network Analysis' (Sage publications); Wasserman and Faust (1994) ' Social Network Analysis' (Cambridge University press) - especially the early chapters; Carrington, Scott, Wasserman (2005) ' Methods and Models for Social Networks' (Cambridge University press) - especially the introductory chapters. Other useful information on social network analysis can be found on www.insna.org and the links from that site. Two aspects of Social Network Analysis of particular interest to me are as follows: I: Statistical models for social networks One of the reasons for modelling a social network is to detect or to quantify the extent of reciprocation and clustering that may occur between nodes and to see how this might differ from what would be expected from a random grouping of nodes. Garry Robins et al have developed p* models for the statistical analysis of social networks and pnet software to fit these models. An introductory paper on p* models can be downloaded as a .pdf here http://www.sna.unimelb.edu.au/publications/ERGM11.1.pdf and more information about p* models and the software to fit them is available here: http://www.sna.unimelb.edu.au . Other software for modelling social network data includes Siena/Stocnet and (occasionally) multilevel modelling software such as MLwiN. See the website of Tom Snijders for more information on these: http://stat.gamma.rug.nl/ I am interested in phd topics that involve the modelling of networks. Such topics could involve the analysis of real or simulated data (or both). II: Social networks in geographical space. Any individual has demographic and socio-economic characteristics and individuals can be grouped, geographically (e.g. into neighbourhoods) and/or by their social network, which may be quite different to the geographical grouping. Methods to disentangle geographical and network effects on socio-economic outcomes such as health or employment are therefore of interest in order to understand the nature and extent of variations in these outcomes at the individual and group levels. This research may be developed using real or simulated data or both. I am interested to discuss any proposals relating to this theme. Best regards, Mark. _____________________________________________________________________ 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.