2015-09-01cmonthEurope/Londontag:micratag:social_sciences2015-08-012015-10-01British Summer TimeBST01:003600000Greenwich Mean TimeGMT00:000even:z8-idsnqulz-7ebb73The Manchester Business School Organizational Ethnography Network LaunchKeynote addresses:
Tony Watson - Emeritus Professor of Sociology, Work and Organisations, Nottingham University Business School
Fabian Muniesa - Senior Researcher (Professor), Centre de Sociologie de l’Innovation, Mines ParisTech, Paris
Dr Alexandra Ouroussoff - Visiting Research Fellow, Brunel University, London
confirmedHigher Education2015-09-1611:00:002015-09-1616:30:002015-09-1612:00:002015-09-1617:30:002015-09-1612:00:002015-09-1617:30:00D. Damian O'Dohertydamian.odoherty@mbs.ac.uk0161 306 3489yi3-i260v82f-hcq1eghum-columba-imageii4-i260v82i-15un76180180B3Manchester Business School EastManchesterUnited KingdomGBGBRref:T1265POINT(53.468864 -2.235535)15even:i2e-ielh9vm7-apvq68CMIST afternoon seminar: How many classes? Statistical modelling of a social network and a terrorist network, with a latent class model and Bayesian model comparisons
This talk discusses the assessment of the number of classes in a social network, through the latent class model extension of the exponential random graph model. The assessment uses a new Bayesian method of model comparisons, based on the posterior distribution of the deviance for each of the competing models.
The approach is applied to a well-known social network in which the number of classes is known a priori, and to the Noordin Top terrorist network, analysed at length in the book by Everton (2012).
The performance of the model comparison method is illustrated with simulations from single population models and normal mixture models.
This work is joint with Duy Vu and Brian Francis.
References
Aitkin, M. (2010) Statistical Inference: an Integrated Bayesian/Likelihood Approach. CRC Press.
Aitkin, M., Vu, D. and Francis, B. (2014) Statistical modelling of the group structure of social networks. Social Networks 38, 74-87.
Aitkin, M., Vu, D. and Francis, B. (2015) A new Bayesian approach for determining the number of components in a finite mixture. Metron (to appear).
DOI :10.1007/s40300-015-0068-1
Aitkin, M., Vu, D. and Francis, B. (2015) Statistical modelling of a terrorist network. Submitted.
Everton, S.F. (2012) Disrupting Dark Networks. Cambridge University Press.
No registration needed. All Welcome. Tea & coffee provided from 3.45confirmedHigher Education2015-09-2215:00:002015-09-2216:30:002015-09-2216:00:002015-09-2217:30:002015-09-2216:00:002015-09-2217:30:00Dr Sarah King-Helesarah.king-hele@manchester.ac.uk0161 275 0279s3z-i0npzflv-p5dtx3HBS180x180Humanities Bridgeford StreetHBS buildingn40-i0npzfm1-5bna1z2002002.07Humanities Bridgeford StreetManchesterUnited KingdomGBGBRref:T1265POINT(53.466465 -2.236522)15Professorial Fellow School of Mathematics and Statistics, University of Melbournehttp://www.ms.unimelb.edu.au/~maitkin/Prof. Murray Aitkineven:o22-ieldm1bg-dyzhk6Social Anthropology Seminar - The experience of the Bush : Hunters and social aesthetics in Burkina FasoSocial Anthropology Seminar
Monday, 28 September 2015
4:15-6:00pm
(Tea and Coffee will be available outside the boardroom at 4:00pm)
Lorenzo Ferrarini, University of Manchester
The experience of the Bush : Hunters and social aesthetics in Burkina Faso
This paper deals with the role of sensory experience for the initiated donso hunters of Western Burkina Faso. It is an enquiry into their perceptual skills and suggests that these form the conditions for the donso to relate in a specific way to their environment, and have a role in their social formation. This subject also raises important methodological questions: what kind of access can ethnographers have to people’s embodied experience? What is the role of enskilment and apprenticeship versus the verbal transmission of information? What are the limits of a sensory ethnography? I propose answers based on the one hand on my practice of hunting among the donso, and on the other on an ecological approach to perception.
2.016/2.017, Second Floor Boardroom, Arthur Lewis Building
ALL WELCOME!confirmedHigher Education2015-09-2815:00:002015-09-2817:00:002015-09-2816:00:002015-09-2818:00:002015-09-2816:00:002015-09-2818:00:00Val Lenfernaval.lenferna@manchester.ac.uk0161 275 4883 / 7058kgv-i1wb6amr-gfg9trArthur Lewis Buildinghgw-i1wb6amw-g1j3b11801802.016 / 2.017 - 2nd Floor BoardroomArthur Lewis BuildingManchesterUnited KingdomGBGBRref:T1265POINT(53.466702 -2.235739)15SpeakerUniversity of ManchesterLorenzo Ferrarinieven:o47-iev86nqs-yxxq00CMIST afternoon seminar: When “contexts” are geographical areas, is multilevel model still a good choice to model hierarchical data, or a new approach is needed?Abstract:
It is very common that our research uses hierarchical data where the higher level units or “contexts” are defined as geographical areas—for example, individuals nest into census units or houses into districts. In such situations, we need to think about three questions:
(1) are lower-level units correlated with each other if they are in the same “context” or group?
(2) are the interactions or correlations among lower-level units strictly bounded within “contexts” or groups?
(3) are contexts themselves independent of each other? The first effect is referred to as a vertical group dependence effect. The latter two can be considered as horizontal dependence effects at each level of the data hierarchy.
If the last two dependence effects were suspected, standard multilevel models would not be a good modelling choice. Instead, an integrated spatial and multilevel model could be used to deal with the vertical and horizontal dependence simultaneously.
confirmedHigher Education2015-09-2915:00:002015-09-2916:30:002015-09-2916:00:002015-09-2917:30:002015-09-2916:00:002015-09-2917:30:00Sarah King-Helesarah.king-hele@manchester.ac.uk0161 275 0279l41-i0nq3dp2-j55cslHBS2180x180Exterior of Humanities Bridgeford StreetThe HBS builidingg42-i0nq3dp7-xj0dgr1801802.07Humanities Bridgeford StreetManchesterUnited KingdomGBGBRref:T1265POINT(53.466465 -2.236522)15Research AssociateSheffield Methods Institute, the University of SheffieldDr Guanpeng Dong