Network analysis of the Jose Celestino Mutis Universities collaboration network

Transcripción

1 Network analysis of the Jose Celestino Mutis Universities collaboration network Fifth Annual French Complex Systems Summer School Paris, France 15th July, 2011 José David Meisel Felipe Montes

2 José Celestino Mutis Network Association of universities Objective: to promote the quality and development of higher education Plans Programs Projects around teaching Research and management

3 General Objectives Analyze the organizational structure of the MUTIS universities (units) collaborative network. Analyze the influence of type of units (attribute) and structural predictor (contact frequency, G.W. Terms) in the integration of the units in the network.

4 Methods: Data collection Survey: 234 unit directors in 8 universities (6 in Colombia,1 in USA, 1 in Mexico) and the direction of Mutis network. Organization characteristics Relationships among the university units Barriers to partnering Respondents were chosen by considering their position in the university unit.

5 Methods: Measures Relationship: Existence of links between the university units(dichotomic variable). Integration: described the current relationship. Unlinked (do not worked at all), communication (share information only), cooperation (work together as an informal work group to achieve common goals), collaboration (work together as a formal team to achieve common goals) and partnership (work together as a formal team across multiple projects to achieve common goals). Contact (frequency): no contact (0), >semi-annual (1), trimesterly (2), monthly (3), weekly (4), daily (5). Organizational attributes: Type of unit: Direction (1), Administrative (2), Academic (3), Foreign affairs (4)

6 Data management and analysis Visual and Descriptive analysis Based in the relationships sociometric network different measures were analyzed: centrality (degree, betweeness and closeness) and density. Identify the more important actors in the network. Exponential Random Graph Model (ERGM) The parameters of (ERGM) were estimated using Markov chain Monte Carlo (MCMC) methods. In particular, the package allows users to obtain approximate (or, in some cases, exact) maximum likelihood estimates (MLEs) The ERGM model allows prediction of a collaborative tie between 2 organizations based on organization and network characteristics.

7 Visual and Descriptive Analysis

8 Relationship Network Type Directive Administrative Academic Foreign affairs office Color Degree=Size of node Density = 0.004

9 Relationship Network Daily relationship Master Programs Type Directive Administrative Academic Foreign affairs office Color Tompkins Cortland Community College Virtual Education Programs

10 Relationship Network Weekly relationship Type Directive Administrative Academic Foreign affairs office Color PhD program Weekly Call to directors

11 Relationship Network Monthly relationship internships Type Directive Administrative Academic Foreign affairs office Color Project Contracts supervision

12 Relationship Network Trimesterly relationship Directors meeting Looking for new research projects? Type Directive Administrative Academic Foreign affairs office Color

13 Relationship Network > semiannual relationship Type Directive Administrative Academic Foreign affairs office Color Degree=Size of node Accountability Databases and books transfer

14 Relationship Network Type Directive Administrative Academic Foreign affairs office Color Degree=Size of node Density = Rectories

15 Network Measures N = 312 E = 387 In-Degree Out-Degree Closeness Betweeness

16 Centrality and Prestige Centrality OutDegree Closeness Betweenness InDegree Rectoría UNIMINUTO Biblioteca UI Rectoría UNIMINUTO Rectoría UNIMINUTO Vicerrectoria Adm UNAB Auditoria UI Proy interinst Doctorado UI Rectoría UAO Vicep Asociada Prog Institucionales TC3 IEVD UNIMINUTO Prog Ing Sistemas UAM Posgrados ITESM Posgrados UNAB CERES UNIMINUTO Dir Investigaciones UAO Dir Financiera UNAB Fac Ingenieria UI Coor educ ejec y proy especiales ITESM Fac Ingenieria UTB Rectoría UNAB Contabilidad UI Cartera UI UNAB Virtual Rectoria UI Type Directive Administrative Academic Foreign affairs office Fac Ingenieria UTB Prog Psicología UNAB Prog Ing sistemas UI Rectoría UTB Prestige Prog Ing sistemas UI Centro de Idiomas UI Fac Ing Físico mecánicas UNAB Rectoría UAM Vicerrectoría Gen Adm y Financiera Dir Tec Monterrey sede Bogotá ITESM Dir Financiera UTB UNIMINUTO Relaciones Inter TC3 VicerRectoría Gen Académica Dir. Red Mutis Prog Ing Industrial UI Prog Ing Sistemas UNAB UNIMINUTO Coor académica posgrados ITESM Prog Ing Mecanica UI Fac Ingenieria UI VicerRectoría Gen Adm y Financiera UNIMINUTO Prog Comunicación Social UI Prog Ing Industrial UNIMINUTO Dir Investigacion e Innovacion UTB Rectoría ITESM Rectoría UAM Prog Contaduria Pub UI Dir investigacion UNAB Rectoría TC3 Vicerrectoría Gen Adm y Financiera UNIMINUTO Facturación y cartera UTB Posgrados UNAB UTB virtual Relaciones Inter UNAB Fac Ciencias Econ y Adm UTB Dir adminsitrativa y financiera UAM Relaciones Inter UNIMINUTO Fac Estudios Tï ½cnicos y Dir investigacion UNAB Tecnológicos UNAB Dir Internacionalizacion UTB UNAB Virtual Relaciones Inter UAO Prog Adm Negocios Inter UI IEVD UNIMINUTO Prog Ing Sistemas UNAB Fac Humanidades UTB Fac Ciencias Naturales UI Rectoría UTB Dir Internacionalizacion UTB Vicerrectoria UAM Dir Desarrollo Empre y Prot Social UTB Relaciones Inter UI Prog Ing Sistemas UAM Rectoria UI Comunicaciones_Jefe UTB Prog Ing Mecatrónica UNAB Dir adm y financiera ITESM Rectorïía UTB Edu Continuada UAM Facultad de Ingeniería UNIMINUTO Dir Administrativa UAO Color

17 Measures (Integration) The collaboration measure (the primary outcome) was adapted from the work of Harris et al. (2008) and Slonim et al. (2007).

18 Integration Network Communication (info only) Accountability (semiannual) Internships (monthly) Type Directive Administrative Academic Foreign affairs office Color Degree=Size of node Contracts supervision (monthly)

19 Integration Network Cooperation (informal group) Project (monthly) PhD program (weekly, trimestrely) Type Directive Administrative Academic Foreign affairs office Color Degree=Size of node

20 Integration Network Collaboration (formal team) Type Directive Administrative Academic Foreign affairs office Color Degree=Size of node

21 Integration Network Partnerships (multiple projects) Virtual education (Daily) Type Directive Administrative Academic Foreign affairs office Color Degree=Size of node Accountability (Semiannual) Master Program (Daily) PhD program (weekly)

22 Descriptive Analysis Contact Integration 43% 6% 16% 15% 20% Daily Weekly Monthly Trimesterly semi-annually or more 33% 16% 26% 25% Communication Cooperation Collaboration Partnership

23 Stochastic Model

24 Exponential Random Graph Model This model helped us address the question: What is the probability of a tie to exist, given a set of conditions that we can specify? Hypotheses Units that work with other similar (e.g. directives or academics) are more likely to form a integration tie with one another. The higher the frequency of contacts, the higher the likelihood of integration.

25 ERGM: Predictors used Attributes: Sector or group in which was classified the unit (Type of unit). Structural predictors: Frequency of contact (Contact) Geometrically weighted terms GWDegree (geometrically weighted degree statistic). GWESP (geometrically weighted edgewise shared partner) GWDSP (geometrically weighted dyad-wise shared partner)

26 ERGM: Results Coefficient Logit Model 1: Null model Std. Error Odds Model 2: attribute predictors Logit Std. Error Model 3: Attribute and structural predictors Odds Logit Std. Error Odds Edges -6,089*** , *** ,0014-7,771*** 0,586 0,0004 Type: Directive *** ,9734-0,974** 0,583 0,2742 Type: Administrative *** ,7273-0,927** 0,329 0,2836 Type: Academic * ,5985-0,25 0,489 0,4378 Type: Foreign affairs *** ,8367-1,144** ,2417 Contact 9,06*** 0,517 0,9999 GWIdegree 0,396 0,484 0,6047 GWOdegree -3,28*** 0,105 0,0361 GWESP (Clustering) 0,268*** 0,069 0,5666 GWDSP (Structural equivalence) Model Fit Log Likelihood AIC BIC Log Likelihood AIC BIC -0,038** 0,015 0,4905 Log Likelihood ,4 2918,8 2966,2-280,91 581,84 676,67 * P < 0.05; ** P < 0.01; *** P < AIC BIC

27 ERGM: Structural Model Goodness of fit

28 Discussion Directives units are the more important nodes to achieve Mutis objectives. Units are more likely to be more integrated within the same types when type is the only attribute considered. Units that contact one another more often were more likely to be more integrated. Units that are more likely to be integrated tend to form cluster with other units. There isn t a clear tendency for units that have higher outdegree to be more integrated. A pair of units in the network doesn t tend to share ties with the same set of partners.

29 Merci!

30 References Brownson, R., Parra, D., Dauti, M., Harris, J., Hallal, P., Hoehner, C., Carvalho, D., Reis, R., Ramos, L., Ribeiro, I., Soares, J. and Pratt, M. (2010). Assembling the puzzle for promoting physical activity in Brazil: a social network analysis. Proceso de publicación. Journal of Physical Activity and Health, 2010, 7(Suppl 2), S242- S253. Hunter, D. R., Goodreau, S. M., & Handcock, M. S. (2005). Goodness of Fit of Social Network Models 1. Recuperado a partir de Hunter, D. R., Handcock, M. S., Butts, C. T., Goodreau, S. M., & Morris, M. (2008). ergm: A Package to Fit, Simulate and Diagnose Exponential-Family Models for Networks. Journal Of Statistical Software, 24(3), nihpa Robins, G., Pattison, Pip, Kalish, Y., & Lusher, D. (2007). An introduction to exponential random graph (p*) models for social networks. Social Networks, 29(2), doi: /j.socnet Robins, G., Snijders, T., Wang, P., Handcock, M., & Pattison, P. (2007). Recent developments in exponential random graph (p) models for social networks. Social Networks, 29(2), doi: /j.socnet Hunter, D. R., Goodreau, S. M., & Handcock, M. S. (2005). Goodness of Fit of Social Network Models 1. Recuperado a partir de Robins, G., Pattison, Pip, Kalish, Y., & Lusher, D. (2007). An introduction to exponential random graph (p*) models for social networks. Social Networks, 29(2), doi: /j.socnet Robins, G., Snijders, T., Wang, P., Handcock, M., & Pattison, P. (2007). Recent developments in exponential random graph (p) models for social networks. Social Networks, 29(2), doi: /j.socnet