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THAI Técnicas de Investigación Cualitativa y Mixta S5. Análisis de redes sociales y métodos mixtos Alejandra Martínez Monés 28 de septiembre 2010.

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Presentation on theme: "THAI Técnicas de Investigación Cualitativa y Mixta S5. Análisis de redes sociales y métodos mixtos Alejandra Martínez Monés 28 de septiembre 2010."— Presentation transcript:

1 THAI Técnicas de Investigación Cualitativa y Mixta S5. Análisis de redes sociales y métodos mixtos Alejandra Martínez Monés 28 de septiembre 2010

2 2 Index Social Network Analysis An example of a mixed method Tools

3 3 Social network analysis (SNA) Considers relations and mutual effects of actors within groups and organisations – Based on empirical data – Different levels of analysis (individual, sub-group, community) Formal methods, mainly based on graph theory and graph algorithms Fundamentals were presented as „Sociometry“ (Moreno, 1951) – Sociogram – Sociomatrix

4 4 Social network analysis Social network Set of actors (a person, a department, a company) and relationships among them Examples: – “is a friend of” – “is a neighbor of” – “distributes goods to” – “is a member of”

5 5 Social Network Analysis Graphical representation - Sociograms

6 6 Social network analysis Types of networks Mode – One-mode networks: one set of actors – Two-mode networks: two sets of actors. Affiliation networks: relationships between actors and activities Complete vs. egocentric networks

7 7 Social Network Analysis Application areas to computer science Human oriented disciplines – Computer supported collaborative learning (CSCL) – Computer supported cooperative work (CSCW) Network Analysis – Identification of bottlenecks in computer networks – Fault-tolerance and –handling in distributed systems Knowledge Structures – Growing interest in analysis of dynamic knowledge structures, such as Wikipedia

8 8 Social network analysis Some indicators Centrality of actors – Degree based – Proximity / Closeness based – Betweenness based Centralization of a network Prestige of actors – Indegree and proximity Groupings: Cliques, Clusters, Positions

9 9 Social network analysis Indicators - Examples Individual: – Degree centrality: Activity of a node C D (n i ) = d(n i ) =  x i+ – Normalized degree centrality C’ D (n i ) = d(n i ) / (g-1)

10 10 Social network analysis Indicators - Examples Global: – Density: Global activity of the network  = 2 L / g (g -1) L, number of links; g, number of nodes – Degree Centralization: Dependency of a single actor C D =  1<=i<=g [C D (n*) – C D ( n i )] / (g-1) (g-2), C D (n*) = max i C D ( n i )

11 11 Social Network Analysis Sociograms Who is central in this network?

12 12 Social network analysis Visualisation techniques Teacher Group 1 Group 2 Group 3 Intra-group Inter-group  = 24,45% C D = 63,6% C D (x00) = 81,9 % C D (x21) = 9,1 % C D (x32) = 9,1 %

13 13 Social network analysis Data Collection and Transformations Computer-mediated communication – Discussion Forums – Mailinglists – Web 2.0 applications, such as xing, facebook etc. Archival records / artifacts – Bibliographies – Wikis – Versioning systems (e.g. CVS) Automatically processable Potential for transformation between differenet network types

14 14 Social network analysis Limitations of the method Frequently not all of the interaction takes place inside a computer environment – People going for a coffee and discussing their homework Interpretation is hard without „insider knowledge“, i.e. replication is difficult Combination with other methods is useful  „triangulation“

15 15 Index Social Network Analysis An example of a mixed method Tools

16 16 Research context 16 CSCL – Computer Supported Collaborative Learning – Emphasises interactions among learners F2F / Distance / Blended – Technology and models to support the whole lifecycle – Validation in authentic scenarios

17 17 Research Context Evaluation of CSCL situations Overall research question: How to help teachers in monitoring participatory aspects of learning by means of technology? “Validation” research question: How is the evolution of participation structures in a classroom supported by technology? 17

18  Integrate context  Study of real situations  Participants’ point of view  New forms of interaction  Visualisation processes  Participatory aspects  Scalable and efficient processes Research context What we needed … Mixed evaluation method – Ethnographic data sources – Qualitative analysis – Automatically recorded data – Quantitative analysis – Tools – Social network analysis

19 19 InterviewsQuestionnaires Observations Automatic data Phases End of project Throughout the experience Prepara- tion Analysis methods SNA Event logs Socio- metries Socio- metries Face to face interact. Qualitati ve Scheme of categories Quantita tive Data sources After milestones Critics about the project Final Previous concepts (individual) Initial After milestones Daily work Final Conclusions Mixed evaluation method

20 20 Index Social network analysis An example of a mixed method Tools – SAMSA – Quest, Iloca, Nudist

21 21 SAMSA Usage overview Actions Filter Socio- metries Filter f2f interacions Filter SAMSA Datos Configuration parameters SNA Indexes Sociograms Generic representation of actions Generic representation of actions Output files (Ucinet, NetDraw) Logs from CSCL tools

22 22 SAMSA Configuration Dates, actors, objects … Types of relationships Indirect: mediated by objects (shared workspaces) Direct: chats, forums, etc. Person-object: use of resources

23 SAMSA Configuration Example of SAMSA configuration screen

24 24 IDTitle Thread_I D Parent_I D date Student Name Schoo l 21 Solutions of it is Energy Conservation? 302007-04-30A41A 22 I think it is too late for us to solve the global warming 3212007-05-19A5A 19.05.2007 00:00:00 Samsa in use Workshop on interaction analysis approaches (CSCL 2009)

25 Analysis. Thread’s leader Thread 6: thread beginner is the thread leader C Di (A25) = 46,7 C Ci (A25) = 62,5

26 Analysis. Thread’s leader Thread 3 C Di (A41) = 25,0 C Ci (A41) = 52,0 Thread beginner C Di (A5) = 58,3 C Ci (A5) = 70,6 Thread leader

27 Analysis. Thread’s leader Thread 28 – School B

28 Detecting roles Dynamizer C Do (B20) = 16 C Do-sessions (B20) = 30,8% C Di (B20) = 4 (17 th value) Isolated Non-participative

29 29 Other analysis experiences Analysis of collaboration in a clasroom using f2f observations Analysis of collaboration in a course mediated by BSCW – Both in blended and distance settings Analysis of collaboration in a problem-solving CSCL tool 29

30 30 Other SNA Software UCINet – Whole Network Analysis – NetDraw – Visualization – http://www.analytictech.com/downloaduc6.htm Pajek – Network Visualization (Large Networks) – http://pajek.imfm.si/doku.php SAMSA – SNA applied to CSCL scenarios – amartine@infor.uva.es

31 31 Index Social network analysis An example of a mixed method Tools – SAMSA – Quest, Iloca, Nudist

32 Observations SAMSA Teacher / evaluator DL File (UCINET format) CSCL tool QUEST obs2xml Participants Answer to questionnaires Interactions through the computer el2xml Event log Interaction maps NUD*IST New categories Pedagogical tool Evaluation tool or module File STATISTIC PACKAGE Categories Statistic indexes Tools - Quest SNA indexes & sociograms Actions (XML) Designs questionnaires RTF files Tables XML file

33 Observations SAMSA Teacher / evaluator Configuration parameters DL File (UCINET format) CSCL tool QUEST obs2xml Participants Respuestas cuestionarios Interactions through the computer el2xml Event log Interaction maps NUD*IST New categories Pedagogical tool Evaluation tool or module File STATISTIC PACKAGE Categories Statistic indexes Tools – Iloca and Nud*IST SNA indexes & sociograms Actions (XML) iloca

34 34 Tools Qualitative analysis Many tools NVivo (antes Nud*IST) allows to analyse qualitative data: – Textual – Video, audio Supports the researcher in “making sense” of the data.

35 35 Nud*IST Example – Coding data


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