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Técnicas y Herramientas de Apoyo a la investigación (THA) II. Técnicas de Investigación Cualitativa Sesión 3. Análisis de redes sociales Alejandra Martínez.

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Presentation on theme: "Técnicas y Herramientas de Apoyo a la investigación (THA) II. Técnicas de Investigación Cualitativa Sesión 3. Análisis de redes sociales Alejandra Martínez."— Presentation transcript:

1 Técnicas y Herramientas de Apoyo a la investigación (THA) II. Técnicas de Investigación Cualitativa Sesión 3. Análisis de redes sociales Alejandra Martínez Monés Noviembre 2009

2 2 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

3 3 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

4 4 Social Network Analysis Graphical representation - Sociograms

5 5 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

6 6 Application areas of SNA for 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

7 7 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

8 8 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)

9 9 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 )

10 10 Social Network Analysis Sociograms Who is central in this network?

11 11 SNA – 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 %

12 12 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

13 13 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

14 14 Indirect networks through BSCW Subproject 2Final project Density =17,54% = 35,48% CentralizationC D =87,03%C D = 68,21% Example – AO case study

15 15 SNA Software UCINet – Whole Network Analysis – NetDraw – Visualization – Pajek – Network Visualization (Large Networks) – SAMSA – SNA applied to CSCL scenarios –

16 Teacher / evaluator CSCL tool Interaction through the computer Participants SAMSA Interaction maps Configuration parameters Actions (Generic XML format) Event logs QUEST Answers to questionnaires SNA indexes Sociograms Other SNA tools (UCINET NETDRAW) DL file Sociometries SNA tools - SAMSA

17 17 SAMSA - Example Raw source data (BSCW logfile) User:[158009, 'stm1x06'] object:[162008, 'stm1x04_Diagrama_Estados'] Type:ReadEvent Time: Members:[[158339, 'stm1x17', 'l5QcDnyhBmzkc'], [158099, 'stm1x09', 'SgBa8D7t4R3XQ'], [116766, 'stm1x00', 'Q5OG42nMsUCog'],... Path: [[158012, ':stm1x06'], [156970, 'Ingeniería de software (03/04)'], [158541, 'El proyecto de la herramienta de encuestas'], [160233, 'Elaboración (primera iteración)'], [161426, '6. Diagramas de Estado']] On:[161426, '6. Diagramas de Estado'] Touched:[158012, ':stm1x06'] Icon:'/bscw_resources/icons/e_read.gif' Class:Document Content:application/pdf User:[158249, 'stm1x14'] object:[161927, 'stm1-junio03.pdf'] Type:ReadEvent Time: Members:[[158339, 'stm1x17', 'l5QcDnyhBmzkc'], [158099, 'stm1x09', 'SgBa8D7t4R3XQ'], [116766, 'stm1x00', 'Q5OG42nMsUCog'], [158129, 'stm1x10', 'kn/7H8eEIaSD6'], [157859, 'stm1x01', '8zEhF2Cl/XRlI'], [158399, 'stm1x19', '2GUzLTh.vkZNw'], [158009, 'stm1x06', '.MH/OmIqgz7HQ'], [158039, 'stm1x07', 'nhiUpoSQdFnWA'], [158249, 'stm1x14', 'bMckxPSGUQwts'], [158429, 'stm1x20', 'zpRVdxqaDKDkw'], [157979, 'stm1x05', 'qpE8BEv6DvK1M'], [158159, 'stm1x11', 'Ttibbr4C9YDdw'], [158309, 'stm1x16', 'gwkeGlWoNsn3Y'], [157949, 'stm1x04', 'ZbRd75nzzT39c'], [158219, 'stm1x13', 'dI/2GRyZPEbbI'], [158369, 'stm1x18', '1VpwTzrnjvteI'], [157919, 'stm1x03', 'JtWyaVVOJNo7E'], [158279, 'stm1x15', 'dE2Y.IRtuK30g'], [157889, 'stm1x02', 'xyAQS9GZf62Es'], [158069, 'stm1x08', 'wksiMtOrd/PK2'], [158189, 'stm1x12', 'RwmThVDSMhk0g']] Path:[[158252, ':stm1x14'], [156970, 'Ingeniería de software (03/04)'], [158473, 'Material académico'], [158531, 'exámenes']] On:[158531, 'exámenes'] Touched:[158252, ':stm1x14'] Icon:'/bscw_resources/icons/e_read.gif' Class:Document Content:application/pdf

18 18 SAMSA - Example XML generic format 08:42 :laox38 Curso GENERAL'],[17938, ] Comentarios... SERIA MAS... None

19 19 SAMSA - Example Social Network (SAMSA output) DL N=19 FORMAT = FULLMATRIX DIAGONAL PRESENT LABELS: x00 x21 x22 x23 x24 x25 x26 x27 x28 x29 x31 x32 x33 x34 x35 x36 x37 x38 x39

20 20 SAMSA - Example SNA indexes (SAMSA output) GRUPO OUTDEGREE INDEGREE OUTFARNESS INFARNESS OUTCLOSENES INCLOSENESS x00 44,00 3,00 55,00 101,00 32,73 17,82 x21 1,00 0,00 65,00 342,00 27,69 5,26 x22 25,00 8,00 72,00 100,00 25,00 18,00 x23 5,00 22,00 67,00 91,00 26,87 19,78 x24 3,00 3,00 77,00 113,00 23,38 15,93 x25 1,00 3,00 81,00 113,00 22,22 15,93 x26 0,00 16,00 342,00 69,00 5,26 26,09 x27 3,00 1,00 77,00 113,00 23,38 15,93 x28 2,00 4,00 79,00 113,00 22,78 15,93 x29 1,00 6,00 70,00 108,00 25,71 16,67 x31 0,00 2,00 342,00 97,00 5,26 18,56 Densidad normalizada: 14,33 % Densidad: 29,53 % La centralizacion(InDegree): 97,84 % La centralizacion(OutDegree): 226,85 %

21 21 SAMSA - Example Sociogram (NetDraw)

22 22 Tools - Quest Quest (Questionnaire web-based tool) Contact person: Eduardo Gómez Sánchez

23 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

24 24 Quest Set of tools for: – Managing complete lifecycle of questionnaires Editing Web Publishing Filling in and results storing Results processing – Managing administrative aspects Course definition Group formation At GSIC we use it for supporting collaborative learning scenarios

25 25 Quest QuestFormCreator: – Java Stand-alone application – Visual questionnaire authoring tool – Questionnaires can be saved as: HTML file (for communications) XML file (for web publishing)

26 26 Quest Quest questionnaires: – Divided in sections – Each section composed of questions: Free text Numeric Checkboxes Choice list Matrix …

27 27 Design of questionnaires QuestFormCreator

28 28 QuestServer Web application Client side – Web browser: FireFox as best choice

29 29 QuestServer Users – Administrator: Creates/removes students, removes courses – Teacher Creates/removes courses Invite/remove students/teaches Creates groups Publishes and closes questionnaires Publishes results Requests results files – Student Fills questionnaires in, browses tables with results

30 30 Tools - Iloca Iloca (Interactive Learning Observer for Computer Analysis)

31 31 Conceptos a observar: P.P.O: Preocupado por el observador CyL: Colabora y se levanta para hacer cosas. T: Teclea. T/R: Teclea con el ratón. NT: No teclea. C: Colaboran CyP: Contesta y pregunta. S.: Solo P.: Pregunta A.: Aclaraciones de Yannis A C.: Aclaración conjunta. I.D.: Intervención en el debate. R.: Ratón A. en Y.: Aclaración en el ordenador de Yannis. I.: Intervención I/O: Intervención sobre el comentario de otro grupo. Observación Directa Observadores: Grupo EMIC Taller Buendía Palencia Nª Alumnos/as: 21 Zona de reunión Interacción con… Profesor Grupo 08 Grupo 07 Grupo 06 Grupo 05 Grupo 03Grupo 04 Grupo 02 P P P P P P P P.P.O C C C C

32 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 - Overview SNA indexes & sociograms Actions (XML) iloca

33 33 Nudist – Qualitative analysis Non-Numerical Unstructured Data Indexing, Search and Theorizing o Nudist Vivo de Qualitative Solutions Research - QSR

34 34


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