1. Social Network Analysis as an evaluation tool 10 June 2015Rick Davies @ UNICEF, FLorence1

2. “The heart of all major discoveries in the physical sciences is the discovery of novel methods of representation” Steve Toulmin (1953) “The Philosophy of Science: An introduction. 10 June 2015Rick Davies @ UNICEF, FLorence2

3. Agenda 1.What is a network [5] 2.My own perspectives on networks [4] 3.How to represent networks [8] 4.How to gather data on networks [12] 5.Making sense of complex networks [5] 6.Causal perspectives on networks 7.SNA software [10] 10 June 2015Rick Davies @ UNICEF, FLorence3

4. 1. What is a network? 10 June 2015Rick Davies @ UNICEF, FLorence4 Any set of entities Connected by some kind of relationship Forming a larger structure Common purpose is not necessary

5. Historical networks 5

6. Company Board networks 6

7. Metabolic networks 7

8. Where does SNA come from? Mathematics: Graph Theory, Euler, 1735 Sociology: Sociometry, Moreno, 1930s Computer programs: UCINET, 1970s> Conferences: INSNA, 1970s> Social Network apps, 2000>, LinkedIn, etc, 10 June 2015Rick Davies @ UNICEF, FLorence8

9. 2. Five Arguments For The Use Of A Network Perspective (2003) 1.Social Network Analysis is about social relationship, so are most development programs 2.There is a range of methods for describing networks 3.There is a range of theories about social and other networks 4.Network can be seen, and analysed, at many scales 5.Networks are not linear 10 June 2015Rick Davies @ UNICEF, FLorence9

10. Nov 2005Rick Davies at NDN10 Background meta-theory (2005) Better representations of program designs will lead to better programs Because… Better quality program designs => More “evaluable” designs => Improved M&E of those programs => Program improvements

11. Assumptions (2010) 1.Evaluation involves comparisons. For example, between plans and achievements 2.Better descriptions will enable more informed comparisons and better evaluations 3.Projects described in terms of networks of actors will be more useful than those described as abstract and disembodied processes of change 4.SNA provides a flexible array of tools for describing networks of actors 11

12. Caveats: Its not just about theory (2015) SNA provides particular kinds of data not easily available elsewhere Examination of well presented data can prompt improvements in theory Ideally, there is an ongoing dialogue between theory and data 10 June 2015Rick Davies @ UNICEF, FLorence12

13. 3. Ways of representing networks Diagrams Matrices Mathematical measures Data formats readable by software 10 June 2015Rick Davies @ UNICEF, FLorence13

14. 14 A matrix view of 16 NGOs in Ghana In social network analysis, this is a “one-mode” network, showing … Actors x Actors Cell values = frequency of references to each other, in their Progress Reports between 2005-6 Cells can represent many other aspects of their relationships Matrices can hold data on thousands of relationships Up to 120 here But it is hard to read.

15. Measuring Effectiveness, Melbourne, Sept 2007 15 The same data, presented in diagram form.. Weak links Strong links Reciprocated links Well connected actors Unconnected and marginalised actors Groups of actors

16. Mathematical measures 10 June 2015Rick Davies @ UNICEF, FLorence16 Individual’s positions within a network e.g. Degree centrality Closeness centrality Betweenness centrality The structure of a whole network e.g. Density Network centralisation Cliques

17. 10 June 2015Rick Davies @ UNICEF, FLorence17

18. Types of matrices (&diagrams) Adjacency matrices Actors x Actors e.g. Ghana NGOs Affiliation matrices Actors x Objectives e.g. Ghana PRSP Which can be converted to…. Actors x Actors (by shared objectives) Objectives x Objectives (by shared actors) 10 June 2015Rick Davies @ UNICEF, FLorence18

19. 19 GPRS – top priority governance objectives

20. …and networks of networks Modular matrices e.g. Actors x Objectives + Objectives x Activities + Activities x Projects 10 June 2015Rick Davies @ UNICEF, FLorence20

21. 4. Sources of network data and their uses Funding agreements Progress reports Workshop participant lists Workshop planning exercises Workshop drawing exercises Card sorting exercises Online SNA surveys Household survey data WWW sources 21

22. October 2006 Rick Davies www.mande.co.uk/networkmodels.htm 22 PETRRA funding relationships

23. Visualising data from Progress Reports 23

24. July 2007UK Social Network Conference24 M4P project, Hanoi Nodes = workshops Links = movement of participants from one workshop to another Thicker link = more participants moving Node colour = type of workshop (by topic )

25. July 2007UK Social Network Conference25 MSC Training Workshop, Accra Nodes = workshop participants Links = intend to talk to each other after workshop, but had not worked together before. (=Old minus new links) Thick lines = reciprocated intentions

26. Output to Purpose relationships 26

27. Sept 2005Rick Davies at EES SS27 Objectives x programs (Vietnam)

28. Rick Davies, Uppsala, April 2008, Part 3 28 Network model based on discussion

29. 29 Categorisation of districts in Indonesia

30. Online survey of NGOs & Issue coalitions Key: Blue squares = coalitions; Red circles = NGO respondents; Purple triangles = NGO respondents who are also listed as coalitions; Grey lines = NGO membership in a coalition.

31. Sensemaker self-signified stories 10 June 2015Rick Davies @ UNICEF, FLorence31

32. Websites linked by hypertext 10 June 2015Rick Davies @ UNICEF, FLorence32

33. 5. Making sense of complex networks Aggregating Grouping actors into higher level categories Filtering by link, type, strength and other attributes Consider filtered networks as flooded landscapes By node type, degree and other attributes Clustering Using layout algorithms and cluster identification procedures 33

34. October 2006 Rick Davies www.mande.co.uk/networkmodels.htm 34 PETRRA funding relationships

35. October 2006 Rick Davies www.mande.co.uk/networkmodels.htm 35 PETRRA funding relationships simplified

36. 36 G-RAP Baseline survey circa 2006

37. Meeting participants 37 Orange = participating organisations Blue, red and green = meetings Links = meetings attended Meetings can be filtered out in temporal sequence

38. 6. Causal perspectives on networks Actors’ attributes + their network position + wider network structure => outcomes Multiple pathways to the same outcome Impact mediated via x other actors Incomplete knowledge of network structrure 10 June 2015Rick Davies @ UNICEF, FLorence38

39. Aspects of networks Links may have direction, or not Connections may or may not be reciprocated In-degree and out-degree may differ E.g. NGO and donor relationship Links may be singular or multiplex Distance is in links (degrees), not cm/inches Connected links create pathways Structure is important, so layout is important, to minimise cross-overs 39

40. 10 June 2015Rick Davies @ UNICEF, FLorence40

41. Pathways through networks A way of telling a simplified story, that Can be identified by one person, as they see it Or generated bottom up, by asking all to prioritise their relationships 41

42. 11th June 2009Rick Davies at BOND42 Which can be captured in tabular form …as a Social Framework, a Logical Framework re-designed as if people and their relationships mattered

43. 9. Software for network visualisation and analysis Criteria Free User friendly manual On-screen network drawing is easy Imports and exports multiple formats Ability to display attributes by colour/size/shape Continuing to be developed Multiple layout options Multiple network measures 43 Who has used what software? Hands up exercise

44. 44

45. NodeXL, an Excel Template Free, manual exists, continuing development You can give nodes and links as many different attributes as desired Including objective descriptions, results data And these can be visualised in the colour/size/shape of nodes and links 45

46. 46

47. yED, diagramming software Free Wide range of ways of visualising differences in nodes and links Copes with Excel files that vary in their layout Diagrams can be uploaded as interactive web pages 47

48. 48

49. UCINET & NetDraw The most widely used SNA package Lots of data manipulation and network analysis tools Lots of layout and filtering options Continually being developed Steep learning curve Courses available 49

50. 50

51. Visualyzer Very easy on screen drawing User friendly manual Attributes of nodes and links can be made visible by mouse-over, clicks and coding Expensive Not under continuing development 51

52. 52 The attributes of the actors can be made visible by cursor rollover+shift

53. Sept 2005Rick Davies at EES SS53 http://www.dilbert.com/

54. A common problem…? 10 June 2015Rick Davies @ UNICEF, FLorence54

55. which can be dealt with by filtering By kinds of actors Types Centrality measures By kinds of relationships Types Strengths 10 June 2015Rick Davies @ UNICEF, FLorence55