Network analysis methods for assessment & measurement

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Presentation slides for a webinar produced by the Leadership Learning Community. Full audio is available on their site, at http://www.leadershiplearning.org/blog/eleanor-cooney/2012-12-17/2013-webinar-network-analysis-snaona-methods-assessment-measurement

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<ul><li>1.Network Analysis Methods forAssessment &amp; MeasurementJanuary 14, 2012Patti AnklamWith June Holley and Claire Reinelt</li></ul> <p>2. Webinar GoalsShare current thinking about how network analysis is used in designing and evaluating nonprofit programsProvide examples of network analysis used in assessment and measurement contextsStimulate thinking about correlating network analysis with measurement and evaluation outputs and outcomes 2 3. Network Thinking &amp; Non-Profits 4. The Evolution of Network Thinking4 5. What is Network Analysis? Social network analysis (SNA) is a collectionof techniques, tools, and methods to mapand measure the relationships amongpeople and organizations Organizational network analysis (ONA)often refers to the use of SNA methods inthe context of organization dynamics anddevelopment In practice, we use these tools to mapconnections among people and ideas,issues, and other entities as well as thesocial and organizational connections5 6. Network Analysis: The Method in a Nutshell Step Activities/Tools Design Identify boundariesClarify and design questions Collect Data SurveysInterviewsFacebook, LinkedInEmail logs Analyze data to generate (Netdraw/UCINET, NodeXL, Gephi maps and metricsmany others) Review dataValidate; look for questions Prepare evaluation Match network results with contextand stories Move into action Weaving &amp; other interventions 6 7. Survey Example 7 8. Survey Example Demographic Component 8 9. Survey Example Affiliation Component 9 10. Survey Example Network Questions 10 11. Network Questions Probe Relationships11 12. Analyses Outputs: Map PatternsMulti-HubHub and SpokeStove-piped (Siloed) Core/Periphery12 13. Quick View: What an Analysis Can Tell Overall very well connected One region distinctlyclustered with fewconnects to otherregions Staff are highlycentral Identification ofkey connectors 13 14. Reasons for a Network Analysis: Examples1. Assessment, Planning, &amp; Weaving2. Measure changes over time3. Sense-making &amp; story- finding4. Positioning and working with individuals in the network 14 15. Assessment, Planning, &amp; WeavingStrategic Purpose Assess the networks capacity for collaboration, informationtransfer, innovation Identify key individuals Establish goals for enhancing connectivity Create an action plan 15 16. Assessment: Capacity for CollaborationCurrent Funder Interaction NetworkFuture Funder Interaction NetworkWhen funders indicate with whom they would like to work in the near future, the network becomes more robust.Funders are saying they want to work more together.Source: Transcending Boundaries: Strengthening Impact. The Full Potential of a Justice Network (Research &amp; Network-Building Project Report,April 2011, Criminal Justice Funders Network). Courtesy of June Holley.16 17. Assessment: Affiliation NetworkStrategic Purpose Identify potentialrelationships amongpeople based onshared events,meetings, ideas, orareas of expertise Nonprofits use this tosee whichorganizations attachto different ideas Forms the basis fornetwork weaving 17 18. Drill Down Into Affiliation Network Identify people withcommon interest basis for buildingcommunities ofpractice See which peopleshare interest inmultiple issues ortopics A way for the networkto reveal itself andhave richconversations18 19. Measuring Changes Over TimeVery WellBoston Green &amp; Healthy Building Network WellSomewhat2005 2007 24 9 32124 9 321 31 3 31 18 7 3 18676 25 25 3328 4 3328 4 26 26829 17 829 2 17 21112111223162723162710 1910 19 21 21 15 1513133414341430305 20225 2022Source: Boston Green &amp; Healthy Building Network, Beth Tener and Al Nierenberg, January 2008 Maps copyright 2012 New Directions Collaborative19 20. Analyses Outputs: MetricsOverall network metricsIndividual position metrics Look at the whole network Look at positions ofand its components:individuals in the network: Overall cohesion # of connections Degrees of separation Favorability of position Good for comparing Good for identifyinggroups within networks orpeople who are wellfor comparing changes in a positioned to influence thenetwork over timenetwork or to move information around 20 21. How the Metrics Enhance the Maps2011Year # DensityAvg #ties2009 552.2% 1.22010 902.7% 2.42011 855.3% 4.52012 828% 6.88201020092012 21 22. Sense-Making &amp; Emergence Barr Foundation Fellows Program See changes over time, but really to see how the network has supportedemergence Work to shift Barr staff from the center Pat BrandesSource: Networking a City, Marianne Hughes &amp; Didi Goldenhar, Stanford Social Innovation Review, Summer 201222 23. Sense-Making: New School Development in Boston An intentional network may This person has helped me accomplishwork-related tasks. have no other purpose than to enable emergence Maps that show the evolving relationships within a network help to identify powerful network storiesSource: Networking a City, Stanford Social Innovation Review, Summer 201223 24. Positioning: The Individual View Node Betweenness Indegree OutDegree62 792.6726 3080 660.4817 32 Centrality metrics6423 530.61 333.36 20 203314identify people with71 321.4221 2056 316.4220 18the most ties (in-degree and out-degree) Those positioned tomove informationaround in thenetwork or be in theknow (betweenness) Can identify people tolead task teams, toprovide resources to,or to train as weavers 24 25. Tracking Individuals ChangesI learned something from this person that made me a better leader. 200920052007200820092010 25 26. Tracking Individuals ChangesI learned something from this person that made me a better leader. 201126 27. Network Analysis &amp; Measuring Outcomes 28. Summary What We KnowWhat We Can Measure and Show in an Analysis: Measure the cohesion of the network overall: High-level structure (stove-piped, core/periphery, highly clustered) Average degree of separation Average number of connections each person has Identify individuals by their centrality to the network: Core or periphery? How do you bring people in from the outside? Broker? Connector? Facilitator? Bottleneck? Number and diversity of connections See changes over time 28 29. Things We Can Do With What We KnowWays to change patterns in Practices from the KM/OD Repertoire networksWeaving. Create intentionalConvene. Make introductions through meetings and webinars, face-to- connections face eventsIncrease the flow of knowledge Establish collaborative workspaces, install instant messaging systems,make existing knowledge bases more accessible and usable;implement social software or social network softwareCreate awareness Provide expertise directoriesConnect disconnected clustersWeave: establish knowledge brokering roles; expand communicationchannelsCreate more trusted relationshipsAssign people to work on projects togetherAlter the behavior of individual nodes Create awareness of the impact of an individuals place in a network; foster network literacyIncrease diversity Add nodes; connect and create networks; encourage people to bring knowledge in from their networks in the world29 30. Measurement Challenges Maps area snapshot in time Targets and thresholds How much cohesion is enough?Is there a point at whichincreasing the number of tiesmakes the network less efficient? Is it reasonable to set a target forthe cohesion metric? Tying Network Metrics toOutcomes We have to think of the metrics asindicators and as correlates of Source: Dave Snowden, Cynefin Advanced Practitioners Course December 2012other survey questions 30 31. Questions? 31 32. patti@pattianklam.com http://www.pattianklam.com claire@leadershiplearning.org http://www.leadershiplearning.org/ June@networkweaving.com http://www.networkweaving.comThank you.Question </p>