FROM HIERARCHIES TO NETWORKS: USE SOCIAL NETWORK ANALYSIS (SNA) TO NAVIGATE ORGANIZATIONAL CHARTS. Felix Ankel

Transcription

1 FROM HIERARCHIES TO NETWORKS: USE SOCIAL NETWORK ANALYSIS (SNA) TO NAVIGATE ORGANIZATIONAL CHARTS Felix Ankel

2 Dreyfus model of skill acquisition

3 Dreyfus model of skill acquisition

4 Dreyfus model of skill acquisition

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11 THINK OF YOUR NEXT CLER VISIT 1. Patient safety 2. Transitions in care 3. Fatigue management 4. Quality improvement 5. Professionalism 6. Supervision

12 SOCIAL NETWORK ANALYSIS Advice: Prominent players whom others depend on to solve problems and provide technical help Trust: Which members share sensitive information in a crises Communication: Daily interaction of network members

13 FROM HIERARCHIES TO NETWORKS: USE SOCIAL NETWORK ANALYSIS (SNA) TO NAVIGATE ORGANIZATIONAL CHARTS Felix Ankel

14 From Hierarchies to Networks: The Use of Social Network Analysis (SNA) to Navigate Organizational Charts Presented by Kimberly A. Fredericks, Ph.D

15 Who Do You Know?

16 Survey Network

17 Network by Category

18 Introduction To speak of social life is to speak of the association between people their associating in work and in play, in love and in war, to trade or to worship, to help or to hinder. It is in the social relations men establish that their interests find expression and their desires become realized. Peter M. Blau Exchange and Power in Social Life, 1964 "If we ever get to the point of charting a whole city or a whole nation, we would have a picture of a vast solar system of intangible structures, powerfully influencing conduct, as gravitation does in space. Such an invisible structure underlies society and has its influence in determining the conduct of society as a whole." J.L. Moreno, New York Times, April 13, 1933

19 What is Social Network Analysis? Network analysis is: > the study of social relations among a set of actors > a theoretical perspective on social life, that interdependence matters a set of tools used to implement these ideas These patterns of connections form a social space and SNA provides us with a set of tools to empirically test these propositions

20 What are social relations? A social relation is anything that links two actors. Examples include: Kinship Co-membership Friendship Talking with Love Hate Exchange Trust Coauthorship Fighting Social Relations can be reciprocal or non-reciprocal

21 What is a network? oa network is the sum of the actor s social relations. Relations are also referred to as: edges, arcs, lines and ties. oan actor can be an individual, group, organization or some other formation of individuals who interact with one another. Actors are also referred to as: nodes, vertices, or points

22 What properties of relations do we study? The substantive topics cross all areas of sociology. But we can identify types of questions that social network researchers ask: 1) Social network analysts often study relations as systems. That is, what is of interest is how the pattern of relations among actors affects individual behavior or system properties. For example...social cohesion, intergroup relations

23 What properties of relations do we study? 2) Networks as social contexts How does the network environment affect an actor s behavior? Examples: Peer influence on delinquency Corporate interlocks and political participation International trade and war

24 What properties of relations do we study? 3) Conduits for diffusion Relations are like wires or pipes: risks and resources flow through relations. This can have very wide implications: Diffusion of innovations (fads, rumors, etc.) Disease diffusion (STDs)

25 Assumptions in Network Analysis Social life is based on interdependence, not independence social entities make non-random connections -patterns of connections matter- they support resource flows -both direct and indirect ties matter- strength of weak ties Social structure matters, it enables and constrains action Access is related to power, influence, and position brokerage In Networks Perception Matters!

26 Basic Examples Network Analysis An example from James Moody and Cross and Parker

27 What is a High School?

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30 Another Example from Cross and Parker

31 Total Network

32 Fragmented Network

33 Types of Networks One-Mode Network -One set of Actors Two-Mode Network -Two sets of social entities Affiliation Network -The second mode in an affiliation network is a set of events.

34 Traditional vs. Network Data Name Sex Age Degree Bob Male 32 2 Carol Female 27 1 Ted Male 29 1 Alice Female 28 3 Who Reports Liking Whom Choice Chooser Bob Carol Ted Alice Bob Carol Ted Alice

35 Foundations of Network Analysis From pictures to matrices b d b d a c e a c e a b Undirected, binary a b c d e a b Directed, binary a b c d e 1 1 c d e c d e

36 Scattered Networks If group C knows of a job opportunity, people in Groups A and B may never hear of it. Group A This is low social capital - small, dense networks with no hierarchy. You Group B Somebody who knows of an opportunity Group C

37 A Primer Hub and Spoke Network Networks: A Primer Potentially fewer connections, but ties to the other networks expand opportunities in any of the networks. You Somebody who knows of an opp Group A Somebody who needs An opportunity Group B Group C A broker connects people in one group and somebody else in another who needs that information. His social capital has increased.

38 Multiple Hubs Network The highest social capital is achieved when your networks are networks of other well connected people. With only three connections you are Likely to receive information from anyone in the entire network! You You are probably only two or three steps away from anyone, which also gives you early access as well.

39 Core Periphery Network Emerges after many years of network weaving by multiple hubs. Core contains key individuals or organizations who have developed strong ties among themselves. Periphery contains groups of nodes that are usually tied to the core through looser ties

40 All networks are not created equal o o o o Human nature tends to build networks that are ineffective from a leverage standpoint. More contacts are not necessarily better (in fact, less is more ). Not all possible relations are actually activated. This leads to structural holes. Different types of contacts (i.e., heterophily) are often better than strong contacts.

41 All networks are not created equal Successful people (and organizations) across every discipline, industry, and profession have network structures that enable them to be more influential and have access to more opportunities, sooner. The result is more success, more money, more prestige, more influence earlier and longer. There are many different types of networks- advice, trust, communications, collaboration, information,- based on purpose of the network, the role of the network, the function, or the form The effectiveness of a network depends on the optimal arrangement of ties depending on what the network is trying to achieve. This can be quantified and evaluated using SNA

42 Basic Principles Ties often are asymmetrically reciprocal, differing in content and intensity. Ties link network members indirectly as well as directly; hence ties must be analyzed within the context of larger network structures. The structuring of social ties creates nonrandom networks; hence network clusters, boundaries, and cross-linkages arise. Cross-linkages connect clusters as well as individuals. Asymmetric ties and complex networks distribute scarce resources differentially. Network structure collaborative and competitive activities to secure scarce resources.

43 What Does this Mean We study and use networks to share information, for social support, for advice, for collaboration, for coordination, for workflow, for diffusion of innovation Adoption of knowledge and practice flows from relationshipscommunities of practice which are networks that involve membership, trust, and knowledge Diffusion of Innovation for new practices, new drugs, new protocols SNA can analyze and support each of these

44 Information Example from Stacey Friedman, PhD

45 Referrals Example from Stacey Friedman, PhD

46 Data Collection Questionnaire -Free vs. Fixed Choice -Ratings vs. Complete Rankings Interview -Face to Face -Telephone Observation -Small Groups -Face-to-face Interactions Archival Records -Records of Interactions -Past political interactions among nations -Previously published citations (Triangulation and Response Rates)

47 Analyzing Networks Ego - Networks A respondent and the set of people they have relations with. Measures: Similarity Size Density Pattern of ties Centrality i.e. Do Densely-Knit Networks Provide More Support? (structure); Do More Central People Get More Support?(network); Do Women Provide More Support? (composition); Do Face-to-Face Ties Provide More Support Than Internet Ties? (relational); Are People More Isolated Now? (ego)

48 Analyzing Networks Complete/Whole Networks The connections among all members of a population. Measures: Graph properties Density Sub-groups (Cliques/Blocks) Positions Centrality Flows i.e. What is real structure of organization? How does information flow through a villiage

49 Size and Density Size: The number of actors in a network. Size is critical to networks because of the limited resources and capacities each actor has for building and maintaining ties. Density: The proportion of ties that could be present that actually are. (Total # of ties/total # of possible ties) Density indicates how close a network is to realizing its potential (reported as percentage)

50 Measures of Centrality Centrality is the degree to which an actor is in a central role in a network. It describes power, status or popularity (values range from 0 to 1) 1. Indegree: The total number of actors who have ties to the actor we are focused on (# of times an actor is chosen by others). It measures power and popularity.

51 Measures of Centrality 2. Betweenness: How much an actor is indirectly linked to others of the group or stands between other actors. It measures information control. 3. Closeness: How close an actor is to everyone else in the network. Measures independence from the control of others.

52 Measures of Function What flows through the network? What is the network used for? Information or Resources Time, Money, Support, Advice, (Innovation, Research) Disease Influence Knowledge

53 Data Analysis Questions 1. How can properties of networks and actors be described and summarized? Statistical Network Descriptives -Density, Network Visualization Techniques 2. How can the association between ties within one network and between networks be described and modeled? the prediction of ties within a network or the prediction of one network using other networks. Several types of procedures are available: correlation (QAP) and regression methods (QAP), and exponential random graph models (p1 type models) 3. How can the association between network ties and (endogenous or exogenous) actor characteristics be described and modeled? regression methods, and exponential random graph models with actor attributes (p* modeling) 4. How can actors and their (endogenous or exogenous) characteristics be compared or categorized? Available procedures are ANOVAs and the statistical counterparts of the nonstatistical analysis of network positions and roles (e.g., cohesive subgroups based on logistic regression or stochastic blockmodeling) 5. How do networks develop over time and how do they influence each other? Network Dynamics

54 Lets Go Back and Examine our Network Map and Answer the Following Questions: 1. What type of network is it (e.g., scattered, hub and spoke, multiple hubs, core-periphery)? 2. Who are the main brokers? Why do you think they are in these roles? 3. Would you change this network or do you think it works the way it is? What would it take to change it? 4. Note any other observations you might have about the network.

55 Appendix Some SNA Network Studies

56 Chambers D, Wilson P, Thompson C, Harden M (2012) Social Network Analysis in Healthcare Settings: A Systematic Scoping Review. PLoS ONE 7(8): e doi: /journal.pone

57 Appendix