Grappling with Grouping III Social Network Analysis

Transcription

1 Grappling with Grouping III Social Network Analysis David Henry University of Illinois at Chicago Allison Dymnicki American Institutes for Research

2 Acknowledgments Families and Communities Research Group Patrick Tolan Deborah Gorman-Smith Michael Schoeny Social Network and Normative Influence Projects Fern Chertok Daneen Deptula Allison Dymnicki Jane Jegerski Christopher Keys Kimberly Kobus Jennifer Watling Neal Zachary Neal Michael Schoeny

3 Acknowledgments This work was supported by grants from the Centers for Disease Control and Prevention and the National Institute of Justice. The content of this presentation is solely the responsibility of the authors and does not necessarily represent the official views of the funders. For more information, visit

4 Grappling with Grouping I. Cluster Analysis II. Clustering methods for binary variables III. Social Network Analysis Central theme: Clustering approximates uniqueness in the same way that a sample mean approximates a population.

5 Network Analysis Widely used in community psychology research 28 studies since 2000 in just two journals (AJCP, JCP) Search terms: Network Analysis Similar search using the term cluster analysis returned 14 studies.

6 Community Studies Employing Network Analysis Study Variables Type of Analysis Swindle et al., 2000 Positive and negative social transactions in networks of HIV+ persons Rating scales Hirsch et al., 2002 Differences in strength of ties by race Ego network Ying, 2002 Social network composition of Taiwanese graduate students Rating scales Langhout, 2003 A single case study using ego networks Ego network Fleisher & Krienert, 2004 Violence among female gang members increases before pregnancy and decreases afterward Qualitative Interviews Criticism, practical support were significant predictors of Levendosky et al., 2004 mental health for battered women. Rating scales Toohey et al., 2004 Differences between nearly homeless and housed women on beliefs about netweok members as housing resources Rating scales Zea et al., 2004 Target specific factors were related to the probability of disclosure. Rating scales Chia, 2006 Sociometric nominations in a work organization Sociometric Knowlton & Latkin, 2007 Ego networks Ego network Dominguez & Maya Lariego, 2008 Ego network support characteristics Ego network Pernice Duca, 2008 SocialSupportin Support in clubhouse mental health programs Rating scales

7 Community Studies Employing Network Analysis Study Variables Type of Analysis Toro et al., 2008 Social Support in homeless adults ego networks Ego network Campo et al., 2009 Convergent and discriminant validity with other measures Rating scales Network drug use contributed to perceptions of neighborhood Latkin et al., 2009 disorder. Ego network Neal, 2009 Density, centrality, and relational aggression Informant Trotter & Allen, 2009 Ego networks, qualitative analysis Ego network Crowe, 2010 Personal and Organizational Community Networks Sociometric Lugue et al., 2010 Community Cancer Network Nt Sociometric i Prelow et al., 2010 Social Support buffered ecological risk effects on psychological distress Rating scales Haines et al., 2011 Network characteristics of an interdisciplinary collaboration based on multiple types of relationships Sociometric Nealet et al. (2011) Cohesionvs vs. structuralsimilarityin similarity in teacher advice networks Sociometric

8 Variety of Network Methods in Community Psychology Studies Type Sociometric nominations 5 Informant-based Methods 1 Ego-networks 7 Rating Scales 8 Qualitative 1 Number

9 Outline Overview of methods Sociometrics Informants Ego-networks Dynamic For each (-1) Theory/method/measures Software Strengths and limitations

10 Network Analysis with Sociometrics Data source: Relationships Who are your friends? (Kobus & Henry, 2009) What organizations a o do you belong to? (Crowe, 2010) Analysis: Matrix and Graph Representations A B C D E F G A B B C E D E F G C F G Advancing Advancing Health Health Practice Practice and and Policy Policy Through through Collaborative Research A D

11 Sociometrics: Network Measures X DENSITY 12 / g(g( g 1) where X = relationships (ties) = 12 g = network size (# of potential relationships) = 42 A B C D E F G Σ A B C D E F G E C F G A B D Σ Advancing Advancing Health Health Practice Practice and and Policy Policy Through through Collaborative Research

12 Sociometrics: Networks Measures ( g 1) M L L Mutuality Index L( g 1) L L ( 2 M = number of mutual relationships = 5 g = network size = 7 L = sum of fthe outdegree of the total t network = 12 L 2 = sum of squares of the outdegree of the total network =22 A B C D E F G Σ A B C D E F G E C F G A B D Σ

13 Sociometrics: Network Measures Boundary Density (Hirsch, 1980) T Tactual BD Tpossible T actual = number of actual ties across subgroups = 2 T possible = number of possible ties across subgroups = 24 A B C D E F G Σ A B C D E F G Σ E C F G A B D

14 Sociometrics: Measures of Individuals Mean Geodesic Distance where D = Distance and B = Reachability In: 1.33 for F, 2.0 for D and 2.16 for G j j D B ij ij or j j D B ji ij A B C D E F G Σ A B C D E F E C F G A B D G Σ

15 Sociometrics: Measures of Individuals Position F A Member C Liaison G Isolate E B D Liaisons > Members or Isolates on tobacco and alcohol use (2 studies) Members and Isolates more influenced by peer substance use than Liaisons.

16 Sociometrics: Statistical Models Example Dyads have a tendency to become triads: birds of a feather? or friends of friends? We can model the likelihood of triad closure, but the chance models are complex Random graph models and double permutation tests provide alternatives suitable for predicting network structures or individual ties.

17 Network Analysis with Sociometrics i Strengths Unbiased assessment of social influence Patterns of diffusion and communication Rich measurement and theory Limitationsit ti Missing nominators compromise accuracy Costly assessment Complex coding and analysis Requires bounded social space

18 Network Analysis with Sociometrics Software Stand-alone alone Programs UCINET ( Krackplot (Freeware visualization software) ( edu/user/krack/krackplot sh tml) R ( igraph sna Excel NodeXL: Freeware d l /

19 Network Analysis from Informants Data Source: Who hangs out together? Informants Compilation Informant Port Kit Tunner Port Kit Tunner 1 Port Kit 0.5 Informant 2 Port 1 1 Kit 1 Variations Cognitive Social Structures (Krakhardt, 1987) Social Cognitive Mapping (Cairns et al., 1985)

20 Informants: Examples Cairns, Leung, Buchannan, and Cairns (1995) used social cognitive mapping to study the fluidity, reliability, and interrelations of social networks of 4 th and 7 th graders over a 3-week period. Neal (2009) used Cognitive Social Structures to study the influence of centrality and density on relational aggression in a sample of 3 rd through 8th grade children.

21 Informants: Software Cognitive Social Structures consensus aggregation across k informant matrices R R ij i,j k can be done in Excel or R. See Krackhardt (1987) for specific instructions. Social Cognitive Mapping Contact Man-Chi Leung, Ph.D. at UNC (manchi.leung@unc.edu ) for a copy of the SCM 4.0 program and manual.

22 Network Analysis from Informants Strengths: Provides valid estimates of network ties with comparatively few informants. Missing data does not decrease accuracy Economical to administer Limitations Difficult to assess directed relations Requires bounded social space (e.g., classrooms, schools, organizations)

23 Ego Network Analysis Theory: Best for unbounded networks where saturation is not possible Data Source: Prompts for different social functions Demographics, relationships, frequency Behavior of network members Analysis: Network size, density, diversity, boundary density, heterogeneity, position, behavior of network members

24 Ego Networks: Measures Heterogeneity n A 1 e Heterogeneity ia 1 n where A = a categorical attribute (e.g., gender, race) A k = number of individuals with the attribute e = number of individuals with valid data on A n = total number of traits of A in the ego network k 2

25 Ego Networks: Examples Dominguez & Maya-Lariego, 2008 Ego networks of host individuals and immigrants in the U.S. and Spain Host individuals had lower centrality than did immigrants according to multiple measures. Tolan, Gorman-Smith, & Henry, 2003 Ego network assessments of delinquent involvement in adolescent males Network violence predicted future individual violence.

26 Ego Networks Strengths Does not require assessment of entire network Can provide social network and social support information Does not require bounded social space. Limitations Possible bias in the direction of the individual s behavior Ego is central by definition, so meaning of position and centrality are problematic.

27 Ego Networks: Software Like informant-based network data, ego networks populate matrices and graphs of the type we have been discussing. Ego network data can be visualized in Krackplot and other programs and analyzed using any software program you would use to analyze sociometric data. Because ego networks tend to be smaller than networks derived from sociometric studies, analyses can often be conducted by hand or using Excel or SPSS.

28 Dynamic Social Network Analysis Theory Social relationships are dynamic Most SNA is static Static analysis may miss important characteristics of the social world. Examples Is liaison a position or a transition state? Changes in parent groups over the course of intervention.

29 Group 212: Pre

30 Group 212: Session 4

31 Group 212: Session 9

32 Group 212: Session 14

33 SAFE-E Group # of contacts Density

34 Dynamic SNA Methods Berger-Wolf method α (Persistence) β (turnover) γ (membership) Software tnet package in R does analysis of time-stamped ties - p DNA (Discourse Network Analyzer)

35 Summary Saturation Possible? Absentees or non- participants? Multiple Time Points N Y N Y N Y Sociometrics Informants Ego-Networks +? Dynamic SNA???? - +