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Structure of social psych - Lanning Social Psych Winter Conf 2015
The document presents an analysis of social psychology through a social network lens, focusing on citation networks within the Journal of Personality and Social Psychology (JPSP). It explores the dynamics of research communities, citation relationships, and network centrality, concluding with a discussion of the applicability of community structuring in social psychology research topics. The findings suggest a complex interconnectedness of scholarly inquiry across three main sections of JPSP, highlighting both overlapping and distinct areas of focus within the field.
Structure of social psych - Lanning Social Psych Winter Conf 2015
- 1. Mapping the connectednessof social psychology: A social network approach Kevin Lanning Fred Rhodewalt Social Psychology Winter Conference Park City, UT January 2015 Note: Slides preceded by # were not included in the presentation due to time constraints. Updated analyses and a more detailed report are available from lanning@cal.berkeley.edu
- 2. Some heterogeneous startingpoints The trend towards team science Our evolved nature in communities The incompleteness of keywords …& arbitrariness in the assignment of papers to Associate Editors and Reviewers …& inadequacy of self-reports of expertise The artificiality of, and limits imposed by, the “organizational model” of academic units that shapes our (and student) lives The power of empiricism The availability of new tools for network analysis
- 3. The reciprocal relevanceof social psychology and network science Historical Lewin, Heider, Milgram, … Structural A language for the study of communities Networks inform one of the critical issues of our time: the study of inequality Who should buy/sell insurance? Who do we cite?
- 4. Interpreting citation networks Citations Dyadic,directed acts Occur within context Multiple levels of analysis Level of analysis Concept / parameter Relevance / interpretation Network (dynamic) Preferential attachment Developmental trajectories of topics, scholars Network (static) Giant component, density Connectedness of a research area Community Modules, cliques Topics, subdisciplines, categories Path Diameter, path length Distance and proximity of nodes… Node: Author, paper, journal, department (In)degree, centrality Forms of influence, impact, eminence
- 5. Analyzing citation networks Adescriptive, large data technique Nodes: Paper vs author vs journal vs … • Ambiguity of names (M Zuckerman, J Block, etc) • Here, primarily use paper/doi as identifier Edges: Many possible sources • Here, bibliographic couplings Two types of networks • Source -> Reference • The citation network (directed, biphasic, large, sparse) … can be reduced to • Source <-> Source • A structural network (undirected, single mode, small, dense)
- 6. Overview of analyses Datasets •JPSP 2014 • (JPSP 1981) • ((Annual Review chapters on personality)) • ((SPSSI papers)) • (((Scholar network at a public university in the southeast))) Tools (open source) • Gephi for network articulation & visualization • Lada Adamic’s MOOC on SNA • Cfinder for complex representation of communities
- 7. JPSP and thestructure of social/personality psychology Three sections of JPSP Attitudes and Social Cognition Interpersonal Relations and Group Processes Personality Processes and Individual Differences Is this partitioning valid? Weak form of hypothesis • Papers are “closer” within than they are between sections Strong form of hypothesis • An optimal partitioning of the space • Natural, discrete categories of scholarly inquiry
- 8. Properties of theJPSP 2014 -> reference citation network Biphasic, directed 6159 Nodes • 118 articles • 10024 citations • 7248 with doi • 6041 unique references (cited in 1 or more papers) 7248 Edges • Sparse: Density rounds to 0 (7248/(6159 * 6158)) Average path = 3.7, diameter is 6 (undirected) All articles are linked in a giant component
- 9. Papers most frequentlycited in JPSP 2014 cites reference 19 Preacher, K. J. Hayes, A. F. (2008). … indirect effects in ... mediator models. BRM, 40, 879-891. 14 Buhrmester, M. Kwang, T. Gosling, S. D. (2011). Amazon's Mechanical Turk. Pers. Psych Sci, 6, 3-5. 13 Blanz, M. (1999). Accessibility & fit determine salience of social categoriz. EJ Social Psych, 29, 43-74 10 Baumeister, R. F. Leary, M. R. (1995). The need to belong: attachments … Psych Bull, 117, 497-529. 10 Altemeyer, B. (1998). The other “authoritarian personality”. In M. Zanna (Ed.), Adv in Exper. Soc Psy. . 10 Simmons, J. P. Nelson, L. D. Simonsohn, U. (2011). False-positive psychology Psych Sci, 22, 1359-66. 9 Franco, F. M. Maass, A. (1999). Intentional control over prejudice: When the choice of the measure matters. European Journal of Social Psychology, 29, 469-477. 9 Watson, D. Clark, L. A. Tellegen, A. (1988). The PANAS Scales. JPSP, 54, 1063-1070. 8 Preacher, K. J. Hayes, A. F. (2004). SPSS and SAS … mediation models, BRM, 36, 717-731. 8 Shiner, R. Caspi, A. Goldberg, L. R. (2007). The power of personality. Pers. on Psych Sci, 2, 313-345.
- 10. Properties of theJPSP <-> JPSP structural network Single mode, undirected, small 118 Nodes (articles) 1421 Edges Edges are weighted by number of common citations The network is dense The average paper is directly linked to 24 others (20.6% of all possible links) Average path is 1.9, diameter is 4
- 11. Connections within andacross JPSP sections JPSP Section(s) Papers (nodes) Edges Density Density between sections Attitudes 30 170 39.1% -- Attitudes & Interpersonal 73 686 26.1 21.1 Attitudes & Personality 75 605 21.8 14.4 Interpersonal 43 243 26.9 -- Interpersonal & Personality 88 784 20.5 15.6 Personality 45 241 24.3 -- All sections 118 1421 20.6 16.8
- 12. What counts asa good ‘visualization’? (Tufte)
- 13. On graph visualizations inGephi • A powerful tool for conveying network structure • But easily manipulable, and no clearcut rules for ideal representations • Nodes ranked by degree (3 depictions) • linear vs nonlinear spline, labels vs none
- 14. Different forms ofnetwork centrality Degree and weighted degree: Number of direct links, possibly weighted by total shared cites PR (Page Rank, Eigenvector Centrality): Recursive measures in which the importance of a paper is dependent upon the importance of the papers which refer to it BC (Betweenness Centrality): Extent to which a node bridges different areas of scholarship, introduces work to a new audience, etc.
- 15. Nodes graded byBetweenness, Weighted degree, and (unweighted) PageRank
- 16. Most central papersin JPSP on 3 metrics Id source.title BC WD PR Rauthmann_J.p.107.677 The Situational Eight DIAMONDS 1 3 1 Gebauer_J.p.107.1064 Cross-cultural variations in Big Five r religiosity 2 2 7 Wakslak_C.a.107.41 Using abstract language signals power. 3 11 2 Barasch_A.a.107.393 Selfish or selfless? On the signal value of emotion in altruism 4 18 9 McClure_M.i.106.89 …attachment anxiety hurts relational opportunities. 5 7 4 Frimer_J.i.106.790 Moral actor, selfish agent. 8 13 5 Dunning_D.i.107.122 Trust at 0 acquaintance: respect not expectation of reward. 9 9 3 Lemay_Jr._E.i.106.37 Diminishing self-disclosure to maintain security in partners' care. 16 1 8 Lemay_Jr._E.i.107.638 Accuracy/bias in self-perceived responsiveness -> security in romantic rs. 18 5 22 Hui_C.i.106.546 When relationship commitment fails to promote partners' interests. 24 3 16
- 17. The challenge ofcommunities Partitioning a continuous universe Three approaches • A priori • Three JPSP areas • Top down (divisive) • Modularity assessment of whole graph • All inclusive, too Procrustean • Bottom up (agglomerative) • Begin with cliques • May allow for overlapping categories • Not all inclusive, may be too selective
- 18. Modularity analyses ofJPSP 2014 • Results not robust • A 7 community solution is representative • 2 primarily attitudes • 2 primarily interpersonal • 1 personality • 2 mixed Community Att Int Pers I 7 2 0 II 10 2 1 III 0 16 2 IV 4 10 3 V 0 2 26 VI 4 2 3 VII 5 9 10
- 19. #Modularity: Clusters I& II (attitudes) • Defaults to 7 communities (10 attitudes, 2 interpersonal, 1 personality) Malaviya_P.a.106.1 Do hedonic motives moderate regulatory f Zou_X.a.106.183 In pursuit of progress: Promotion motiva Schumann_K.i.107.432 Religious magnanimity: Reminding people Leander_N.a.107.229 Indifferent reactions: Regulatory respon Zhang_Y.a.107.965 Embodied motivation: Using a goal system Goldenberg_A.a.107.581 How group-based emotions are shaped by c Moscatelli_S.a.107.248 Badly off or better off than them? The i Malka_A.p.106.1031 Do needs for security and certainty pred Campbell_T.a.107.809 Solution aversion: On the relation betwe Topolinski_S.a.106.885 Oral approach?avoidance: Affective conse Krpan_D.a.107.978 Too close for comfort: Stimulus valence Buechel_E.a.106.20 More intense experiences, less intense f Hodges_B.i.106.218 Speaking from ignorance: Not agreeing wi (Includes 7 papers in attitudes, 2 in interpersonal) Barasch_A.a.107.393 Selfish or selfless? On the signal value Jung_M.a.107.414 Paying more when paying for others. Yeager_D.a.107.559 Boring but important: A self- transcenden Clark_C.a.106.501 Free to punish: A motivated account of f Leith_S.a.107.597 Changing theories of change: Strategic s Schumann_K.i.107.475 Addressing the empathy deficit: Beliefs Rheinschmidt_M.i.107.101 Social class and academic achievement in Hag�_S.a.107.994 Too young to correct: A developmental te Yeager_D.a.106.867 The far-reaching effects of believing pe
- 20. #Modularity: Clusters III& IV (interpersonal) • Defaults to 7 communities (4 attitudes, 10 interpersonal, 3 personality) Wakslak_C.a.107.41 Using abstract language signals power. Thomas_K.i.107.657 The psychology of coordination and commo Friesen_J.i.106.590 Seeking structure in social organization Kross_E.p.106.304 Self-talk as a regulatory mechanism: How Halevy_N.i.106.398 How decisions happen: Focal points and b Acar-Burkay_S.p.107.719 Trusting others: The polarization effect Sweeny_K.p.106.1015 Mapping individual differences in the ex Claypool_H.i.106.571 Social exclusion and stereotyping: Why a Yogeeswaran_K.i.106.772 The devil is in the details: Abstract ve Hsee_C.a.106.699 Approach aversion: Negative hedonic reac Critcher_C.i.107.454 The involuntary excluder effect: Those i Campbell_T.i.106.272 Too much experience: A desensitization b Uskul_A.i.106.752 Responses to social exclusion in cultura Yan_D.a.106.514 Future events are far away: Exploring th Mittal_C.i.107.621 Sense of control under uncertainty depen Case_C.i.107.1033 Divide and conquer: When and why leaders Holtgraves_T.a.107.219 Interpreting uncertainty terms. (Includes 16 interpersonal, 2 personality) McClure_M.i.106.89 Anxiety doesn?t become you: How attachme Lemay_Jr._E.i.106.37 Diminishing self-disclosure to maintain Marigold_D.i.107.56 You can?t always give what you want: The Lemay_Jr._E.i.107.63 8 Accuracy and bias in self- perceptions of Cortes_K.i.106.380 Self-regulating the effortful ?social do Hui_C.i.106.546 The Manhattan effect: When relationship Wiltermuth_S.p.107.925 ?I?d only let you down?: Guilt proneness Nelissen_R.i.106.257 Relational utility as a moderator of gui VanderDrift_L.i.106.927 Relational consequences of personal goal Eastwick_P.i.106.728 Relational mate value: Consensus and uni West_T.i.107.81 A little similarity goes a long way: The Eastwick_P.i.106.429 Is a meta-analysis a foundation, or just Forest_A.i.107.1013 Discount and disengage: How chronic nega Overall_N.i.106.235 Attachment anxiety and reactions to rela Solomon_B.p.107.516 You are so beautiful...to me: Seeing bey Meltzer_A.i.106.418 Sex differences in the implications of p West_T.i.107.825 Anxiety perseverance in intergroup inter Meltzer_A.i.106.435 Men still value physical attractiveness
- 21. #Modularity: Cluster V(personality) • Defaults to 7 communities Includes 2 interpersonal, 26 personality) Rauthmann_J.p.107 .677 The Situational Eight DIAMONDS: A taxono Foschi_R.p.106.339 Does sociability predict civic involveme Gebauer_J.p.107.10 64 Cross-cultural variations in Big Five re Hilbig_B.p.107.529 Personality and prosocial behavior: Link Dittmar_H.p.107.87 9 The relationship between materialism and Wang_S.i.107.864 Psychological well-being and job stress Solomon_B.p.106.9 78 Why do personality traits predict divorc Pauletti_R.p.106.843 Influences of gender identity on childre Cohen_T.p.107.943 Moral character in the workplace. Bardi_A.p.106.131 Value stability and change during self-c Mund_M.p.107.352 Treating personality-relationship transa Van_den_Akker_A.p.107.73 6 Mean-level personality development acros Manczak_E.p.106.169 Regulatory focus in the life story: Prev Israel_S.p.106.484 Translating personality psychology to he B�langer_J.p.107.494 The psychology of martyrdom: Making the Ng_W.p.107.326 What matters to the rich and the poor? S Callan_M.p.107.142 Making sense of misfortune: Deservingnes Specht_J.p.107.540 On the consistency of personality types Jeronimus_B.p.107.751 Mutual reinforcement between neuroticism Schriber_R.p.106.112 Personality and self-insight in individu Human_L.p.106.286 To thine own self be true: Psychological Colonnesi_C.p.106.624 Positive and negative expressions of shy Church_A.p.106.997 A four-culture study of self-enhancement Major_J.p.106.638 Linear and nonlinear associations betwee Saucier_G.p.107.199 Human attribute concepts: Relative ubiqu Luhmann_M.p.107.163 Honey, I got fired! A longitudinal dyadi Ku_L.p.106.803 To have or to learn? The effects of mate Nadler_A.i.106.58 Helping them stay where they are: Status
- 22. #Modularity: Clusters VIand VII (mixed) • Defaults to 7 communities (5 attitudes, 9 interpersonal, 10 personality) Frimer_J.i.106.790 Moral actor, selfish agent. Dunning_D.i.107.122 Trust at zero acquaintance: More a matte Koopmann- Holm_B.p.107.1092 Focusing on the negative: Cultural diffe Landau_M.a.106.679 The college journey and academic engagem Goldstein_N.i.106.941 Perceived perspective taking: When other Sullivan_D.a.107.767 The dramaturgical perspective in relatio Przybylski_A.p.106.441 Competence-impeding electronic games and Kogan_A.p.107.1051 Vagal activity is quadratically related de_Melo_C.i.106.73 Reading people?s minds from emotion expr Orehek_E.i.107.265 Interdependent self-construals mitigate DeWall_C.p.107.339 Explaining the relationship between reli Lambert_A.a.106.655 Toward a greater understanding of the em Peer_E.a.106.202 ?I cheated, but only a little?: Partial Swann_Jr._W.i.106.713 Contemplating the ultimate sacrifice: Id Mojzisch_A.i.106.961 The consistency principle in interperson Birnbaum_G.p.106.822 When sex goes wrong: A behavioral system Morris_K.p.107.181 Trio of terror (pregnancy, menstruation, Kesebir_P.p.106.610 A quiet ego quiets death anxiety: Humili Sellier_A.a.107.791 So what if the clock strikes? Scheduling Gramzow_R.p.106.458 Boasts are a boost: Achievement prime se Goff_P.i.106.526 The essence of innocence: Consequences o Swann_Jr._W.i.106.912 What makes a group worth dying for? Iden Goodwin_G.p.106.148 Moral character predominates in person p Hull_J.p.107.300 A longitudinal study of risk-glorifying (Includes 4 attitudes, 2 interpersonal, 3 personality) Ratner_K.a.106.897 Visualizing minimal ingroup and outgroup Wildschut_T.i.107.844 Collective nostalgia: A group-level emot Critcher_C.a.106.359 If he can do it, so can they: Exposure t Kawakami_K.a.107.1 An eye for the I: Preferential attention Steiger_A.p.106.325 Low and decreasing self-esteem during ad Klauer_K.a.107.21 How malleable is categorization by race? Chung_J.p.106.469 Continuity and change in self-esteem dur Mannes_A.i.107.276 The wisdom of select crowds. Koenig_A.p.107.371 Evidence for the social role theory of s
- 23. A complex networkview (Palla et al, 2005) Communities as cliques • Each node is linked to at least k other nodes • Family resemblance Nodes (papers) may belong to multiple communities Overlapping communities also constitute a network • Multiple levels of categorization Open source software at Cfinder.org
- 24. #Applying Cfinder • Explorethresholds for filtering data • Here, minimum edge weight of 2 • Investigate network structure for various values of k • Here, k > = 5 • Communities are groups in which each paper is connected by at least 2 common citations to at least 4 other papers within the community • Here, 8 communities in two separate components
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- 27. #Attitude II ->3 k-cliques (Includes 7 papers in attitudes, 2 in interpersonal) Jung_M.a.107.4 14 Paying more when paying for others. 5 Clark_C.a.106.50 1 Free to punish: A motivated account of f 5 Barasch_A.a.107 .393 Selfish or selfless? On the signal value 5,6 Yeager_D.a.107. 559 Boring but important: A self- transcenden 5,7 Schumann_K.i.107.47 5 Addressing the empathy deficit: Beliefs 6,7 Yeager_D.a.106.867 The far-reaching effects of believing pe 7 Rheinschmidt_M.i.10 7.101 Social class and academic achievement in 7 Leith_S.a.107.59 7 Changing theories of change: Strategic s 7 Hag�_S.a.107.994 Too young to correct: A developmental te
- 28. A second JPSPdataset • JPSP 1981 • First year of three areas • Assess density within, between sections • Compare with 2014 • Prospective analyses • Which measures of centrality are most important? • Do 1981 parameters of centrality, clique membership predict citations 1982 - > 2014? • Inspect community structure of 1981 papers
- 29. #Properties of theJPSP 1981 -> reference citation network • JPSP1981 -> reference network • Biphasic, directed • 2879/6153 references have doi • 2418 Nodes (206 articles, 2212 unique references) • 2879 Edges (sparse: density rounds to 0) • Average path = 7.6, diameter is 18 (undirected) • 2226 articles (92% linked in a giant component • JPSP1981 <-> JPSP1981 network • Single-mode, undirected • 206 nodes, 855 edges (density = 4%) • 183 (88%) of nodes linked in a giant component • Average path = 3.1, diameter is 8
- 30. Connections within andacross JPSP sections JPSP Section(s) Papers (nodes) Edges Density Density between sections Density Density between sections 1981 2014 Attitudes 65 239 11.50% 39.1% -- Attitudes & Interpersonal 102 305 5.9 2.0 26.1 21.1 Attitudes & Personality 169 716 5 3.4 21.8 14.4 Interpersonal 37 19 2.9 26.9 -- Interpersonal & Personality 141 337 3.4 1.9 20.5 15.6 Personality 104 245 4.6 24.3 -- All sections 206 855 4 2.7 20.6 16.8
- 31. #Most central papers– JPSP 1981 Id source.title BC WD PR cites Kuhl_J.p.40.155 Motivational and functional helplessness: The mode 1 6 6 159 Tunnell_G.p.40.1126 Sex role and cognitive schemata: Person perception 2 4 3 1 Eagly_A.p.40.384 Sex differences in conformity: Surveillance by the 3 8 10 45 Romer_D.p.41.562 A person-situation causal analysis of self-reports 4 24 14 7 Polivy_J.p.41.803 On the induction of emotion in the laboratory: Dis 5 25 15 56 Wilson_T.a.40.53 Awareness and self-perception: Verbal reports on i 7 20 5 0 Harvey_J.p.40.346 How fundamental is "the fundamental attribution er 9 12 4 35 Yarkin_K.a.41.243 Cognitive sets, attribution, and social interactio 10 9 1 11 Fazio_R.a.41.232 Self-perceptions following social interaction. 13 9 2 140 Major_B.p.41.988 A different perspective on androgyny: Evaluations 21 1 20 11 Harrington_D.p.41.744 Creativity, masculinity, femininity, and three mod 55 2 32 14 Heilbrun_A.p.41.1106 Gender differences in the functional linkage betwe 64 5 40 15 Lubinski_D.p.40.722 The relationship between androgyny and subjective 113 3 63 46
- 32. #Most cited papersfrom JPSP 1981 (1981 -> present) Id source.title BC WD PR cites Bandura_A.p.41.586 Cultivating competence, self-efficacy, and intrinsic interest through proximal self-motivation. 134 150 168 461 Petty_R.a.41.847 Personal involvement as a determinant of argument- based persuasion. 30 18 26 436 Kinder_D.a.40.414 Prejudice and politics: Symbolic racism versus racial threats to the good life. 145 184 190 417 Cheek_J.p.41.330 Shyness and sociability. 26 83 44 406 Wong_P.a.40.650 When people ask "why" questions, and the heuristics of attributional search. 23 11 11 375 Sternberg_R.a.41.37 People's conceptions of intelligence. 145 184 190 301 Brewer_M.a.41.656 Perceptions of the elderly: Stereotypes as prototypes. 130 88 121 290 Batson_C.i.40.290 Is empathic emotion a source of altruistic motivation? 38 83 85 235 Hoffman_M.p.40.121 Is altruism part of human nature? 25 76 54 191 Kuhl_J.p.40.155 Motivational and functional helplessness: The moderating effect of state versus action orientation. 1 6 6 159
- 33. Little relationship between1981 centrality and subsequent cites source.citedby weighted.degree 0.11 betweenness.centrality 0.13 page.rank 0.12 ncliques 0.07
- 34. #Applying Cfinder to1981 data • Explore thresholds for filtering data • Here, minimum edge weight of 1 (unfiltered) • Investigate network structure for various values of k • Here, k > = 8 • Communities are groups in which each paper is connected by at least 1 common citations to at least 7 other papers within the community • Here, 7 communities in one component connecting 60 papers
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- 40. #Other datasets Annual Reviewarticles on personality • Author as the unit of analysis (Smith,J) • 33 source papers published 1977-2012 Journal of Social Issues and Analyses of Social Issues and Public Policy (journals of SPSSI/APA Division 9 • 855 source papers published 2001-2013 • By citation (Smith,J 2006) • By author (Smith,J) Analyses of first authors only.
- 41. #Big data, smallworld: Personality in the Annual Review Scope • 6,294 references by 2,803 unique authors • Of these, 219 self-cites (3.5%) are excluded Connectedness • All authors are connected, and separated by no more than 5 degrees • Average path is 3 Nodes and text are colored by community. Node size represents Eigenvector centrality. Layout determined by Force Atlas 2 algorithm. All analyses and visualizations done in Gephi.
- 42. #Personality in theAnnual Review: Most cited authors 115 authors with 5 or more cites
- 43. #Personality in the Annualreview: Communities as constellations / The five factor paradigm Between 12 and 15 communities are identified. One of the largest is anchored by source papers of Wiggins, Carson, Digman, and Ozer.
- 44. #Personality in theAnnual review: Minnesota and Berkeley schools In one analysis, the two largest communities
- 45. #Proximity and distancein citation networks • Proximity may occur for several reasons; distance is less ambiguous • Closeness of Block and Mischel in the personality space (right) • Greatest distances among source papers • Parke (‘83, Social and Personality Development) • -> Rorer (‘83, Personality Structure and Assessment) • -> Butcher (‘96, Personality: Individual Differences and Clinical Assessment).
- 46. #Analyses of SPSSIjournals • All papers published in JSI, ASAP from 2001-2013. • N sources = 855 • 38854 references(45.4 per source) • - 2,042 self-references (5.3%) • - 3,198 (8.2%) unusable: references to news articles, government institutes, or without a date ____________________________ • 33,615 usable citations (86.5%) • 24,263 unique papers • 14,702 unique first authors
- 47. #The SPSSI author network: Most cited Includes68 authors with 20 or more citations. Nodes ranked by eigenvector centrality
- 48. #SPSSI citation network:Connectedness • Of the 24,263 papers, 24,075 (99.2%) are linked in a single giant component • Papers are separated by an average of 4.2 links
- 49. The SPSSI author network:Allport and Lewin communities compared Lewin community includes authors with 5 or more cites; Allport includes authors with 13+ cites. Nodes ranked by eigenvector centrality
- 50. #(How) has ASAPchanged SPSSI? Total JSI ASAP only ASAP unique authors 696 491 233 205 unique cited 14568 11704 4848 2864 unique scholars (nodes) 14702 11778 4942 2924
- 51. The communities ofdepartments bear little resemblance to the existing colleges of the university #A network of departments: Global and clique-based perspectives
- 52. #Summary, concluding thoughts Communities •Discrete clusters are artificial • A valid structure of social psychology will require some more data, and much more thought Impact • Beyond great persons • Different measures of centrality have distinct interpretations Connectedness • To see small worlds, you need big data Distance • May be more interpretable than proximity Obsolescence • Bigger data and much more sophisticated methods lie ahead
Editor's Notes
- #2 Let’s talk about us. My focus is on the structure of social psychology based on citation analyses, but also on citations as a form of social behavior. Something of a workshop, but what is really needed is not a workshop in methods, but a workshop on gaining access to and working with understudied populations – June.
- #3 Science is a social endeavor, in collaborations (growing) also the act of citing and learning from another. Personality, altruism, cooperation in dyads, but in communities, as in Apicella (gift-giving among the Hadza) My experience over the last five years as editor of a SPSSI journal. My admin post at FAU University functions (such as advising) are run by anecdote and tradition, not by data
- #4 Social networks is really a natural home for me, and for us. It brings together ideas such as Lewin’s topology, notions of distance, forces; Heider’s balance theory and the study of triads, and Milgram’s study of small worlds together with new advances in big data Consider a scenario in which you have a small probability of a very large financial loss, and a high probability of a trivial financial gain, What should you do? If you are not very wealthy, the very large loss can be catastrophic, and so you buy insurance – and you will probably become a little poorer. If you are very, very wealthy, it becomes rational for you not to buy but to sell insurance – and you become a little richer. This insurance game is a trivial example of how the rich get richer, the so-called Matthew effect. In networks it is called preferential attachment. Lewin might have described it as a force, akin to gravity, which contributes to inequalities – a power distribution of resources – in a variety of ways. Preferential attachment governs citations too.
- #5 Context might includes the intellectual structure as well as perceived social norms These may be consequential. In the 1.7 million record JSTOR database, King et al (2014) found that men cite themselves 56% more often than women, is it indeed true that no man is an island. Finally, we can think about scholarly eminence in a richer way than simple citation counts, one which recognizes diverse mechanisms of social and scholarly influence.
- #6 Large data: 70% of work is in data cleaning and prep Sources for edges: Clickstreams, shared text (Green’s analysis of early Psych Review, J Hist Beh Sci, 2014)
- #8 Not merely that this is the best way to divide the journal, or area, into discrete categories, but more Argument for 1: Swann & Selye (2005) – personality’s emerging symbiosis with social Argument for 2: Tracy, Robins, & Sherman (2009): persy and social psychology differ primarily along lines of Cronbach’s two disciplines of scientific psychology – in essence, the correlational and experimental
- #9 Force Atlas 2 algorithm – gravity and springs pushing papers away from each other No paper is an island
- #10 Largely methodological papers.
- #12 Support for the weak version of the hypothesis Relative homogeneity or coherence of the attitudes section Distance of the personality section from the other two We’ll look at this in a few other ways shortly
- #13 An ugly word, Visual representation of data. An important idea, however.
- #14 The layout is based on an algorithm (force atlas 2) in which the positioning of pairs of nodes is based on their pairwise degree of separation, like springs pushing them apart. The layout is then modified for legibility, so that names are minimally overlapping. And keep in mind that this is a 2D representation of a multidimensional space. Gephi is open source and quite cool. (possibly open it up here)
- #15 These diagrams aren’t pictures of molecules, but the same small network depicted 3 times, with font size is ordered by three different parameters or conceptions of centrality. These illustrate some of the different forms of scholarly impact. The simplest of these is citation counts. In the top diagram, the node labeled ID has the highest in-degree or number of cites. The more interesting measures are recursive. Imagine – it is perhaps too easy to imagine- that you pick up or click on an article at random on your desk, then move to another cited within it. Then the likelihood that you’ll come across a given article is its eigenvector centrality, or the closely related PageRank. One feature of these approaches is that if you are reading a particular paper (ID in the graph here) that refers only to 1 other paper, then you will certainly land upon the second paper (PR). Still another approach is betweenness, in which the measure of centrality is the extent to which one ties together different parts of the network.
- #16 Spline applied
- #17 Note on id field
- #19 Dependent on random seed 5-7 communities, with some variability in membership across analyses
- #24 Adjacent complete subgraphs combine into communities
- #30 Force Atlas 2 algorithm – gravity and springs pushing papers away No paper is an island
- #31 In 1981 data, attitudes section was already more homogeneous than the other two. The few papers in the interpersonal section were not coherent, closer to the personality section than to Support for the weak version of the hypothesis9 Relative homogeneity or coherence of the attitudes section Distance of the personality section from the other two We’ll look at this in a few other ways shortly
- #33 Quinones-Vidal (2005) half-life of JPSP papers was about 8 years in 1981. Incidentally – this value is getting larger, not smaller over time, suggesting that the weight of history effect > any accelerating change effect
- #36 Personality (2) and persy/attitudes (5)
- #37 From 2-> 5 -> 6, persy -> persy/att -> attitudes
- #38 Here, group 6 – attitudes – is on the right,
- #41 Papers in Annual Review with the word Personality in the title. All articles in two journals. I am treating the datasets separately here.
- #42 The data. Despite the restriction to 1st authors, the datasets become large. Further, they require some curating to get into usable form. The self-citation rate is a little higher than that reported (2.6%) by King Layout The layout is based on an algorithm (force atlas 2) in which the positioning of pairs of nodes is based on their pairwise degree of separation, like springs pushing them apart. The layout is then modified for legibility, so that names are minimally overlapping. And keep in mind that this is a 2D representation of a multidimensional space. Gephi is open source and quite cool.
- #43 For all of the measures, I’ve shaded the top ten in the table. The key idea here is that the simple citation counts and recursive measures give similar results, led by Block and Mischel. I’ve italicized Mischel and Funder here because they are authors of AR chapters as well as targets of citation, and the measures of impact might be somewhat inflated by this (that is, AR papers are particularly likely to cite earlier AR papers).
- #44 The most common approach to assessing the community structure of networks treat these as mutually-exclusive, all-inclusive clusters or categories. My results here are not particularly stable or robust - in ten successive attempts at partitioning the space into discrete modules, a random seed led to solutions with between 12 and 15 categories. It is possible that other approaches to extracting modules would lead to stable and efficient results, but another possibility is that a simple Aristotelian partitioning of the space doesn’t seem to do a very good job. Fuzzy categories or regions anchored in dimensions or prototypicality would likely provide a more stable articulation of the space. Regardless, one of the more stable communities in the analyses might be characterized as the five-factor paradigm.
- #45 The ‘school’ metaphor is not bad for thinking about regions of the space. In one analysis, the two largest regions are largely defined by personality psychologists in residence or trained in Minnesota and at Berkeley, particularly the Institute of Personality Assessment and Research, now known as IPSR. One individual, Harrison Gough, is an historical bridge between these two communities.
- #46 For those of us familiar with the personality literature, the idea that Block and Mischel should appear close together in the network is counterintuitive. Yet the things which bring nodes together, or which forge co-citation are of different types. (In this, there is an analogy with other forms of social bonds, including attachment). Yet if proximity is difficult to interpret, distance is less so, as it suggests that the authors have little in common.
- #47 A much larger database, and not constrained by the vagaries of the selection of particular Annual Review articles. I first consider this at the level of individual citation, then aggregate these by author.
- #48 68 authors were cited 20 or more times; some statistics are presented on the next slide.
- #49 A sense of this network can be obtained in a low-budget animation of zooming in on the most important authors. As you can see, the most cited paper – or rather book – is Allport’s The nature of prejudice.
- #50 Where is Lewin? – ranked 23rd in EC, 35th in PR, 21st in weighted cites Lewin is revered, but his influence appears increasingly indirect In comparison to the Allport community, the Lewin community appears to include a disproportionate number of historians of psychology.
- #52 Here, I consider communities among the 46 departments of the university, in which links arise because faculty members in two departments cite the same sources. In the illustration at left, colors correspond to colleges; this graph was generated using Gephi. In this graph, departments in the College of Arts and Letters, like those of the tiny Honors College, are on the periphery, literally marginalized by the approach. The seven departments in the College of Education are in close proximity, as are the six departments in the College of Business. This is not true, however, for the five departments of the College of Design and Social Inquiry, in green. (The Colleges of Medicine and Nursing each consist of a single department). At right are illustrated three overlapping cliques which lie at the core of the graph, generated from c-finder. In this first image, I’ve filtered out all but the strongest links (those joined by a minimum link weight of 15) in order to reveal only the most robust links between departments. With this minimum link weight of 15, three overlapping k-cliques appear, together comprising 11 departments, each of which is linked to three or more other departments. The first of these communities includes Geosciences, Biology, Urban and Regional Planning, Computer and Electrical Engineering and Computer Science (here, EECS), and Medicine. This is the most tightly connected large group of departments in the University, but these five areas span four colleges. I examine these departments more closely below. These strong cliques of departments are strikingly heterogeneous with respect to the colleges from which they are drawn; in the two cliques with five departments, each is drawn from four different colleges. The remaining clique includes three departments in the College of Business together with psychology. The communities of departments bear little resemblance to the existing colleges of the university.
- #53 Small worlds: The law of large numbers applies in how we get to the truth of our connectedness. Giant components and small worlds are more apparent as our data become more complete. Citation networks in personality and social psychology are small worlds in which virtually all of us can be connected On articulating the space Clustering: “Communities” are fuzzy, artificial, and lack robustness Distance and proximity Obsolescence Primitive: small big data – King studied 1.6 million cites. Others have looked at similar qs in a much more sophisticated way. First authors as opposed to all authors Authors as compared with full citation Boyack - more coherent networks can be obtained if one also assesses how far apart they are cited in the source...for example, in the beginning of the introduction or in the methods section.