The structure of (personality and) social psychology:

An empirical investigation using social network analysis

Kevin LanningSPSP social dynamics preconference

Long Beach, CAFebruary 2015

Slides posted at

www.slideshare.net/lanningk/JPSPstructure2015

Overview

•Networks, citations, bibliometrics

• JPSP and the structure of social (&,/,- personality) psychology

•Foretelling which papers will get cited

•Communities and the category structure of scholarship• (omitted from presentation due to time constraints)

•The problem of Big Data Reduction

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Why networks?

•Community as a level of analysis

•The reciprocal relevance of social psychology and network science

•Historical: Lewin, Heider, Milgram, …

•Contemporary: Inequality in complex systems

•The power of empiricism

•The availability of new tools for network analysis

Why scholarly networks?

• Science as a social endeavor

• A citation is a dyadic, directed act which occurs in a cultural context

• The need for a better map of scholarship

• From arbitrary keywordsto a tool for fostering social and intellectualcapital

Levels of analysis in citation networks

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 Degree, centrality

Forms of influence, impact,

eminence

Two types of scholarly network

The citation network• Source -> Reference

• Directed, biphasic, large, sparse

… here, a loss of older (no doi) cites

The structural network• Source <-> Source

• Bibliometric couplings

• Undirected, single mode, small, dense

Smith,

2014

Thomas,

2014

Abe, 2011 Baker, 1971 Coe, 2009 Davis, 1999

Reed,

2014

Smith,

2014

Thomas,

2014

Abe, 2011 Coe, 2009 Davis, 1999

Reed,

2014

Reed,

2014

Smith,

2014

Thomas,

2014

JP

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How many tribes in social-personality psychology?

‘SSP’

A singular social psychology

‘SPSP’

At the very least, an ‘&’ rather than a ‘/’ or ‘-’

‘SAIPP’

The three sections of JPSP as a valid model

Weak vs. strong forms of hypothesis.

Method

Develop and examine JPSP 2014 structural networknb: The procedure for culling references from PsycInfo is posted at

https://github.com/kevinlanning/StructureOfSocialPsychology/blob/master/ParsefromPsycInfo.Rmd

Properties of the JPSP 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

Results from the citation network:Papers most frequently cited 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.

9Franco, 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.

Properties of the JPSP <-> 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

Connections within/between JPSP sections

JPSP Section(s) Papers

(nodes)

Edges Density Density between

sections

Attitudes 30 170 39.1% --

Interpersonal 43 243 26.9 --

Personality 45 241 24.3 --

Attitudes & Interpers 73 686 26.1 21.2

Attitudes & Personality 75 605 21.8 14.4

Interpers & Personality 88 784 20.5 15.5

All sections 118 1421 20.6 16.8

Greater density within than between sections: The typical ‘Attitudes’ paper

shares refs with ~ 40% of papers in Attitudes, ~ 20% in the other sections

So what?

• Relative homogeneity provides support for the weak form of validity of the three areas

• But unclear just how distinct the areas are

A longitudinal approach

• Are the three areas, or personality and social, growing more separate?

• Method

• Analysis of 1981*, 1994, 1999, 2004, 2009 and 2014 volumes

• Comparison of citations within areas to citations between areas over time

JPSP connectedness over time: Detail

1981 1994 1999 2004 2009 2014

w/in Attitudes 11.5% 21.0% 30.9% 27.8% 21.9% 39.1%

Interpersonal 2.9% 6.5% 15.8% 24.1% 20.1% 26.9%

Personality 4.6% 16.4% 14.1% 15.2% 19.8% 24.3%

bet A & I 2.0% 7.2% 14.7% 19.6% 16.1% 21.2%

A & P 3.4% 6.4% 11.3% 6.5% 12.0% 14.4%

I & P 1.9% 7.1% 9.8% 9.3% 12.9% 15.5%

The Attitudes and Interpersonal sections are closer to each other

than either is to the Personality section

JPSP connectedness over time: Summary

1981 1994 1999 2004 2009 2014

Within sections 6% 16.3% 17.3% 22.4% 20.4% 28%

Between 2.7% 6.8% 11.4% 11.4% 13.6% 16.8%

Within/between 2.3 2.4 1.5 2.0 1.5 1.7

in 2014, a paper in JPSP was ~ 70% more likely to share

a reference with a paper in the same section than in

one of the other sections

JPSP connectedness over time: ‘Controlling’ for network size

1981 1994 1999 2004 2009 2014

N edges 2879 4938 4551 4761 6880 7248

Within/between 2.3 2.39 1.52 1.95 1.49 1.68

N selected edges 4547 4551 4550 4551 4547

Within/between 2.44 1.52 2.02 1.52 1.70

Relative homogeneity of discrete areas holds up after randomly slicing ~ 35% of references.

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Predicting citations

•Does the location of a paper in a network predict future citations?•Concepts of network centrality

•A second use of the longitudinal data•Prospective analyses

• 1994, 1999, 2004, 2009 properties ->citations to 2014

Different forms of network 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.

Most central papers in JPSP 2014 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.106.37Diminishing self-disclosure to maintain security in partners' care. 16 1 8

Lemay_Jr._E.i.107.638Accuracy/bias in self-perceived responsiveness -> security in romantic rs. 18 5 22

Hui_C.i.106.546When relationship commitment fails to promote partners' interests. 24 3 16

Nodes graded by Betweenness, Weighted degree, and (unweighted) PageRank

Node properties -> future cites:correlations and regression coefficients

1994 1999 2004 2009 across years

year -- -- -- -- -0.28

nrefs 0.2 0.24 0.18 0.2 0.14

PageRank 0.22 0.24 0.24 0.22 0.17

Betweenness 0.15 0.18 0.05 0.15 0.12

Degree 0.22 0.21 0.18 0.22 0.12

year -- -- -- -- -0.06

nrefs 0.16 0.19 0.14 0.16 0.16

PageRank 0.17 0.23 0.32 0.17 0.20

Betweenness -0.18 -0.10 -0.24 -0.18 -0.10

Degree 0.15 0.00 0.05 0.15 0.01

R2 0.08 0.08 0.10 0.08 0.14

Adjusted R2 0.05 0.06 0.07 0.05 0.13

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The challenge of communities

Partitioning a continuous universeThree 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

Modularity analyses of JPSP 2014

• Results not robust• Number of communities is dependent upon random seed

• 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

Modularity in SPSSI journals: Allport & Lewin

Lewin community includes authors with 5 or

more cites; Allport includes authors with 13+

cites. Nodes ranked by eigenvector centrality

A complex systems view (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

Exploring community structurein the JPSP 2014 data

• Explore thresholds 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 connectedby at least 2 common citations to at least 4 other papers within the community• Here, 8 communities in two separate components

Cinder

Personality

Pers’y & relationships

Interpersonal processes

Mixed

Cinder

Attitudes

Attitudes

Mixed

Mixed

Personality

Th

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Big

Data

Red

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On data visualizations

Same data, two different formats

JPSP 2014 structural network. Node size f(PageRank), degree >= 30. Spline

applied in right panel.

Big Data requires Big Data reduction

many ‘truths’ can be told

non-arbitrary principles for constructing data visualizations are needed