MEANINGFUL LINKS: USING NETWORK ANALYSIS TO ARTICULATE THE STRUCTURE OF by Jared Hesse

A Thesis Submitted to the Faculty of The Harriet L. Wilkes Honors College in Partial Fulfillment of the Requirements for the Degree of Bachelor of Art in Liberal Arts and Sciences with a Concentration in Psychology

The Harriet L. Wilkes Honors College of Florida Atlantic University Jupiter, Florida May, 2014

MEANINGUFL LINKS: USING NETWORK ANALYSIS TO ARTICULATE THE STRUCTURE OF by Jared Hesse

This thesis was prepared under the direction of the candidate’s thesis advisor, Dr. Kevin Lanning, and has been approved by members of his supervisory committee. It was submitted to the faculty of the Wilkes Honors College and was accepted in partial fulfillment of the requirements for the degree of Bachelor of Arts in Liberal Arts and Sciences.

SUPERVISORY COMMITTEE:

______Dr. Kevin Lanning

______Dr. John Hess

______Dean Jeffrey Buller, Wilkes Honors College

______Date

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Acknowledgements

I would like to thank Dr. Kevin Lanning for his constant encouragement and critical support while writing this thesis. He has encouraged me to push the boundaries of my research to their highest potential and to never settle for an answer that fails to satisfy the research question. Dr. John Hess has been an inspiration to my work as his unrelenting critical perspective has taught me that surface analyses of any kind are inadequate and that academic success is a product of constantly expanding one’s conceptual ideas further and further. I am also grateful for the entire psychology department at the Harriet L. Wilkes Honors College as they have had a tremendous positive impact on my academic career in psychology. I would also like to thank Jaclyn

Goldstein, Kadeem Ricketts, and Blake Bailey for their unwavering friendships during my undergraduate stay and for serving as remarkable inspirations by cheering me on to work harder and become the best possible version of myself that I could hope to be.

Lastly, I would like to thank my family for their dedication, love, and steadfast support in any endeavor that I set my mind to.

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ABSTRACT

Author: Jared Hesse

Title: Meaningful Links: Using Network Analysis to Articulate the

Structure of Personality Psychology

Institution: Wilkes Honors College of Florida Atlantic University

Thesis Advisor: Dr. Kevin Lanning

Degree: Bachelor of Arts in Liberal Arts and Sciences

Concentration: Psychology

Year: 2014

The current study is presented as an exploratory network analysis of personality psychology using a network composed of 54 source papers and references. The network contains 2852 distinct papers with 4455 connections between them. The articles used were papers from the Annual Review of Psychology, which dealt with the subject of personality from 1950 to 2012. References from the source papers were pulled and mapped onto a network that graphically illustrated the links between different citations.

From the network it was possible to deduce, both visually and statistically, distinctively clustered communities, the relative influence of certain , and the researchers that bridged disciplinary gaps within the field of personality research. By using methods such as network analysis it is possible create an alternative map of the field of personality psychology and science as a whole.

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Table of Contents

Introduction ...... 6

Methods...... 16

Results ...... 17

Discussion ...... 23

Appendix ...... 29

References ...... 48

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Meaningful Links: Using Network Analysis to Articulate the Structure of

Personality Psychology

Network analysis is an emergent method which seeks to illustrate and interpret the data in the world around us in terms of relationships between entities. With the advent of

Big Data, the social sciences have exploded with research into how our interconnected lives can articulate larger ideas about society. The sheer volume of data available, in tandem with refined tools of analysis, can be used to describe higher-order societal changes which transcend the boundaries of any one academic discipline. A collection of data on individuals’ movements through airports could be tracked and visualized to understand how a pandemic spreads. Networks can use data longitudinally to express the changing shape of interpersonal relationships, country allegiances, and illustrate how various forms of contagion, social and medical, can spread through a network of individuals (Lazer et al., 2009).

The value of network analysis goes beyond the pictorial depiction of the studied relationship. Questions regarding the size, density, and overall structure of networks can lead to new insights beyond often stunning visual displays. Network analysis has an extremely high degree of interdisciplinary reach and potential, and is a strong holistic approach to the questions it seeks to answer (Easley & Kleinberg, 2010; Newman, 2010).

As a testament to their interdisciplinary nature, networks have been used in an assortment of venues, academic and otherwise, including: mapping the spread of pandemic diseases, creating better search results for Google, and charting the pathways of neuronal activity in the brain (Barabási, 2012). Webster, Dzedzy, & Crosier (2013) used social network analysis to examine gender differences in hiring in a sample of American Association of

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Universities (AAU) colleges. The researchers were able to discern which universities were most pivotal in closing the gender gap in hiring as well as what hiring pictorially looks like in a network space. By analyzing the number of hiring paths that go through certain universities the researchers were able to find correlations between how “central” a university was and their productivity, prestige, and gender of professors.

The process of mapping scientific disciplines has recently gained traction in the practical realm as it allows researchers to find peers with similar research interests and to see how certain scientific communities are partitioned. Analyzing citations and mapping them in a network is the base method in mapping a scientific community. Klavans &

Boyack (2010) describes three methods that are classically used in citation network analysis: co-citation analysis, bibliographic coupling, and direct citation. Co-citation analysis describes the frequency which two articles are cited by another article. If two articles are frequently cited by others they can be seen as related by their mutual citations.

Bibliographic coupling refers to when two papers share a citation in common. This indicates that the two citing papers may be similar in their subject matter as they both reference the same material. Lastly, direct citation describes mapping citations as from one paper to another, only considering direct citation links and disregarding mutual citations. Direct citation is considered to be the weakest method of scientific mapping with co-citation analysis and bibliographic coupling being tied for the most effective way

(Klavans & Boyack, 2010). Bibliographic coupling’s effectiveness is dampened by the fact that it is a retroactive measurement of outgoing citations from source papers. Co- citation analysis accounts for this by considering incoming citations of older papers. By

7 analyzing a large citation network using both bibliographic couplings and co-citation analysis the time confounds for both can be somewhat mitigated.

In bibliographic coupling, the citations in papers act as one type of connection that can form the basis of a network. If “A” cites “B” it creates a link between the two papers. If “C” cites “B”, “A” is then connected to “C” via “B”, by analyzing these citation networks, it is possible to glean new information regarding the importance and longevity of certain authors in a network of papers. Unnoticed links between researchers and disciplines may arise as a result of common citations between authors on other

“sides” of the psychological field. Even a citation that is critical of another author has significance in that the citing author found the citee important enough to be criticized.

Through this method of research, we can better understand how fields of psychology change over time, and we only need to look at their references. Numerous studies have already examined citation networks in scientific journals and have articulated robust metrics, such as PageRank and network centrality, for measuring the importance of a person or paper (Rahm & Thor, 2005; Ding, 2009; Radev et al., 2009). The present study seeks to use journal articles from the Annual Review of Psychology to understand how the structure of personality psychology has changed and what authors have been most important, or central, in the evolution of the field.

The Annual Review of Psychology

The Annual Review of Psychology was chosen for the present study because of its longevity as a journal and because of its importance in the growth of the psychological field. The Annual Review of Psychology is the second-most cited journal in the field of

8 psychology, according to the 2012 Journal Citation Reports which compare and rank the impact factors of over 10,000 different journals (Thomson Reuters, 2013). Each year, from 1954 to 2012, the Annual Review has published in their review a paper titled

“Personality,” or a similar title (see Appendix), that is written by prominent researchers in the field of personality psychology. Annual Review papers serve as a review and critique of the contemporary state of personality psychology, from the lens of the author.

The papers’ generally have over a hundred citations and are well-cited in the field of psychology. As such, Annual Review articles constitute an appropriate starting point for constructing a network, not only because of the volume of citations the papers contain, but due to the methodological-bridging nature of the reviews. The Annual Review citations can show how longitudinally important a is, how psychological topics are related through citations, and the relative impact of a psychologist’s review of the field.

Networks and Their Composition

A network is composed of a collection of nodes, or “sources”, whose connections, usually called “links” or “ties,” denote specific relationships. “Relationship” is itself an extremely flexible construct, but despite the wide variety of possible relationships, most network ties can be categorized into either state-type ties or event-type ties (Borgatti &

Halgin, 2011). A state-type tie refers to connections based on somewhat continuous states such as the kinship, cognitive awareness, or affective mood toward a certain person. An event-type tie is a connection that is based in directional action, such as sales or nominations. For example, a small network composed of nodes “A”, “B”, and “C” could

9 have state-type connections which describe friendship: a link from “A” to “B” would illustrate a direct, friendly connection between them.

Additionally, not all networks illustrate directed relationships in their links; networks can further be categorized as either directed or undirected (Borgatti & Halgin,

2011). An example of a directed network would be a network of who initiates phone calls between groups of friends. In this network one friend receives another call from another.

There is no transitivity in this unidirectional relationship. Conversely, an undirected network has non-directional relationships between nodes that can flow in both directions.

Thus, if the aforementioned “A”, “B”, and “C” network was undirected, a link between

“A” and “B” would illustrate mutual friendship. Conversely, in a directed network a link from “A” to “B” would only show a unidirectional relationship that is not reciprocated.

Furthermore, in a directed network nodes have a dimension called degree, which describes the number of links a node has. In-degree refers to the number of incoming links to a node while out-degree describes the number of outgoing links from a node

(Barabási, 2012).

A network’s structure can be densely packed, spread extremely thin, or even be broken up into several smaller clusters. A network’s density is defined by the number of actual ties between nodes over the number of possible ties between nodes. This can be thought of as potential connections in a network vs. actual connections. An acquaintance network composed of shoppers in a super market may have a large amount of nodes, but might have low density due to the number of strangers in the sample. Conversely, a network of family members at a reunion may have high density as nearly all family members would know each other to some degree. A low density network of a research

10 area could imply a lack of communication or collaboration between researchers.

Similarly, a high density research network could show an extremely tight-knit community of collaborators. High density could also illustrate echo chamber effects in a research network if the citations are too centralized to the point of not branching out.

In a very tightly packed network, perhaps one made up of co-workers, the concentration gradient of nodes would be very high in the center of the network space and would begin petering out as the distance from the center cluster of co-workers increases. Links between nodes have varying levels of strength to them, that is to say that ties can be thought of as either strong or weak (Easley & Kleinberg, 2010). A node with strong ties is generally found toward the center of the network as it is usually the most well-connected node in the network space. These strongly connected nodes can be seen as the hub of the network as their level of connectivity implies a centrality to the relationship being studied in the network.

A node with weaker ties is usually farther from the core of the network, but could potentially bridge the gap between two strongly-connected network clusters. A weakly tied node with a very low level of overall in-degree and out-degree may seem unimportant on face value, but can actually tell more about the network than a node with higher degree. For instance, in the aforementioned hypothetical network of co-workers, a single node could be a conduit between members of Human Resources and Research &

Development departments. The only links between these departments are the weak ones which depend upon this single node, which could be seen as a mediator that bridges the gaps between these two departments. One could posit that the loss of this node could

11 cause a collapse in the stability of the company’s communication. Thus, a weakly tied node that bridges the gap between clusters may be of extreme importance to the network.

In other words, the strength of a tie does not imply a judgment of usefulness or lack thereof in a node. Granovetter (1973) posits that a weakly connected node has an extreme degree of importance to a network because it can act as a bridge between clusters in the network, and that weakly connected nodes have a greater potential to diffuse information, or any variable of study, throughout different groups in a network. Strongly connected nodes create in-group effects as their connection is generally limited to a tightly-packed group, whereas weakly connected nodes can bridge the gaps between otherwise disparate groups in a network. In a citation network, a weakly connected node’s importance could be relatively high if it links disparate fields of personality psychology. However, if a weakly connected node does not bridge any communities and is an “island” in the network space the paper could be either unimportant or confounded by the citations in the sample.

Communities & Modularity

The community structure of networks may be revealing. If a network was created that was composed of one’s Facebook “friends,” intuitive and non-intuitive groups within the network may arise: peers, co-workers, and acquaintances may be easily discernible by how they cluster together in the network, but there may also be groups such as “friends that know A, but not B” that are not easily recognized. These subgroups are called communities. The detection of communities in network analysis aims to discern

12 subgroups of nodes, “such that the amount of interaction within group is more than the interaction outside it” (Sahebi & Cohen, 2011).

The simple fact that a network has tightly-knit communities can describe the nature of the network on a broader scale. In Lazer et al. (2009), community detection was used to distinguish political leanings in blogs, with two large communities of liberals and conservatives, and a third smaller community of independents, whose weak ties bridged the gap between the two major parties. Community detection itself is done algorithmically and looks for natural subgroups and divisions in the data (Carrington,

Scott, & Wasserman, 2005). This is done without modification from the experimenter.

Therefore, resulting conclusions about the communities must be based solely off the data presented. The presence of communities, or lack thereof, can again speak to the larger picture of the network.

One approach to detecting communities relies on modularity. A community’s modularity is defined as, “the number of edges falling within groups minus the expected number in an equivalent network with the edges place at random” (Newman, 2006). This means that a group with high modularity would have more inter-connected links between the nodes that comprise it than there would be simply by chance. Modularity is regarded as one of the most widely-used and fastest tools for community detection in network analysis, but is somewhat limited in detecting extremely small communities (Newman,

2006; Brandes et al., 2008).

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Measuring Importance: PageRank

In bibliographic networks, a paper’s importance is generally based off of its net incoming citations. This metric fails to account for the overall importance of the paper.

An alternative method to measuring the importance of a paper is to compute a node’s

PageRank. PageRank is based upon the efficacy that a node ties communities together, as opposed to simply its degree. Using in-degree citations to measure the importance of a paper is confounded by variables such as time of publishing and controversy generated by the paper (Ding et al., 2009). PageRank seeks to mitigate the issue of in-degree citation as a measure of importance by eschewing net citations in favor of, “counting citations or backlinks to a given page…this gives some approximation of a page’s importance or quality. PageRank extends this idea by not counting links from all pages equally, and by normalizing by the number of links on a page” (Brin & Page, 1998).

PageRank was utilized in both Ding et al., (2009) and Radev et al., (2009) as a method of measuring the importance of authors in citation networks. Both studies praised PageRank for its link weighting: PageRank determines the significance of a tie in a network by the relative importance of the citation. This means that if “A” was cited by four smaller papers, but “B” was cited by one extremely well-cited paper the Page Rank for “B” could be larger than “A” due to the impact of the larger paper being higher than four relatively un-linked papers.

Network Parameters in Bibliographic Analysis

In the present study, the aforementioned network parameters have been applied in terms of bibliographic analysis. A network of citations is an inherently directed network

14 as it follows links from one author to the next in a linear fashion. The directed nature signifies that the distance between two authors in a citation network can be interpreted as meaningful. Closer authors will generally share similarity while authors farther apart generally differ in their research areas. In line with this, this suggests that strongly and weakly tied nodes can be seen as authors of importance in the citation network. A strongly tied author is both well-cited inwards and outwards. An author with weak ties could show an author that bridges the methodological gaps in the field of psychology.

The density of a citation network can show the connectedness, or lack thereof, of researchers in that network. Low density may imply that there is not enough cross- research area citation and high density could illustrate a relatively well connected psychological field. Community detection in a citation network serves to describe the different research areas in a citation network. If multiple authors cluster together in a community then it is possible to infer that authors are connected on some level, whether it is academic, personal, critical, or otherwise. By using network parameters in bibliographic analysis we can look beyond the visual representation of citations and postulate higher-order conclusions about the structure of the scientific field from the citations presented.

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Methods

The source articles used in this study were journal articles pulled from the Annual

Review of Psychology which contained the word “personality” in their title; including 54 source articles from 1950 to 2012 (See Table 1). References were pulled from each individual article then compiled into a single file including the first authors’ last name, publication year, and citing document. We began by examining networks in which the individual papers were nodes, but due to the unique nature of the Annual Review (see

Discussion, it became apparent that a network of individual authors would be more informative. Each node in the network represents a specific author, and the ties between two nodes represent a directional citation from one author to another. Self-citations have been removed from the network as they can confound the statistical methods associated with a paper as well as inflate a psychologist’s apparent importance. The entire network is made up of 2852 distinct papers with 4455 connections between them. The network itself was created in Gephi, an open-source network visualizer designed to handle larger networks. Gephi is widely used as a network visualization method and has excellent,

“high quality layout algorithms, data filtering, clustering, [and] statistics,” (Bastian,

Heymann, & Jacomy, 2009).

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Results

Figure A is a pictorial representation of the entire network. In Figure A the size of the nodes in the network is determined by the number of incoming citations a paper receives (in-degree), with larger nodes being cited more often. The color of the nodes determines which community a node belongs to and, if a node is pink, if it is a source paper or not. Figure B is an ego network of psychologist E.J. Phares. An ego network can be thought of as a cross-section of a network that focuses solely on node and its subsequent outgoing connections. Figure C is an in-degree network of only those papers with seven or more incoming citations. Figure D is a visualization of the entire network with connections to Richard Lanyon highlighted.

The Overall Network (A)

Twelve distinctive communities were automatically detected in Gephi. From these detected communities it is possible to draw qualitative inferences about the content that ties these algorithmically detected sub-groups together. Communities were detected by modularity class. The density of the network was computed to be 0.001.

Community 1: Biological/Evolutionary Personality and Trait-Theory

The first community’s largest nodes are articles that were written by David Buss,

William Revelle, , and Douglas Kenrick. Kenrick and Buss both are evolutionary psychologists. Revelle has investigated Eysenck’s Personality Inventory, and Eysenck has, in turn, published critical reviews of Revelle’s own conceptual framework (Eysenck & Folkard, 1980; Rocklin & Revelle, 1981; Kenrick et al., 1990;

Buss & Shackleford, 1997). Both Revelle and Eysenck have made diverse contributions

17 in psychology; they, like Buss and Kenrick, share a conception of personality grounded in its biological basis.

Community 2: Cultural Influences on Personality

The third community’s major nodes are Harry Triandis, Ed Diener, and Steven

Heine. Triandis, Diener, and Heine all study cultural influences on personality as well as the subjective usage of personality tests across cultures (Diener & Diener, 1995; Heine et al., 2002; Triandis & Suh, 2002). In this community, Eunkook Suh acts as a weakly tied node that brings together Triandis, Diener, and Heine. Triandis, Diener, and Suh have collaborated on papers in the past (Suh et al., 1998) and Suh acts as a bridge between them and Heine. In the community Suh’s weak ties connect Diener, Triandis, and Heine.

Community 3: Minnesota and the MMPI

The third community includes James Butcher, one of the co-creators of the

MMPI, and Lee Anna Clark. As stated before, Butcher worked on developing the scales for the MMPI, while Clark has worked extensively on adaptation of the MMPI scales to different cultures (Clark & Shiota, 1996; Butcher et al., 2003). Furthermore, Butcher is linked to several collaborators on the MMPI, including Yoseff Ben-Porath and sometimes

MMPI critic Thomas Widiger (Widiger & Spitzer, 1991). Another link between Butcher,

Clark, and Ben-Porath is that they have all spent time in Minnesota in their careers, either as faculty or students.

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Community 4: The Developmental/Interactionist View

The fourth community stems almost entirely from David Magnusson and includes connections to both developmental psychologist Robert Cairns and the late .

Magnusson has referenced Lewin’s field theory numerous times in his arguments for how situations influence personality and Robert Cairns’s developmental work with teenagers deals with how social influences shape their personality development (Lewin, 1939;

Magnusson & Ekehammer, 1978; Cairns, 1986).

Community 5: Memories and Cognitive Influences on Personality

The fifth community’s source papers are from Lawrence Pervin and Jefferson

Singer. Pervin and Singer both work in the field of memory, motivational, and cognitive influences on personality (Pervin, 1989; Singer, 1990). Pervin and Singer are both linked to Seymour Epstein, developer of the Cognitive-Experiential Self Theory, and Nancy

Cantor whom bridges the tie between Pervin and Singer with her work on the effects of autobiographical memory reconstruction on personality (Cantor & Mischel, 1977;

Epstein, 1991).

Community 6: Response Styles on Psychological Tests

The sixth community includes source papers from Douglas Jackson and Leonard

Rorer. Rorer deals with the inherent issues in response styles on psychological tests while

Jackson himself has created a personality inventory and done additional research on response style (Rorer, 1965; Jackson, 1976). This citation could suggest either a critical relationship between both psychologists or, due to Jackson’s inventory occurring after

Rorer’s critiques, may imply a scholarly nod to the issues discovered by Rorer.

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Community 7: Adolescence & Family

The seventh community is one that primarily deals with adolescence, the family environment, and the developmental of personality. The main nodes are Willard Hartup,

W. Andrew Collins, and Ross Parke. Hartup, Collins, and Parke have all written extensively about adolescence, family relationships, and the process of psychological development (Collins & Russell, 1991; Parke, 1998; Bukowski, Newcomb, & Hartup,

1998). Parke and Collins have reciprocated citations, whereas Hartup is only cited directly by Collins. Hartup and Collins eventually collaborate on research about adolescent aggression after their respective Annual Reviews are published (Verbeek,

Hartup, & Collins 2000).

Community 8: Richard Lanyon

The eighth community has the source paper written by Richard Lanyon and his papers cited therein. Lanyon has written numerous Annual Review-style papers (Lanyon,

1986; Goodstein & Lanyon, 1999) that seek to understand the current state of personality assessment in its totality. Lanyon’s importance is further articulated in the later discussion on PageRank.

Community 9: Ego and Moral Development

The ninth community is one that is clearly based in ego and moral development.

This community’s central nodes are Jack Block, Jane Loevinger, and Martin Hoffman.

Hoffman and Loevinger are the only source papers. Loevinger is held in high esteem for her stage theory on ego development, which Jack Block has extoled for its help in his own personality theory, and Hoffman has been cited as an influence on Loevinger’s work

20 on personality stages (Loevinger & Knoll, 1983; Block, 1995). Both Hoffman and

Loevinger heavily cite Block in their Annual Review papers.

Community 10: Gender Differences in Personality

The tenth community had source papers from Ravenna Helson, Frank Barron, and

Lee Sechrest. Helson has published extensively on personality differences between males and females and was heavily cited in Barron’s paper, Sechrest looks at sex differences in personality as well. Barron and Helson both worked at Berkeley’s Institute for

Personality Assessment and Research. Barron’s Annual Review focused on creativity and sex differences (Helson & Moane, 1987; Barrett et al., 2000).

Community 11: E.J. Phares

The eleventh community, much like that of Robert Lanyon, is solely composed of

E.J. Phares and his outgoing citations. Phares’s citations span a large cross-section of personality research, including research on gender studies, trust, and locus of control

(Rotter, 1967; Naditch, 1974; Bem, 1977). Phares’s holistic approach to describing the field will be touched on again in the discussion of the strongest connected node in the network.

Community 12: The Big-Five and Structural Models

The twelfth community is one that is dominated by the nodes with the highest amounts of citations throughout the entire network, despite the relative lack of source papers. This Big-Five community includes a list of classic personality researchers involved in the five-factor model, its criticisms, and personality models in competition

21 with it: John Digman, Jerome Wiggins, Paul Costa, Daniel Ozer, Ray Cattell, Oliver

John, Timothy Leary, Lewis Goldberg, Robert McCrae, and Robert Carson are all important nodes on this network. Cattell, Digman, Goldberg, Costa, and McCrae are all closely linked to the five-factor model of personality, while Carson, Leary, and Wiggins are known, in large part, for their interest in circumplex models.

Strongest Connected Node

E.J. Phares was the highest-ranking strongest connected node in the entire network.

PageRank

Robert Lanyon had the highest PageRank in the entire network with a rank of

0.031.

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Discussion

The value in the automatically detected communities extends past their immediate usage for identifying different fields in the references. By utilizing a method which can algorithmically detect research similarities, it could be possible to find connections that span entire disciplines, as opposed to only fields in one. A future research direction would be to analyze citations from multiple journals in different scientific areas. One could visualize citation links connecting a sociologist working on juvenile criminal behavior, a chemist trying to discover the biological basis of criminal behavior, and a lawyer working on criminal policy. Through the process of network analysis unseen links that can dismantle the artificial boundaries of disciplines can appear. Although citation networks do have inherent limitations regarding time, the value of discovering both intuitive and non-intuitive links is one that can inform the structure of science as a whole in novel and beneficial ways.

The nature of the detected communities speaks to a broader idea that certain psychological researchers have a tendency to only regard the field in terms of their specific area. Although the amount of times a paper may be cited can be seen as an adequate metric for measuring relative impact, the similarities between Figure C and the

Big-Five community suggest an echo chamber-like effect, especially among these most- cited papers. However, communities are limited by their mutual exclusivity and inclusivity. A node can only be a part of one community at a time, which means that authors that rightfully belong to two research areas or more are not shown as such by community detection. Similarly, all nodes are considered in community detection and they must be placed into the different communities. Thus, certain nodes may be

23 uniformly forced, out of necessity, into communities that may be ill-fitting to their actual research areas. However, communities in the present network did map onto the author’s specific research areas with good efficacy, even though most authors would fit into more communities than just one.

From the citation networks it is possible to argue that there may be a tendency for authors to frame the field of personality in light of their particular research theories, or at least in only their specific areas of research. However, E.J. Phares and Robert Lanyon’s

Annual Review papers suggest more holistic approaches that could be used to show how the field has grown; as opposed to simply relating the most widely-known names. This is somewhat supported by the graph’s overall low density. This seems to illustrate a non- cohesive community as the entire structure is relatively unconnected in regards to its possible connections.

In Phares’s ego network (Figure B) it is visually apparent that Phares citations span ten separate communities while the citations from the higher-degreed papers generally cite around two or three communities. Phares’s paper would not suffice as a contender for psychological importance in the field of personality, but in terms of describing the field adequately his holistic approach is unparalleled by any other paper.

Therefore, although Phares himself may only be a mediator or vessel on the network, his analysis captured the state of personality models as well as competing theories and ideas.

In Robert Lanyon’s network (Figure D) we can see how PageRank may be an adequate metric for understanding relative psychological importance. Lanyon’s connections span through numerous communities and this is more than likely a

24 consequence of his past research in assessing the state of personality research. The importance of Lanyon himself, unlike Phares, could be argued in his favor as he received numerous incoming citations as well. This could elucidate a stronger model of importance using PageRank. PageRank seemed to capture Lanyon as both a high-degree and bridging node. His Annual Review’s breadth captured more different aspects of the discipline than the ones written by his contemporaries and he as a psychologist also managed to be well-received and cited. It is important to note that both Lanyon and

Phares are textbook authors. Their broad, universal approach to the Annual Review may be a consequence of their already ecumenical style of psychology.

Challenges and Potential Sources of Error

In Meehl (1990) the researcher relates an anecdote about being “slapped” with the title of dust-bowl empiricist, a title which he gleefully accepts. In an exploratory study of this nature the idea of dust-bowl empiricism is a salient one: the inferences based off network analysis are driven by data, with little theoretical backing behind them. It is a process that can further articulate future research in the field of psychology, but is based solely off induction. The future of studying large quantities of psychological data may continue using this brute-force approach in order to describe higher order properties of the data.

As such, this exploratory study is limited by the same limitations of the data.

Bibliographic couplings are inherently directional in nature due to the fact that they can only refer to papers in the past, the use of authors instead of papers is only a partial remedy. This could be a possible explanation for why the most-cited papers are generally

25 the oldest in the network. Furthermore, despite the relative usability of the Annual Review of Psychology it still does not include the entire breadth of personality and has undoubtedly left certain researchers to the wayside. Future research could rectify these issues by looking at citations on a strictly by-year-and-journal basis that would sample multiple journals across one year or by only sampling from a wider range of psychological journals.

References were retrieved from the source papers through either extraction via an

Excel formula or from conversion of document to editable text using Optical Character

Recognition (OCR). Newer journal articles had digitized references that would only need to be stripped of their extraneous characters in order to be imported into the network.

However, as the papers became older, the ease of extracting references diminished.

Newer papers have digitalized citations that are simple to import into a network while older papers’ citations must be either transcribed by hand or extracted from the source file with OCR. OCR is the more desirable option as it is faster than manually transferring citations from papers. During this process it is possible for certain characters in the reference to be changed as the process is not a perfectly accurate one. Overt errors such as a misspelled name can be noticed after and rectified, however a numerical or single letter being changed could possibly be disregarded unless the source material was extensively combed.

Additionally, due to the fact of the changing nature of references themselves over the decades from 1950 to 2012, another source of error is found in whether or not two references are the same. When pulling references from any of the papers, only the name and year were taken as the thousands of “raw” citations themselves were in a variety of

26 different formats. By creating a unified year and author citation the data was able to be more effectively visualized and investigated in its network. This comes at the cost of at times not being able to discern what journal article is exactly being referenced. This is rectified by combing through the citing document and identifying the paper, which was used for the nodes more central to the network. The last possible error source could be from human or mechanical error during the process of pulling and organizing the references into editable documents.

The potential misfires when importing the data into the network can be conceptualized within the realm of signal . A “hit” is a citation that is accurately retrieved and placed into the network; this is the most desirable result.

Equally, a “miss” is a citation that has been overlooked and did not make it into the final network. The relative impact of a “miss” on the network is low to medium as the omission of one citation may not hurt the relative impact of a more well-cited paper, but could potentially neglect an important node that could bridge communities. Correct rejections are beneficial to the network. These are references that were corrupted by OCR and were detected before being inserted into the network. One of the potentially most detrimental errors to the network is a “false alarm”, where an incorrect reference is seen as being correct. If the reference is merely a source paper to a relatively unconnected and isolated node, it can have very little impact on the network structure as a whole.

However, false alarms can be dangerous to the network structure when they create duplicates of existing papers, create connections between communities that do not actually exist, or artificially pad a paper’s degree. Due to the methodologically

27 explorative nature of the study, these kinds of errors have been controlled for at great lengths, but can still possibly exist at numerous steps in the creation of the network.

28

Appendix

Table 1 – Source Papers

Author Title Year 1. Hampson, Sarah E. Personality processes: 2012 Mechanisms by which personality traits "get outside the skin." 2. Carver, Charles S.; Personality and coping. 2010 Connor-Smith, Jennifer 3. McAdams, Dan P.; Personality development: 2010 Olson, Bradley D. Continuity and change over the life course. 4. Heine, Steven J.; Personality: The universal 2009 Buchtel, Emma E. and the culturally specific. 5. Clark, Lee Anna Assessment and Diagnosis of 2007 Personality Disorder: Perennial Issues and an Emerging Reconceptualization. 6. Ozer, Daniel J.; Benet- Personality and the 2006 Martínez, Verónica prediction of consequential outcomes. 7. Caspi, Avshalom; Personality Development: 2005 Roberts, Brent W.; Stability and Change. Shiner, Rebecca L. 8. Cervone, Daniel Personality Architecture: 2005 Within-Person Structures and Processes. 9. Diener, Ed; Oishi, Personality, culture, and 2003 Shigehiro; Lucas, Richard subjective well-being: E. Emotional and cognitive evaluations of life. 10. Triandis, Harry C.; Suh, Cultural influences on 2002 Eunkook M. personality. 11. Funder, David C. Personality. 2001 12. Mischel, Walter; Shoda, Reconciling processing 1998 Yuichi dynamics and personality dispositions. 13. Butcher, James N.; Personality: Individual 1996 Rouse, Steven V. differences and clinical assessment. 14. Hartup, Willard W.; van Personality development in 1995 Lieshout, Cornelis F. M. social context. 15. Revelle, William Personality processes. 1995

29

16. Ozer, Daniel J.; Reise, Personality assessment. 1994 Steven P. 17. Magnusson, David; A holistic view of 1993 Törestad, Bertil personality: A model revisited. 18. Wiggins, Jerry S.; Pincus, Personality: Structure and 1992 Aaron L. assessment. 19. Buss, David M. Evolutionary personality 1991 psychology. 20. Collins, W. Andrew; Social and personality 1990 Gunnar, Megan R. development. 21. Digman, John M. Personality structure: 1990 Emergence of the five-factor model. 22. Carson, Robert C. Personality. 1987 23. Singer, Jerome L.; Personality: Developments 1987 Kolligian, John in the study of private experience. 24. Pervin, Lawrence A. Personality: Current 1985 controversies, issues, and directions. 25. Lanyon, Richard I. Personality assessment. 1984 26. Loevinger, Jane; Knoll, Personality: Stages, traits, 1983 Elizabeth and the self. 27. Parke, Ross D.; Asher, Social and personality 1983 Steven R. development. 28. Rorer, Leonard G.; Personality structure and 1983 Widiger, Thomas A. assessment. 29. Barron, Frank; Creativity, , and 1981 Harrington, David M. personality. 30. Jackson, Douglas N.; Personality structure and 1980 Paunonen, Sampo V. assessment. 31. Helson, Ravenna; Personality. 1978 Mitchell, Valory 32. Hoffman, Martin L. Personality and social 1977 development. 33. Phares, E. Jerry; Lamiell, Personality. 1977 James T. 34. Sechrest, Lee Personality. 1976 35. Carlson, Rae Personality. 1975 36. Holzman, Philip S. Personality. 1974 37. Edwards, Allen L.; Measurement of personality 1973 Abbott, Robert D. traits: Theory and technique. 38. Singer, Jerome L.; Singer, Personality. 1972 Dorothy G. 39. Sarason, Irwin G.; Smith, Personality. 1971 Ronald E.

30

40. Dahlstrom, W. Grant Personality 1970 41. Fiske, Donald W.; Theory and techniques of 1970 Pearson, Pamela H. personality measurement. 42. Adelson, Joseph Personality. 1969 43. Klein, George S.; Barr, Personality. 1967 Harriet L.; Wolitzky, David L. 44. Jenness, Arthur Personality dynamics. 1962 45. Blake, Robert R.; Personality. 1959 Mouton, Jane S. 46. Jensen, Arthur R. Personality. 1958 47. Eriksen, Charles W. Personality. 1957 48. McClelland, David C. Personality. 1956 49. Nuttin, Joseph Personality. 1955 50. Child, Irvin L. Personality. 1954 51. Bronfenbrenner, Urie Personality. 1953 52. Eysenck, H. J. Personality. 1952 53. Mackinnon, Donald W. Personality. 1950 54. Sears, Robert R. Personality. 1950

31

Figure A (Entire Network)

32

Figure B (Phares Ego Network)

33

Figure C (Degree Network)

34

Figure D (Lanyon PageRank)

35

Community 1

36

Community 2

37

Community 3

38

Community 4

39

Community 5

40

Community 6

41

Community 7

42

Community 8

43

Community 9

44

Community 10

45

Community 11

46

Community 12

47

References

Barabási, A. (2012). Network Science. Retrieved from

http://barabasilab.neu.edu/networksciencebook/downlPDF.html

Barrett, L. F., Lane, R. D., Sechrest, L., & Schwartz, G. E. (2000). Sex differences in

emotional awareness. Personality and Bulletin, 26(9),

1027-1035.

Bastian, M., Heymann, S., & Jacomy, M. (2009, May). Gephi: an open source

software for exploring and manipulating networks. In ICWSM (pp. 361-362).

Bem, S. L. (1977). On the utility of alternative procedures for assessing psychological

androgyny. Journal of consulting and , 45(2), 196.

Block, J. (1995). A contrarian view of the five-factor approach to personality

description. Psychological bulletin, 117(2), 187.

Borgatti, S. P., & Halgin, D. S. (2011). On network theory. Organization Science,

22(5), 1168-1181.

Boyack, K. W., & Klavans, R. (2010). Co‐citation analysis, bibliographic coupling,

and direct citation: Which citation approach represents the research front most

accurately?. Journal of the American Society for Information Science and

Technology, 61(12), 2389-2404.

Brandes, U., Delling, D., Gaertler, M., Gorke, R., Hoefer, M., Nikoloski, Z., &

Wagner, D. (2008). On modularity clustering. Knowledge and Data

Engineering, IEEE Transactions on, 20(2), 172-188.

48

Brin, S., & Page, L. (1998). The anatomy of a large-scale hypertextual Web search

engine. Computer networks and ISDN systems, 30(1), 107-117.

Bukowski, W. M., Newcomb, A. F., & Hartup, W. W. (Eds.). (1998). The company

they keep: Friendships in childhood and adolescence. Cambridge University

Press.

Buss, D. M., & Shackelford, T. K. (1997). Human aggression in evolutionary

psychological perspective. Clinical Psychology Review, 17(6), 605-619.

Butcher, J. N., Graham, J. R., Ben-Porath, Y. S., Tellegen, A., & Dahlstrom, W. G.

(2003). MMPI-2: minnesota multiphasic personality inventory-2. University

of Minnesota Press.

Cairns, R. B. (1986). A contemporary perspective on social development. Children's

social behavior: Development, assessment, and modification, 3-47.

Cantor, N., & Mischel, W. (1977). Traits as prototypes: Effects on recognition

memory. Journal of Personality and Social Psychology, 35(1), 38.

Carrington, P. J., Scott, J., & Wasserman, S. (Eds.). (2005). Models and methods in

social network analysis. Cambridge university press.

Clark, L. A., & Shiota, N. (1996). Adaptation and validation of the Japanese MMPI-

2.

Collins, W. A., & Russell, G. (1991). Mother-child and father-child relationships in

middle childhood and adolescence: A developmental analysis. Developmental

Review, 11(2), 99-136.

49

Diener, E., & Diener, M. (1995). Cross-cultural correlates of life satisfaction and self-

esteem. Journal of personality and social psychology, 68(4), 653.

Ding, Y., Yan, E., Frazho, A., & Caverlee, J. (2009). PageRank for ranking authors in

co‐citation networks. Journal of the American Society for Information Science

and Technology, 60(11), 2229-2243.

Easley, D., & Kleinberg, J. (2010). Networks, crowds, and markets. Cambridge Univ

Press, 6(1), 6-1.

Epstein, S. (1991). Cognitive-experiential self-theory: An integrative theory of

personality. The relational self: Theoretical convergences in psychoanalysis

and social psychology, 111-137.

Eysenck, M. W., & Folkard, S. (1980). Personality, time of day, and caffeine: some

theoretical and conceptual problems in Revelle et al. Journal of experimental

psychology. General, 109(1), 32.

Goodstein, L. D., & Lanyon, R. I. (1999). Applications of personality assessment to

the workplace: A review. Journal of Business and Psychology, 13(3), 291-

322.

Granovetter, M. (1973). The strength of weak ties. American journal of sociology,

78(6), l.

Heine, S. J., Lehman, D. R., Peng, K., & Greenholtz, J. (2002). What's wrong with

cross-cultural comparisons of subjective Likert scales?: The reference-group

effect. Journal of personality and social psychology, 82(6), 903.

50

Helson, R., & Moane, G. (1987). Personality change in women from college to

midlife. Journal of Personality and Social Psychology, 53(1), 176.

Jackson, D. N. (1976). Jackson personality inventory. Port Huron, MI: Research

Psychologists Press.

Kenrick, D. T., Sadalla, E. K., Groth, G., & Trost, M. R. (1990). Evolution, traits, and

the stages of human courtship: Qualifying the parental investment model.

Journal of personality, 58(1), 97-116.

Lanyon, R. I., & Goodstein, L. D. (1997). Personality assessment . John Wiley &

Sons.

Lazer, D., Pentland, A. S., Adamic, L., Aral, S., Barabasi, A. L., Brewer, D., ... &

Van Alstyne, M. (2009). Life in the network: the coming age of computational

social science. Science (New York, NY), 323(5915), 721.

Lewin, K. (1939). Field theory and experiment in social psychology: Concepts and

methods. American journal of sociology, 868-896.

Loevinger, J., & Knoll, E. (1983). Personality: Stages, traits, and the self. Annual

Review of Psychology, 34(1), 195-222.

Magnusson, D., & Ekehammar, B. (1978). Similar situations—Similar behaviors?: A

study of the intraindividual congruence between situation and

situation reactions. Journal of Research in Personality, 12(1), 41-48.

Meehl, P. E. (1972). Second-order relevance. American Psychologist, 27(10), 932.

51

Naditch, M. P. (1974). Locus of control, relative discontent and hypertension. Social

psychiatry, 9(3), 111-117.

Newman, M. E. (2006). Modularity and community structure in networks.

Proceedings of the National Academy of Sciences, 103(23), 8577-8582.

Newman, M. (2010). Networks: an introduction. Oxford University Press.

Parke, R. D., & Buriel, R. (1998). Socialization in the family: Ethnic and ecological

perspectives. Handbook of child psychology.

Pervin, L. A. (1989). Goal concepts in personality and social psychology. Lawrence

Erlbaum Associates, Inc.

Radev, D. R., Joseph, M. T., Gibson, B., & Muthukrishnan, P. (2009). A Bibliometric

and Network Analysis of the field of Computational Linguistics. Journal of

the American Society for Information Science and Technology, 1001, 48109-

1092.

Rahm, E., & Thor, A. (2005). Citation analysis of database publications. ACM

Sigmod Record, 34(4), 48-53.

Rocklin, T., & Revelle, W. (1981). The measurement of extroversion: A comparison

of the Eysenck Personality Inventory and the Eysenck Personality

Questionnaire. British Journal of Social Psychology, 20(4), 279-284.

Rorer, L. G. (1965). The great response-style myth. Psychological bulletin, 63(3),

129.

52

Rotter, J. B. (1967). A new scale for the measurement of interpersonal trust. Journal

of personality, 35(4), 651-665.

Sahebi, S., & Cohen, W. (2011). Community-based recommendations: a solution to

the cold start problem. In Workshop on Recommender Systems and the Social

Web, RSWEB.

Singer, J. A. (1990). Affective Responses to Autobiographical Memories and Their

Relationship to Long‐Term Goals. Journal of Personality, 58(3), 535-563.

Suh, E., Diener, E., Oishi, S., & Triandis, H. C. (1998). The shifting basis of life

satisfaction judgments across cultures: versus norms. Journal of

Personality and Social Psychology, 74(2), 482.

Thomson Reuters. (2013). Journal Citation Reports. Retrieved from

http://www.annualreviews.org/page/about/isi-rankings

Triandis, H. C., & Suh, E. M. (2002). Cultural influences on personality. Annual

review of psychology, 53(1), 133-160.

Verbeek, P., Hartup, W. W., & Collins, W. A. (2000). Conflict management in

children and adolescents. Natural conflict resolution, 3453.

Webster, G. D., Dzedzy, A. M., & Crosier, B. S. (2013). What social network

analysis can reveal about hiring decision in social psychology. Talk given at

the 14th annual conference of the Society for Personality and Social

Psychology, New Orleans, LA

53

Widiger, T. A., & Spitzer, R. L. (1991). Sex bias in the diagnosis of personality

disorders: Conceptual and methodological issues. Clinical Psychology

Review, 11(1), 1-22.

54