academic tenure: the researcher personality archetype · 2018-03-23 · academy year 2012-2013...

FACULTY OF PSYCHOLOGY AND

EDUCATIONAL SCIENCES

Faculty of Psychology and Educational SciencesAcademy year 2012-2013Second examination period

Academic Tenure:The researcher personality archetype

Thesis submitted for the degree of Master of Psychology,option Theoretical and Experimental Psychology

by Koen De Couck

Promotor: Prof. dr. Wouter DuyckCo-Promotor: Prof. dr. Frederik Anseel

RESEARCHER PERSONALITY ARCHETYPE ii

Abstract

In the last decade funding and tenure has become increasingly more exclusive. More

candidates applied, which means recognizing good scientists became more critical. The

Hirsch-index (Hirsch, 2005) is the most common measure of academic success, but lacks

predictive validity amongst junior applicants. Three-quarters of Flemish Ph.D. grant

applicants are denied funding each year (Barbé, 2010; Bijzonder Onderzoeksfonds [BOF],

2013). Of those that do get selected, only half finishes their Ph.D. (Groenvynck,

Vandevelde, De Boyser, et al., 2010; Nelson & Lovitts, 2001; Smallwood, 2004;

Van der Haert, Ortis, Emplit, Halloin, & Dehon, 2011). We predict latent traits can boost

the h-index’ predictive validity. To test this hypothesis, we explored the effect of

twenty-three latent variables on academic productivity, impact and h-index.

Demographics, university ranking, personality, satisfaction with life, professional integrity

and measures of online social networking are examined in a sample of male and female

academics. Compared to other Facebook users, we find that the researcher archetype is

associated with lower openness, extraversion, agreeableness and neuroticism as well as a

heightened sense of fair-mindedness. Academic social networks are larger, with more status

updates. University ranking significantly predicted h-index (implying large training

effects), while neuroticism predicted academic productivity, with gender x neuroticism and

age x neuroticism interactions. A researcher’s capacity for research is modified by his/her

capacity to deal with stress, a predictor that is particularly profound in males and junior

researchers. No sex differences were found in productivity, impact or h-index, although

males were found to be more professionally mobile than female colleagues. Based on these

results, four recommendations are offered to improve Ph.D. selection tools, aid career

development and prevent academic drop-out.

Keywords: scientometrics, academics, PhD, h-index, personality, networks

RESEARCHER PERSONALITY ARCHETYPE 1

Academic tenure: The researcher personality archetype

In recent years the region of Flanders witnessed a major proliferation of candidate

researchers. According to ECOOM, the actual inflow has doubled in ten years’ time,

reaching a temporary peak in 2007 of over 2 thousand new researchers per year

(Groenvynck, Vandevelde, Van Rossem, et al., 2011). At the same time a large shift has

occurred in how Flemish research is supported: twenty years ago nearly thirty percent of

researchers earned wages as a research assistant. Recently, this number has gone down to a

mere eight percent. Instead, researchers are increasingly more dependent on financial

grants. Since 2008, nearly half of all Flemish researchers are supported through the

combined mandate capacity of the Fonds of Wetenschappelijk Onderzoek (FWO),

Bijzonder Onderzoeksfonds (BOF) or other research projects (Groenvynck, Vandevelde,

De Boyser, et al., 2010). The sharp rise of candidacies has forced these agencies to use

harsher selection measures in order to optimally allocate their limited funds (Barbé, 2009).

Research funding has become harder to come by, as success rates fell from fifty percent in

2002 to a mere twenty percent today (Barbé, 2010; BOF, 2013). Coincidentally, the

development of better selection tools for junior researchers has been booming in the last

decade (Froghi et al., 2012).

How does one measure true academic excellence? Lately, there is a growing emphasis

on using quantitative impact factors. They are usually praised as objective and

time-efficient instruments. The greater availability of online citation databases makes them

after all relatively easy to calculate. The most commonly used ranking is the Hirsch-index

(Hirsch, 2005). A researcher’s h-index is defined as the Np papers of that researcher, which

have at least h citations each, with the other (Np - h) papers having no more than h

citations each. For example, a junior researcher having produced one paper, cited once, will

receive an h-index of one. To increase his h-index to two, that researcher would need two

papers, each of which cited at least two times. Private funding agencies frequently employ

the h-index to assess new applicants and optimize their investment. For senior researchers

the h-index is consulted in nearly seventy-five percent of cases deciding tenure and

professorship appointments (Abbott et al., 2010). These decisions often hold great impact

on the life of the selectee and constitute a significant investment on the part of the


institution. Therefore, academic selections are often grueling and time-consuming

procedures. Meanwhile on a higher level, governments like the UK, Italy and Australia are

using a pooled version of the h-index to assess the research quality of their universities and

allocate national research funding accordingly (Anonymous, 2012; Butler, 2003; Wang, Liu,

Ding, & Wang, 2012). In applying a quantitative metric like the h-index, institutions

attempt to objectify the large differences in the number and importance of contributions

made by scientists working in the same discipline. However past studies suggest a number

of potentially unfair individual confounds. Clearly, academic achievement is multiply

determined by intrinsic factors, such as motivation and skill, and factors outside the

individual, such as the quality of training and access to resources. Inadvertently these

indices may well be selecting for more than we know. As such, we should at least

investigate the joint contribution of intrinsic and extrinsic factors to these indices to better

understand what attributes they select for.

Demographics and reputational ranking

Some of the earliest comprehensive work in the correlational structure of the h-index

was done by Helmreich, Spence, Beane, Lucker, and Matthews (1980). Among their

findings was a correlation between reputational ranking of a researcher’s institution and

citation. Not surprisingly, researchers active in respectable institutions were more highly

cited. Haslam et al. (2008) provided an overview of more citation impact predictors. Their

variables included author characteristics (i.e., gender, nationality, eminence), institutional

factors (i.e., university prestige, grant support), features of the article and research

approach. They found strong article-level predictors of first author eminence, having a

more senior later author, journal prestige, article length, and number and recency of

references. Interestingly, neither gender, nationality, the amount of collaboration nor

university prestige had any effect on the amount of citations. This seems to contradicts

earlier findings, which may point to recent changes in common research practices.

Age is definitely the most important demographic factor, as both the number of

produced articles as citations can only increase over time (Stroebe, 2010). At a minimum,

any analysis needs to at least control for age. Yet there will always be less data for junior


researchers as compared to senior colleagues. A researcher may not become truly active

until some years after finishing his/her Ph.D., a predictor that should be included (Nosek

et al., 2010). Similarly McNally (2010) found the variability in productivity and impact

increases as a function of academic seniority. In the early stage of one’s career,

productivity and impact alone simply do not explain enough of the variance to justify

adequate decisions.

Personality

Aside from university prestige, Helmreich’s original study also found positive citation

effects of ’achievement motivation’, ’mastery and work needs’ and perhaps more unusual: a

negative correlation to ’competitiveness’ (i.e., less competitive researchers were cited

more)(Helmreich et al., 1980). Their study was amongst the first to also include

personality variables, although the notion was only vaguely defined at the time. Not so

uncommon in psychology, his measurement scales were self-made and less robust than

current Big Five personality models (Barrick & M. K. Mount, 1991; Gosling, Rentfrow, &

Swann, 2003; Judge, Higgins, Thoresen, & Barrick, 1999). As part of a much larger

meta-analysis Feist and Gorman (1998) reported results of 26 personality studies, showing

that scientists typically have higher conscientiousness and lower openness than

non-scientists. While this combination was indicative of scientific interest, its relation to

scientific achievement was not reported on. Indeed, as he himself notes, the psychology of

science is still a ’fledgling field’, which gained prominence only in the last five to ten years

(Feist, 2011). Of these studies only a handful focus on junior researchers. For example,

Scevak, Cantwell, Bourke, and Reid (2007) reported a metacognitive profile specifically

associated to doctoral students. They found above average coping scores, suggesting a link

at least with higher stress resistance. More interestingly however, three subgroups could be

discerned: a non-problematic group, an anxious and dependent group and a third group

consisting of weaker and at-risk candidates. While this study would suggest that early

detection is possible, its only criterium was a student’s chance of completing his/her Ph.D.

No scientific impact measures were mentioned. In fact until this day, investigation into the

causal link between individual traits and scientific attainment is still largely lacking.


A few important traits of attainment can however be discerned from the broader

research in job success. Findings from Judge et al. (1999) indicated conscientiousness

positively influenced career success, while neuroticism generally had a negative effect.

Simonton (2008) believed scientific success represented a specific subset within general job

success. If so, then certain personality traits would predispose some people to a strong

research career, more so than others. Earlier research had shown Eysenck’s psychoticism

and extraversion scores were significantly lower for scientists than non-scientists (Feist &

Gorman, 1998). Simonton (2008) expanded on these results, showing that psychoticism and

extraversion also constitute the dominant traits to scientific success, with medium-to large

Cohen’s d (Cohen, 1988). These reliability estimates are about as good as can be expected

of effects in behavioral sciences. As a way of comparison, Simonton (2008) compared the

lower-end estimate of genetic contribution as having about the same magnitude as the

relation between psychotherapy and subsequent well-being, whereas the upper-end estimate

is about the same size as the correlation between height and weight among U.S. adults.

Much of this effect is entirely inherent. Substantial genetic contributions were found to the

original choice of a scientific profession (12%) and scientific succesfulness (30%) (Simonton,

2008). However in his discussion Simonton (2008) explicitly stresses the need for Big Five

studies to try replicate these results in the near future: "It should be apparent that the

literature needs more investigations that apply the Big Five Factors (and their facets)

directly to the prediction of scientific training and performance - a desideratum underlined

by the availability of appropriate heritability estimates" (Simonton, 2008, p. 42). Similar

high impact estimates were found for general intelligence ratings and traits of sociability,

self-acceptance and dominance. These aspects correlate with another important factor: an

academic’s sense of professional integrity (Simonton, 2008).

Professional integrity

Consider for a moment the selection of scientists as a human resource problem.

Traditional personnel selection involves making an evidence-based choice of which

candidate best fits the job. This process typically involves a job analysis and candidate

profile. Common selection tools include structured interviews, ability tests, work samples,


personality and integrity tests. The predictive validity of these measures to job performance

is generally high: work sample (r = .54), structured interviews (r = .51), conscientiousness

tests (r = .31) and integrity tests (r = .41) offer some of the highest reliabilities available

(Schmidt & Hunter, 1998). These validity gains become even larger when used in

conjunction (Schmidt & Hunter, 1998), leading to recent recommendations for composite

selection instruments (Hattrup, 2012; M. Izquierdo & A. Izquierdo, 2006). In academia

though, focus remains squarely on work samples equivalents, occasionally supplemented by

structured interviews. Integrity and personality tests are generally unheard of. Indeed,

little or no research even exists as to their effectiveness in academic settings. Nevertheless,

when issues of integrity combine with high publication pressure, problems may arise.

Recent web surveys of UK academics show a general loss of trust, and growing skepticism

about the culture of academic accountability (McNay, 2007). The majority believes

colleagues cheat in order to step up their published quota. (Anderson, Martinson, &

De Vries, 2007; Fanelli, 2009). Their concern seems to hold, as meta-analyses find one-third

of researchers anonymously admit to questionable research practices (Fanelli, 2009). The

absence of integrity testing seems at odd with the strong academic outlash against cases of

scientific fraud (Baier & Dupraz, 2007). At the core of many such cases there is high

perceived pressure and a personal belief that he/she can get away with it (Levelt, Drenth,

& Noort, 2012; Lock, 1995; Miller & Hersen, 1992). Equally many are reluctant, but will

conceal certain results to keep up with the high pace of academic publishing (Stapel, 2012).

Networking

The h-index’ emphasis on productivity and getting cited leaves many frustrated. In a

recent survey by the journal Nature, 63% of researchers indicated general dissatisfaction

with the present use of metrics. Half of the respondents admitted that this form of

evaluation also affected their work behavior (51%), and the majority is convinced the

system can be cheated (71%). Meanwhile many indicated that in their view other aspects -

like public visibility and collaborative work outside of their institution- ought to be

considered as well (Abbott et al., 2010; Newton, 2000). With the introduction of online

media and communication, it is these areas in particular in which research practice has


seen the most change. Internet has become a prominent tool for research, just as it has for

private business (Amor, 2001; Modahl, 1999). Contemporary researchers now actively

maintain online correspondence with collaborators. They exchange ideas, stay informed

and collaborate through online social networks (Jahnke & Koch, 2009). Many researchers

actively use at least to some extent Facebook, a popular social networking website.

Facebook has an astonishing penetration rate, which passed one billion users last year (one

in seven of the world’s population)(Williams, 2012). Are Facebook users as a group

representative for the wider population? They aren’t, as research shows unequal

participation in social networks, according to age, gender, education and race (Hargittai,

2007). Young adults are much more wired than their older counterparts (Fox, 2004;

Madden, 2006). Thus, their participation in social networks is proportionally higher.

Women are also more likely to use social networks than men, including Facebook

(Hargittai, 2007; McAndrew, 2012). Finally, Facebook users are more likely to be

Americans with college-level educations (Hargittai, 2007). Facebook’s initial focus on

American college students and then high school students left out less educated people by

design. As such a social network like Facebook is more likely to represent young,

college-level American professionals. It is an excellent recruiting platform for junior

researchers, for whom a college degree forms a general prerequisite. A Facebook sample

also provides a high-fidelity base to differentiate within young academic professionals. To

grant agencies and academic boards, it is this group which carries their interest.

Sex differences

Gender differences within academia forms the last of our variables of interest. Early

studies in the eighties still found significant differences, with men being more active and

receiving more recognition for their work (Helmreich et al., 1980). The gender gap in

academia has since then been found to steadily shrink (Ferber, 2003). Multivariate analysis

by Nakhaie (2002) and D’Amico, Vermigli, and Canetto (2011) suggests most of the

remaining gender differences are largely accounted for by discipline, rank, years since

Ph.D., type of university and time set aside for research. McNally (2010) found research

quality is largely independent of gender. Haslam et al. (2008) even found no evidence that


gender differences still exist, at least in the amount of received citations.

Research framework and hypothesis

Academic selection tools are in high demand, leading to a specialised scientometric

literature of impact indices (Froghi et al., 2012). The h-index (Hirsch, 2005) is the most

widely used today, with properties that have been extensively studied. Part of those

studies included challenges to its assumed objectivity. However, these were so far limited to

easily obtained apparent traits: gender, nationality, race (Haslam et al., 2008). None of

which proved significant. This study is innovative in its ability to investigate large amounts

of inherent traits and behaviors: personality, integrity and online networking, in addition

to university rankings and gender effects. As far as we know this is the first study to

investigate the effect of latent variables on the h-index. It is also the first study to even

consider a researcher’s online social network, although the importance of academic

networking had previously already been asserted (Abbott et al., 2010; Newton, 2000).

Our first hypothesis would be that personality, integrity and networking will influence

an individual’s original decision to pursue an academic career. Based on past personality

studies on academic samples (Feist & Gorman, 1998; Scevak et al., 2007; Simonton, 2008),

we expect the following personality differences for academics versus the general population:

higher conscientiousness, combined with lower openness, extraversion and neuroticism. We

will refer to this set as the ’researcher archetype’.

Our second hypothesis is that a combination of certain personality traits, integrity

and networking will influence productivity and popularity, and therefore a researcher’s

h-index. This would allow certain individuals to not only choose an academic profession,

but also distinguish themselves as compared to their peers. Previous studies would suggest

particularly high extremes of conscientiousness with low extraversion and neuroticism

predict academic success (Feist & Gorman, 1998; Judge et al., 1999). In addition, the

influence of integrity and networking on the h-index also remained largely unknown so far.

We predict less scrupulous individuals (low fair-mindedness, low self-disclosure) to have

both higher productivity, popularity and h-indexes due to the sensationalizing of their

research results. Networking would boost these factors as well but in a different way,


rewarding researchers that actively promote or collaborate on research.

Our final hypothesis relates to academic gender effects. We predict that recent efforts

to promote equal opportunities will have either strongly reduced or removed the

professional differences in academic productivity, popularity or h-index between sexes.

Gender effects should still be prominent in personality traits though, with women

characterised by higher conscientiousness, agreeableness and neuroticism. Female

researchers are also predicted to be more engaged in network features that strongly

represent community ties (e.g., Facebook events or photo tags).

All of these correlates - personality (Bachrach, Kosinski, Graepel, Kohli, & Stillwell,

2012), integrity (DiMicco & Millen, 2007; Rosenberg & Egbert, 2011) and networking

(Sparrowe, Liden, Wayne, & Kraimer, 2001) - are known to be expressed in online social

networks. A Facebook sample provides a means of testing large groups of young,

college-level professionals. We will show that correlated traits and behavior can boost the

h-index’ prediction power, to better accommodate these two questions: which candidates

will likely make succesful scientists? What trait-based interventions could best support a

knowledge economy workforce?

Method

Data collection

Participant data was gathered using myPersonality, a Facebook application that

offers its users personality assessment and feedback (Stillwell & Kosinski, 2011). Users were

not specifically approached or recruited, nor were they paid to install the application or to

participate in research. Instead these users were self-motivated by the prospect of receiving

reliable feedback test scores. Users had the opportunity to explicitly opt-in for their test

and user data to be anonymously used in research. When selected, the application retrieved

Facebook profile demographic information: age, gender, location, current employment and

social network data through the Facebook application programming interface (API).

Network variables included network size, network density, betweenness (= the normalized

centrality of the researcher in his/her social network), brokerage (= the normalized number

of alters’ pairs that are not directly connected) and diads (= the normalized amount of


friend diads within the network). We also retrieved six Facebook-specific indicators of

network activity, namely the number of likes, status updates, attended events, Facebook

group membership, number of jobs, number of attended schools and number of Facebook

photo tags. Table 1 provides an overview of all variables. For reasons of privacy, only data

that users had previously indicated as willing to share was retrieved.

Table 1

Proportion of missing data: variable completeness (N = 7273)

Size Complete Size Complete Size Completen % n % n %

Demographic data Academic data Integrity dataSex 7252 99.7 Number of articles 169 2.3 Fair-mindedness 119 1.6Age 4760 65.4 Number of citations 169 2.3 Self-disclosure 119 1.6Location 7252 99.7 h-index 169 2.3Employer 7273 100.0 QS ranking 3817 52.5Latent traits Network data Online participationOpenness 6864 94.4 Network size 709 9.7 N likes 2257 31.0Conscientiousness 6864 94.4 density 709 9.7 N status 1422 19.6Extraversion 6864 94.4 betweenness 709 9.7 N event 429 5.8Agreeableness 6864 94.4 brokerage 709 9.7 N group 1903 26.2Neuroticism 6864 94.4 diads 2388 32.8 N work 7252 99.7I.Q. 66 0.9 N edu 5618 77.2Satisfaction with life 227 3.1 N tags 2083 28.6

For this study, a target subsample was taken of participants active at universities

worldwide (N = 7273). The employer specified on their Facebook profile had to contain

either the word ’university’, or a non-ambiguous translated equivalent from 23 considered

languages (e.g., universiteit, université, universität, universitat, università). Employer data

was manually checked afterwards to remove any non-academic sources, and insure high

quality of the sample. We identified 1637 unique university employers. Next, each

university’s international ranking was retrieved from the QS World University Rankings

website1. In each case we registered the most recent QS ranking, representing each

institution’s quality of postgraduate studies. No QS ranking was registered if the ranking

score was only approximate (e.g., ’601+’), a group indicator (e.g., ’601-700’) or rounded

down to zero.

Finally, information on participant’s academic merit was gathered by cross-referencing

Facebook user names with public Google scholar profiles. A self-made Python script1www.topuniversities.com


retrieved users associated h-index as well as number of publications, number of citations

and each researcher’s associated academic discipline. Afterwards, human raters checked the

results against name ambiguity and aliasing. Facebook and Google Scholar profiles were

matched on name, picture, age and employer. Variables were written to the dataset only

when a match was made with absolute certainty. Although a mere fraction of our Facebook

sample had verifiable Google scholar profiles (2.3%, n = 169), this method has the

advantage of providing high-quality academic data. Google scholar profile users add, track

and periodically review their own list of articles, effectively acting as their own aliasing

filter (Google, 2013). Many scientific disciplines were represented in the sample, the largest

group being Psychology (24.0%). Note that because of differences in scientific practice,

comparing performance across disciplines is often challenging. In such cases, Google scholar

is also the most appropriate data source recommended by the literature (Harzing, 2012).

Sample

Our sample contained Facebook data from 7273 academic researchers. Some

participants were removed (< 20 years, n = 31, less than .01% of the sample), while some

had their age reevaluated as ’missing’ when deemed inaccurate (> 71 years, n= 26, less

than .01% of the sample). In terms of academic indexes, we were able to retrieve h-index,

publication and citation data from 169 individuals. To respect user privacy we only

retrieved variables that users were willing to share. As such the actual sample size varied

for different variables (e.g., 94.4% of users agreed to share personality data, while only

9.7% agreed to share social network information). Sample sizes are displayed in Table 1 on

page 9. The sample was relatively young (M = 32, Median = 30, SD = 9.53, 20 - 71

years), with a 75% majority being within the 16-34 age group. There was a gender

asymmetry of women (62%) over men (38%). Both age and gender distributions

correspond however to distributions typically found on Facebook (McAndrew, 2012). The

sample included researchers of 33 nationalities, 75% of which being USA citizens.


Instruments of assessment

The Big Five personality traits of openness (O), conscientiousness (C), extraversion

(E), agreeableness (A), and neuroticism (N) were measured using the 100-item IPIP

representation of the NEO-PI-R scale (Goldberg et al., 2006), offered through the

myPersonality application. The instrument is based on Costa and McCrae’s Five Factor

Model (Costa & McCrae, 2006), a common framework for both traditional and online

personality research. Self-report ratings using this questionnaire have been widely used and

extensively validated (Goldberg et al., 2006). Furthermore, the myPersonality application

itself ensures high test result validity by removing inattention effects, language

incompetency or random responding. The resulting quality of the responses is high: the

scales’ reliabilities are on average higher than reported in test manuals (Goldberg, 1999)

and the discriminant validity (average r = .16) is at least as good as those obtained using

traditional samples (average r = .20) (John & Srivastava, 1999). Internal reliability values

are equally high (Cronbach’s alpha: O = .83, C = .91, E = .93, A = .87, N = .92).

IQ estimates were calculated using the MyIQ test, a timed 20-item version of Raven’s

Matrices, validated and developed by the University of Cambridge’s Psychometrics Centre.

Integrity assessment was done using Orpheus, a broad spectrum 190-item work-based

combined personality and integrity questionnaire. Developed by the UK’s leading

psychometrician John Rust, Orpheus’ biggest advantage is its robust psychometric

properties (Rust, 1998). Here, we were particularly interested in the two main integrity

scales of Fair-mindedness (= impartial attitude in decision making, fm) and Self-disclosure

(= the extent to which one conducts his/her life transparently, sd).

To measure global life satisfaction we used the Satisfaction With Life Scale (SWLS)

(Diener, Emmons, Larsen, & Griffin, 1985). The SWLS has been used in psychological

research for over twenty years to study global judgments of wellbeing, and has excellent

internal (Cronbach’s alpha = .87) and temporal reliability (2-month test-retest r = .82),

with the added advantage that it can be used for different age groups (Diener et al., 1985;

Neto, 1993).


Results

Distribution and skewness

One challenge is that all three academic predictors (number of articles, number of

citations and h-index) produce a distribution that violates the assumption of

homoscedasticity: there is less variability in these impact scores among junior researchers

because their work has had no opportunity yet for citation. If uncorrected, this threatens

the analysis and interpretation of regression estimates, and damages the comparability of

scores for researcher samples with a wide age range. All three are also known to display

significant skewness (Helmreich et al., 1980). In addition QS-ranking also displayed

significant deviation, likely the result of the ranking process itself. To remove

heteroscedasticity and skewness, we used the natural log of the indicators, loge (X+ 0.5)

when creating regression estimates. This transformation was effective in mitigating both.

As for the 25 non-academic variables: the assumption of normality was tested by

Kolmogorov-Smirnov and Shapiro-Wilks tests. All but one variable (Self disclosure) were

all significantly non-normal. Deviations from normality were not unexpected however given

the sample size, and QQ-plots showed a strong normality fit with only minor skew and tail

deviations. Unlike the academic predictors these deviations were judged as non-critical to

the model assumptions and allowed.

Correlations

A correlation matrix was computed for the full sample of researchers, shown in

Table 2. At this point of the analysis, three of the five network variables (i.e., betweenness,

brokerage and diads) were dropped because of high multicollinearity to either network size

or density. The correlations within each sex were almost identical; justifying the use of one

combined group. Effect sizes range from 0 to .85 (absolute values), corresponding mostly to

small-to-medium effects. Correlations between the academic variables are expected, since

the h-index is computed based on a combination of number of articles and citations, and

all three naturally increase with age. From an assessment point of view, one should note

the negative correlation of h-index to extraversion (r(167) = -.19, p < .001), hinting at the


fact that the more withdrawn individuals make for highly appraised researchers.

Introverted individuals seem to produce more scientific work (r(167) = -.17, p < .001), just

as the more competitive individuals (low agreeableness) (r(167) = -.19, p < .001). None of

these variables correlated however to a researcher’s popularity (= number of citations).

There is also a noteworthy absense of correlations to QS ranking.

As for integrity, fair-minded researchers report significantly higher wellbeing (r(58) =

.37, p < .001). The medium effect size of this correlation is as high as can be expected.

More competitive individuals are more likely to have a problematic professional integrity

(r(118) = .17, p < .001; r(118) = .24, p < .001). Another noteworthy finding is the

medium-sized correlation between researchers’ IQ and network density (r(32) = .40, p <

.001). Highly intelligent researchers in academia maintain dense Facebook networks in

which information is exchanged.

Facebook usage is definitely more widespread amongst younger generations of

researchers, confirmed through the negative correlations with age. Many personality traits

are also shown to interact with aspects of Facebook usage. These correlations already

provide rudimentary insight into the researcher archetype.


Table 2

Pearson correlations for the researcher sample

articles citation h-index age QS swl IQ O C E A Narticles 0.62 0.85 0.43 0.05 0.09 -0.09 -0.10 -0.17 -0.20 0.01

(< .001) (< .001) (< .001) (0.56) (0.88) (insuf) (0.23) (0.18) (0.03) (0.01) (0.85)citations 0.62 0.74 0.31 0.02 0.07 -0.03 0.00 -0.14 -0.04 0.11

(< .001) (< .001) (0.91) (0.79) (0.001) (insuf) (0.68) (0.99) (0.08) (0.65) (0.17)h-index 0.85 0.74 0.41 0.08 0.10 -0.06 -0.06 -0.19 -0.12 0.11

(< .001) (< .001) (< .001) (0.36) (0.88) (insuf) (0.42) (0.41) (0.01) (0.13) (0.14)age 0.43 0.31 0.41 0.06 0.17 0.16 0.00 0.05 -0.04 0.04 -0.02

(< .001) (0.001) (< .001) (0.003) (0.03) (0.22) (0.96) (0.001) (0.01) (0.005) (0.23)QS 0.05 0.02 0.08 0.06 0.15 -0.07 -0.02 -0.03 -0.02 -0.05 -0.01

(0.56) (0.79) (0.36) (0.003) (0.12) (0.70) (0.19) (0.05) (0.21) (0.005) (0.43)SWL 0.09 0.07 0.10 0.17 0.15 -0.33 0.07 0.14 0.28 0.17 -0.36

(0.88) (0.91) (0.88) (0.03) (0.12) (0.21) (0.29) (0.04) (< .001) (0.01) (< .001)IQ 0.16 -0.07 -0.33 -0.05 -0.09 0.01 0.18 -0.02

(insuf) (insuf) (insuf) (0.22) (0.70) (0.21) (0.73) (0.52) (0.94) (0.18) (0.90)O -0.09 -0.03 -0.06 0.00 -0.02 0.07 -0.05 0.55 0.58 0.57 0.37

(0.23) (0.68) (0.42) (0.96) (0.19) (0.29) (0.73) (< .001) (< .001) (< .001) (< .001)C -0.10 0.00 -0.06 0.05 -0.03 0.14 -0.09 0.55 0.57 0.60 0.25

(0.18) (0.99) (0.41) (0.001) (0.05) (0.04) (0.52) (< .001) (< .001) (< .001) (< .001)E -0.17 -0.14 -0.19 -0.04 -0.02 0.28 0.01 0.58 0.57 0.56 0.16

(0.03) (0.08) (0.01) (0.01) (0.21) (< .001) (0.94) (< .001) (< .001) (< .001) (< .001)A -0.20 -0.04 -0.12 0.04 -0.05 0.17 0.18 0.57 0.60 0.56 0.16

(0.01) (0.65) (0.13) (0.005) (0.005) (0.01) (0.18) (< .001) (< .001) (< .001) (< .001)N 0.01 0.11 0.11 -0.02 -0.01 -0.36 -0.02 0.37 0.25 0.16 0.16

(0.85) (0.17) (0.14) (0.23) (0.43) (< .001) (0.90) (< .001) (< .001) (< .001) (< .001)net size -0.16 -0.07 -0.16 -0.12 -0.11 -0.03 -0.27 0.02 0.01 0.22 0.00 -0.11

(0.54) (0.80) (0.55) (0.002) (0.06) (0.89) (0.12) (0.55) (0.72) (< .001) (0.95) (0.003)density 0.17 -0.07 0.14 0.00 -0.01 0.14 0.40 0.04 0.10 0.04 0.04 0.11

(0.53) (0.81) (0.61) (0.90) (0.82) (0.47) (0.02) (0.28) (0.01) (0.35) (0.28) (0.003)SD 0.09 -0.16 0.05 0.20 0.02 0.12 -0.02 0.17 -0.16

(insuf) (insuf) (insuf) (0.40) (0.22) (0.69) (0.42) (0.81) (0.20) (0.79) (0.06) (0.09)FM 0.17 0.01 0.37 0.09 0.13 0.19 0.21 0.24 -0.30

(insuf) (insuf) (insuf) (0.10) (0.93) (0.004) (0.73) (0.16) (0.04) (0.02) (0.01) (< .001)N likes 0.01 -0.11 -0.02 -0.13 -0.05 -0.02 -0.03 0.04 -0.04 0.00 -0.06 0.03

(0.94) (0.49) (0.91) (< .001) (0.13) (0.88) (0.83) (0.05) (0.05) (0.98) (0.02) (0.21)N status 0.02 -0.11 0.01 -0.20 -0.08 -0.25 -0.02 -0.04 -0.04 0.02 -0.08 0.05

(0.93) (0.56) (0.96) (< .001) (0.05) (0.12) (0.92) (0.18) (0.12) (0.55) (0.004) (0.07)N event -0.04 -0.19 -0.08 -0.18 -0.03 0.17 -0.54 0.05 -0.05 0.12 0.06 -0.09

(0.92) (0.61) (0.83) (< .001) (0.68) (0.50) (0.02) (0.32) (0.33) (0.03) (0.25) (0.12)N group -0.05 -0.09 -0.06 -0.15 -0.01 -0.05 0.03 0.07 -0.05 0.11 0.02 0.02

(0.77) (0.56) (0.72) (< .001) (0.73) (0.68) (0.83) (0.003) (0.03) (< .001) (0.46) (0.37)N work 0.08 0.14 0.05 -0.08 -0.02 0.03 0.09 0.10 0.05 0.08 0.07 0.03

(0.29) (0.07) (0.50) (< .001) (0.13) (0.68) (0.50) (< .001) (< .001) (< .001) (< .001) (0.004)N edu 0.00 0.13 -0.01 -0.07 -0.04 0.04 0.21 0.15 0.08 0.14 0.13 0.07

(0.99) (0.14) (0.91) (< .001) (0.04) (0.63) (0.10) (< .001) (< .001) (< .001) (< .001) (< .001)N tags -0.20 -0.08 -0.19 -0.23 0.02 0.11 0.11 0.06 0.03 0.14 0.10 -0.03

(0.20) (0.61) (0.22) (< .001) (0.55) (0.36) (0.48) (0.02) (0.18) (< .001) (< .001) (0.17)Note. Significance is color-coded (red / gray). Near-significant results are gray (p < .10).Correlation size is intensity-coded.


net size density SD FM N likes N status N event N group N work N edu N tags-0.16 0.17 0.01 0.02 -0.04 -0.05 0.08 0.00 -0.20 articles(0.54) (0.53) (insuf) (insuf) (0.94) (0.93) (0.92) (0.77) (0.29) (0.99) (0.20)-0.07 -0.07 -0.11 -0.11 -0.19 -0.09 0.14 0.13 -0.08 citation(0.80) (0.81) (insuf) (insuf) (0.49) (0.56) (0.61) (0.56) (0.07) (0.14) (0.61)-0.16 0.14 -0.02 0.01 -0.08 -0.06 0.05 -0.01 -0.19 h-index(0.55) (0.61) (insuf) (insuf) (0.91) (0.96) (0.83) (0.72) (0.50) (0.91) (0.22)-0.12 0.00 0.09 0.17 -0.13 -0.20 -0.18 -0.15 -0.08 -0.07 -0.23 age

(0.002) (0.90) (0.40) (0.10) (< .001) (< .001) (< .001) (< .001) (< .001) (< .001) (< .001)-0.11 -0.01 -0.16 0.01 -0.05 -0.08 -0.03 -0.01 -0.02 -0.04 0.02 QS(0.06) (0.82) (0.22) (0.93) (0.13) (0.05) (0.68) (0.73) (0.13) (0.04) (0.55)-0.03 0.14 0.05 0.37 -0.02 -0.25 0.17 -0.05 0.03 0.04 0.11 SWL(0.89) (0.47) (0.69) (0.004) (0.88) (0.12) (0.50) (0.68) (0.68) (0.63) (0.36)-0.27 0.40 0.20 0.09 -0.03 -0.02 -0.54 0.03 0.09 0.21 0.11 IQ(0.12) (0.02) (0.42) (0.73) (0.83) (0.92) (0.02) (0.83) (0.50) (0.10) (0.48)0.02 0.04 0.02 0.13 0.04 -0.04 0.05 0.07 0.10 0.15 0.06 O

(0.55) (0.28) (0.81) (0.16) (0.05) (0.18) (0.32) (0.003) (< .001) (< .001) (0.02)0.01 0.10 0.12 0.19 -0.04 -0.04 -0.05 -0.05 0.05 0.08 0.03 C

(0.72) (0.01) (0.20) (0.04) (0.05) (0.12) (0.33) (0.03) (< .001) (< .001) (0.18)0.22 0.04 -0.02 0.21 0.00 0.02 0.12 0.11 0.08 0.14 0.14 E

(< .001) (0.35) (0.79) (0.02) (0.98) (0.55) (0.03) (< .001) (< .001) (< .001) (< .001)0.00 0.04 0.17 0.24 -0.06 -0.08 0.06 0.02 0.07 0.13 0.10 A

(0.95) (0.28) (0.06) (0.01) (0.02) (0.004) (0.25) (0.46) (< .001) (< .001) (< .001)-0.11 0.11 -0.16 -0.30 0.03 0.05 -0.09 0.02 0.03 0.07 -0.03 N

(0.003) (0.003) (0.09) (< .001) (0.21) (0.07) (0.12) (0.37) (0.004) (< .001) (0.17)-0.37 -0.18 -0.04 0.02 0.26 0.20 0.29 0.07 0.11 0.36 net size

(< .001) (0.43) (0.85) (0.54) (< .001) (0.002) (< .001) (0.07) (0.006) (< .001)-0.37 -0.02 -0.12 -0.03 -0.02 -0.10 -0.05 -0.05 -0.08 -0.14 density

(< .001) (0.94) (0.60) (0.43) (0.65) (0.14) (0.16) (0.21) (0.08) (< .001)-0.18 -0.02 0.33 0.24 0.20 -0.46 -0.07 0.01 -0.20 0.09 SD(0.43) (0.94) (< .001) (0.06) (0.27) (0.25) (0.62) (0.94) (0.05) (0.55)-0.04 -0.12 0.33 -0.10 0.07 -0.06 0.16 0.02 -0.04 0.06 FM(0.85) (0.60) (< .001) (0.46) (0.70) (0.90) (0.27) (0.84) (0.72) (0.67)0.02 -0.03 0.24 -0.10 0.30 -0.04 0.41 0.09 0.00 0.03 N like

(0.54) (0.43) (0.06) (0.46) (< .001) (0.40) (< .001) (< .001) (0.85) (0.31)0.26 -0.02 0.20 0.07 0.30 0.13 0.26 0.12 0.00 0.30 N status

(< .001) (0.65) (0.27) (0.70) (< .001) (0.03) (< .001) (< .001) (0.96) (< .001)0.20 -0.10 -0.46 -0.06 -0.04 0.13 0.21 -0.01 0.03 0.20 N event

(0.002) (0.14) (0.25) (0.90) (0.40) (0.03) (< .001) (0.79) (0.53) (< .001)0.29 -0.05 -0.07 0.16 0.41 0.26 0.21 0.16 0.06 0.24 N group

(< .001) (0.16) (0.62) (0.27) (< .001) (< .001) (< .001) (< .001) (0.007) (< .001)0.07 -0.05 0.01 0.02 0.09 0.12 -0.01 0.16 0.50 0.12 N work

(0.07) (0.21) (0.94) (0.84) (< .001) (< .001) (0.79) (< .001) (< .001) (< .001)0.11 -0.08 -0.20 -0.04 0.00 0.00 0.03 0.06 0.50 0.15 N edu

(0.006) (0.03) (0.05) (0.72) (0.85) (0.96) (0.53) (0.007) (< .001) (< .001)0.36 -0.14 0.09 0.06 0.03 0.30 0.20 0.24 0.12 0.15 N tags

(< .001) (< .001) (0.55) (0.67) (0.31) (< .001) (< .001) (< .001) (< .001) (< .001)Significance is color-coded (red / gray). Near-significant results are gray (p < .10).Correlation size is intensity-coded.


Statistics

Means and other statistics of the researcher sample are shown in Table 3. These

measures were compared against a reference sample of all Facebook users within the same

age range (20-71 years, N = 1,213,690). Because of multiple comparisons, a conservative

Bonferroni correction was applied.

Table 3

T-test results for researchers characteristics

Researchers Population

M Median SD M Median SD t(df) p*

IQ 122.1 125.1 14.4 113.1 115.2 15.1 5.01 (67.1) < .001

O 62.0 66.0 23.2 66.2 67.0 17.3 -15.16 (6915.2) < .001

E 53.3 56.0 24.4 61.1 63.0 20.6 -26.42 (6929.6) < .001

A 55.4 57.0 22.2 60.2 61.0 17.8 -18.12 (6923.38) < .001

N 36.9 37.0 20.6 43.1 44.0 19.4 -24.68 (6946.2) < .001

Network size 395 314 323 317 240 282 6.34 (731.0) < .001

N likes 131 71 196 153 67 292 -5.17 (2425.4) < .001

N status 163.9 108 188.2 133.5 86 157.3 6.05 (1451.3) < .001

N group 38 23 42 29 14 42 9.01 (1957.6) < .001

N work 1.3 1 0.86 1.1 1 0.58 17.61 (7367.1) < .001

N education 1.8 1 1.2 1.3 1 0.81 27.29 (5678.3) < .001

N tags 118.0 27 203.8 45.5 3 120.5 16.22 (2094.0) < .001

SWL 4.7 5 1.3 4.4 4.4 1.4 4.00 (229.6) .0016

FM 3.6 4.0 6.4 1.7 1.5 6.2 3.21 (120.4) 0.03

N event 23 3 64 25 4 83 -0.71 (474.9) ns

C 58.4 62.0 23.5 59.2 58 18.4 -2.83 (6921.1) ns

Density 0.052 0.020 0.11 0.048 0.025 0.089 0.97 (726.0) ns

SD -0.2 0.0 6.8 0.6 1 6.7 -1.30 (726.0) ns

Given the non-normality of many of these variables, we chose to check the results


with Bonferroni corrected, non-parametric tests. We applied the Mann-Whitney test as the

non-parametric equivalent of the independent samples t-test. Overall similar results were

obtained: sixteen out of nineteen variables remained unaffected. However the difference in

conscientiousness now reached significance (t(6921) = -2.83, p = .006), as did network

density (t(726) = .97, p < .001). The difference in likes became non-significant (t(2425) =

-5.17, p = .95).

Table 3 provides deeper insight into the academic job profile. Not surprising,

academics hold higher intelligence scores. They also appear more content with life. As for

integrity, researchers generally possess a heightened sense of fairness (fair-mindedness),

which could play a role in situations ranging from tenure decisions to reactions to fraud.

They display significantly lower trait scores for openness, extraversion, agreeableness and

neuroticism. The difference in conscientiousness was non-significant with standard t-tests.

These trait differences are also depicted in Figure 1 on page 19.

Significant differences are found in how academics use Facebook social networks, as

compared to the average user. In general, academics support larger social networks, though

not particularly more dense. Consistent with larger networks, they also join more Facebook

groups. They make less use of Facebook ’likes’, instead using the platform’s status updates

to communicate their experience to others. We also find a large difference in photo tags,

which may indicate photos are used as an equally popular means of communication. The

increased employability most likely depicts their high professional mobility, as academics

tend to move from one institution to another. Similarly the increased number of schools

likely results from a high mobility in training.

Sex differences

Significant researcher sex differences were found on a number of measures (Table 4).

Note however the absense of sex differences for academic variables: after three

decennia the gender gap in academia seems finally bridged. As expected, the area showing

the largest gender differences was personality. Female researchers proved to be more

conscientious, more extraverted, more agreeable and more emotional. As can be expected

from more extraverted individuals, female researchers are more often tagged in Facebook


Table 4

T-test results for researcher gender effects

Men Women

M Median SD M Median SD t(df) p*

C 54.3 56 24.0 60.9 63 22.8 -11.16 (5102.7) < .001

E 49.9 50 25.3 55.3 56 23.7 -8.88 (5042.9) < .001

A 51.8 55 22.8 57.5 60 21.5 -10.21 (5076.5) < .001

N 33.0 31 20.5 39.2 38 20.4 -12.03 (5282.3) < .001

N work 1.37 1 0.97 1.27 1 0.79 4.39 (4902.5) < .001

N education 1.89 1 1.31 1.72 1 1.18 4.77 (4228.0) < .001

N tags 101.9 22 174.9 129.4 32 221.3 -3.15 (2053.9) 0.03

N articles 38.2 23 41.4 24.3 16 33.9 2.38 (155.1) ns

N citations 732.3 111 1872.0 380.9 113 807.5 1.68 (151.8) ns

h-index 8.4 5 8.7 6.5 5 6.2 1.65 (163.9) ns

QS ranking 53.5 52.19 23.9 54.1 52.9 24.4 -0.82 (3251.6) ns

SWL 4.5 4.8 1.4 4.9 5.2 1.2 -2.22 (162.7) ns

age 32.1 30 9.5 32.5 30 9.6 -1.35 (4070.4) ns

IQ 123.0 128.7 15.7 121.4 123.8 12.3 0.48 (59.7) ns

O 60.9 66 24.5 62.7 66 22.4 -3.03 (4942.8) ns

Network size 410 311 348 386 318 309 0.91 (465.7) ns

Density 0.054 0.021 0.11 0.051 0.019 0.11 0.39 (500.6) ns

SD -0.36 -1.25 7.39 -0.091 1 6.6 -0.20 (76.2) ns

FM 3.4 4 6.8 3.7 4 6.2 -0.30 (77.9) ns

N likes 134.4 70 190.8 129.1 72.5 199.6 0.64 (2069.1) ns

N status 148.5 90 188.4 176.0 122 187.3 -2.74 (1338.2) ns

N event 21.8 4 64.0 23.8 3 64.5 -0.33 (376) ns

N group 40.6 24 47.7 35.5 22 38.5 2.47 (1386.9) ns


photos. Yet, while male and female academics do not differ in academic indicators, there is

still a difference in professional mobility: male academics indicate both more places of

education and professional employment than female colleagues. At this point it should be

noted that the differences we showed in personality to the general population (Table 3)

cannot be reduced to an overrepresentation of women in the researcher sample. If the effect

was gender-related, then females’ higher conscientiousness, agreeableness and neuroticism

would produce high researcher trait scores. The fact that we find significant lower scores in

a predominantly female sample, only stresses the strength of a so-called academic profile.

Figure 1 . Big Five personality profile for academics (N = 6864). Left: Researcher profile.

Right: Sex differences.

Multivariate exploration of researcher traits

To gain further insight into the variability between different researchers, we

performed a principal component analysis (PCA). Because PCA breaks on missing data, a

reduced dataset was selected holding the most-complete variables of openness,

conscientiousness, extraversion, agreeableness, neuroticism, QS ranking, network size,

network density, likes, groups, number of workplaces, number of schools, and photo tags.

This combination optimized the number of variables we could use for the largest possible

sample size (n = 266). The result can be seen in Figure 2.

The variability seems uniformly spread over most of these variables, with network size

and density explaining a similar amount of variability as personality traits. This supports


Figure 2 . PCA results for the reduced, 13-dimensional dataset (n = 266).

the idea that network variables may be valuable sources of information, similar to

personality traits. The trait of neuroticism is remarkable in that it shows less variability

amongst researchers. It also seems nearly independent from the other traits, and opposite

to network size. This would mean that while researchers generally support larger networks

(Table 3), this tendency decreases as a result of (sensitivity to) stress.

Figure 2 also demonstrates the factor of mobility, produced by the number of schools

a researcher attended, and the number of listed work places. In the space of the two

principal components these two variables are shown to overlap, justifying their combined

interpretation as a single ’mobility’ factor. Fairly little variability is explained by QS

ranking, the quality rating of a researcher’s institution. It shows a trend for researchers

from high ranking institutions to host smaller, but denser social networks. They generally


are also more stress-prone, probably a result from their high-profile positions.

The first two principal components only explain 30% of the variability though,

leaving a lot of the individual differences absent in this 2PC-space. A minimum of four

components are required to provide a solution with at least 51% explained variance.

A new perspective is gained from performing k-medoid clustering on the researcher

data. Instead of comparing the explanatory power of the different variables, this method

allows to identify similar groups of researchers. We applied the partitioning around

medoids clustering algorithm (PAM). Just like PCA, PAM is sensitive to missing data and

requires a reduced dataset with complete data. Here we chose to add the academic

indicator of h-index, at the cost of sample size. The PAM dataset included h-index, QS

ranking, age and the personality traits of openness, conscientiousness, extraversion,

agreeableness en neuroticism (n = 82). The result can be seen in Figure 3.

Figure 3 . PAM clustering solutions for 2 clusters (n = 82). Left: Clusplot, right:

Silhouette plot. Note the smaller cluster of high-performing individuals.

The best clustering solution was realized when considering two groups, as indicated

by the silhouette plot (Figure 3). Solutions with > 2 groups proved unsatisfactory as they

produced both negative silhouette values and overlapping clusters. Here, the amount of


explained variance in the principal component space amounts to 55% of the total

variability.

From these clustering results (Figure 3) we can observe two groups of researchers, one

substantially larger than the other. The division seems to correspond to the first principal

component, which contains a medium-size positive loading for the h-index (.24) and high

negative loadings for four of the personality traits: openness (-.48), conscientiousness(-.44),

extraversion (-.48) and agreeableness (-.49) but no contribution of age or QS ranking. Also

worth to mention is the second principal component, containing high factor loadings for the

h-index (.64), age (.65) and neuroticism (.35).

Thus, Figure 3 reveals a small minority of individuals characterized by very low

personality scores in four of the Big Five traits. This group is very competitive (low

agreeableness) and introverted (low extraversion), but also rather laid back (low

conscientiousness) and conservative in thinking (low openness). They all have high

h-indexes. This is not a seniority or age-effect, as age did not contribute towards the

variability explained by the first principal component. These individuals likely profit from

the unique combination of traits that proves useful in conducting research.

Meanwhile, though not defining a particular group, we find that the h-index goes well

with both age and the trait of neuroticism. While it is only natural for the h-index and

physical age to coincide, the loading of neuroticism indicates a possible link with stress

coping. Thus, we can reasonable conclude that the h-index does in fact resonate with

specific combinations of personality traits.

Regression results for publications, citations, h-index and QS ranking

The reported parametric statistics of publications, citations, h-index and QS ranking

all use the log-transformed indices. Regression sample size was restricted to researchers

with known academic indices, latent trait personality scores and QS-ranking (n = 82).

A multivariate regression was first performed on the means to help protect against

inflating the Type 1 error rate in follow-up linear regressions and post-hoc comparisons. As

evidenced by Table 2, the correlation observed amongst the dependent variables

(productivity and citation), suggests the appropriateness of a multivariate approach. The


Pearson correlations between articles and citations (i.e., .62, Table 2) support the

assumption that the dependent variables would be correlated with each other in the

moderate range (i.e., .20 - .60). The high Pearson correlations with h-index (i.e., r(169) =

.85, p < .001 and r(169) = .74, p < .001) makes h-index a near-linear combination of

productivity and citations. Under these circumstances, h-index becomes statistically

redundant.

A multivariate linear regression was conducted to examine the effect of gender and

other latent variables as openness, conscientiousness, extraversion, agreeableness,

neuroticism and QS ranking (IV’s) on productivity and citations (DV’s). Age was included

as a possible covariate. Note that the correlations between openness, conscientiousness,

extraversion and agreeableness present a statistical issue of multicollinearity (Table 2). A

multivariate effect was found for age (b = .32, t(57) = 3.26, p = .002), but no other

predictor. QS ranking approached significance (b = .08, t(57) = 1.69, p = .10). The overall

model fit was adjusted R2= .28, F(24,57) = 2.31, p = .005. When corrected for

multicollinearity, QS ranking passed the threshold of significance (b = .08, t(69) = 2.14, p

= .04), with the model fit increasing to adjusted R2= .33, F(12,69) = 4.30, p < .001. As

such the only linear effect on academic performance seems to relate to the academic

resources at one’s disposal, while no single personality trait on its own benefits your

h-index.

Latent traits could conceivably have different effects depending on productivity and

citations. Therefore we performed least squares linear regressions on productivity (number

of articles) and popularity (number of citations) as follow-up tests to multivariate

regression. In each case we examined the effect of gender, age, QS ranking and all five

personality traits (IV’s), plus their interactions.

A linear regression for productivity revealed a significant effect of age (b = .17, t(56)

= 2.70, p = .009) and neuroticism (b = .06, t(56) = 2.23, p = .03), with interactions for

gender x neuroticism (b = .02, t(56) = 1.72, p = .09) and age x neuroticism (b = -.002,

t(56) = -1.80, p = .07) closely approaching significance. The model fit was adjusted R2=

.38, F(25,56) = 2.99, p < .001. Similar to the multivariate case, correcting for

multicollinearity showed significant effects for these interactions: gender x neuroticism (b =


.03, t(68) = 2.49, p = .015) and age x neuroticism (b = -.001, t(68) = -2.05, p = .04), while

still maintaining the effects of age (b = .13, t(68) = 2.70, p = .008) and neuroticism (b =

.04, t(68) = 2.00, p = .05). Model fit increased to adjusted R2 = .42, F(13,68) = 5.50, p <

.001. Thus, a researcher’s capacity to output research is modified by his/her capacity to

deal with stress. The picture is more complicated however as gender and age interact with

this trait. Neuroticism’s effect is much more profound for young academics, particularly

men (Figure 4).

Figure 4 . Interaction effects of neuroticism on researcher productivity (number of articles)

(n = 82). Left: neuroticism x gender. Right: neuroticism x age. Categories are defined

against median age (= 30 years).

In contrast to productivity, follow-up linear regression for citation revealed only a

significant effect of age (b = .30, t(56) = 2.42, p = .02). Model fit was adjusted R2 = .27,

F(25,56) = 2.18, p = .007. Correcting for multicollinearity did not change these results.

Citation is only affected by a researcher’s age, with the higher seniority and academic

visibility that implies.

To conclude, we were interested to see whether the combined latent traits of

employed researchers could also predict a university’s QS ranking. We performed linear

regression on QS rankings, examining the effect of gender, age and all five personality traits

plus their interactions. As this analysis did not depend on the availability of Google

Scholar data, it allowed for greater sample size (n = 2295) and test power. Again, only age


emerged as a significant factor (b = .006, t(2277) = 2.05, p = .04). Model fit was adjusted

R2 = .004, F(17,2277) = 1.66, p = .04, indicating that the traits of individual researchers

do not significantly build towards predicting a university’s QS ranking. Correcting for

multicollinearity did not change these results.

Discussion

Before selection begins, everyone is still a success. All of the applicants gunning for

Ph.D.’s already attained academic honours: certificates, scholarships, honor roll, summa

cum laude, and parents who commend their talent. In Flanders, about one quarter of these

applicants will see their dream fulfilled and get to start a sponsored Ph.D. (Barbé, 2010;

BOF, 2013). Yet, about half of our best and brightest will drop-out before ever finishing it

(Groenvynck, Vandevelde, De Boyser, et al., 2010; Van der Haert et al., 2011). Flanders

performs slightly worse than its neighbours, with the Dutch Association of Universities

(VSNU) and the Deutsche Forschungsgemeinschaft (DFG) reporting a 25-30% and 30%

drop-out respectively (Deutsche Forschungsgemeinschaft [DFG], 2013;

Vereniging van Universiteiten [VSNU], 2011). While it is true that many reasons for

pursuing a Ph.D. exist, incl. family, financial or health issues, the most common argument

remains a personal mismatch to academic demands (Verlinden et al., 2005). Ph.D. drop-out

is costly. Each case represents loss of financial and time investments by student, mentor

and university. In addition, much early potential is unduly declined and prevented from

reaching the doctoral pipeline. The question then is one of optimizing attributed resources,

and minimizing the talent spill. Insight in the associated traits of academic success can help

predict which candidate personalities will flourish in a competitive research environment.

Such early investment carries over in tenure: high-productive doctoral students contribute

the most to their faculty. And when their own time comes to apply for tenure, traits of

personality, integrity and networking will have shaped their academic candidacy.

What determines a best-fit academic profile? Past studies mostly limited themselves

to easily accessible application data: age, gender, race and nationality. None proved very

succesful at predicting later academic success (Haslam et al., 2008). In order to address

what underlying factors are involved we analyzed common latent predictors of job


performance: personality, professional integrity and an individual’s ability to network.

These factors are not arbitrary. In our initial review, we noted how personality and

integrity instruments are in fact proven tools for private job recruitment (Schmidt &

Hunter, 1998). Several jobs have evidence-based trait profiles associated with successful

careers: leadership positions (Dilchert, 2007) or positions involving high interpersonal

interaction (M. Mount, Barrick, & Steward, 1998). No single trait drives these successful

careers, rather a specific combination of traits (Dilchert, 2007).

Personality

Our first hypothesis related to the combination of traits specific to an academic

profession. Just as is the case for leaders (Dilchert, 2007) or social workers (M. Mount

et al., 1998), we found that academics display a distinct personality signature. Researchers

are characterised by lower scores for openness, extraversion, agreeableness and neuroticism

as compared to Facebook peers. Therefore, in a negative sense, the researcher archetype is

more conservative (low O), socially secluded (low E), antagonistic (low A) and somewhat

emotionally distant (low N). These traits are turned to strengths as - respectively - a

reliance on proven methods, high personal independence, a highly competitive spirit and

greater stress-resistance. These results replicate previous reports of reduced extraversion

and neuroticism in academic samples reported by Scevak et al. (2007) and Simonton

(2008). It partly complies with the meta-analysis results from Feist and Gorman (1998),

who also found lower openness but also noted high conscientiousness scores. In our sample,

conscientiousness did not significantly differ for academics as compared to the other

Facebook users. This might have been an effect inherent to using Facebook, as young adult

Facebook users had previously been found to be generally low conscientious (Ryan, 2011).

However, in all fairness, Ryan (2011) also found Facebook users to be more extraverted,

while Correa, Hinsley, and De Zuniga (2010) found high openness and high neuroticism for

using social media. These differences are sufficient to assume a deeper truth to the

researcher archetype that extends beyond just the Facebook website. Furthermore,

Facebook’s high penetration rate of one in seven people (Williams, 2012) makes systematic

personality biases unlikely. A third reason is the strength and consistency of the pattern,


as it even overcomes the long-established gender differences in personality originally

reported by Feingold (1994) and Costa, Terracciano, and McCrae (2001). Towards

application this might be useful, as it allows for quantified compairisons to the researcher

archetype. University counseling services can use this information to host trait-specific

workshops (e.g., a stronger focus on presentation styles for introverts). Meanwhile,

neuroticism testing could assist in preventing stress-related drop-out and reduce the risk of

job burnout amongst senior researchers.

We also predicted that a combination of traits would influence a researcher’s

productivity and popularity, thus modifying his/her h-index. This effect seems primarily

focussed on productivity, as our results showed a significant contribution of an individual’s

neuroticism. The way a researcher deals with stress seems therefore crucial in determining

his/her academic publishing. The trait of neuroticism is of particular interest as it is also

lower for researchers, and shows less variation as compared to other traits. That implies

that for most cases an early selection had already occured. It also shows up as almost

independent from the other personality traits in PCA and clustering. That would indicate

stress management ought to be approached as an independent skill, crucial to academic

success. Its central role fits perfectly with self-report measures from Scevak et al. (2007),

reporting on a group of at-risk doctoral candidates experiencing difficulty specifically in

handling stress-related deadlines.

Aside from the interesting main effect, we also found evidence for two interactions.

First, an interaction of neuroticism x gender shows greater reactivity to stress for men as

compared to women. This likely results from the well-known sex difference in neuroticism.

Females already have a higher range of neuroticism. They experience greater distress over

deadlines and work diligently to meet them. Men - in general- have a more laid back

attitude. Neuroticism acts as a filter to men, in which types more receptable to stress cues

proceed to publish significantly more material (note that productivity is expressed on a

logarithmic scale). It may appear as if men are also publishing more. However, recall that

we found no evidence for a sex difference in productivity (Table 4). Second, an interaction

of neuroticism x age shows greater reactivity to stress for young researchers. The most

likely explanation would be that young researchers are still learning to adapt to academic


demands. The logarithmic scale should again be taken into account though, which tends to

compress the effect for senior researchers having the largest number of articles.

We also located a small group of high-performing individuals, sharing a combination

of low scores on at least four latent personality traits. These scores also showed up in

t-tests and relate to openness, extraversion and agreeableness (with a possible influence of

conscientiousness). Clustering also revealed an association between the h-index and high

neuroticism. Neuroticism’s effect on the h-index is only indirect, as a multivariate

regression showed no independent main effect for neuroticism on the h-index. Instead, high

sensitivity to stress prompts more published work, which in turn seems to boost the

h-index.

This brings us to our first, practical recommendation: personality testing in academic

settings could likely be used as a viable orientation instrument, preferably focusing on

neuroticism and stress coping strategies. Future studies will need to focus on the

discriminative power and desirability of such an instrument. (1)

QS ranking

While multivariate regression showed no effects of latent traits to the h-index, it did

reveal a significant effect of the university’s QS ranking. Most likely, the influence of

QS-ranking works as a self-sustained process. More prominent universities attract more

prominent experts in their field, while at the same time these scientists gain access to

superior training and academic resources, becoming even better. This finding provides

added incentive for universities looking to grow to invest in doctoral schools and effective

tutoring of junior academics. After all, the best predictor to the h-index seems to still be

the quality of training. Project management and time management skills, being able to

cope with pressure, are all examples of relevant skills that young scholars need to learn.

With QS ranking as the most significant predictor, we feel universities’ goal of promoting

domestic talent is best served through graduate services investment; including doctoral

schools, open talks and promoting high-quality training for graduate and postgraduate

attendees. This could be combined with our first recommendation, by supplying stress

management techniques and a university counseling service that insures high spirits. (2)


Professional integrity

In regards to our first hypothesis, we found an heightened sense of fair-mindedness

also contributed to the researcher archetype. Academics typically share a higher sense of

fairness than average Facebook users, appreciating fair play and balanced decisions. This

may go a long way towards explaining the loss of trust and skepticism about academic

acountability reported by McNay (2007). That is, researchers in general are highly

sensitive to issues of fairness and would share strong disapproval of illegitimate practices by

others. Their reported concerns likely do not reflect on an actual majority of colleagues

(Anderson, Martinson, & De Vries, 2007; Fanelli, 2009), but rather forms a strong reaction

to a minority that is known to cheat (Fanelli, 2009). Academics’ high sense of professional

integrity obviously means good news for academia, but may also be what is hindering

others to come forward (Stapel, 2012). Curiously, the more fair-minded researchers also

reported significantly higher wellbeing (Table 2). The medium effect size of this particular

correlation is as high as can be expected, and could be a definite reason to consider

integrity assessment. The more competitive individuals proved more likely to have a

problematic professional integrity. Most likely because their sensitivity to performance

pressure, and drive to outshine other colleagues. This parallels findings by Anderson,

Ronning, De Vries, and Martinson (2007), who showed that high competition promotes

careless and questionable research conduct. Unfortunately, there was insufficient data to

investigate a possible effect between scales of professional integrity and academic variables.

As this merely represents a lack of sufficient variable sample size, it remains an obvious

avenue for future studies.

However, given their heightened sense of fairness, we believe academia could benefit

from follow up in integrity assessment. Aside from promoting good practice and a fair

atmosphere, it would restore much of the academic trust and leave researchers happier

because of it. (3)

Networking

Just as was the case for personality and integrity, we found characteristic traits for

researchers in relation to online networking. Specifically, we found researchers’ networks


are larger-than-average, with a more active use of communicative features such as status

updates or tags. Other Facebook features such as ’likes’ were less used, perhaps because

they convey a preference rather than communicate customizable information. When

considering the variance in the principal component analysis (Figure 2), the variability

seemed uniformly spread over most of the variables, with network size and density

explaining a similar amount of variability as personality traits. This supports the idea that

network variables may be valuable additional source of information, similar to personality

traits. This idea is supported by Zhong, Hardin, and Sun (2011), who found that social

network users were also more likely to be multitaskers. Additionally, those who spend time

on social networks also spend more time browsing the web in general, more time on work

and more time communicating to their peers online (Zhong et al., 2011). Since research

often involves long periods of collaborative works, a researcher’s ability to network and

communicate findings would be important. In addition, social networks may serve as

promoting platform were findings can be shared with a wider audience.

From PCA we learned neuroticism relates to active social networks of low size/high

density, while the majority of academics actually have large size networks. Given the

central role of neuroticism as predictor to academic productivity, it is likely that smaller,

denser networks actually assist some researchers with higher productivity rates. More

studies are definitely needed into the role of social networks for academic innovation. (4)

Sex differences

Perhaps the most encouraging finding was the lack of gender differences in all

academic predictors across analyses. Whereas previously studies reported gender

differences for doctorate success and academic attainment (Helmreich et al., 1980; Knox,

1970), they seem to have gradually phased out (Haslam et al., 2008; McNally, 2010). In

accordance with that literature, we find no evidence for sex differences in either

productivity, popularity or h-index amongst our sample. This confirms our third and final

hypothesis. Note that this finding is especially encouraging given that this sample mainly

represents younger generations of academic professionals. Given these results, we are

hopeful academic boards will see fit to provide junior researchers with equal prospects to


academic opportunities. Recent appointments of tenure already show no more evidence of

sex bias (Steinpreis, Anders, & Ritzke, 2005). Yet, while male and female academics do not

differ in academic indicators, we did find discrepancies in professional mobility: male

academics indicated both more places of education and professional employment than

female colleagues. Further research will need to expose the underlying reason, which might

be a difference in personal preference or still some lingering difference in professional

opportunities. In time though, the only differences that are likely the persist are differences

in personality (Costa, Terracciano, & McCrae, 2001; Feingold, 1994).

Future studies could also investigate whether gender differences have gone from all

academic fields, something we lacked sufficient data on. We were limited by the much

smaller data availability of academic variables (n = 169). This is solely the result of some

of the harsh limitations within a master thesis timeframe, and could easily be expanded on.

Similarly, follow-up will need to see if our other results fully generalize to all individual

fields. Interdisciplinary differences surrounding co-authorship, article length, self-citations

and even the range of h-index are common. This difference in range can even be

substantial (Harzing, 2012). Collapsing h-indexes for different fields into one variable

therefore weakens the resulting analyses. This no doubt lessened our ability to detect

inherent effects. We recommend for future research to replicate our analysis both between

and within academic fields. This will permit the development of more sophisticated tools

that predict future academic success, with potential for wide-scale professional application.

Obviously this study only provides a debut into the psychology of science. This

discipline of psychology is itself fairly new. In addition, this is the first study to our

knowledge to examine latent variables, combined with the power of a big data approach.

More such studies will be needed. Luckily, the increasing availability of large, online

databases provides new blood to the psychology of science. Sample sizes of thousands are

available to fix the limited power of old-school, self-recruited samples. The permeation of

public online access also offers a new alternative to private questionnaires: users can now

be approached directly through online social networks. The opportunities for new research

are numerous.


Conclusion

Based on this study we’re able to offer the following recommendations:

1. Personality testing in academic settings would likely make a viable orientation

instrument, preferably focusing on neuroticism and stress coping strategies. The researcher

archetype may serve as conceptual framework to other trait-specific interventions

specifically tailored to academics. Trait information, particularly neuroticism, can also

boost early detection of young academic potential. Follow-up studies will be needed

however on the discriminative power and desirability of such a personality instrument,

before it can be used in professional settings. As a starting point, the 20 IPIP-items of

neuroticism are provided in Attachment 1. These items are public domain, and thus free

for future researchers to use.

2. Graduate services investment as a way for universities to foster domestic

talent. This includes doctoral schools, open talks and promoting high-quality training for

graduate and postgraduate attendees. A combination with our first recommendation is

possible, by supplying stress management techniques and a university counseling service

that insures high spirits.

3. Professional integrity testing in academic settings deserves greater attention.

Potential beneficiaries include both the applicant, his/her research group and the wider

scientific community. Researchers are more sensitive to impartial decision making, and a

persistent correlation exists between reported well-being and a high integrity environment.

More competitive individuals are more likely to bend the rules on this point. As such, they

ideally form the focus for best practice campaigns. Again as a starting point, one might

consider Orpheus, a 190-item personality questionnaire for occupational settings that also

includes four professional integrity scales.

4. Continued research into academic networking, and its implications for fostering

academic collaboration and wider, public visibility.


References

Abbott, A., Cyranoski, D., Jones, N., Maher, B., Schiermeier, Q., & Van Noorden, R.

(2010). Do metrics matter? Nature, 465, 860–862.

Amor, D. (2001). The e-business (r)evolution : living and working in an interconnected

world (2nd Ed.) New Jersey, NJ: Prentice Hall.

Anderson, M., Martinson, B., & De Vries, R. (2007). Normative dissonance in science:

results from a national survey of U.S. scientists. Journal of Empirical Research on

Human Research Ethics, 2 (4), 3–14.

Anderson, M., Ronning, E. A., De Vries, R., & Martinson, B. C. (2007). The perverse

effects of competition on scientists’ work and relationships. Science and engineering

ethics, 13 (4), 437–61. doi:10.1007/s11948-007-9042-5

Anonymous. (2012). REF 2014 Research Excellence Framework. Retrieved August 1, 2013,

from http://www.ref.ac.uk/

Bachrach, Y., Kosinski, M., Graepel, T., Kohli, P., & Stillwell, D. (2012). Personality and

patterns of Facebook usage. In Proceedings of the 3rd annual ACM web science

conference on - WebSci ’12 (pp. 24–32). New York, NY: ACM Press.

doi:10.1145/2380718.2380722

Baier, E. & Dupraz, L. (2007). Individual and institutional liability of researchers in the

case of scientific fraud: values and ethics. Higher Education Management and Policy,

19 (3), 27–42.

Barbé, K. (2009). Memorandum FWO. Retrieved August 1, 2013, from

http://www.fwo.be/Nieuws-Relevante-informatie.aspx

Barbé, K. (2010). Het FWO in beeld - een overzicht van de bestedingen in 2002-2010.

Retrieved August 1, 2013, from

http://www.fwo.be/Nieuws-Relevante-informatie.aspx

Barrick, M. R. & Mount, M. K. (1991). The big five personality dimensions and job

performance: a meta-analysis. Personnel Psychology, 44 (1), 1–26.

doi:10.1111/j.1744-6570.1991.tb00688.x

Bijzonder Onderzoeksfonds. (2013). personal communication.


Butler, L. (2003). Explaining Australia’s increased share of ISI publications - the effects of

a funding formula based on publication counts. Research Evaluation and Policy

Project (REPP), 32 (1), 143–155.

Cohen, J. (1988). Statistical power analysis for behavioral sciences (2nd Ed.). Hillsdale, NJ:

Lawrence Erlbaum Associates.

Correa, T., Hinsley, A. W., & De Zuniga, H. (2010). Who interacts on the web?: the

intersection of usersfffdfffdfffd personality and social media use. Computers in Human

Behavior, 26 (2), 247–253.

Costa, P. & McCrae, R. (2006). Revised NEO Personality Inventory (NEO PI-R): Manual

(UK Edition). Oxford, United Kingdom: Hogrefe.

Costa, P., Terracciano, A., & McCrae, R. (2001). Gender differences in personality traits

across cultures: robust and surprising findings. Journal of personality and social

psychology, 81 (2), 322–331.

D’Amico, R., Vermigli, P., & Canetto, S. S. (2011). Publication productivity and career

advancement by female and male psychology faculty: The case of Italy. Journal of

Diversity in Higher Education, 4 (3), 175–184. doi:10.1037/a0022570

Deutsche Forschungsgemeinschaft. (2013). personal communication.

Diener, E., Emmons, R. A., Larsen, R. J., & Griffin, S. (1985). The satisfaction with life

scale. Journal of Personality Assessment, 49 (1), 71–75.

Dilchert, S. (2007). Peaks and Valleys: Predicting interests in leadership and managerial

positions from personality profiles. International Journal of Selection and

Assessment, 15 (3), 317–334. doi:10.1111/j.1468-2389.2007.00391.x

DiMicco, J. M. & Millen, D. R. (2007). Identity management: multiple presentations of self

in facebook. In Proceedings of the 2007 international acm conference on supporting

group work. New York, NY.

Fanelli, D. (2009). How many scientists fabricate and falsify research? A systematic review

and meta-analysis of survey data. PloS one, 4 (5), 1–11.

doi:10.1371/journal.pone.0005738

Feingold, A. (1994). Gender differences in personality: a meta-analysis. Psychological

bulletin, 116 (3), 429–456.


Feist, G. (2011). Psychology of science as a new subdiscipline in psychology. Current

Directions in Psychological Science, 20 (5), 330–334.

Feist, G. & Gorman, M. (1998). The psychology of science: review and integration of a

nascent discipline. Review of General Psychology, 2 (1), 3–47.

doi:10.1037/1089-2680.2.1.3

Ferber, M. A. (2003). Women’s uneven progress in academia - problems and solutions. In

Conference on equal rites, unequal outcomes - women in american research

universities. Cambridge, MA.

Fox, S. (2004). Older americans and the internet. Retrieved August 1, 2013, from

http://www.pewinternet.org/Reports/2004/Older-Americans-and-the-Internet.aspx

Froghi, S., Ahmed, K., Finch, A., Fitzpatrick, J. M., Khan, M. S., & Dasgupta, P. (2012).

Indicators for research performance evaluation: an overview. BJU International, 109,

321–326.

Goldberg, L. R. (1999). A broad-bandwidth, public-domain, personality inventory

measuring the lower-level facets of several five-factor models. In I. Mervielde, I.

Deary, F. D. Fruyt, & F. Ostendorf (Eds.), Personality psychology in Europe 7

(pp. 7–28). Tilburg, the Netherlands: University Press.

Goldberg, L. R., Johnson, J. A., Eber, H. W., Hogan, R., Ashton, M. C., Cloninger, C. R.,

& Gough, H. G. (2006). The international personality item pool and the future of

public-domain personality measures. Journal of Research in Personality, 40, 84–96.

Google. (2013). Google scholar citations. Retrieved August 1, 2013, from

http://www.google.com/intl/en/scholar/citations.html

Gosling, S. D., Rentfrow, P. J., & Swann, W. B. (2003). A very brief measure of the

Big-Five personality domains. Journal of Research in Personality, 37 (6), 504–528.

doi:10.1016/S0092-6566(03)00046-1

Groenvynck, H., Vandevelde, K., De Boyser, K., De Grande, H., Leyman, A., Derycke, H.,

& R., V. R. (2010). Basisindicatoren HRRF - ECOOM-UGent databank 2010. Gent,

Belgium: Human Resources in Research.


Groenvynck, H., Vandevelde, K., Van Rossem, R., Leyman, A., De Grande, H.,

Derycke, H., & De Boyser, K. (2011). Doctoraatstrajecten in Vlaanderen - 20 jaar

investeren in kennispotentieel. Gent, Belgium: Academia Press.

Hargittai, E. (2007). Whose space? differences among users and non-users of social network

sites. Journal of Computer-Mediated Communication, 13 (1), 276–297.

doi:10.1111/j.1083-6101.2007.00396.x

Harzing, A. (2012). Citation analysis across disciplines: the impact of different data sources

and citation metrics. Retrieved August 1, 2013, from

http://www.harzing.com/data_metrics_comparison.htm

Haslam, N., Ban, L., Kaufmann, L., Loughnan, S., Peters, K., Whelan, J., & Wilson, S.

(2008). What makes an article influential? Predicting impact in social and personality

psychology. Scientometrics, 76 (1), 169–185. doi:10.1007/s11192-007-1892-8

Hattrup, K. (2012). Using composite predictors in personnel selection. In N. Schmitt (Ed.),

The Oxford handbook of personnel assessment and selection (pp. 297–319). New York,

NY: Oxford University Press.

Helmreich, R. L., Spence, J., Beane, W. E., Lucker, G. W., & Matthews, K. (1980).

Making it in academic psychology: demographic and personality correlates of

attainment. Journal of Personality and Social Psychology, 39 (5), 896–908.

Hirsch, J. E. (2005). An index to quantify an individual’s scientific research output.

Proceedings of the National Academy of Sciences of the United States of America,

102 (46), 16569–72. doi:10.1073/pnas.0507655102

Izquierdo, M. & Izquierdo, A. (2006). Utility of composite and multiple criteria measuring

discriminative efficiency in personnel selection. Psicothema, 18 (2), 243–248.

Jahnke, I. & Koch, M. (2009). Web 2.0 goes academia: does web 2.0 make a difference?

International Journal of Web Based Communities, 5 (4), 484–500.

John, O. P. & Srivastava, S. (1999). The Big Five trait taxonomy: history, measurement,

and theoretical perspectives. In L. A. Pervin & O. P. John (Eds.), Handbook of

personality: theory and research (pp. 102–138). New York, NY: Guilford.


Judge, T. A., Higgins, C. A., Thoresen, C. J., & Barrick, M. R. (1999). The Big Five

personality traits, general mental ability, and career success across the life span.

Personnel Psychology, 52 (3), 621–652. doi:10.1111/j.1744-6570.1999.tb00174.x

Knox, W. J. (1970). Obtaining a PhD in psychology. American Psychologist, 25 (11),

1026–1032. doi:10.1037/h0030145

Levelt, W. J. M., Drenth, P., & Noort, E. (2012). Flawed science: the fraudulent research

practices of social psychologist Diederik Stapel. Tilburg, the Netherlands:

Commissioned by the Tilburg University, University of Amsterdam and the

University of Groningen.

Lock, S. (1995). Lessons from the Pearce affair: handling scientific fraud. British Medical

Journal, 310, 1547–1548.

Madden, M. (2006). Internet penetration and impact. Retrieved August 1, 2013, from

http://www.pewinternet.org/PPF/r/182/report_display.asp

McAndrew, F. (2012). Who does what on Facebook? age, sex, and relationship status as

predictors of facebook use. Computers in Human Behavior, 28 (6), 2359–2365.

McNally, G. (2010). Scholarly productivity, impact, and quality among academic

psychologists at group of eight universities. Australian Journal of Psychology, 62 (4),

204–215. doi:10.1080/00049531003667406

McNay, I. (2007). Values, principles and integrity: academic and professional standards in

UK higher education. Higher Education Management and Policy, 3, 43–66.

Miller, D. & Hersen, M. (1992). Research fraud in the behavioral and biomedical sciences.

New York, NY: John Wiley and Sons.

Modahl, M. (1999). Now or never: how companies must change today to win the battle for

internet consumers. New York, NY: HarperCollins Publishers.

Mount, M., Barrick, M., & Steward, G. (1998). Five-factor model of personality and

performance in jobs involving interpersonal interactions. Human Performance,

11 (2/3), 145–165.

Nakhaie, M. (2002). Gender differences in publication among university professors in

Canada. Canadian Review of Sociology, 39 (2), 151–179.


Nelson, C. & Lovitts, B. (2001). 10 ways to keep graduate students from quitting. The

Chronicle of Higher Education, June 29, B20.

Neto, F. (1993). The satisfaction with life scale: psychometrics properties in an adolescent

sample. Journal of Youth and Adolescence, 22 (2), 125–134.

Newton, J. (2000). Feeding the beast or improving quality ?: academics’ perceptions of

quality assurance and quality monitoring. Quality in Higher Education, 6 (2),

153–163.

Nosek, B. A., Graham, J., Lindner, N. M., Kesebir, S., Hawkins, C. B., Hahn, C., . . .

Tenney, E. R. (2010). Cumulative and career-stage citation impact of

social-personality psychology programs and their members. Personality & social

psychology bulletin, 36 (10), 1283–1300. doi:10.1177/0146167210378111

Rosenberg, J. & Egbert, N. (2011). Online impression management: personality traits and

concerns for secondary goals as predictors of self-presentation tactics on Facebook.

Journal of Computer-Mediated Communication, 17 (1), 1–18.

doi:10.1111/j.1083-6101.2011.01560.x

Rust, J. (1998). The validation of the Orpheus minor scales in the working population.

Social Behavior and Personality: an international journal, 4, 399–406.

Ryan, S., T. Xenos. (2011). Who uses facebook? an investigation into the relationship

between the big five, shyness, narcissism, loneliness, and facebook usage. Computers

in Human Behavior, 27 (5), 1658–1664.

Scevak, J., Cantwell, C., Bourke, S., & Reid, A. (2007). A metacognitive profile of doctoral

students. In EARLI 12th biennial conference for research on learning and instruction:

developing potentials for learning. Budapest, Hungary.

Schmidt, F. L. & Hunter, J. E. (1998). The validity and utility of selection methods in

personnel psychology: Practical and theoretical implications of 85 years of research

findings. Psychological Bulletin, 124 (2), 262–274. doi:10.1037//0033-2909.124.2.262

Simonton, D. K. (2008). Scientific talent, training, and performance: Intellect, personality,

and genetic endowment. Review of General Psychology, 12 (1), 28–46.

doi:10.1037/1089-2680.12.1.28

Smallwood, S. (2004). Doctor dropout. Chronicle of Higher Education, 50 (19), A10–A12.


Sparrowe, R. T., Liden, R. C., Wayne, S. J., & Kraimer, M. L. (2001). Social networks and

the performance of individuals and groups. Academy of Management Journal, 44 (2),

316–325. doi:10.2307/3069458

Stapel, D. (2012). Ontsporing. Amsterdam, The Netherlands: Prometheus.

Steinpreis, R., Anders, K. A., & Ritzke, D. (2005). The impact of gender on the review of

the curricula vitae of job applicants and tenure candidates: a national empirical

study. Sex roles, 41 (7-8), 509–528.

Stillwell, D. & Kosinski, M. (2011). myPersonality research wiki. Retrieved August 1, 2013,

from http://mypersonality.org/wiki

Stroebe, W. (2010). The graying of academia: will it reduce scientific productivity? The

American psychologist, 65 (7), 660–73. doi:10.1037/a0021086

Van der Haert, M., Ortis, E. A., Emplit, P., Halloin, V., & Dehon, C. (2011). Dropout and

degree completion in doctoral study: a competing risks survival analysis (Working

Paper No. 2011-034). ECARES.

Vereniging van Universiteiten. (2011). Promovendi. Retrieved August 1, 2013, from

http://www.vsnu.nl/f_c_promovendi.html

Verlinden, A., Billiet, J., Smedts, D., Pyck, H., Page, H., & Van de Velde, M. C. (2005).

Doctoreren in Vlaanderen. Research report. Katholieke Universiteit Leuven and

Universiteit Gent.

Wang, X., Liu, D., Ding, K., & Wang, X. (2012). Science funding and research output: a

study on 10 countries. Scientometrics, 91 (2), 591–599.

Williams, R. (2012). Revealed: the third largest ’country’ in the world - Facebook hits one

billion users. Retrieved August 1, 2013, from

http://www.independent.co.uk/life-style/gadgets-and-tech/news/revealed-the-third-

largest-country-in-the-world--facebook-hits-one-billion-users-8197597.html

Zhong, B., Hardin, M., & Sun, T. (2011). Less effortful thinking leads to more social

networking? the associations between the use of social network sites and personality

traits. Computers in Human Behavior, 27 (3), 1265–1271.

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