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Mapping Creativity: Creativity Measurements NetworkAnalysisIgor Reszka Pinheiro a & Roberto Moraes Cruz aa Universidade Federal de Santa Catarina , BrazilPublished online: 08 Aug 2014.

To cite this article: Igor Reszka Pinheiro & Roberto Moraes Cruz (2014) Mapping Creativity: Creativity Measurements NetworkAnalysis, Creativity Research Journal, 26:3, 263-275, DOI: 10.1080/10400419.2014.929404

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Mapping Creativity: Creativity MeasurementsNetwork Analysis

Igor Reszka Pinheiro and Roberto Moraes Cruz

Universidade Federal de Santa Catarina, Brazil

This article borrowed network analysis tools to discover how the construct formed bythe set of all measures of creativity configures itself. To this end, using a variant ofthe meta-analytical method, a database was compiled simulating 42,381 responses to974 variables centered on 64 creativity measures. Results, although preliminary, indicatethe existence of a core dimension filled with variables that indicate novelty, which issurrounded by the paired dimensions of negative affect and social leadership, and highcognitive performance and positive affect. As for the measurement instruments, it wasfound that, although tests of divergent thinking, self-reported biographies, and com-posite scores are the most appropriate tools to gauge creativity itself, both attitudeand personality inventories are best for diagnosing the different kinds of creators.

It is no joke saying that creativity exists only in people’sminds. In spite of the most fascinating and unusual com-binations of gastronomic spices, musical notes, or colorson a canvas, no one can smell, hear, or see this phenom-enon itself, only imagine it and theorize it. In concreteterms, then, as happened in the case of intelligence, itcan be said that creativity is nothing more than whatits tests measure because, after all, all its theories resist-ant to scientific scrutiny are grounded in an accurate andclear operational definition of the construct (Higgins,1996; Kuhn, 1992; Pasquali, 2003).

Thus, although for Guilford’s psychometric perspec-tive (1950, 1953) creativity is the expression of divergentthinking, which is operationalized as the quantity, thediversity, and the elaboration of answers to open ques-tions, for Amabile’s componential theory (1982, 1983)creativity is the synergistic relationship between motiv-ation and the relevant skills, which are accessed mostlythrough pair expert consensus. Other than these defini-tions, there are also in the literature on creativity the-ories based on personality traits measured by lists ofadjectives, on the potential of eminence measured by

biographical inventories, on specific cognitive processesas the ones measured by word association, and also onthe ex post facto analysis of portfolios through checklists(Cropley, 2000; Hocevar & Bachelor, 1989; Sternberg &Lubart, 1999).

Whether due to academic humility, or due to the lackof substantial evidence in favor of any of these theories,it is not rare (Almeida, Prieto, Ferrando, Oliveira, &Ferrandiz, 2008; Bechtereva, Danko & Medvedev,2007; Cropley, 2000; Kim, 2006; Plucker & Runco,1998; Wechsler, 1998; among others) to suggest thatthe phenomenon of creativity cannot be described byany of these tests alone, but only through a battery ofjoint measures. Thereby, the complexity of the phe-nomenon would be more fully encompassed, and alsocreativity itself would be observed by a perspectiveless subject to the particularities derived from theadministration of each test.

This article, therefore, by taking to the extreme thework of other authors who have already used scorescomposed by two or more measures of creativity (Jung,Grazioplene, Caprihan, Chavez, & Haier, 2010;Zampetakis, Bouranta, & Moustakis, 2010), aimed tofind out, after all, how the construct formed by the setof all, or at least the most representative, tests of creativ-ity is configured. Thereby, a couple of questions areraised: Is there a central element in the configurationshown by the intersection of these instruments? If so,

This article was supported by the Psychology Department of

Universidade Federal de Santa Catarina and CAPES.Correspondence should be sent to Igor Reszka Pinheiro, Rua

Heitor Luz 97, apto 205, Centro, Florianopolis–SC 88015-500, Brazil.

E-mail: pinheiro_ir@yahoo.com.br

CREATIVITY RESEARCH JOURNAL, 26(3), 263–275, 2014

Copyright # Taylor & Francis Group, LLC

ISSN: 1040-0419 print=1532-6934 online

DOI: 10.1080/10400419.2014.929404

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what guidelines coordinate the orbit of the peripheralelements?

To answer these questions, the various instrumentsused to measure this phenomenon are reviewed andthen a tool developed by sociology, network analysis(Hanneman & Riddle, 2005; Michaelson & Contractor,1992; Scott et al., 2005), is employed to map the spatialstructure between variables self-proclaimed creativityand all other elements directly connected to them. Thisshould identify the competition, complementarity, oropposition between the various operational definitionsof creativity, as well as to compile guidelines for theoptimal selection of tests in a battery or, even, forcomposing new measurement instruments.

CREATIVITY MEASUREMENT INSTRUMENTS

Measuring creativity is basically determining the beha-viors necessary for someone to be considered creative,and then observing their degree among the other subjectswho perform these same behaviors (Kirschenbaum,1998). Consistent with this objective, several instrumentswere developed, some of which were compiled by reviewsthat added up to 250 of them (Wechsler, 1998), whatshows, in addition to the growing interest in creativityresearch, a collective methodological need (Simonton,1999). This myriad of instruments can be sorted accord-ing to many ways, as through the construct dimensional-ity—contact, awareness, or interest (Kirschenbaum,1998)—through the psychometric object investigated—product, process, or person (Cropley, 2000)—or eventhrough the epistemological approach employed—psychometric, historiometric, systemic, etc. (Pinheiro &Cruz, 2009).

Possibly, one of the most cited individual attempts toorganize the conceptual basis of creativity measurementinstruments is the proposal of Hocevar and Bachelor(1989), which was previously drafted only by Hocevar(1981). This classification splits the measures into eighttaxonomic groups, namely: (a) tests of divergent think-ing; (b) attitude and interest inventories; (c) personalityinventories; (d) biographical inventories; (e) ratings byteachers, peers, and supervisors; (f) judgments of pro-ducts; (g) eminence; and (h) self-reported creative activi-ties and achievements. Tests that belong to each of theseclasses share much of their outlining theory, which helpsto ensure the reliability of concurrent measurements andthus, the cohesion of the results from different studies.

Starting by the tests most commonly used in research,the measures of divergent thinking include associativefluency, alternative uses, virtual thinking, unusual appli-cations, and the Torrance Tests of Creative Thinking(TTCT; Alencar, 1996) battery. Initially designed as atool to measure fluency, originality, flexibility and

elaboration, the TTCT were revised and extended toaccess, also, the presence of emotion, fantasy, move-ment, analogies, internal and unusual perspectives,mood, color, expressiveness, variety, and resistance toclosure (Kim, 2006; Wechsler, 1998). In both its formats,verbal and figurative, the application consists of the pres-entation of pictures to be questioned, of toys to beimproved, and of drawings to be completed or built.The scores are assigned by trained judges, often certifiedones, according to the number, the type, and the statisti-cal infrequency of the relevant answers (Nakano &Wechsler, 2006). Even being informally considered theclosest gold standard of creativity (Nassif & Quevillon,2008), criticism regarding the use of TTCT still mentionthe dependence of the divergent thinking to convergentthinking (Brown, 1989; Cropley, 2000), the inseparabilityof originality and fluency scores (Michael & Wright,1989), the variability of results caused by respondentsdomain (Clapham, 2004), and also that there is evidencethat the factor structure of the test depends more on theformat chosen than on the theoretical dimensionaccessed (Almeida et al., 2008).

Shifting focus now to the second category ofmeasures, the inventories of attitude and interest, othercatalysts of creativity like intrinsic motivation arevalued, especially because they consist of effective strate-gies for coping with environmental difficulties (Amabile,1983; Matlin, 2004; Sternberg, 2006). In order, then, tocheck people’s level and type of interest for certainactivities, instruments such as the Group Inventory forFinding Creative Talent, the Group Inventory forFinding Interests, and the Preschool Interest Descrip-tion are answered on a Likert-type scale, indicating thepresence of elements such as the sense of humor, thevariety of hobbies, the taste for arts, and the unusualwills (Alencar, 1996; Alencar & Fleith, 2003).

The instruments of the third class, the personalityinventories, in their turn, are distinguished from theprevious set by focusing on the factors that differentiateindividuals by describing the way they are, not the beha-viors they agree with. In general, these instruments aremade up of lists of adjectives that should be selected orwritten, being evaluated those features that are associa-ted with creativity as autonomy, self-confidence, initiat-ive, perseverance, spontaneity, and emotional sensitivity(Alencar, 1996). Examples of this group that can be citedare the Adjective Check List Creative Personality Scale,the Creativity Checklist, and the Creativity BehaviorInventory (Cropley, 2000). Despite all the virtues of thisclass of instruments, it is worth remembering that theconstant and dubious association between creativityand madness is derived from this particular kind ofpersonality measures (Post, 1994; Santosa et al., 2007).

Then, the biographical inventories, the fourth groupand probably the first instruments developed to measure

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creativity, still consist in the main mechanism toattribute scientific creativity. Either by the investigationof the childhood period, the family history, or theacademic production, this class of measures carries outthe ex post facto analysis of an event that demonstratesthe manifestation of the creativity phenomenon,enabling the obtainment of an index through an equa-tion of inputs (references) and outputs (citations; Soler,2007). This criterion is widely used in the selection forscholarships, jobs, and other processes based on merit.Thereby, the idea underlying this class of tests is the veryplausible assumption that the best indicator of futurecreative output is, precisely, the creative behavior ofthe past (Hocevar & Bachelor, 1989; Plucker & Renzulli,1999).

As for the measurement process of the fifth group,the ratings by teachers, peers, and supervisors, it occursin a much more subjective way and thus prone to per-sonal interferences. Widely used in the school context,a formal example of this type of measure is the Teacher’sEvaluation of Creativity Sheet. In this instrument, basedon the TTCT, a teacher, after receiving explanationregarding the concepts of fluency, flexibility, originality,and elaboration, lists the five students who demonstratethe greatest mastery of each of these dimensions, as wellas the five who demonstrate the least (Alencar, 1996).Other than this, there are numerous measures builtspecifically for research in the business context, amongwhich can be cited the widely used scales of Zhou andGeorge (2001), of Andrews and Smith (1996), and ofPerry-Smith (2006).

Next, the sixth group, the judgments of product, isthe second most widely used modality of creativity tests.These instruments are primarily based on the scrutiny ofjudges who carry out an individual assessment, whoseopinion will only subsequently be crossed. Accordingto Amabile (1982), four requirements must be met tomaintain reliance between the views: (a) all judges mustbe experts in the judging criteria; (b) all judges must usetheir implicit theories about creativity to make theirjudgments, (c) the evaluation of each judge shall beconducted individually; and (d) judges should evaluateeach product in relation to others and not based on anabsolute standard in the specified domain. If, on theone hand, this class of tests obviously tends to an subjec-tive assessment, on the other, this is the kind of measurethat induces less biased responses, as the lack of explicitcriteria prevents the deliberate adjustment of the respon-dents (Nagel, 2001). Conversely, in other highly struc-tured tests of creativity in which infrequency is used asa measure of originality, there is the real possibility ofincoherent answers being offered only aiming at goodscores (Silvia et al., 2008).

Finally, both the seventh and the eighth categories,the eminence and self-reported creative achievements,

respectively, focus again the human beings, however,unlike thebiographical inventories,measuringcreativitybythe social recognition or general unpublishedworks.Whilethe eminence group verifies the consequences of creativity,rather than its causes, such as titles or awards, theself-report instruments look for evidence of productionin daily activities, as the creative potential manifests itselfin all spheres of life (Hocevar & Bachelor, 1989). A recentexample of this last category is the Escala de Criatividadeao Longo da Vida (Shansis et al., 2003), which covers upthe peak creativity and extent of involvement in both theprofessional and the personal facets.

Moreover, it is still valid to include a ninth category increativity tests, the biometric features of neuroscience, inwhich stand out the Electroencephalography, the Func-tional Magnetic Resonance Imaging, and the PositronEmission Tomography (Fink, Benedek, Grabner, Staudt,& Neubauer, 2007; Kounios & Beeman, 2009). All ofthese features, each in its own way, detect the activity ofdifferent brain areas, relating the thoughts during creativeactivity to the flow of information processed. Thesetools have been employed in research indicating thatcreativity manifests itself not only at the right hemisphereof the brain, and that there is a clear relationship betweenthis phenomenon and the synaptic density (Dietrich,2007).

In general, then, irrespective of the technique used,the nature of the creativity measurement instruments iseither statistical or speculative, once classes 1, 2, 3, 8,and 9 follow the logic of Classical Test Theory, andthe other groups, 4, 5, 6, and 7, consist basically ofexperts opinions (El-Murad & West, 2004). Wheneveran instrument is investigated by its own, the primarilyfocus is to understand how to access creativity, but whenit is scrutinized in a grouping of two or more of thesetools, the question of what, in fact, is being measuredarises. Unfortunately, however, there appears to be nopublished work investigating what the complete setof creativity measures actually comprises. Thus, thisresearch, with the aid of network analysis, attemptedto fill the gaps between all the above nine taxonomicgroups by drawing the most detailed map of the creativ-ity dimensionality.

METHOD

Given the impossibility1 of administrating to the sameset of subjects, on a single testing battery, the required

1Preliminary calculations showed that the amount of time required

for the proper administration of all the selected tests on the same sub-

jects would reach the tens of hours, making impracticable the recruit-

ment of volunteers, and also severely damaging ecological validity of

any results.

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amount of creativity measures to compose a generalmap of this construct dimensionality, as well as of intuit-ively interpreting the quite large volume of informationgenerated, two formal techniques were used for themanagement of the data. First, by means of ameta-analysis variant, the retro-analysis,2 as many cor-relation coefficients from creativity’s measurementinstruments as possible were compiled and, after that,through network analysis, this data set was graphicallyorganized, having its centrality inspected by thefragmentation metric.

Following the guidelines of systematic review pro-posed by Hunter and Schmidt (1990), it was held asearch for papers indexed in all3 databases availableon CAPES Periodicos web portal, until the year of2011, containing the word creativity in the fields title,abstract, or keywords, of their own search engines. Allpapers that contained correlation data between twomeasures of creativity, or a measure of creativity andadjacent phenomena, were selected if they met thecriterion of objectivity, as those capable of replicationafter complete reading. Among the selected items,papers that did not presented its variables averagevalues, standard deviations, or complete correlationmatrices were excluded, as well as those which sampleswere not pointed out or were less than 100, those writtenin languages other than English, Portuguese, Spanish, orItalian, and those which only correlate a creativity

measure with another fundamental one of the subjects(such as age, sex etc.).

The remaining texts, then, had their Pearson corre-lation matrices subjected to the Inverse TransformSampling technique (Devroye, 1986), through whichwas created databases equivalent to their primaryresearch ones, by means of MVN software (Uebersax,2006). In the rare cases (20 units) in which actual corre-lation tables did not consist of positive definite matrices,adjustment were made to their nearest variation, as pro-posed by Mishra (2007). Then, the created databases’raw scores were normalized and compiled into a singletable containing all variables in the selected papers. Thisall-in-one database was, then, scanned to discern differ-ent sets of elements directly linked by at least one pair ofvariables. From this point forward, only scores from thelargest cohesive group were retained.

Thus, the final dataset, the one composed by thejuxtaposition of all created scores of which variables werekept, was subjected to the process of multiple imputationby Multivariate Imputation by Chained Equations(MICE) method (Raghunathan, Lepkowski, Hoewyk, &Solenberger, 2001; Stuart, Azur, Frangakis, & Leaf,2009), as no computer was able to process, at once, thehuge amount of data gathered. By means of SPSS 17, thisdatabase missingness pattern was examined and, startingfrom the variable with the greatest number of juxtaposedscores, the missing values imputation was performed foronly one variable at a time. Thus, by the fully conditionalspecification option, a set of values was created for onesingle variable every 10 iterations, being itself and allothers specified as predictors. This process was repeatedfor the second variable with the greatest number of juxta-posed scores, and so on. After a first cycle of imputationshas been carried out with each of the variables, the sameprocedure was repeated. This resulted in 10 databasescompletely filled, with no missing data.

Still using SPSS, the 10 complete databases pooledcorrelation matrix was then calculated and exported toMicrosoft Excel. In this last program, each cell of thecorrelation matrix was multiplied by 100 (to avoid dec-imals), and also added, again in each cell, the smallestexisting value plus one (to avoid numbers equal to orbelow zero). This annulment of the correlations direc-tion, besides avoiding the usual semantic changes whenperforming meta-analyzes (e.g., being obligated to turnneuroticism into emotional stability), was a requirementof the programs that perform network analysis, as theircalculations are based mostly on positive Euclideandistances between actors.

Thus, the resulting quadrangular table was importedinto Ucinet 6 (Borgatti, Everett, & Freeman, 2002) soft-ware and its auxiliary programs KeyPlayer 2 (Borgatti &Dreyfus, 2002), and NetDraw (Borgatti, 2002), withinwhich took place network analysis itself. Using the first

2Technique analogous to meta-analysis in which effect sizes of

primary research are used to recreate databases subject to agglutination,

imputation and generalized further analysis. Unpublished research

showed systematic deviations of retro-analysis results from traditional

meta or mega-analytical procedures only when it was recreated sets of

answers simulating samples of 100 subjects or less.3Academic OneFile, Academic Search Premier, Academy of

Operative Dentistry, Agri2000, Agricola, AGRIS, Alexander Street

Press, American Academy of Audiology, American Academy of

Periodontology, American Academy of Psychiatry and the Law,

AAAS, AACN, AAVLD, ACM, ACS, EconLit, AIP, AMA, APS,

APA, ASBMB, American Society for Cell Biology, ASIP, ASN, Amer-

ican Society of Agronomy, ASA, ASCE, ASH, Annual Bulletin of

Historical Literature, Annual Reviews, Applied Science and Tech-

nology Text, arXiv.org, APEL, ACM, Association of Clinical Scien-

tists, ASTM International, Banco de Teses da Capes, AGROBASE,

BRASPAT, BDPA, Bergell House, Bentham Science, Berkshire

Encyclopedia of Extreme Sports, Biological Abstracts, BioOne, Book-

list, BMJ, CAB Abstracts, CSA, Cambridge University Press, Cell

Press Journals, Clase, Classical Reviews, Cold Spring Harbor Labora-

toty, Ei, DII, Duke University Press, EBSCO, ESA, ERIC, ECCO,

Emerald, Endocrine Society, FDI, Faseb, FSTA, Fuel and Energy

Abstracts, Gale, GSA, Guilford Press, HLAS Online, HighWire,

IndexPsi, Informs, INSPEC, IEEE Xplore, IOP, ICE, IPDL, INIS,

Jama, JCR, JSTOR, Karger, Kirkus Reviews, LILAC, Maney Pub-

lishing, Mary Ann Liebert, MathSci, PubMed, Micromedex Health-

care Series, MLA, NCJRS, Nature, OSA, OECD, OVID, Oxford

University, PloS One, Press, PePSIC, Philosopher’s Index, Project

MUSE, RILM, RePEc, RIPM, RCPSYCH, RSC, Sage, Science

Direct, SciELO, SciFinder, Scitation, Scorpus, SpringerLink, Wiley

InterScience e WorldSciNet.

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application, the variables’ hierarchical clustering wasinvestigated by the maximum distance method throughthe algorithm developed by Johnson (1967), with whichthe clustering levels are organized in descending order ofsimilarity. Using the second, the centrality of the ele-ments was calculated through their fragmentationpotential, which expresses the heterogeneity coefficientof the set after deleting each node (Borgatti, 2006).Using the third, the graphical representation of thenetwork formed by every node relationships wasbuilt, through its standard iterative metric multidimen-sional scaling procedure (Freeman, 2000; Scott, 2000).The three results were finally compiled into a singleimage to thereby make the creativity map moreintelligible.

RESULTS

Among the 118 surveyed databases, 26 offered resultsafter searching the term creativity: AAAS (30 papers),ACM (100 papers), Annual Reviews (39 papers),EBSCO (1,408 papers), Emerald (118 papers), Gale(73 papers), High Wire (212 papers) IEEE Xplore (228papers), IOP (17 papers), Jstore (210 papers), Jama (3papers), Karger (48 papers), LILAC (25 papers), Nature(2 papers), OVID (2,814 papers), Oxford (55 papers),PloS ONE (22 papers), PubMed (133 papers), RCPsych(11 papers), Sage (216 papers), SciELO (126 papers),Science Direct (1,200 papers), Scitation (71 papers),Springer Link (264 papers), Wiley Interscience (457papers), and WorldSciNet (20 papers). After visualinspection of all the material, however, only 699 papersremained in this research, as the others did not qualifyinto the category of correlational studies. Out of this,some 495 articles were also excluded due to duplicates(the same paper available in more than one database),the absence of some essential information (means, stan-dard deviations, samples, or values of the correlationmatrix itself), or because they are written in languagesother than English, Portuguese, Spanish or Italian.

Thus, based on the remaining 2044 papers, 1,845 vari-ables were cataloged and grouped into 107 disconnectedgroups. To enable the imputation of missing data onlythe largest of these groups was kept, which turned outto be composed of 974 variables, 64 of them beingentitled creativity by the authors of their source papers.The measurement of these variables were labeled ascreativity, subjective creativity, creative performance,creative behavior, creative identity, creative thinking, indi-vidual creativity, collective creativity, creative synergy,

creative process, product creativity, creative content, cam-paign creativity, or creativity self-assessment. The termcreative self-efficacy was not included in this groupbecause, according to the theory presented by theauthors who have used this variable, this measure is clo-ser to self-efficacy than to creativity itself (Carmeli &Schaubroeck, 2007; Robbins & Kegley, 2010; Tierney& Farmer, 2004). Thus, the generation of the remainingprimary research equivalent databases created scoressimulating the response of 42,381 subjects, and thosewere supplemented by multiple imputation procedure.

From this latter database-adapted correlation matrix,we obtained, then, by the network analysis’ hierarchicalclustering procedure, the merging levels shown inFigure 1. As can be seen in this image, apparently, struc-turally equivalent groups coalesce constantly until a firstinflection point, in which can be distinguished 9 clusters,but only when it reaches 5 clusters can one perceive amarked drop in the similarity levels. Thus, althoughthe first point, the EI index, which expresses therelationship intensity between external and internal linksof a cluster (Krackhardt & Stern, 1988), still approxi-mates 1, being equal to .76, in the second one it isalready halfway to 0, being equal to .52.

Thereby, based on the division of 5 clusters, there arein the first group, other than 3 variables labeled ascreativity, elements such as the neuroticism, the Myers-Briggs Type Indicator (MBTI) judgment dimension,the primitive defenses, the negative mood, the schizoty-pal personality, the trait anxiety, the fear, the negativeaffect, the children’s anxiety, the cognitive disorganiza-tion, the unusual experiences, the children’s depression,the worry, the outcome expectation, the futility, theconcentration, the social anhedonia, the emotionalexhaustion, the work strain, the ability to hide negativeemotions, the unclear means, the expertise heterogen-eity, the multiple means, the quickness, the ethnicdiversity, the ingenuity, the depth, the relationshipconflict, the intrinsic satisfaction, the intrinsic workmotivation, and the task routinization. Therefore, it

FIGURE 1 Clustering levels’ inflection line.

4The full list of papers included in this study is too large to appear

in the references, but it is, however, available for consultation with the

first author of this article.

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can be distinguished, among this cluster’s variables, thestrong presence of a negative affective dimension, whichis characterized by the internalization of troubled socialrelations, by the deep but messy thoughts, by thediversity, and also by the self-recognition.

In the second cluster, other than 12 variables labeledas creativity, there can be found elements such as theneuroticism again, the utility test score, the SAT score,the stress, the magnitude of innovation, the feedbackvalence, the picture arrangement ability, the conceptmastery, the affiliative humor, the American CollegeTesting score, the checking ability at work, the fluidintelligence, the general cognitive ability=IQ, theanxiety, the letter listing, the word association, theperceived team pressure, the domain-relevant skills,the word vocabulary, the decision-making perfor-mance, the word fluency, the English compositionscore, the socioeconomic status, the number compari-son, the knowledge of the operating environment, theproblem solving, the innovation radicalness, the infor-mation acquisition ability, the disciplined imagination,the formalization, the Raven’s advanced progressivematrices score, the general knowledge, the task focus,the contingent reward, the intrinsic motivation again,the preference for working hard, the self-criticism,and the need for rehearsal. Therefore, it stands outamong the variables in this cluster, the high cognitiveperformance dimension, which is characterized by sev-eral scores of intelligence and general knowledge, bythe verbal fluency, by the depth obtained from disci-pline, effort, and focus, and also by some introvertedpersonality traits such as intrinsic motivation andself-criticism.

The third cluster, for its turn, other than 32 measuresof creativity, shows variables as the perceived control oftime, the openness to experience, the psychoticism, thetransformational leadership, the novelty, the emotionalambivalence, the confidence on long-range planning,the conscientiousness, the tenacity, the social interac-tion, the figural fluency, the figural flexibility, the impul-sive nonconformity, the originality, the attitude towarddivergent thinking, the technological uncertainty, thecreative self-efficacy, the pictorial social knowledge,the positive mood, the sadness, the turnover intention,the unique responses, the unmet career expectations, theself-efficacy, the upward striving, the participation indecision-making, the peripherical anagram perfor-mance, the job dissatisfaction, the estimated dreamrecall, the inspirational motivation, the leader proto-typicality, the substance abuse, the variability, the cross-application experiences, the support for creativity, theendurance, the risk-taking behavior, the plasticity,the absorption, the demographic dissimilarity, theself-confidence, the role ambiguity, and the risk toler-ance. Therefore, although this cluster covers a broad

variety of features, it can be discerned through itsvariables mainly the dimension of novelty, which isidentified by the multiplicity of experiences, the impetusfor change, the development of unusual combinations,the perceived control of time, and the belief in theirown creativity.

Next, the fourth cluster displays, other than 8 vari-ables labeled as creativity, elements such as the groupidentity, the agreeableness, the organizational commit-ment, the perception of unhelpful coworkers, the roleoverload, the mature defenses, the cooperative-teamperceptions, the additive fantasy, the conjunctive values,the process conflict, the contextual behaviors, the nega-tive work environment, the ability to identify, the stab-ility, the organizational citizenship, the social-cognitiveflexibility, the team performance, the task conflict, theefficiency, the facilitator’s role, the entrepreneurialintentions, the adhocracy perceptions, the coworkertrust, the group cohesion, the supervisor close monitor-ing, the personal initiative, the affective commitment,the expertise integration, the leader humor, the rela-tional capital, the emotional social support, the denial,the competitive intensity, the environmental uncer-tainty, the job insecurity, the idealism, and uncertaintyavoidance. Therefore, it can be found among these vari-ables, a clear dimension of the social leadership beha-vior, which is characterized by the commitment to thecommunity, by the warmth and flexibility in the rela-tionships, by the entrepreneurial intention, by theidealism, and by the integration of skills prioritization,given the perception of risks posed by the environmentand the coworker’s incompetence.

Finally, the fifth cluster consists of, other than thelast 9 variables entitled creativity, elements such as theself-esteem, the creativity motivation, the extraversion,the positive mood, the ability to communicate ideas,the work involvement, the joviality, the emotional con-flict, the politicality, the membership esteem, the positiveaffectivity, the minority group identification, the clarityif feelings, the ability to influence others, the idea gener-ation, the leader emergence, the idea validation, themarket turbulence, the ability to have ideas, the open-ness, the counterproductivity, the willingness to takerisks, the immediate consequences, the injury rate, thepropensity for switching, the presence of creativecoworkers, the empowering leadership, the nurturance,the sociability, the authority acceptance, the psychologi-cal empowerment, the preference for prematureevaluations, the noncontrolling supervision, the ruleconformity, the identification with the leader, theemotional stability, the preference for group work, andthe job satisfaction. Therefore, the set of variables in thiscluster distinguished a positive affective dimension thatis highlighted by superior levels of sociability, by thesatisfaction with the leadership, by the self-esteem, and

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by the participation in the idea generation process,even if the contributions are limited to the immediateconsequences ones.

As shown in Figure 2, if it is then taken to accountthe size of each of these clusters in relation to the totalset of variables used in the analysis, i.e., 10%, 28%,31%, 12%, and 19%, respectively, there is a dispro-portional creativity measurements concentration only inthe cluster number 3. This cluster, whose dimensionality,in fact, is the closest to creativity as it is theorized, notonly brings together the largest number of instrumentsfor measuring this phenomenon, but also is the only onewhose configuration would be compromised if thesevariables were excluded, given their high potential forfragmentation (Mann-Whitney U¼ 3,382.5, z¼�2.12,p¼ .03). Conversely, in any other remaining cluster, 1,2, 4, or 5, when looking for statistically significant differ-ences regarding the same fragmentation value betweencreativity labeled variables and all others, nothing isfound (U¼ 117.5, z¼�.35, p¼ .73; U¼ 1,385, z¼�.68,p¼ .50; U¼ 419, z¼�.14, p¼ .89; U¼ 616, z¼�1.17,p¼ .24, respectively), making it impossible to state thatthese elements play a central role in these last four groups.

All clusters and their dimensions, however, to someextent, interact to form the network of variables in thecreativity universe, because when they are combinedinto a single entity, the difference in the fragmentationpotential of this phenomenon presents a larger effect sizeand a smaller associated probability than those calcu-lated based on the cluster number 3 alone (U¼24,203.5, z¼�2.26, p¼ .02). This result, more than justcorroborating the method used to construct a variablemap centered on creativity, demonstrates the real com-plementarity of the various instruments used to accessit, as the measures outside the central cluster, somehow,also cooperate to the cohesion, and therefore on thedefinition, of this phenomenon as a whole.

Thus, as shown in Table 1, four out of the five dis-cerned clusters are presented among the 25 measures

of creativity with greater fragmentation potential, andamong the top 10 only five belong to the groupnumber 3. Furthermore, also examining this table’s data,it can be seen that the different types of measures cata-loged by Hocevar and Bachelor (1989) tend to focus onspecific clusters and, therefore, that restrictions on theoverall network configuration are generally converted intoconstraints on the variety of data collection techniquesavailable. Thereby, it appears that, although clusters 1and 5 concentrate most of the self-reported inventoriesof attitude (Creative effort and Creativity) and personalityFIGURE 2 Creativity labeled variables among different clusters.

TABLE 1

Creativity Labeled Variables with Major Fragmentation Potential

Frag Variable Cluster Sample Reference

4.31 Creativity rating 4 Farmer, Tierney, and

Kung-McIntyre (2003)

4.24 Verbal TTCT (Form A) 3 Clapham (2004)

4.23 Total creativity 3 Batey, Furnham, and

Safiullina (2010)

3.65 Consens. Assess.

Technique

3 Madjar and Ortiz-Walters

(2008)

3.56 Creat. Person. Scale

(ACL)

3 Burch, Hemsley, Pavelis,

and Corr (2006)

3.47 Total creativity 3 Zampetakis, Bouranta, and

Moustakis (2010)

3.17 Long creativity checklist 5 Goldsmith and Matherly

(1991)

3.17 Short creativity checklist 5 Goldsmith and Matherly

(1991)

3.08 Creativity 5 Nilniyom (2007)

3.08 Creative ideas 5 Giambatista and Bhappu

(2010)

3.07 How Creative Are You? 5 Clapham (2004)

2.96 Subjective creativity 3 Zhou and George (2001)

2.89 Biograph. Inv. of Creat.

Behaviours

3 Batey and Furnham (2008)

2.86 Creative performance 3 Oldham and Cummings

(1996)

2.85 Figural TTCT (Form A) 3 Clapham (2004)

2.81 Creative

accopmplishments

3 King, Walker, and Broyles

(1996)

2.79 Composite Creativity

Index

3 Jung et al. (2010)

2.69 Composite Creativity

Personality Scale

3 Oral, Kaufman, and Agars

(2007)

2.57 Individual creativity 3 Kurtzberg and Mueller

(2005)

2.54 Team creative synergy 3 Kurtzberg and Mueller

(2005)

2.39 Creative effort 1 Hirst, Dick, and

Knippenberg (2009)

2.38 Creativity (IPIP) 1 Dewett and Gruys (2007)

2.38 Creativity temperament

(CPI)

1 Charyton and Snelbecker

(2007)

2.25 Creative Achievement

Questionnaire

3 Hirsh and Peterson (2008)

2.24 Creativity Behavior

Inventory

3 Dollinger, Dollinger, and

Centeno (2005)

Note. Frag¼Fragmentation metric.

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(Creativity temperament – CPI and Long creativitychecklist, for example), there can be found in cluster 4a measure that is, at the same time, the judgment ofproducts and the rating by supervisors (because peopleare the ‘‘products’’ to be evaluated), and finally, in thecluster 3 reside the tools to access divergent thinking(both TTCT), the self-reported biographies (BiographicalInventory of Creative Behaviours and Creative Achieve-ment Questionnaire, for example), and most of themeasures composed by two primary scores (variables withthe words total or composite in their names).

By cross checking information from the hierarchicalclustering with the fragmentation metric, apparently,then, we discerned a core of measures designed toaccess the infrequency of significant behaviors anddaily achievements, which is orbited by the instrumentsdeveloped to evaluate personality, attitudes, and theproducts socially judged as creative. This configurationbecomes even clearer through the visual inspection ofthe network formed by this set of variables, as can beseen in Figure 3, which shows the clusters individually,and also as a whole. In this image, that is limited to the50 variables with greatest fragmentation potential ofeach cluster plus the 64 measures of creativity due toreadability, it can be seen that the cluster number 1 (a)tends to the fourth quadrant of a trigonometric circle,that the cluster 2 (b) bends toward the third quadrant,that the cluster 3 (c) concentrates itself in the center, thatthe cluster 4 (d) locates itself in the second quadrant, and,finally, that the cluster 5 (e) rests on the first quadrant.

In general, then, although cluster 3 (c) is individuallythe only one structurally similar to the wholenetwork (f), if all other groups are used collectively ina balanced manner, a graph focusing the center of

the construct defined by these variables can also beobtained. Rather than measure creativity itself,however, the role of the instruments present in clusters1, 2, 4, and 5 seems to be helping in the diagnosis of thisphenomenon deviations, indicating possible paths toreach the desired nucleus. Therefore, in short, the map(f) obtained through this research: (1) indicated thevariables in cluster 3 as the most appropriate to measurecreativity, and; (2) suggested that people whose scoreswere bent to any peripheral cluster just required betterperformance in the elements typically presented in theopposite cluster to reach the core phenomenon.

DISCUSSION

Considering that the main purpose of any map isto guide people from a particular location to its finaldestination, it can be said that the four peripheralclusters defined in this research offer as much infor-mation, or even more, about creativity as the very coreof this phenomenon, because once discerned a person’spersonality kind of deviance, the direction that he orshe should be headed to compensate it can be inferred.The map of creativity shown here (Figure 3f), then, firstof all, proposes a way of organizing the always referredcomplex personality of the creators (Csikszentmihalyi,1996), which seems to pair the dimensions of negative affectand social leadership, and high cognitive performance andpositive affect. To find someone’s location among thesedimensions, however, more than stick to the prevalenceof scores in each of these clusters, it is necessary toexamine the specific aspects that theoretically link allfour to creativity.

FIGURE 3 Metric multidimensional scaling of the 50 variables with highest fragmentation potential of each cluster plus the 64 measures of

creativity (Circles: Cluster1; Squares: Cluster 2, Diamonds: Cluster 3; Boxes: Cluster 4; and Triangles: Cluster 5; Source: NetDraw).

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Thus, as shown by George and Zhou (2002), it can bestated that the first dimension—which is characterizedby the feelings of anxiety, by the bad mood, by the fear,by the troubled relationships, and, in general, by thenegative affection—is directly linked to creativity everytime it appears to be influenced by reward distribution.The rationale behind this idea is that complications orenvironmental pressures in general, serve as evidencethat there are problems to be solved, which wouldbe ignored without such stimuli (Perkins, 1999). Thispoint of view understands creativity as the abilityto solve problems (Pinheiro & Pinheiro, 2006), whichmore properly is accessed through measures focusingattitudes and personalities traits, as shown in Table 1.

Next, symmetrically opposite to the previous, thesocial leading dimension refers to the abilities to train,to retain, and to manipulate networks of relationships,skills accessed by measures of idealism, of commitment,of kindness, of clarity of objectives, and also of creati-vity through the ratings of supervisors. Theoreticallycloser to the concept of innovation than to creativityitself, this dimension stood out only in modernity, bywhich time the individual geniuses have lost terrain tothe large collective enterprises (De Masi, 2003). Takinginto account Sternberg’s (2006) theory of investment increativity, leading acts, therefore, to guide the socialmovement, which allows the breaking of environmentalinertia and so the acceptance of new paradigms.

The dimension of cognitive performance, for its turn,characterized by scores of intelligence, of verbal fluency,of perseverance, and of self-criticism, is regularly linkedto creative through the concept of utility, which coversmainly the topic of value judgment (Sternberg, 2000;Ward, 2007). From this perspective, creativity can bedifferentiated from any ordinary novelty especiallybecause the first imply profit or recognition from peerexperts or the general society (Pinheiro & Pinheiro,2006). Thus, despite the fact that this cluster’s creativitymeasures have low fragmentation potential, they are allbased on productivity metrics, the only true way toaccess ideas actual acceptance and common importance,as Simonton (1975) argues.

At last, complementary to the previous dimension isthe positive affect, which can be distinguished by thevariables of emotional stability, of self-esteem, of intrin-sic motivation, and in general, by the sensation of fittingthe environment. This dimension, other than beingroutinely referred as a factor of personal fulfillmentamong creative behaviors (De Masi, 2003; Ostrower,1999), is also directly connected with the theoreticalframeworks of Csikszentmihalyi’s (1999) systemsperspective, of Amabile’s (1983) componential perspec-tive, and especially of Sternberg’s (1999) integrativeview. According to these authors, satisfaction withthe environment signals the harmonic acceptance

of a person in his context, which is a fundamentalcharacteristic for creativity, as both under or over-utilization of someone qualities determines his or herproductivity.

Therefore, by using a battery composed of instru-ments for measuring creativity or its adjacent phenom-ena, rather than quantifying the distance that someoneis away from creative behaviors, the approximatedirection in which this person should set off to, finally,join the dimension of novelty, can be defined. Quitesimilar to Pinheiro’s (2009) general model of creativity,the map of creativity resulting from this researchsuggests, then, that the dominant personality traits arecomplemented by stimulating the vectors directed to theiropposite trigonometric quadrant, as shown in Figure 4.In this simplified image, while the logical thinking patternis complete, and not canceled out, by the intuitivethinking pattern, the extraverted thinking pattern is alsocomplete, and not canceled out, by the reflective thinkingpattern. Thus, as Kim (2006) pointed out, the testsof creativity, in fact, are not meant only for measuringit, but mainly to catalyze it, as an accurate diagnosisis always necessary for the formalization of effectivetraining programs.

If, however, the creativity measurement objective isnot grounded in pedagogical practices, but in specificclassificatory needs, it is perceived as more practicaland precise the individual instruments with greaterfragmentation potential presented in the cluster number3 (see Table 1). Therefore, the widely cited TTCT,Consensual Assessment Technique, Creativity PersonalityScale, and Biographical Inventory of Creative Behaviors,or even one of the scales used in research within thebusiness context, such as the Zhou and George’s one(2001) or the Oldham and Cummings’ one (1996). Theseinstruments, scored mostly based on the subjective assess-ment of creativity, suggest that the core of this pheno-menon is basically composed of implicit theories about it

FIGURE 4 Pinheiro’s General Model of Creativity Vectors Covered

by a Simplification of the Creativity Map (Source: Author).

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(Amabile, 1982), and therefore that creativity really doesexists only in people’s minds. An instructive exampleabout this peculiarity can be found in the aforementionedZhou and George’s 13-item scale (2001), in which is askedif someone suggests new ways to reach a goal, without anyclarification about what is a new way or even a goal.

The dimension of novelty or originality, by thispoint of view, then, is characterized as a subjectivemicro-universe of the other areas on the creativitymap, in which coexist, even if at constant friction,variables easily suitable for all four peripherals clusters.This combination, in its turn, seems to be possible onlybecause of two elements that rests exclusively in thecentral cluster, being the risk tolerance, which moder-ates the openness to new experiences, the emotionalambivalence, and the appreciation of what is different,and the belief in people’s own creativity, which moder-ates the perceived control of peoples’ own time, thebehavioral plasticity, and the desire for change. Thus,without risk tolerance (Andrews & Smith, 1996;Charyton & Snelbecker, 2007) and a belief in one’sown creativity (Gong, Huang, & Farh, 2009; Tierney& Farmer, 2004) the confrontation of antagonisticelements would cause the inhibition of the weaker onesor the cancellation of them all.

Therefore, the X that signals the center of thecreativity map, differently than what is widely assumed(Batey & Furnham, 2008; Goldsmith & Matherly,2001; Kurtzberg, 2005; Oldham & Cummings, 1996;Pinheiro & Pinheiro, 2006; Zampetakis et al., 2010;among others), is not the point of intersection betweenoriginality and usefulness, but only the landmark ofnovelty, which is the unlikely alliance between mostideas confrontations’ surviving opposites. The require-ment of usefulness, absent, for example, in cluster num-ber 3’s strong measures of divergent thinking, seems tobe just a pragmatic snip of creativity, as it is quite hardto justify its research in the cases in which thisphenomenon has no positive or negative influence inany productivity metric. Considering that, creativity isthe novelty linked to usefulness if, and only if, intelli-gence, for example, is the reasoning linked to usefulness,memory is the recalling linked to usefulness, andpsychosis is, also, diseases linked to usefulness.

Thus, either because of information reorganization(Baxter, 2000; Munari, 1998), contradiction removing(Manzini, 1993; Schwartz, 1992), or innovation pro-posal (Boden, 1999), the creativity’ score addresses,fundamentally, the statistical infrequency of any parti-cular product or idea. Some of the creativity measuresin this same nucleus, however, do not limit themselvesto access uniqueness, also being good candidates fordiagnosing the deviance patterns shown in Figure 4, asis the case of the tools that combine two or more basicscores of this phenomenon.

A proper example of this type of composite measurewith high level of fragmentation is the instrument ofZampetakis et al. (2010), in which the normalized meanof eight items from the scale of Zhou and George (2001)and Gough’s (1979) Creative Personality Scale areadded. While the first scale focuses the analysis of theoriginality, the second bends toward the definition ofthe personality that generated such novelty, and thus,they are partly overlapping, but still directed to differentaspects of the creativity construct. Thereby, in conson-ance with the advocates of the multidimensional assess-ment of creativity (Almeida et al., 2008; Bechtereva et al.,2007; Cropley, 2000; Kim, 2006; Plucker & Runco,1998; Wechsler, 1998), this research suggests that, ifthere are sufficient resources, the test batteries are,in fact, invaluable tools for the measurement, not justof the phenomenon itself, but also of the personality ofthose responsible for the creation.

FINAL REMARKS

This article, aimed at discovering how the constructformed by the set of all creativity measures configuresitself, used the tools of network analysis to map thisphenomenon’s dimensions and, therefore, define therelationship between its core and its periphery. It wasdetermined then, by the method employed, that thenovelty consists of the dimension with the greatest frag-mentation potential among the elements of creativity,and that the dimensions of negative affect and socialleadership, and cognitive performance and positiveaffect are paired two by two, respectively, around thecenter (see Figure 4).

Designed as a fundamentally exploratory research, allresults must be interpreted only as preliminary indica-tions, however, given that different methodologicalchoices would lead also to different conclusions. There-fore, among the choices made that are worthy of men-tion, the procedure for data imputation, which enteredhypothetical information based on the restricted valuesobtained empirically, the cut-off point used in the hier-archical clustering, which in the absence of well-definedstopping criteria (Cheung & Chan, 2005; Milligan &Cooper, 1985) was based solely on visual inspection,and the employment of the fragmentation potential,which, despite having demonstrated excellent consist-ency in numerous pretests, is just one among many othernetwork analysis’ measures of centrality.

In spite of these limitations, the obtained data arebroadly consistent with the literature on creativity,indicating, as Borsboom, Cramer, Schmittmann, Epskamp,and Waldorp (2011), Cramer, Waldorp, Van der Maas,and Borsboom (2010), and Schimttmann and colleagues(2013) did, that network analysis is a valid and promis-

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ing strategy in psychometrics. This suggests then,to those interested in following this research line, thedefinition of methodological strategies to overcome thisstudy’s barriers, or even the development of specificstatistical solutions for psychology, because all networkanalysis resources mentioned so far are borrowed fromthe fields of sociology and economics.

Even before such advances, it is pertinent, however,to consider the findings regarding the representativenessof the instruments used in this analysis, because theevidence obtained may lead to the construction of com-posite measures best suited to the purposes of measuringcreativity and diagnosing personality deviations. Aimingat that, we suggest the use of the combination of at leastone measure of divergent thinking, like the TTCT or theWallach-Kogan Creativity Test, and another personalityinventory, such as the Creativity Personality Scale orthe Creativity Temperament Scale (adapted from theCalifornia Psychological Inventory). Some options ofcomposed measures already available for use are theHow Creative Are You? (Clapham, 2004), the aforemen-tioned Zampetakis and colleagues (2010) tool, and,even though it is restricted only to divergent thinkingmeasures, the Batey, Furnham, and Safiullina (2010)composite score.

Thus, being a psychological construct, after allnothing more than a way to conceptually organize theobserved information (Cronbach & Meehl, 1955), crea-tivity measurement instruments perform their role betterand better, which is to facilitate the intersection of whatis concrete about the human creative potential. As sug-gested by Pinheiro and Cruz (2009), maybe this is theshortest path to get to the core of creativity, once noindividual theory about this phenomenon is as compre-hensive or compelling as the social enterprise of progress-ive evidence accumulation. The creativity map displayedin this article, thus, by making visible the invisible pat-tern of relationships between the variables that definethis construct, finally offers a less abstract north to thosewilling to take the next steps in that enterprise.

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