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Huge volume of evidence supportive of FFM Nevertheless there has been a significant body of criticism We conducted an exploratory factor analysis of seven major personality inventories, together with four supplementary scales, comprising 1,772 items in total This analysis has a number of advantages but also significant limitations There are numerous studies which find evidence of more than five factors within questionnaires designed within the FFM framework: DeYoung, Quilty & Peterson (2007) analyzed the NEO- PI-R and the AB5C-IPIP and found ten aspects (factors) extracted from 75 facets Jang, Livesley, Angleiter, Reiman, & Vernon (2002) found evidence of at least 10 genetic factors underlying the NEO-PI-R There are also numerous studies which point to more than five major factors of personality based on item level analyses (e.g. Ashton, Lee & Goldberg, 2004), analyses of other scales such as the PRF, and California Q-set (Block, 1995), or combinations of different inventories (Hopwood, Wright and Donnellan, 2011) A test of a theory should in principle be able to falsify it If personality is conceived of as a set of latent variables, it should be analyzed using a common factor model, not principal components. The latter is an observed variable model It assumes no error of measurement Despite claims to the contrary principal components solutions frequently differ from common factor solutions (Widaman, 1993) Simulations show that the most accurate exploratory estimates of the number of factors are parallel analysis, and Velicer’s minimum average partial test (Peres-Neto, Jackson & Summers, 2005; Velicer, Eaton & Fava, 2000; Zwick & Velicer, 1986). Yet the eigenvalue-greater-than-1 rule (Kaiser, 1960) and scree test (Cattell, 1966) are most frequently used. The most frequently chosen method of rotation is Varimax which assumes orthogonality of personality factors, which rarely reflects the natural properties of the data (Yates, 1987). Almost certainly a combination of oblique methods are preferable since they should correctly locate factor axes whether they are orthogonal or oblique (Sass & Schmitt, 2010; Schmitt & Sass, 2011) Suboptimal estimators are often employed Generally preferred methods are: Continuous and multivariate normal data: maximum likelihood (Bollen, 1989) Ordered-categorical data: diagonally weighted least squares (Flora & Curren, 2004) Items or scales which are identical need to be eliminated With item level data, appropriate estimators such as diagonally weighted least squares perform best with large sample sizes It is known that with large factor loadings and many indicators small sample sizes can provide reasonable estimates (MacCallum, Widaman, Zhang & Hong, 1999), but consistently large loadings are unlikely with large numbers of indicators Database consolidated from instruments administered to the Eugene-Springfield Community Sample, with a total sample of 972 Measures: NEOPI-R, 16PF, MPQ,JPI-R, HEXACO,6PFQ, Social Dominance Orientation, Altemeyer’s Right-Wing Authoritarianism Scale, MachiavellianismIPIP, Need for cognition-IPIP totalling 1,772 items. Initially 137 facets were placed into 23 groups based on the similarity of facet descriptions Grouping should increase over-determination of factors, and reduce the number of items with low communalities both of which are known to improve correspondence of solutions to population factors. It should also reduce the risk of determinants tending to zero which when using diagonally weighted least squares estimates, as required for categorical data, may lead to improper solutions Items were sorted to remove identical items Computed tetrachoric and polychoric correlations Inspected all item pairs which correlated above 0.40 to determine whether they were identical One of each pair of identical items was removed 355 items were removed so 1,417 remained To maximize sample size Multivariate Imputation by Chained Equations (MICE) was used Used weighted least squares and variances estimation (WLSMV, Muthen, du Toit & Spisic, 1997) with Geomin rotations. Parallel analysis and the MAP test were used to estimate the number of factors Solutions were refined such that: Each factor was defined by at least 3 salient loadings All salient loadings were ≥ 0.3 There were no Heywood cases Cross-factor loadings were minimized A total of 121 facets were retained, and 448 items were removed leaving 969 items Facets were judged to be uni-dimensional if they fit a one-factor CFA model. Fit criteria were: standardized-root-mean-square-residual ≤ 0.05, root-mean-square-error-of-approximation about 0.06, Tucker-Lewis Index, and Comparative Fit Index ≥ 0.95, WRMR ≤ 1.00 Multidimensional facets were either modified or subject to EFA until they provided unidimensional facets. Resulted in 136 uni-dimensional facets Each of three panel members inspected the item content of all facets and identified facets which were identical or which were not coherent. Disattenuated correlations for all pairs of facets were calculated and presented in an ordered list from highest to lowest. Panel members considered each pair beginning with those with highest correlation. For pairs judged to be identical one of the pair was retained. This process continued until each of 10 consecutive pairs were judged to be unique measures. Finally the panel met and reached a consensus decision on which facets to retain 78 facets were retained EFA used the maximum likelihood estimator, followed by four rotations: Geomin, CFEquamax, CF-Parsim, and Oblimin. Solutions were inspected for consistency No. of factors was determined from PA (11), MAP (12), and EFA fit statistics which suggested 11-13 factors 11 factor solution was supported by PA (the best indicator of the number of factors: Timmerman & Lorenzo-Seva, 2011), was consistent across rotations, all factors had at least three indicators, and it explained 57% of variance in the facet scales. Scale facets 1 Openness/Intellect Intellectual Curiosity 0.634 Understanding 0.614 Aesthetic Interests 0.519 Cognitive Interests 0.469 Creativity 0.459 Absorption 0.441 Fantasy 0.374 Perseverance 0.356 Scale facets Planfulness Practical vs. Imaginative Impulse Control Caution Orderliness Detail Conscious Self Discipline Spontaneity Competence Dutifulness Unconventionality 2 Conscientiousness 0.643 0.571 0.559 0.517 0.513 0.477 0.471 -0.430 0.411 0.355 -0.323 Scale facets 3 Assertiveness/ Dominance Assertiveness 0.648 Non-judgmental -0.636 Dominance 0.413 Social Confidence 0.367 Forgiveness -0.357 Stubbornness 0.338 Extraversion: Assertiveness (DeYoung et al., 2007); Agency (Depue & Collins, 1999) Scale facets Sociability Leadership People vs. Things Self Reliance Dependent Affability Social Boldness Social Dependence Cooperativeness 4 Sociability/Dependence 0.706 0.425 0.558 -0.565 0.540 0.515 0.510 0.405 0.396 Extraversion: Communality(Depue & Collins, 1999); Scale facets 5 Positive Affect Optimism 0.623 Surgency 0.577 Vigour 0.492 Seriousness -0.490 Liveliness 0.352 Positive Emotionality (Tellegen, 1985); Extraversion: Enthusiasm (DeYoung et al., 2007); Positive Emotions (Depue & Collins, 1999) Scale facets 6 Internalizing Anxiety 0.710 Worry 0.663 Depression 0.658 Rumination 0.589 Anger 0.474 Self Consciousness 0.449 Neuroticism: Withdrawal (DeYoung et al., 2007) Scale facets 7 Externalizing Emotional Reactivity 0.820 Negativity 0.782 Callousness 0.726 Psychopathy 0.692 Conflicted Relationships 0.655 Nervous Anxiety 0.617 Inferiority 0.600 Neuroticism: Volatility (DeYoung et al., 2007) Scale facets 8 Social Astuteness/ Manipulativeness Narcissism/Modesty(-ve) 0.667 Deviousness 0.591 Altruism -0.453 Empathy -0.369 Honesty-Humility (Ashton, Lee & Goldberg, 2004) Scale facets 9 Resilience Fearfulness -0.564 Achievement 0.491 Diligence 0.477 Sensitivity -0.405 Risk taking 0.398 Tolerance 0.337 Ego-Resiliency (Block & Block, 1980); Hardiness (Kobasa, Maddi & Kahn, 1982) Scale facets 10 Prejudice Traditionalism 0.762 Intolerance 0.714 Authoritarianism 0.658 Punitiveness 0.541 Complexity -0.457 Dishonest-Opportunism -0.368 Openness-to-Change -0.346 Scale facets 11 Aggression/ Impulsivity Aggression 0.670 Risk 0.559 Harm Avoidance -0.537 Antagonism 0.522 Alienation 0.517 Fewer markers of Impulsivity than predicted by Whiteside and Lynam’s (2001) UPPS model: Urgency, Premeditation, Perseverance and Sensation Seeking. Multiple response formats may have caused method artefacts Missing facets Use of single raters (Connelly & Ones, 2010)