Download An item level exploratory factor analysis of the sphere of personality

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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
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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
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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)
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A test of a theory should in principle be able
to falsify it
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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)
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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.
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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)
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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)
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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
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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.
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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
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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
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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
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Solutions were refined such that:
 Each factor was defined by at least 3 salient
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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
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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
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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
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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.
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Multiple response formats may have caused
method artefacts
Missing facets
Use of single raters (Connelly & Ones, 2010)