Global Sex Differences in Personality: Replication with an Open Online Dataset

Abstract

Objective: Sex differences in personality are a matter of continuing debate. In a study on the US standardization sample of Cattell’s 16PF (fifth edition), Del Giudice and colleagues (2012; PLoS ONE, 7, e29265) estimated global sex differences in personality with multigroup covariance and mean structure analysis (MG-CMSA). The study found a surprisingly large multivariate effect, D = 2.71. Here we replicated the original analysis with an open online dataset employing an equivalent version of the 16PF.
Method: We closely replicated the original MG-MCSA analysis on N = 21,567 US participants (63% females, age 16-90); for robustness, we also analyzed N = 31,637 participants across English-speaking countries (61% females, age 16-90).
Results: The size of global sex differences was D = 2.06 in the US and D = 2.10 across English-speaking countries. Parcel-allocation variability analysis showed that results were robust to changes in parceling (US: median D = 2.09, IQR [1.89, 2.37]; English-speaking countries: median D = 2.17, IQR [1.98, 2.47]).
Conclusions: Our results corroborate the original study (with a comparable if somewhat smaller effect size) and provide new information on the impact of parcel allocation. We discuss the implications of these and similar findings for the psychology of sex differences

Full text

Journal of Personality. 2019;00:1–15.
|
1
wileyonlinelibrary.com/journal/jopy
1
|
INTRODUCTION
More than a century after the publication of the first sys-
tematic studies (see Allen, 1930; Thompson, 1903), sex dif-
ferences in personality continue to be passionately debated.
Taken as groups, do men and women show substantially
different patterns of feelings, thoughts, and behaviors? Or
does the overlap between the sexes dwarf whatever dis-
crepancies exist? The latter is closer to the prevailing view
in psychology, which embraces the “gender similarities
hypothesis” that men and women are similar on most psy-
chological variables (Hyde, 2005, 2014). Personality has
implications for a multitude of important life outcomes,
from physical and mental health to occupational choices
and work performance (Friedman & Kern, 2014; Kotov,
Gamez, Schmidt, & Watson, 2010; Soto, 2019). Moreover,
current ideas about the causes and effects of gender stereo-
types are inevitably shaped by assumptions about the mag-
nitude of sex differences across domains (e.g., Fiske, 2017;
Haines, Deaux, & Lofaro, 2016; Hyde, Bigler, Joel, Tate,
Received: 4 April 2019
|
Revised: 13 June 2019
|
Accepted: 6 July 2019
DOI: 10.1111/jopy.12500
ORIGINAL ARTICLE
Global sex differences in personality: Replication with an open
online dataset
TimKaiser1
|
MarcoDel Giudice2
|
TomBooth3
This is an open access article under the terms of the Creat ive Commo ns Attri butio n‐NonCo mmercial License, which permits use, distribution and reproduction in any medium,
provided the original work is properly cited and is not used for commercial purposes.
© 2019 The Authors. Journal of Personality published by Wiley Periodicals, Inc.
1Department of Psychology,University of
Salzburg, Salzburg, Austria
2Department of Psychology,University of
New Mexico, Albuquerque, New Mexico
3Department of Psychology,University of
Edinburgh, Edinburgh, UK
Correspondence
Tim Kaiser, Department of Psychology,
University of Salzburg, Hellbrunnerstrasse
34, 5020 Salzburg, Austria.
Email: tim.kaiser@sbg.ac.at
Abstract
Objective: Sex differences in personality are a matter of continuing debate. In a
study on the United States standardization sample of Cattell's 16PF (fifth edition),
Del Giudice and colleagues (2012; PLoS ONE, 7, e29265) estimated global sex dif-
ferences in personality with multigroup covariance and mean structure analysis. The
study found a surprisingly large multivariate effect, D=2.71. Here we replicated the
original analysis with an open online dataset employing an equivalent version of the
16PF.
Method: We closely replicated the original MG‐MCSA analysis on N=21,567 U.S.
participants (63% females, age 16–90); for robustness, we also analyzed N=31,637
participants across English‐speaking countries (61% females, age 16–90).
Results: The size of global sex differences was D =2.06 in the United States and
D = 2.10 across English‐speaking countries. Parcel‐allocation variability analysis
showed that results were robust to changes in parceling (U.S.: median D=2.09, IQR
[1.89, 2.37]; English‐speaking countries: median D=2.17, IQR [1.98, 2.47]).
Conclusions: Our results corroborate the original study (with a comparable if some-
what smaller effect size) and provide new information on the impact of parcel alloca-
tion. We discuss the implications of these and similar findings for the psychology of
sex differences.
KEYWORDS
effect size, gender differences, Mahalanobis' D, multivariate, sex differences

2
|
KAISER Et Al.
& van Anders, 2019; Zell, Strickhouser, Lane, & Teeter,
2016). For all these reasons, quantifying sex differences as
accurately and meaningfully as possible is a crucial task for
personality psychology.
Studies conducted with the Five‐Factor Model of per-
sonality (also known as the Big Five) show that, averaging
across countries, women score about 0.4–0.5 standard de-
viations higher in Agreeableness and Neuroticism (Cohen's
d≈−0.40 to −0.50; by convention, we use negative values to
indicate higher scores in females). Sex differences in the other
domains—Conscientiousness, Extraversion, and Openness—
are smaller, typically 0.1 SD or less (see Del Giudice, 2015;
Hyde, 2014; Kajonius & Mac Giolla, 2017; Lippa, 2010;
Löckenhoff et al., 2014; Schmitt, Realo, Voracek, & Allik,
2008). Assuming normal distributions, these figures imply
that the overlap between male and female distributions ranges
from about 80% to almost 100%, depending on the trait.
Based on these findings, some researchers have declared that
the evidence overwhelmingly indicates similarity, and refutes
the view that human behavior is sexually dimorphic to any
significant degree (e.g., Hyde, 2014; Hyde et al., 2019).
From a methodological standpoint, the standard approach
to measuring sex differences suffers from three important
limitations. First, the effect sizes in the literature are typically
calculated directly from observed scores, with no correction
for measurement error (e.g., Hyde, 2005, 2014; Zell, Krizan,
& Teeter, 2015). This can lead to underestimate their mag-
nitude by a substantial margin. To correct for measurement
error, one can disattenuate the observed effect sizes with
reliability coefficients (e.g., Cronbach's α), or estimate the
effects from latent, error‐free variables (see Del Giudice,
2019). Second, focusing on broad personality factors like
the Big Five domains misses much of the structure of sex
differences in personality, which become more apparent at
the level of narrower traits (aspects or facets in the Big Five
model). Moreover, it is sometimes the case that different
facets of the same domain (e.g., intellect/ideas vs. aesthet-
ics/feelings in the Openness domain) show sex differences
in opposite directions, which tend to cancel each other out
at the level of broad factors (Costa, Terracciano, & McCrae,
2001; Del Giudice, 2015; Kajonius & Johnson, 2018; Soto,
John, Gosling, & Potter, 2011; Weisberg, DeYoung, & Hirsh,
2011). Third, in the standard approach individual personal-
ity traits are considered one at a time, or simply averaged
together (e.g., Zell et al., 2015). However, differences across
multiple correlated traits can add up to yield a much larger
effect size in the multivariate space. This problem can be
readily solved by calculating a multivariate effect size. The
natural choice for sex differences is Mahalanobis' D, which
generalizes Cohen's d for two or more correlated variables. D
is the unsigned standardized distance between the centroids
(multivariate means) of the two groups, and has the same
basic interpretation as d (see Del Giudice, 2009, 2013, 2019;
Hess, Hogarty, Ferron, & Kromrey, 2007; Olejnik & Algina,
2000).
To address these limitations, Del Giudice and colleagues
(Del Giudice, Booth, & Irwing, 2012) used D to estimate the
size of global (i.e., multivariate) sex differences in the United
States standardization sample of the fifth edition of Cattell's
Sixteen Personality Factors questionnaire (16PF), a demo-
graphically representative sample with N=10,261 (50.1%
females; age 16–90years). The 16PF comprises 15 narrow
personality factors, allowing a more fine‐grained analysis of
sex differences than the Big Five domains (the remaining fac-
tor in the 16PF is a measure of cognitive ability). Multigroup
covariance and mean structure analysis (MG‐CMSA) was
used to estimate latent differences and correlations, and test
for measurement invariance and equality of correlation ma-
trices. Both the measurement model and the correlation ma-
trix were invariant across sexes. The multivariate difference
was surprisingly large, amounting to D= 2.71. This effect
size implies a marked statistical separation between males
and females: the estimated proportion of overlap is about
18% of each distribution, and about 10% of the joint distri-
bution (assuming multivariate normality; for details see Del
Giudice, 2019).
The study by Del Giudice and colleagues (2012) was the
first to challenge the idea that sex differences in personality
are small to moderate in magnitude. More recently, research-
ers have started to apply the concept of global sex differences
to datasets based on the Big Five model. In a cross‐cultural
study, Mac Giolla and Kajonius (2018) computed D from ob-
served scores on 30 facets of the Big Five, without error cor-
rection. The size of global sex differences in the United States
was D=1.25, similar to the uncorrected effect (D=1.49) in
Del Giudice and colleagues (2012). Kaiser (2019) also con-
sidered 30 Big Five facets, but used latent scores estimated
with MG‐CMSA and found D= 2.16 in the United States.
These results corroborate the initial findings by Del Giudice
and colleagues (2012), and are consistent with the idea that
sex differences in personality are much larger than previously
assumed. However, the original study has yet to be replicated
using the 16PF model.
In the present study, we set out to replicate the analysis
by Del Giudice and colleagues (2012) with a large dataset
available from the Open Source Psychometrics Project (https
://openp sycho metri cs.org). The dataset employs an equiva-
lent version of Cattell's 16PF constructed with items from the
International Personality Item Pool (IPIP; Goldberg, 1999),
and comprises a total of 49,159 responses from various coun-
tries (more details in the Methods section). In accord with
the original study, we focused primarily on the United States;
we included other English‐speaking countries in a second-
ary analysis. After replicating the original study as closely
as possible, we investigated the robustness of our findings to
changes in the allocation of items to parcels (parcel‐allocation

|
3
KAISER Et Al.
variability [PAV]; Sterba & MacCallum, 2010) by randomly
allocating items to parcels and computing the resulting dis-
tribution of D values. Our working hypothesis was that the
size of global sex differences in personality and the pattern
of univariate sex differences across individual traits would be
similar to those of the original study.
2
|
METHODS
2.1
|
Samples and measures
2.1.1
|
Sample selection and data cleaning
The 16PF dataset was retrieved in December 2018 from
the Open Source Psychometrics website. The full dataset
(N=49,159) was uploaded to the website on May 14, 2014.
The answers were given by anonymous users who took the
free questionnaire on the website. At the beginning of the
questionnaires, respondents were informed that their answers
would be used for research purposes, as follows: “your an-
swers on this test will be stored and used for research, and
possibly shared in a way that preserves your anonymity.”
We began by selecting two samples from the dataset: (1)
respondents from the United States (N = 23,701; 64% fe-
males), and (2) respondents from English‐speaking coun-
tries (N = 34,625; 62% females): United States, United
Kingdom (N = 4,630), Canada (N = 2,420), Australia
(N=2,280), Ireland (N=372), New Zealand (N=287),
and others (N=83). Geographical location had been deter-
mined based on the IP address of the connection (note that
the dataset does not include IP addresses, but only country
codes). Because the main goal of the present study was repli-
cation, we did not consider data from non‐English‐speaking
countries. In addition, the questionnaire was only adminis-
tered in English, and there are reasonable concerns about
the validity of online responses by participants whose pri-
mary language is not English (Feitosa, Joseph, & Newman,
2015). Respondents who did not indicate their sex were ex-
cluded at this stage. To replicate the original analysis (Del
Giudice et al., 2012), we excluded respondents under 16 and
over 90years of age.
To address the problem of careless responding, we ex-
cluded respondents with 25 or more missing answers, and
those who responded to all the items with the same end‐of‐
scale answer (either 1 or 5). The questionnaire also included
a follow‐up question asking respondents to rate the accuracy
of their answers on a 0%–100% scale. We only retained par-
ticipants who rated their accuracy at 50% or more. Together,
these criteria led to the exclusion of approximately 9% of the
initial cases. Since the exclusion criteria we applied were not
pre‐registered, we also repeated the analyses on the full U.S.
and English‐speaking samples as a robustness check.
The size of the final U.S. sample was N=21,567, with 63%
females. The mean age was 26.6years in males (SD=12.2)
and 25.5years in females (SD=11.3). The size of the final
English‐speaking sample was N=31,637, with 61% females.
The mean age was 26.7 years in males (SD = 12.1) and
25.9years in females (SD=11.5). These samples were used
to perform the main analyses described below; the analyses
were then repeated on the full (unselected) samples. All anal-
yses were performed in R 3.5.2 (R Core Team, 2018). To en-
sure reproducibility, both the original data and the annotated
R scripts of our analyses are available at https ://osf.io/4sq79/
?view_only=51e0b a73a0 23484 0a290 2f586 e52f4c2 (ano-
nymized link).
2.1.2
|
Personality measures
The questionnaire comprises 163 items organized into an
Intellect scale (Factor B or Reasoning in the original 16PF)
and 15 primary personality scales with 10 items each. For
each scale, we report the original name in parentheses.
Warmth (A, Warmth), Emotional Stability (C, Emotional
Stability), Assertiveness (E, Dominance), Gregariousness
(F, Liveliness), Dutifulness (G, Rule‐Consciousness),
Friendliness (H, Social Boldness), Sensitivity (I, Sensitivity),
Distrust (L, Vigilance), Imagination (M, Abstractness),
Reserve (N, Privateness), Anxiety (O, Apprehension),
Complexity (Q1, Openness to change), Introversion (Q2, Self‐
Reliance), Orderliness (Q3, Perfectionism), and Emotionality
(Q4, Tension). The items were selected from the IPIP pool to
match the content of the original 16PF scales. The response
format is on a 5‐point Likert scale, from “strongly disagree”
to “strongly agree.” In the original scale construction study,
correlations between the original and equivalent personal-
ity scores ranged from .75 to .99 (corrected for unreliability;
Goldberg, 1999). Cronbach's α values for observed scores in
the present study are reported in Tables 1 and 4.
2.2
|
Data analysis
2.2.1
|
Model fitting and invariance tests
For each of the two samples (U.S. and English‐speaking), we
fit a set of MG‐CMSA models in which 3 item parcels (see
below) loaded on each of 15 correlated factors, following the
a priori structure of the 16PF. No cross‐loadings or correlated
errors were modeled. Models were fit with package lavaan
(v.0.6‐3; Rosseel, 2012) using a maximum likelihood estima-
tor with robust standard errors (MLR). Goodness of fit was
evaluated with χ2, CFI, NNFI, RMSEA, and SRMR. There
is much debate on the use of, and exact values of, cut‐offs
for model fit. Here we applied the commonly suggested cri-
teria of > 0.90–0.95 indicating acceptable to excellent fit for
the CFI and NNFI, and 0.06–0.08 indicating acceptable to

4
|
KAISER Et Al.
TABLE 1 Correlations, univariate effect sizes, and internal consistency values for observed scores (U.S. sample)
 A. C. E. F. G. H. I. L. M. N. O. Q1. Q2. Q3. Q4.
A. Warmth  .23 .15 .37 −.23 .49 .26 −.42 −.04 −.46 −.02 −.26 −.39 −.08 −.39
C. Emot. Stability .25  .34 .17 −.25 .43 −.01 −.45 −.22 −.27 −.74 −.11 −.21 −.17 −.54
E. Assertiveness .19 .40  .29 .08 .50 .03 .00 .01 −.29 −.38 −.24 −.11 −.25 .03
F. Gregariousness .44 .26 .34  .13 .63 .00 −.17 .19 −.39 −.12 −.17 −.50 .11 −.08
G. Dutifulness −.22 −.25 .05 .09  −.08 .02 .33 .50 .11 .03 −.26 .15 .34 .27
H. Friendliness .53 .46 .54 .67 −.13  .04 −.35 −.10 −.62 −.34 −.17 −.54 −.10 −.24
I. Sensitivity .28 −.05 .03 −.01 .08 .07  −.14 .23 −.15 .05 −.47 .11 .01 −.14
L. Distrust −.46 −.42 −.03 −.24 .36 −.39 −.12  .22 .42 .30 .07 .35 .02 .53
M. Imagination −.04 −.26 −.01 .11 .56 −.15 .25 .28  .10 .12 −.46 .20 .34 .10
N. Reserve −.54 −.29 −.33 −.46 .16 −.66 −.16 .45 .13  .14 .13 .47 .04 .16
O. Anxiety −.07 −.76 −.42 −.20 .09 −.40 .04 .29 .21 .18  .14 .09 .06 .46
Q1. Complexity −.31 −.13 −.27 −.18 −.25 −.21 −.53 .09 −.42 .20 .13  −.07 −.08 .24
Q2. Introversion −.46 −.26 −.19 −.54 .21 −.60 .09 .44 .26 .53 .16 −.03  −.03 .19
Q3. Orderliness −.08 −.20 −.23 .12 .41 −.11 .02 .07 .37 .07 .10 −.07 .02  .03
Q4. Emotionality −.43 −.53 −.02 −.15 .27 −.29 −.21 .56 .16 .25 .48 .24 .25 .06 
d−0.35 +0.31 +0.19 +0.01 +0.32 0.00 −0.80 +0.05 +0.11 +0.15 −0.51 −0.20 −0.01 +0.09 −0.09
α.84 .87 .84 .80 .84 .91 .68 .87 .80 .88 .85 .79 .85 .80 .81
Note: Females above the diagonal; males below the diagonal. Positive values of d indicate that males score higher than females. Correlations and effect sizes are uncorrected (i.e., not adjusted for score unreliability). The letters
associated with each scale are the conventional trait identifiers in the 16PF model.

|
5
KAISER Et Al.
excellent fit for the RMSEA and SRMR (e.g., Hu & Bentler,
1999; Schermelleh‐Engel, Moosbrugger, & Müller, 2003).
Measurement invariance across sexes was tested by fit-
ting three models: one without constraints (configural invari-
ance), one with equal loadings (metric invariance), and one
with equal loadings and intercepts (scalar invariance). In de-
ciding whether invariance held across models, we inspected
the change in model fit. Simulation studies have suggested
that a change if CFI of −0.01 or less, and changes in RMSEA
of less than or equal to 0.015 are suggestive that invariance
holds (Chen, 2007).
The invariance of interfactor correlation matrices across
sexes was tested by adding an equality constraint to the model
and comparing fit indices; in addition, we calculated Tucker's
congruence coefficient (CC) between the model‐estimated
male and female matrices to quantify their similarity (see Del
Giudice, 2019).
2.2.2
|
Parceling
To replicate the original study by Del Giudice et al. (2012)
as closely as possible, in the main analysis we employed the
Single Factor method (Landis, Beal, & Tesluk, 2000) to cre-
ate three parcels for each personality scale. Parcels reduce the
number of parameters to estimate and often show improved
characteristics compared with individual items (e.g., higher
reliability, lower likelihood of distributional violations; see
Little, Rhemtulla, Gibson, & Schoemann, 2013). However,
they also introduce an additional source of variation, as es-
timated model parameters may vary across different poten-
tial allocations of items to parcels. The impact of PAV has
been shown to be stronger when sample size is small, item
communalities are low, or there are small numbers of parcels
and/or items per parcel (Sterba & MacCallum, 2010; see also
Sterba, 2019). To assess the robustness of our main findings
with respect to PAV, we generated 100 random item alloca-
tions with three parcels per factor using package semTools v.
0.5‐1 (Jorgensen, Pornprasertmanit, Schoemann, & Rosseel,
2018). For each allocation, we fit a set of MG‐CMSA models
(see above), estimated parameters from the scalar‐invariant
model, and examined the resulting distribution of effect sizes.
2.2.3
|
Effect sizes and related statistics
To compute effect sizes and related statistics, we employed
the R scripts available at https ://doi.org/10.6084/m9.figsh
are.79349 42.v1 and described in Del Giudice (2019). For
latent variable models, we computed Mahalanobis' D with
exact confidence intervals (Reiser, 2001) from group differ-
ences and interfactor correlations estimated from the scalar‐
invariant models. For observed scores, we obtained bootstrap
confidence intervals from 10,000 samples (Kelley, 2005).
Values of D were then used to estimate the overlapping
coefficient OVL (the proportion of each distribution shared
with the other), Cohen's coefficient of overlap OVL2 (the
shared proportion of the joint distribution, 1–U1 in Cohen,
1988), and the common language effect size CL (in this
case, the probability that a randomly picked male will show
a more male‐typical profile than a randomly picked female,
and vice versa; see Del Giudice, 2019; McGraw & Wong,
1992). Finally, we calculated coefficients H2 and EPV2 to
quantify heterogeneity in the contribution of individual per-
sonality traits to global sex differences (as measured by D).
Coefficient H2 ranges from 0 (maximum homogeneity; all
variables contribute equally) to 1 (maximum heterogeneity;
the totality of the effect is explained by just one variable).
The “equivalent proportion of variables” coefficient EPV2
(also on a 0–1 scale) estimates the proportion of equally
contributing variables that would produce the same amount
of heterogeneity, if the other variables in the set made no
contribution. For example, EPV2=.30 means that the same
amount of heterogeneity would obtain if 30% of the variables
contributed equally to the overall effect and the remaining
70% made no contribution (Del Giudice, 2017, 2018, 2019).
3
|
RESULTS
3.1
|
United States
3.1.1
|
Main analysis
We started our analysis of sex differences from observed
scores on the fifteen 16PF scales. Correlations and univari-
ate standardized differences are reported in Table 1. The pat-
tern of observed univariate effect sizes in this sample was
very similar to that in the original study by Del Giudice et al.
(2012), with a correlation of .95 across personality factors.
Male and female correlation matrices showed high similarity
(CC=.99). The uncorrected size of global sex differences
was D=1.18, with 95% CI [1.143, 1.207]. Correction for un-
reliability raised the effect size to D=1.68. The correspond-
ing overlapping coefficients for uncorrected scores were
OVL= .56 and OVL2=.39; for corrected scores they were
OVL=.40 and OVL2=.25. In the common language effect
size metric, these values translate to CL=.80 for uncorrected
scores and .88 for corrected scores.
Fit statistics for the main set of MG‐CMSA models in
the U.S. sample are reported in Table 2. The baseline con-
figural model showed acceptable to excellent fit according
to all indices except the NNFI, which fell just below the .90
cut‐off. Model fit for the sequentially constrained models
suggested that invariance held at all levels. Scalar invariance
was met (Model 3 in Table 2), and the resulting mean and
correlation estimates (Table 3) were used to compute effect
sizes. The correlation between latent univariate effect sizes
in this sample and in the original study was .90. Estimated

6
|
KAISER Et Al.
correlation matrices in males and females were highly sim-
ilar (CC =.99); adding a covariance equality constraint to
the model did not appreciably change the goodness‐of‐fit
(Model 4 in Table 2). The size of global sex differences esti-
mated from latent scores was D=2.06, with 95% CI [2.03,
2.10]. Assuming multivariate normality, this corresponds to
OVL=.30, OVL2=.18, and CL=.93. Heterogeneity coeffi-
cients for D were H2=.81 and EPV2=.24.
3.1.2
|
Robustness checks
The first robustness check we ran was to repeat the MG‐
CMSA analysis on the full U.S. sample, with no exclusion
criteria. The size of sex differences did not change appreci-
ably: uncorrected D=1.15; unreliability corrected D=1.66;
CFA‐estimated D=2.04.
To assess the impact of PAV on the size of sex differences,
we computed D values from 100 models with randomly gen-
erated parcels. The median effect size was D= 2.09, very
close to the one obtained in the main analysis. The interquar-
tile range of D was 1.89–2.37; the full distribution is shown
in Figure 1a.
3.2
|
English‐speaking countries
3.2.1
|
Main analysis
Correlations and univariate standardized differences for
observed scores are reported in Table 4. Again, male and
female correlation matrices showed substantial similarity
(CC=.99). The uncorrected size of global sex differences
was D = 1.19, with 95% CI [1.16, 1.21]. Correction for
unreliability raised the effect size to D = 1.69. The corre-
sponding overlapping coefficients for uncorrected scores
were OVL =.55 and OVL2=.38; for corrected scores they
were OVL =.40 and OVL2=.25. These values translate to
CL=.80 for uncorrected scores and .88 for corrected scores.
Fit statistics for the main set of MG‐CMSA models in the
English‐speaking sample are reported in Table 5. The pat-
tern of model fit was identical to the U.S. sample, with an
NNFI slightly below the .90 cut‐off, and all differences in
fit within the criteria suggesting invariance held across sex.
Scalar invariance was met (Model 3 in Table 5), and the re-
sulting mean and correlation estimates (Table 6) were used to
compute effect sizes. Estimated correlation matrices in males
and females were highly similar (CC=.99); adding a cova-
riance equality constraint to the model did not appreciably
change the goodness‐of‐fit (Model 4 in Table 5). The size
of global sex differences estimated from latent scores was
D= 2.10, with 95% CI [2.07, 2.13]. Assuming multivariate
normality, this corresponds to OVL =.29, OVL2 =.17, and
CL=.93. Heterogeneity coefficients for D were H2=.83 and
EPV2=.23.
3.2.2
|
Robustness Checks
Sex differences in the full English‐speaking sample (no
exclusion criteria) were almost identical to those in the
selected sample: uncorrected D=1.17; unreliability cor-
rected D = 1.68; CFA‐estimated D = 2.10. The median
effect size in the PAV analysis was D= 2.17, again very
close to the value obtained in the main analysis. The inter-
quartile range of D was 1.98–2.47; the full distribution is
shown in Figure 1b.
4
|
DISCUSSION
In this paper, we sought to replicate the findings by Del
Giudice and colleagues (2012) with an open online dataset
that employed an equivalent version of the 16PF question-
naire based on IPIP items (Goldberg, 1999). Invariance
tests and indices of matrix similarity indicated that the cor-
relational structure of personality was equivalent in the two
sexes. This allowed us to aggregate sex differences across
fifteen personality traits into a multivariate effect size. In
the sample of U.S. participants, we estimated a global sex
difference of D = 2.06. Assuming multivariate normal-
ity, the overlap between the sexes implied by this effect
size is about 30% of each distribution and 18% of the joint
distribution. Sex differences were similar in the larger
Model χ2df CFI NNFI RMSEA SRMR
1. Configural
invariance
56,938.27 1,680 .908 .892 .055 .056
2. Metric invariance 57,633.98 1,725 .907 .893 .055 .059
Δ 1 versus 2 −.001 .001 .000 .003
3. Scalar invariance 61,786.38 1,755 .900 .887 .056 .059
Δ 2 versus 3 −.007 −.007 .001 .000
4. Equality of
covariances
62,448.00 1,860 .899 .893 .055 .061
Δ 3 versus 4 −.001 .006 −.001 .002
TABLE 2 Goodness‐of‐fit statistics
for MG‐CMSA models (U.S. sample)

|
7
KAISER Et Al.
TABLE 3 Correlations and univariate effect sizes for MG‐CMSA latent scores (U.S. sample)
 A. C. E. F. G. H. I. L. M. N. O. Q1. Q2. Q3. Q4.
A. Warmth  .36 .21 .53 −.31 .66 .37 −.58 −.07 −.59 −.11 −.36 −.53 −.11 −.55
C. Emot. Stability .36  .47 .22 −.30 .54 −.01 −.58 −.29 −.33 −.90 −.17 −.28 −.26 −.69
E. Assertiveness .27 .55  .38 .11 .60 .04 −.03 .01 −.34 −.52 −.32 −.15 −.31 .01
F. Gregariousness .61 .32 .44  .16 .77 −.03 −.24 .21 −.46 −.17 −.21 −.68 .16 −.12
G. Dutifulness −.27 −.28 .08 .12  −.12 .02 .42 .66 .18 .04 −.32 .18 .47 .33
H. Friendliness .70 .57 .64 .81 −.15  .04 −.47 −.15 −.70 −.44 −.20 −.68 −.14 −.33
I. Sensitivity .37 −.05 .04 −.04 .09 .05  −.20 .37 −.20 .07 −.76 .18 .04 −.23
L. Distrust −.62 −.51 −.04 −.34 .44 −.49 −.16  .30 .53 .39 .12 .46 .06 .68
M. Imagination −.08 −.34 −.04 .11 .71 −.23 .37 .36  .14 .17 −.60 .26 .45 .15
N. Reserve −.68 −.34 −.38 −.57 .19 −.75 −.20 .55 .17  .18 .16 .56 .06 .22
O. Anxiety −.14 −.92 −.57 −.25 .10 −.48 .04 .36 .28 .22  .19 .14 .14 .58
Q1. Complexity −.42 −.18 −.35 −.23 −.30 −.23 −.79 .13 −.55 .23 .17  −.08 −.11 .32
Q2. Introversion −.61 −.32 −.23 −.70 .24 −.74 .15 .54 .34 .64 .20 −.05  −.03 .25
Q3. Orderliness −.12 −.30 −.31 .16 .54 −.17 .03 .12 .51 .10 .19 −.09 .04  .08
Q4. Emotionality −.57 −.66 −.06 −.20 .33 −.37 −.30 .71 .23 .32 .59 .32 .32 .12 
d−0.37 +0.32 +0.26 +0.02 +0.37 0.00 −0.92 +0.02 +0.11 +0.16 −0.55 −0.24 −0.01 +0.07 −0.09
Note: Females above the diagonal; males below the diagonal. Positive values of d indicate that males score higher than females. The letters associated with each factor are the conventional trait identifiers in the 16PF model.

8
|
KAISER Et Al.
TABLE 4 Correlations, univariate effect sizes, and internal consistency values for observed scores (English‐speaking sample)
 A. C. E. F. G. H. I. L. M. N. O. Q1. Q2. Q3. Q4.
A. Warmth  .24 .15 .37 −.22 .49 .26 −.42 −.04 −.46 −.02 −.27 −.39 −.08 −.39
C. Emot. Stability .25  .36 .18 −.25 .44 −.01 −.46 −.22 −.28 −.74 −.12 −.21 −.17 −.55
E. Assertiveness .20 .40  .28 .08 .50 .03 −.02 .00 −.30 −.39 −.25 −.10 −.24 .02
F. Gregariousness .42 .24 .33  .13 .63 −.02 −.16 .18 −.38 −.13 −.16 −.50 .13 −.07
G. Dutifulness −.23 −.26 .05 .08  −.07 .01 .32 .49 .11 .03 −.25 .14 .34 .26
H. Friendliness .53 .46 .54 .66 −.13  .03 −.36 −.11 −.62 −.35 −.17 −.54 −.09 −.24
I. Sensitivity .26 −.05 .04 −.05 .08 .04  −.13 .23 −.14 .05 −.48 .12 .00 −.14
L. Distrust −.46 −.42 −.03 −.22 .36 −.38 −.10  .23 .42 .30 .07 .35 .02 .54
M. Imagination −.07 −.29 −.02 .09 .56 −.18 .25 .30  .11 .12 −.45 .22 .33 .11
N. Reserve −.53 −.29 −.33 −.44 .16 −.66 −.13 .44 .15  .15 .13 .47 .04 .17
O. Anxiety −.07 −.76 −.42 −.19 .10 −.40 .05 .30 .23 .18  .15 .10 .05 .46
Q1. Complexity −.29 −.11 −.28 −.15 −.25 −.19 −.56 .07 −.42 .18 .11  −.07 −.08 .24
Q2. Introversion −.45 −.26 −.19 −.54 .21 −.60 .13 .43 .27 .52 .17 −.05  −.04 .19
Q3. Orderliness −.10 −.20 −.23 .13 .40 −.11 −.01 .07 .36 .07 .11 −.05 .00  .03
Q4. Emotionality −.41 −.54 −.02 −.10 .28 −.27 −.20 .56 .19 .23 .48 .22 .23 .08 
d−0.36 +0.33 +0.22 +0.03 +0.29 +0.03 −0.83 +0.03 +0.06 +0.13 −0.52 −0.17 −0.04 +0.07 −0.09
α.84 .87 .84 .80 .83 .91 .69 .87 .80 .88 .86 .78 .85 .80 .82
Note: Females above the diagonal; males below the diagonal. Positive values of d indicate that males score higher than females. Correlations and effect sizes are uncorrected (i.e., not adjusted for score unreliability). The letters
associated with each scale are the conventional trait identifiers in the 16PF model.

|
9
KAISER Et Al.
sample of respondents from English‐speaking countries
(D=2.10); both effects were robust to exclusion/inclusion
of respondents (based on age and response quality) and to
PAV (Figure 1). Predictably, computing effect sizes from
estimated means and correlations substantially increased
the magnitude of sex differences compared with observed
scores.
While substantial, the multivariate effect size estimated
in the U.S. sample was 24% smaller than the one in the orig-
inal study (D=2.71). This difference could be explained by
a number of factors. To begin, the two questionnaires were
not identical. The coverage of specific personality factors
may differ somewhat between the original 16PF and the
IPIP‐based version analyzed here; the original 16PF scales
were also less reliable than their IPIP counterparts (average
α=.76 vs. .83), which may have contributed to inflate the
size of estimated differences. In addition, the online sample
of Open Source Psychometrics was self‐selected, whereas the
standardization sample of the original study was designed
to be demographically representative of the U.S. popula-
tion (and was more balanced by sex: 50% females vs. 63%).
Cohort effects may have contributed as well: the standardiza-
tion sample analyzed in the original study was collected in
1993 (see Del Giudice et al., 2012), whereas the Open Source
Psychometrics dataset was last updated in 2014. Finally, one
has to consider the possible impact of PAV, which can both
inflate and deflate the size of group differences and was not
examined in the original study (e.g., in the PAV analysis of
the U.S. sample, 25% of the D values were larger than 2.37;
see Figure 1a).
The univariate effect sizes in the U.S. sample were highly
correlated with those of the original study (r = .95 for ob-
served scores, .90 for MG‐MCSA estimates), though gener-
ally smaller in magnitude. Heterogeneity statistics indicated a
somewhat more balanced contribution of individual variables
to the overall effect size (H2=.81–.83 vs. .90 in the original
study; EPV2= .23–.24 vs. .16 in the original study. See Del
Giudice, 2018). Considering the largest univariate effects, fe-
males scored higher in Sensitivity, Anxiety (Apprehension),
Warmth, and Complexity (Openness to change), whereas
males were higher in Dutifulness (Rule‐Consciousness),
Emotional Stability, and Assertiveness (Dominance). As in
FIGURE 1 Parcel‐allocation variability (PAV) analysis of global sex differences. Each panel depicts 100 effect sizes (Mahalanobis' D),
estimated from MG‐CMSA models in which items were randomly allocated to parcels within each personality trait. Median values are shown as
dotted lines
Model χ2df CFI NNFI RMSEA SRMR
1. Configural
invariance
81,312.73 1,680 .909 .893 .055 .055
2. Metric invariance 82,358.52 1,725 .908 .894 .054 .054
Δ 1 versus 2 −.001 .001 −.001 −.001
3. Scalar invariance 88,602.66 1,755 .901 .888 .056 .056
Δ 2 versus 3 −.007 −.006 .002 .002
4. Equality of
covariances
89,637.46 1,860 .900 .893 .055 .055
Δ 3 versus 4 −.001 .005 −.001 −.001
TABLE 5 Goodness‐of‐fit statistics
for MG‐CMSA models (English‐speaking
sample)

10
|
KAISER Et Al.
TABLE 6 Correlations and univariate effect sizes for MG‐CMSA latent scores (English‐speaking sample)
 A. C. E. F. G. H. I. L. M. N. O. Q1. Q2. Q3. Q4.
A. Warmth  .36 .22 .51 −.29 .66 .37 −.58 −.08 −.59 −.11 −.36 −.53 −.12 −.54
C. Emot. Stability .36  .48 .21 −.29 .55 .00 −.59 −.30 −.34 −.90 −.17 −.28 −.25 −.70
E. Assertiveness .28 .54  .36 .11 .60 .05 −.04 .00 −.35 −.54 −.32 −.15 −.30 −.01
F. Gregariousness .59 .30 .42  .17 .76 −.06 −.22 .20 −.45 −.17 −.18 −.67 .18 −.10
G. Dutifulness −.29 −.29 .07 .11  −.10 .01 .41 .65 .17 .03 −.32 .16 .46 .32
H. Friendliness .70 .57 .64 .80 −.16  .02 −.48 −.17 −.69 −.46 −.19 −.69 −.13 −.33
I. Sensitivity .35 −.05 .05 −.10 .11 .01  −.20 .37 −.18 .07 −.77 .20 .01 −.23
L. Distrust −.61 −.52 −.04 −.30 .44 −.48 −.14  .31 .54 .40 .12 .46 .07 .69
M. Imagination −.11 −.38 −.05 .09 .72 −.25 .37 .38  .16 .18 −.59 .28 .44 .17
N. Reserve −.68 −.34 −.38 −.55 .20 −.75 −.16 .55 .20  .20 .16 .56 .07 .23
O. Anxiety −.15 −.92 −.58 −.23 .11 −.49 .06 .36 .30 .22  .19 .15 .13 .58
Q1. Complexity −.40 −.15 −.35 −.19 −.31 −.21 −.81 .10 −.54 .21 .15  −.10 −.10 .32
Q2. Introversion −.60 −.33 −.23 −.71 .25 −.74 .20 .54 .36 .63 .21 −.08  −.04 .25
Q3. Orderliness −.14 −.30 −.32 .18 .53 −.16 −.02 .11 .50 .11 .20 −.07 .02  .08
Q4. Emotionality −.55 −.68 −.05 −.14 .35 −.35 −.29 .71 .27 .30 .59 .29 .30 .15 
d−0.36 +0.35 +0.29 +0.04 +0.33 +0.03 −0.94 +0.01 +0.06 +0.14 −0.56 −0.20 −0.04 +0.06 −0.09
Note: Females above the diagonal; males below the diagonal. Positive values of d indicate that males score higher than females. The letters associated with each factor are the conventional trait identifiers in the 16PF model.

|
11
KAISER Et Al.
the original study, the largest univariate difference was found
on the Sensitivity factor (sensitive, aesthetic, sentimental, in-
tuitive, and tender‐minded vs. utilitarian, objective, unsenti-
mental, and tough‐minded).
This pattern of univariate effects is in line with previous
findings on sex differences based on the Big Five model.
The Sensitivity factor overlaps with both Agreeableness and
“feminine openness/closedness,” a composite of Openness
facets that was consistently higher in women in the cross‐
cultural analysis by Costa and colleagues (2001). In the Big
Five, Warmth is an Extraversion facet that is somewhat higher
in women, whereas Assertiveness is consistently higher in
men (Costa et al., 2001; Kajonius & Johnson, 2018). Sex dif-
ferences in Emotional Stability map on those in (negative)
Neuroticism; notably, Anxiety is the facet of Neuroticism that
shows the largest sex differences (Costa et al., 2001; Kajonius
& Johnson, 2018).
In both the present dataset and the original study (Del
Giudice et al., 2012), males scored higher in Dutifulness,
which may seem surprising since the Dutifulness facet of
Big Five Conscientiousness shows higher scores in females
(Costa et al., 2001; Kajonius & Johnson, 2018). However,
the Dutifulness factor in the 16PF differs from the homon-
ymous facet of the Big Five in being heavily skewed toward
conservatism and respect for authority. Items of this kind in
the IPIP‐based version include: “I believe laws should be
strictly enforced,” “I resist authority” (reverse‐scored), and “I
like to stand during the national anthem”. Accordingly, 16PF
Dutifulness correlates with Big Five Conscientiousness, but
also (negatively) with some Openness facets—including
Actions/Adventurous and Values/Liberalism—that are typi-
cally higher in females (Conn & Rieke, 1994; Kajonius &
Johnson, 2018; see also the supplementary results in Kaiser,
2019). In light of these differences, our finding of higher
male scores in 16PF Dutifulness is consistent with the litera-
ture based on the Big Five.
4.1
|
Implications for gender stereotypes
The present study supports the idea that global sex differ-
ences in personality are considerably larger than commonly
assumed. To put our results in perspective, D values between
2.06 and 2.10 imply that the personality profile of a randomly
picked male will be more male‐typical than that of a ran-
domly picked female about 93% of the times (common lan-
guage effect size). Likewise, knowing the personality profile
of an individual makes it possible to correctly guess his/her
sex about 85% of the times (see Del Giudice, 2019). (Note
that these figures apply to a person's “true” personality pro-
file and not to his/her observed questionnaire scores, which
are contaminated by measurement error. The corresponding
probabilities for uncorrected scores are 80% and 72%.)
Of note, these findings may help answer a long‐stand-
ing question in the literature (e.g., Carothers & Reis, 2013;
Maney, 2016): if psychological differences are dimensional
with no discrete boundaries between the sexes, why do cate-
gorical stereotypes of men's and women's behavior persist in
everyday life? A possible answer is that people have a strong
automatic tendency to use categorical templates to interpret
the world, and for this reason misconstrue the actual struc-
ture of sex differences (Reis & Carothers, 2014). However,
research on stereotypes has consistently found that people
estimate sex differences in personality with high accuracy
(Jussim, Crawford, & Rubinstein, 2015; Löckenhoff et al.,
2014); this does not sit well with the idea that the same ob-
servers exaggerate the separation between the sexes to the
point of perceiving two non‐existent categories.
The existence of large multivariate differences offers an
intriguing explanation of why stereotypes about male and fe-
male psychology are often categorical (or approximately so),
even if the sexes overlap substantially on each individual trait.
To the extent that people are paying attention to global differ-
ences (i.e., evaluating personality profiles instead of individ-
ual traits), they should correctly perceive a relatively sharp
boundary between the sexes, with little overlap in the middle.
Although categorical stereotypes remain inaccurate in a strict
sense, they may provide a reasonable approximation of the
degree of statistical separation between males and females
in the multivariate space. To our knowledge, this hypothesis
has yet to be tested in the literature on gender stereotypes. If
people integrate information about personality into multivar-
iate profiles, they should also be able to classify individuals
as male or female with relatively high accuracy when given
descriptions that include multiple traits. (As noted earlier, the
amount of measurement error in the descriptions would limit
the degree of accuracy that can be achieved in practice.) In
principle, changes in classification accuracy across different
combinations of traits may be exploited to make finer dis-
tinctions between alternative models of information use—for
example, to determine whether people keep trait correlations
into account when making inferences about a person's sex.
4.2
|
Implications for theories of sex
differences
Naturally, the findings of the present study do not speak
directly to the biological and/or cultural origins of sex dif-
ferences. Still, it is the case that researchers who emphasize
the role of sociocultural factors often view sex differences as
small, malleable, and overwhelmed by similarities (see Eagly
& Wood, 2013; Hyde, 2014; Hyde et al., 2019). In contrast,
most biologically oriented scholars argue that differences be-
tween the sexes on specific traits can be large, robust, and
potentially universal (though not necessarily fixed in size), as
a result of sexual selection and other evolutionary pressures
that affect the sexes in divergent ways (see Archer, 2019;
Buss, 1995; Schmitt, 2015).

12
|
KAISER Et Al.
The sociocultural malleability of sex differences is a cen-
tral tenet of social role theory (Eagly & Wood, 1999; Wood &
Eagly, 2012). The theory maintains that most sex differences
in psychology and behavior arise because males and females
are socialized into culturally prescribed roles, which in turn
are historically based on the existence of evolved dimorphism
in bodily size and function. A key prediction of social role
theory is that sex differences should shrink as societies adopt
more gender egalitarian values and socialization patterns. In
the domain of personality, however, cross‐cultural studies
have generally found the opposite pattern—that is, sex dif-
ferences are magnified in more gender egalitarian countries
(Kaiser, 2019; Mac Giolla & Kajonius, 2018; Schmitt, 2015;
Schmitt et al., 2016). From a biological perspective, a plausi-
ble explanation of this and similar finding (e.g., concerning
values and occupational preferences) is that gender egali-
tarian cultures leave men and women freer to express their
evolved predispositions (Schwartz & Rubel, 2005; Schmitt,
2015; Schmitt et al., 2016). At the same time, the apparent
effect of increasing gender equality may be confounded with
that of decreasing ecological stress in more developed coun-
tries (Kaiser, 2019). Yet another hypothesis is that, in less
gender egalitarian societies, people tend to evaluate them-
selves using their own sex as the reference group; as gender
equality increase, the reference group expands to include the
entire population, thus increasing the size (and accuracy) of
self‐reported differences (Lippa, 2010; Lukaszewski, Roney,
Mills, & Bernard, 2013; for a critical evaluation see Schmitt
et al., 2016).
Considered in this context, the present results are consis-
tent with the findings of previous cross‐cultural studies based
on the Big Five model. In the recent study by Kaiser (2019),
MG‐CMSA on 30 Big Five facets yielded D=2.16 for the
U.S. sample, an effect almost identical to the one we found
here (effect sizes ranged from 1.49 in Pakistan to 2.48 in
Russia). Likewise, the uncorrected effect size in Mac Giolla
and Kajonius (2018) was D=1.25 for the U.S. sample, com-
pared to 1.18 in the present study (effect sizes ranged from
0.87 in Malaysia to 1.32 in Norway and Sweden).
4.3
|
Future directions
Now that large sex differences have been found in two inde-
pendent datasets based on the 16PF, it will be important to in-
vestigate the extent to which the size of the effect may depend
on the choice of a personality model (e.g., 16PF vs. Big Five).
So far, multivariate studies of sex differences have yielded
fairly consistent results regardless of the underlying model
(Kaiser, 2019; Mac Giolla & Kajonius, 2018). However, it
will take a number of large‐scale replications before a con-
fident statement can be made. Moreover, to our knowledge,
some popular models (e.g., the six‐factor HEXACO; Lee &
Ashton, 2004) have yet to be approached from a multivariate
perspective. As more data accumulate, it will become pos-
sible to use meta‐analysis to explore patterns of consistency
and variation in a more systematic way.
Another interesting topic for future research is the im-
pact of different modeling approaches on the estimation of
latent differences. For example, exploratory structural equa-
tion modeling (ESEM; Marsh, Morin, Parker, & Kaur, 2014;
Marsh et al., 2009) has been gaining popularity in recent
years. Standard confirmatory models like the ones we em-
ployed in the present study constrain each item to load on one
particular latent factor (the independent clusters assumption);
in contrast, ESEM allows items to freely cross‐load on all the
factors. As a result, interfactor correlations tend to become
substantially smaller (e.g., Booth & Hughes, 2014; Furnham,
Guenole, Levine, & Chamorro‐Premuzic, 2013; Marsh et al.,
2010; Marsh, Nagengast, & Morin, 2013).
Given the importance of correlations among variables in
the calculation of multivariate indices such as D, the choice
of measurement model can be expected to have non‐trivial
consequences. Whether the ESEM approach is well suited to
study sex differences in personality is not clear at this point.
On the one hand, failing to model cross‐loadings may lead to
inflated correlations among factors (Marsh et al., 2010, 2014,
2009). On the other hand, extensive cross‐loadings may end
up altering the nature of the factors and “blur” their content
to some extent. This is relevant because sex differences are
revealed most clearly in narrow, circumscribed traits; not
infrequently, traits that positively correlate with one another
(e.g., different facets of Extraversion in the Big Five; Warmth
and Emotional Stability in the 16PF) show sex differences
of opposite sign (see Del Giudice, 2015; Del Giudice et al.,
2012). If narrow traits are allowed to cross‐load extensively,
their specificity—and hence their ability to differentiate be-
tween males and females—may deteriorate to an unknown
extent. Future research on ESEM should consider the impact
of cross‐loadings on both interfactor correlations and univar-
iate differences, as well as their interplay in the determination
of global sex differences.
From a theoretical standpoint, our findings corroborate
those of recent multivariate cross‐cultural studies, and fur-
ther challenge the received view on sex differences in psy-
chology—which, as noted, is largely modeled on the gender
similarities hypothesis. Importantly, Hyde's hypothesis was
framed in strictly univariate terms (Hyde, 2005, 2014); ac-
cordingly, the standard approach in the literature is to con-
sider individual traits one at a time, or at most average them
together (e.g., Zell et al., 2015). As it has become apparent
over the past few years, a multivariate perspective offers a
strikingly different picture of sex differences and similarities,
not just in personality but in domains such as mate prefer-
ences (Conroy‐Beam, Buss, Pham, & Shackelford, 2015) and
occupational interests (Morris, 2016). An important research
question that naturally lends itself to a multivariate approach

|
13
KAISER Et Al.
is the extent to which sex differences in personality predict
sex differences in life outcomes such as health, well‐being,
and occupational choices (Soto, 2019). It is plausible that
multivariate profiles will prove more predictive than individ-
ual traits, particularly if multiple aspects of personality inter-
act in nonadditive ways to influence the relevant outcomes. In
sum, we believe that the shift from an exclusively univariate
focus to a multivariate one is an exciting opportunity, with
the potential to dramatically improve our understanding of
how personality differences play out in the lives of men and
women.
ACKNOWLEDGMENTS
The authors received no financial support for the research,
authorship, and/or publication of this article.
CONFLICT OF INTERESTS
The authors declared no potential conflicts of interest with
respect to the research, authorship, and/or publication of this
article.
ORCID
Tim Kaiser https://orcid.org/0000-0002-6307-5347
Marco Del Giudice https://orcid.org/0000-0001-8526-1573
REFERENCES
Allen, C. N. (1930). Recent studies in sex differences. Psychological
Bulletin, 27, 394–407. https ://doi.org/10.1037/h0070355
Archer, J. (2019). The reality and evolutionary significance of human
psychological sex differences. Biological Reviews, 94, 1381–1415.
https ://doi.org/10.1111/brv.12507
Booth, T., & Hughes, D. J. (2014). Exploratory structural equation
modeling of personality data. Assessment, 21, 260–271. https ://doi.
org/10.1177/10731 91114 528029
Buss, D. M. (1995). Psychological sex differences: Origins through
sexual selection. American Psychologist, 50, 164–171. https ://doi.
org/10.1037/0003-066X.50.3.164
Carothers, B. J., & Reis, H. T. (2013). Men and women are from earth:
Examining the latent structure of gender. Journal of Personality and
Social Psychology, 10, 385–407. https ://doi.org/10.1037/a0030437
Chen, F. F. (2007). Sensitivity of goodness of fit indexes to lack of mea-
surement invariance. Structural Equation Modeling, 14, 464–504.
https ://doi.org/10.1080/10705 51070 1301834
Cohen, J. (1988). Statistical power analysis for the behavioral sciences
(2nd ed.). Mahwah, NJ: Lawrence Erlbaum Associates.
Conn, S. R., & Rieke, M. L. (Eds.). (1994). The 16PF fifth edition tech-
nical manual. Champagne, IL: Institute for Personality and Ability
Testing Inc.
Conroy‐Beam, D., Buss, D. M., Pham, M. N., & Shackelford, T. K.
(2015). How sexually dimorphic are human mate preferences?
Personality and Social Psychology Bulletin, 41, 1082–1093. https ://
doi.org/10.1177/01461 67215 590987
Costa, P. T., Terracciano, A., & McCrae, R. R. (2001). Gender differ-
ences in personality traits across cultures: Robust and surprising
findings. Journal of Personality and Social Psychology, 81, 322–
331. https ://doi.org/10.1037/0022-3514.81.2.322
Del Giudice, M. (2009). On the real magnitude of psychological sex
differences. Evolutionary Psychology, 7, 264–279. https ://doi.
org/10.1177/14747 04909 00700209
Del Giudice, M. (2013). Multivariate misgivings: Is D a valid mea-
sure of group and sex differences? Evolutionary Psychology, 11,
1067–1076.
Del Giudice, M. (2015). Gender differences in personality and social
behavior. In J. D. Wright (Ed.), International encyclopedia of the
social and behavioral sciences (2nd ed., pp. 750–756). New York,
NY: Elsevier.
Del Giudice, M. (2017). Heterogeneity coefficients for Mahalanobis' D
as a multivariate effect size. Multivariate Behavioral Research, 52,
216–221.
Del Giudice, M. (2018). Addendum to: Heterogeneity coefficients
for Mahalanobis’ D as a multivariate effect size. Multivariate
Behavioral Research, 53, 571–573.
Del Giudice, M. (2019). Measuring sex differences and similarities.
In D. P. VanderLaan & W. I. Wong (Eds.), Gender and sexuality
development: Contemporary theory and research. New York, NY:
Springer.
Del Giudice, M., Booth, T., & Irwing, P. (2012). The distance be-
tween Mars and Venus: Measuring global sex differences in per-
sonality. PLoS ONE, 7, e29265. https ://doi.org/10.1371/journ
al.pone.0029265
Eagly, A. H., & Wood, W. (1999). The origins of sex differ-
ences in human behavior: Evolved dispositions versus so-
cial roles. American Psychologist, 54, 408–423. https ://doi.
org/10.1037/0003-066X.54.6.408
Eagly, A. H., & Wood, W. (2013). The nature–nurture debates: 25
years of challenges in understanding the psychology of gender.
Perspectives on Psychological Science, 8, 340–357. https ://doi.
org/10.1177/17456 91613 484767
Feitosa, J., Joseph, D. L., & Newman, D. A. (2015). Crowdsourcing and
personality measurement equivalence: A warning about countries
whose primary language is not English. Personality and Individual
Differences, 75, 47–52. https ://doi.org/10.1016/j.paid.2014.11.017
Fiske, S. T. (2017). Prejudices in cultural contexts: Shared stereotypes
(gender, age) versus variable stereotypes (race, ethnicity, religion).
Perspectives on Psychological Science, 12, 791–799. https ://doi.
org/10.1177/17456 91617 708204
Friedman, H. S., & Kern, M. L. (2014). Personality, well‐being, and
health. Annual Review of Psychology, 65, 719–742. https ://doi.
org/10.1146/annur ev-psych-010213-115123
Furnham, A., Guenole, N., Levine, S. Z., & Chamorro‐Premuzic, T.
(2013). The NEO Personality Inventory‐Revised: Factor structure
and gender invariance from exploratory structural equation model-
ing analyses in a high‐stakes setting. Assessment, 20, 14–23. https ://
doi.org/10.1177/10731 91112 448213
Goldberg, L. R. (1999). A broad‐bandwidth, public domain, personal-
ity inventory measuring the lower‐level facets of several five‐factor
models. Personality Psychology in Europe, 7, 7–28.
Haines, E. L., Deaux, K., & Lofaro, N. (2016). The times they are a‐
changing… or are they not? A comparison of gender stereotypes,

14
|
KAISER Et Al.
1983–2014. Psychology of Women Quarterly, 40, 353–363. https ://
doi.org/10.1177/03616 84316 634081
Hess, M. R., Hogarty, K. Y., Ferron, J. M., & Kromrey, J. D. (2007).
Interval estimates of multivariate effect sizes: Coverage and inter-
val width estimates under variance heterogeneity and nonnormality.
Educational and Psychological Measurement, 67, 21–40. https ://
doi.org/10.1177/00131 64406 288159
Hu, L.‐T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in
covariance structure analysis: Conventional criteria versus new
alternatives. Structural Equation Modeling, 6, 1–55. https ://doi.
org/10.1080/10705 51990 9540118
Hyde, J. S. (2005). The gender similarities hypothe-
sis. American Psychologist, 60, 581–592. https ://doi.
org/10.1037/0003-066X.60.6.581
Hyde, J. S. (2014). Gender similarities and differences. Annual
Review of Psychology, 65, 373–398. https ://doi.org/10.1146/annur
ev-psych-010213-115057
Hyde, J. S., Bigler, R. S., Joel, D., Tate, C. C., & van Anders, S. M.
(2019). The future of sex and gender in psychology: Five challenges
to the gender binary. American Psychologist, 74(2), 171–193. https
://doi.org/10.1037/amp00 00307
Jorgensen, T. D., Pornprasertmanit, S., Schoemann, A. M., & Rosseel,
Y. (2018). semTools: Useful tools for structural equation modeling.
R package version 0.5‐1. Retrieved from https ://CRAN.R-proje
ct.org/packa ge=semTools
Jussim, L., Crawford, J. T., & Rubinstein, R. S. (2015). Stereotype
(in)accuracy in perceptions of groups and individuals. Current
Directions in Psychological Science, 24, 490–497. https ://doi.
org/10.1177/09637 21415 605257
Kaiser, T. (2019). Nature and evoked culture: Sex differences in per-
sonality are uniquely correlated with ecological stress. Personality
and Individual Differences, 148, 67–72. https ://doi.org/10.1016/j.
paid.2019.05.011
Kajonius, P. J., & Johnson, J. (2018). Sex differences in 30 facets of the
five factor model of personality in the large public (N= 320,128).
Personality and Individual Differences, 129, 126–130.
Kajonius, P., & Mac Giolla, E. (2017). Personality traits across coun-
tries: Support for similarities rather than differences. PLoS ONE, 12,
e0179646. https ://doi.org/10.1371/journ al.pone.0179646
Kelley, K. (2005). The effects of nonnormal distributions on confidence
intervals around the standardized mean difference: Bootstrap and
parametric confidence intervals. Educational and Psychological
Measurement, 65, 51–69. https ://doi.org/10.1177/00131 64404
264850
Kotov, R., Gamez, W., Schmidt, F., & Watson, D. (2010). Linking “big”
personality traits to anxiety, depressive, and substance use disorders:
A meta‐analysis. Psychological Bulletin, 136, 768–821. https ://doi.
org/10.1037/a0020327
Landis, R. S., Beal, D. J., & Tesluk, P. E. (2000). A comparison of
approaches to forming composite measures in structural equation
models. Organizational Behavioral Research, 3, 186–207. https ://
doi.org/10.1177/10944 28100 32003
Lee, K., & Ashton, M. C. (2004). Psychometric properties of the
HEXACO personality inventory. Multivariate Behavioral Research,
39, 329–358. https ://doi.org/10.1207/s1532 7906m br3902_8
Lippa, R. A. (2010). Gender differences in personality and interests:
When, where, and why? Social and Personality Psychology Compass,
4, 1098–1110. https ://doi.org/10.1111/j.1751-9004.2010.00320.x
Little, T. D., Rhemtulla, M., Gibson, K., & Schoemann, A. M.
(2013). Why the items versus parcels controversy needn't be one.
Psychological Methods, 18, 285–300. https ://doi.org/10.1037/
a0033266
Löckenhoff, C. E., Chan, W., McCrae, R. R., De Fruyt, F., Jussim,
L., De Bolle, M., … Terracciano, A. (2014). Gender stereotypes
of personality: Universal and accurate? Journal of Cross‐Cultural
Psychology, 45, 675–694. https ://doi.org/10.1177/00220 22113
520075
Lukaszewski, A. W., Roney, J. R., Mills, M. E., & Bernard, L. C. (2013).
At the interface of social cognition and psychometrics: Manipulating
the sex of the reference class modulates sex differences in personal-
ity traits. Journal of Research in Personality, 47, 953–957. https ://
doi.org/10.1016/j.jrp.2013.08.010
Mac Giolla, E., & Kajonius, P. J. (2018). Sex differences in personality
are larger in gender equal countries: Replicating and extending a
surprising finding. International Journal of Psychology. https ://doi.
org/10.1002/ijop.12529
Maney, D. L. (2016). Perils and pitfalls of reporting sex differences.
Philosophical Transactions of the Royal Society of London B, 371,
20150119. https ://doi.org/10.1098/rstb.2015.0119
Marsh, H. W., Lüdtke, O., Muthén, B., Asparouhov, T., Morin, A. J. S.,
Trautwein, U., & Nagengast, B. (2010). A new look at the Big‐Five
factor structure through exploratory structural equation modeling.
Psychological Assessment, 22, 471–491. https ://doi.org/10.1037/
a0019227
Marsh, H. W., Morin, A. J., Parker, P. D., & Kaur, G. (2014). Exploratory
structural equation modeling: An integration of the best features of
exploratory and confirmatory factor analysis. Annual Review of
Clinical Psychology, 10, 85–110. https ://doi.org/10.1146/annur ev-
clinp sy-032813-153700
Marsh, H. W., Muthén, B., Asparouhov, T., Lüdtke, O., Robitzsch, A.,
Morin, A. J., & Trautwein, U. (2009). Exploratory structural equa-
tion modeling, integrating CFA and EFA: Application to students'
evaluations of university teaching. Structural Equation Modeling,
16, 439–476. https ://doi.org/10.1080/10705 51090 3008220
Marsh, H. W., Nagengast, B., & Morin, A. J. (2013). Measurement in-
variance of big–five factors over the life span: ESEM tests of gender,
age, plasticity, maturity, and la dolce vita effects. Developmental
Psychology, 49, 1194–1218. https ://doi.org/10.1037/a0026913
McGraw, K. O., & Wong, S. P. (1992). A common language effect
size statistic. Psychological Bulletin, 111, 361–365. https ://doi.
org/10.1037/0033-2909.111.2.361
Morris, M. L. (2016). Vocational interests in the United States: Sex, age,
ethnicity, and year effects. Journal of Counseling Psychology, 63,
604–615. https ://doi.org/10.1037/cou00 00164
Olejnik, S., & Algina, J. (2000). Measures of effect size for com-
parative studies: Applications, interpretations, and limitations.
Contemporary Educational Psychology, 25, 241–286. https ://doi.
org/10.1006/ceps.2000.1040
R Core Team. (2018). R: A language and environment for statistical
computing. Vienna, Austria: R Foundation for Statistical Computing.
Reis, H. T., & Carothers, B. J. (2014). Black and white or shades of
gray: Are gender differences categorical or dimensional? Current
Directions in Psychological Science, 23, 19–26. https ://doi.
org/10.1177/09637 21413 504105
Reiser, B. (2001). Confidence intervals for the Mahalanobis distance.
Communications in Statistics: Simulation and Computation, 30,
37–45. https ://doi.org/10.1081/SAC-10000 1856
Rosseel, Y. (2012). Lavaan: An R package for structural equation mod-
eling and more. Version 0.5–12 (BETA). Journal of statistical soft-
ware, 48(2), 1–36.

|
15
KAISER Et Al.
Schermelleh‐Engel, K., Moosbrugger, H., & Müller, H. (2003).
Evaluating the fit of structural equation models: Tests of significance
and descriptive goodness‐of‐fit measures. Methods of Psychological
Research Online, 8, 23–74.
Schmitt, D. P. (2015). The evolution of culturally‐variable sex differ-
ences: Men and women are not always different, but when they
are… it appears not to result from patriarchy or sex role socializa-
tion. In T. K. Shackelford & R. D. Hansen (Eds.), The evolution of
sexuality (pp. 221–256). Cham, Switzerland: Springer.
Schmitt, D. P., Long, A. E., McPhearson, A., O'Brien, K., Remmert, B.,
& Shah, S. H. (2016). Personality and gender differences in global
perspective. International Journal of Psychology, 56, 45–56. https ://
doi.org/10.1002/ijop.12265
Schmitt, D. P., Realo, A., Voracek, M., & Allik, J. (2008). Why can't a man
be more like a woman? Sex differences in Big Five personality traits
across 55 cultures. Journal of Personality and Social Psychology,
94, 168–182. https ://doi.org/10.1037/0022-3514.94.1.168
Schwartz, S. H., & Rubel, T. (2005). Sex differences in value pri-
orities: Cross‐cultural and multimethod studies. Journal of
Personality and Social Psychology, 89, 1010–1028. https ://doi.
org/10.1037/0022-3514.89.6.1010
Soto, C. J. (2019). How replicable are links between personality traits
and consequential life outcomes? The life outcomes of personality
replication project. Psychological Science, 30(5), 711–727.
Soto, C. J., John, O. P., Gosling, S. D., & Potter, J. (2011). Age dif-
ferences in personality traits from 10 to 65: Big Five domains and
facets in a large cross‐sectional sample. Journal of Personality
and Social Psychology, 100, 330–348. https ://doi.org/10.1037/
a0021717
Sterba, S. K. (2019). Problems with rationales for parceling that fail
to consider parcel‐allocation variability. Multivariate Behavioral
Research, 54(2), 264–287. https ://doi.org/10.1080/00273
171.2018.1522497
Sterba, S. K., & MacCallum, R. C. (2010). Variability in parameter es-
timates and model fit across repeated allocations of items to par-
cels. Multivariate Behavioral Research, 45, 322–358. https ://doi.
org/10.1080/00273 17100 3680302
Thompson, H. B. (1903). The mental traits of sex: An experimental
investigation of the normal mind in men and women. Chicago, IL:
University of Chicago Press.
Weisberg, Y. J., DeYoung, C. G., & Hirsh, J. B. (2011). Gender differ-
ences in personality across the ten aspects of the Big Five. Frontiers
in Psychology, 2, 178. https ://doi.org/10.3389/fpsyg.2011.00178
Wood, W., & Eagly, A. H. (2012). Biosocial construction of sex differ-
ences and similarities in behavior. Advances in Experimental Social
Psychology, 46, 55–123.
Zell, E., Krizan, Z., & Teeter, S. R. (2015). Evaluating gender similar-
ities and differences using metasynthesis. American Psychologist,
70, 10–20. https ://doi.org/10.1037/a0038208
Zell, E., Strickhouser, J. E., Lane, T. N., & Teeter, S. R. (2016). Mars,
Venus, or Earth? Sexism and the exaggeration of psychological gen-
der differences. Sex Roles, 75, 287–300. https ://doi.org/10.1007/
s11199-016-0622-1
How to cite this article: Kaiser T, Del Giudice M,
Booth T. Global sex differences in personality:
Replication with an open online dataset. Journal of
Personality. 2019;00:1–15. https ://doi.org/10.1111/
jopy.12500