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Sex differences in moral judgements across 67 countries

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Sex differences in moral judgements across 67 countries

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Content University of Southern California
Sex di↵erences in moral
judgements across 67
countries
by
Mohammad Atari
AThesisPresentedtoUSCDornsifeCollegeof
Letters, Arts and Sciences
In Partial Fulfillment of the Requirements for the
Master of Arts in Psychology Degree
University of Southern California
Department of Psychology
Degree Conferral Date:
December 2019
SEX DIFFERENCES IN MORALITY 2
Abstract
Most of the empirical research on sex dierences and cultural variations in morality has
relied on within-culture analyses, or small cross-cultural data, comparing few cultures. To
further broaden the scientific understanding of sex dierences in morality, the current
research relies on two international samples to provide the first large-scale examination of
sex dierences in moral judgements nested within cultures. Using a sample from 67
countries (Study 1; n = 336,691), we found culturally-variable sex dierences in moral
judgements, as conceptualized by the Moral Foundations Theory. Women consistently
scored higher than men on Care, Fairness, and Purity. In contrast, sex dierences in
Loyalty and Authority were negligible and highly variable across cultures. We also
examined country-level sex dierences in moral judgements in relation to cultural,
socio-economic, and gender-equality indicators. In Study 2 (19 countries; n = 11,969), we
largely replicated these results using Bayesian multi-level modeling in a distinct sample.
Specifically, women consistently showed higher concerns for Care, Fairness, and Purity in
their moral judgements than did men. We discuss the implications of these findings on the
ongoing debate about the origin of sex dierences and cultural variations in moral
judgements as well as theoretical and pragmatic implications for moral psychology.
Keywords: morality, gender, sex dierences, culture, Bayesian inference.
SEX DIFFERENCES IN MORALITY 3
Sex dierences in moral judgements across 67 countries
Contents
Abstract 2
Sex dierences in moral judgements across 67 countries 3
Introduction 10
Moral Foundations Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
Sex Dierences in Moral Judgement . . . . . . . . . . . . . . . . . . . . . . . . . . 12
Cultural Dierences in Moral Judgement . . . . . . . . . . . . . . . . . . . . . . . 13
Theories of Culturally-Variable Sex Dierences . . . . . . . . . . . . . . . . . . . . 15
Overview of Present Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
Study 1 18
Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
Participants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
Measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
Procedure and Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
Multi-Level Analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
Country-Level Analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
Study 2 31
Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
Participants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
Measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
Procedure and Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 33
Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
SEX DIFFERENCES IN MORALITY 4
Multi-Level Analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
Country-Level Analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
General Discussion 37
Implications for Moral Psychology . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
Implications for the Origin of Sex Dierences . . . . . . . . . . . . . . . . . . . . 40
Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
References 45
Supplemental Material 61
Abstract 62
Appendix 1: Measurement invariance using maximum likelihood alignment method 63
Introduction 63
Methods 65
Results 67
Care invariance using the alignment method . . . . . . . . . . . . . . . . . . . . . 67
Fairness invariance using the alignment method . . . . . . . . . . . . . . . . . . . 67
Loyalty invariance using the alignment method . . . . . . . . . . . . . . . . . . . 68
Authority invariance using the alignment method . . . . . . . . . . . . . . . . . . 68
Purity invariance using the alignment method . . . . . . . . . . . . . . . . . . . . 69
Measurement Invariance across Sexes . . . . . . . . . . . . . . . . . . . . . . . . . 70
Discussion 73
Appendix 2: Item-Level Multi-Level Analysis: Codes and Visualizations 73
Abstract 165
SEX DIFFERENCES IN MORALITY 5
Appendix 3: Frequentist Multi-Level Modeling of Sex Dierences across 19 Cultures
(Study 2) 166
Introduction 166
Methods 166
Results 167
Care . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167
Fairness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167
Loyalty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167
Authority . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168
Purity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168
Discussion 168
SEX DIFFERENCES IN MORALITY 6
List of Figures
1 Multi-Level Model for Care (Study 1) . . . . . . . . . . . . . . . . . . . . . . 194
2 Multi-Level Model for Fairness (Study 1) . . . . . . . . . . . . . . . . . . . . 195
3 Multi-Level Model for Loyalty (Study 1) . . . . . . . . . . . . . . . . . . . . 196
4 Multi-Level Model for Authority (Study 1) . . . . . . . . . . . . . . . . . . . 197
5 Multi-Level Model for Purity (Study 1) . . . . . . . . . . . . . . . . . . . . . 198
6 Bayesian Multi-Level Models for Care; 0 = female, 1 = male (Study 2) . . . 199
7 Posterior Predictive Checks for Multi-Level Models (Study 2). . . . . . . . . 200
8 Bayesian Multi-Level Models for Fairness; 0 = female, 1 = male (Study 2) . 201
9 Bayesian Multi-Level Models for Loyalty; 0 = female, 1 = male (Study 2) . . 202
10 Bayesian Multi-Level Models for Authority; 0 = female, 1 = male (Study 2) 203
11 Bayesian Multi-Level Models for Purity; 0 = female, 1 = male (Study 2) . . 204
SEX DIFFERENCES IN MORALITY 7
List of Tables
1 Sex Dierences in Moral Foundations in 67 Countries (Study 1) . . . . . . . 169
2 Results of Multi-Level Modeling (Study 1) . . . . . . . . . . . . . . . . . . . 170
3 CorrelationsbetweenSexDierencesinMoralFoundationsandCountry-Level
Indicators (Study 1) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171
4 Results of Bayesian Multi-Level Modeling (Study 2) . . . . . . . . . . . . . . 172
5 CorrelationsbetweenSexDierencesinMoralFoundationsandCountry-Level
Indicators (Study 2) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173
6 Evidence for (or Against) All Hypotheses . . . . . . . . . . . . . . . . . . . . 174
7 Countries and their sample size . . . . . . . . . . . . . . . . . . . . . . . . . 175
8 Approximate measurement invariance (noninvariance) of the intercepts for
the Care items. Non-invariant parameters are parenthesized. Y indicates
item numbers and $ indicates thresholds. See Table 7 for a description of
country numbers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 176
9 Approximate measurement invariance (noninvariance) of the loadings for the
Care items. Non-invariant parameters are parenthesized. See Table 7 for a
description of country numbers . . . . . . . . . . . . . . . . . . . . . . . . . 177
10 Approximatemeasurementinvariance(noninvariance)oftheinterceptsforthe
Fairness items. Non-invariant parameters are parenthesized. Y indicates item
numbers and $ indicates thresholds. See Table 7 for a description of country
numbers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 178
11 Approximate measurement invariance (noninvariance) of the loadings for the
Fairness items. Non-invariant parameters are parenthesized. See Table 7 for
a description of country numbers . . . . . . . . . . . . . . . . . . . . . . . . 179
SEX DIFFERENCES IN MORALITY 8
12 Approximatemeasurementinvariance(noninvariance)oftheinterceptsforthe
Loyalty items. Non-invariant parameters are parenthesized. Y indicates item
numbers and $ indicates thresholds. See Table 7 for a description of country
numbers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180
13 Approximate measurement invariance (noninvariance) of the loadings for the
Loyalty items. Non-invariant parameters are parenthesized. See Table 7 for a
description of country numbers . . . . . . . . . . . . . . . . . . . . . . . . . 181
14 Approximatemeasurementinvariance(noninvariance)oftheinterceptsforthe
Authority items. Non-invariant parameters are parenthesized. Y indicates
item numbers and $ indicates thresholds. See Table 7 for a description of
country numbers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182
15 Approximate measurement invariance (noninvariance) of the loadings for the
Authority items. Non-invariant parameters are parenthesized. See Table 7 for
a description of country numbers . . . . . . . . . . . . . . . . . . . . . . . . 183
16 Approximatemeasurementinvariance(noninvariance)oftheinterceptsforthe
Purity items. Non-invariant parameters are parenthesized. Y indicates item
numbers and $ indicates thresholds. See Table 7 for a description of country
numbers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 184
17 Approximate measurement invariance (noninvariance) of the loadings for the
Purity items. Non-invariant parameters are parenthesized. See Table 7 for a
description of country numbers . . . . . . . . . . . . . . . . . . . . . . . . . 185
18 Results of Measurement Invariance for Purity in Women and Men across 67
countries. RMSEA = Root Mean Square Error of Approximation; CFI =
Comparative Fit Index; Bonferroni-corrected p threshold = 0.0001 . . . . . . 186
19 Results of Measurement Invariance for Authority in Women and Men across
67 countries. RMSEA = Root Mean Square Error of Approximation; CFI =
Comparative Fit Index; Bonferroni-corrected p threshold = 0.0001 . . . . . . 187
SEX DIFFERENCES IN MORALITY 9
20 Results of Measurement Invariance for Loyalty in Women and Men across 67
countries. RMSEA = Root Mean Square Error of Approximation; CFI =
Comparative Fit Index; Bonferroni-corrected p threshold = 0.0001 . . . . . . 188
21 Results of Measurement Invariance for Fairness in Women and Men across
67 countries. RMSEA = Root Mean Square Error of Approximation; CFI =
Comparative Fit Index; Bonferroni-corrected p threshold = 0.0001 . . . . . . 189
22 Results of Measurement Invariance for Care in Women and Men across 67
countries. RMSEA = Root Mean Square Error of Approximation; CFI =
Comparative Fit Index; Bonferroni-corrected p threshold = 0.0001 . . . . . . 190
23 Results of Multi-Level Modeling for Care (Study 2) . . . . . . . . . . . . . . 191
24 Results of Multi-Level Modeling for Fairness (Study 2) . . . . . . . . . . . . 191
25 Results of Multi-Level Modeling for Loyalty (Study 2) . . . . . . . . . . . . . 192
26 Results of Multi-Level Modeling for Authority (Study 2) . . . . . . . . . . . 192
27 Results of Multi-Level Modeling for Purity (Study 2) . . . . . . . . . . . . . 193
SEX DIFFERENCES IN MORALITY 10
Introduction
Men are, on average, more selfish (Grijalva et al., 2015), and women are, on average,
more empathetic (Christov-Moore et al., 2014). Such sex dierences are frequently
mentioned in psychological research as well as public media. It is important to keep in
mind that the direction and magnitude of sex dierences varies across human cultures, and
it is problematic to generalize sex dierences in Western societies, as in most psychological
studies, to rest of the world; as Baumeister (2010) suggests, “the small size of American
gender dierences may be atypical.” Sex dierences, and their variation across cultures, are
debated in morality research (Gilligan, 1982; Kohlberg, 1981). Scholars have examined sex
dierences in a considerable number of morally relevant behaviors, cognitions, and
emotions, but such investigations usually suer from small samples of individuals and small
numbers of tested cultures. Well-powered, cross-cultural studies on sex dierences in moral
judgements are lacking in the field. Drawing upon the Moral Foundations Theory (MFT;
Haidt & Graham, 2007) as an evolutionarily-informed cultural theory of morality, here we
examine sex dierences in moral judgements as conceptualized in MFT (i.e., Care,
Fairness, Loyalty, Authority, and Purity) using a large number of participants, a large
number of cultures, and appropriate statistical methods in a sequence of well-powered,
observational studies. In addition, we investigate the relationship between country-level
indices of cultural characteristics, socioeconomic development, and gender equality, and the
magnitude of sex dierences in moral judgements.
It is important to examine the direction and magnitude of sex dierences in moral
judgements nested across cultures for at least three reasons. First, such studies can shed
light on the origins of sex dierences (see Wood & Eagly, 2002) and the ongoing debates in
social sciences about the universality of sex dierences in psychological characteristics.
Second, it enables us to compare alternative theories of morality (see Gray, Waytz, &
Young, 2012; Sivan, Curry, & Van Lissa, 2018; Tomasello & Vaish, 2013). For example, it
is not yet well-established what aspects of morality produce larger sex dierences across
SEX DIFFERENCES IN MORALITY 11
cultures, or how men’s and women’s intensity of moral emotions are dierent in egalitarian
(vs. non-egalitarian) cultures. Third, such analyses can discover the values people hold in
dierent cultures (see Narvaez, Gleason, Mitchell, & Bentley, 1999), and identify which sex
(and in what culture) is dierent from the global “average” (Schwartz & Rubel-Lifschitz,
2009). Psychologists have argued that widespread variation in psychological characteristics
exists across human societies and that cross-cultural studies can provide valuable insight
into the variability in human societies with respect to sex dierences (Apicella & Barrett,
2016; Wood & Eagly, 2013) and morality (Graham et al., 2013; Purzycki et al., 2018). This
article examines sex dierences in moral judgements across cultures using two dierent
data sets from 67 cultures (Study 1) and 19 cultures (Study 2). In addition, we investigate
how the magnitude of sex dierences in moral judgements across cultures is linked to
country-level cultural, socio-economic, and gender-equality indices.
Moral Foundations Theory
The Moral Foundations Theory (MFT; Graham et al., 2013; Haidt & Joseph, 2004)
was developed by searching for the best links between anthropological and evolutionary
accounts of morality across cultures. This theory posits that moral intuitions derive from
innate psychological mechanisms that coevolved with cultural institutions. Each moral
system produces fast, automatic gut-level reactions of like or dislike when certain
phenomena are perceived in the social world, which in turn guide judgements of right and
wrong. These systems, according to MFT, have evolutionarily adaptive underpinnings
present in all individuals; however, culture shapes the mechanisms through which such
intuitions translate into everyday moral judgements in social contexts. To find the best
candidate foundations for MFT, Haidt and Joseph (2004) initially surveyed lists of virtues
from diverse cultures and time eras, along with taxonomies of morality from anthropology
(Fiske, 1992; Shweder, Much, Mahapatra, & Park, 1997), psychology (Schwartz & Bilsky,
1990), and evolutionary comparative theories regarding human and primate sociality
SEX DIFFERENCES IN MORALITY 12
(Brown, 1991; de Waal, 1996). These systems, according to the MFT, have evolutionarily
adaptive underpinnings present in individuals across cultural norms: Care, Fairness,
Loyalty, Authority, and Purity (Graham et al., 2013).
Although MFT was originally developed as an evolutionarily-informed cultural theory
of morality, it has been substantially applied to the study of political ideology, especially in
the U.S. (e.g., Graham, Haidt, & Nosek, 2009; Graham, Nosek, & Haidt, 2012; McAdams
et al., 2008). MFT provides a useful theoretical framework to examine cross-cultural
variations in morality and how dierent cultures show diering patterns of sex
dierentiation in each moral foundation.
Sex Dierences in Moral Judgement
Sex dierences in values and moral judgements have historically been of interest to
psychologists. Countering Kohlberg’s (1969) ‘morality as justice’ framework, Gilligan
(1982) dierentiated male morality from female morality, and famously argued that male
morality is relatively more cognitive and depersonalized than female morality, which is
more aect-laden and care-based. Some empirical studies have supported Gilligan’s theory
(e.g., Gilligan & Attanucci, 1988; Johnston, 1988), whereas others failed to do so (e.g.,
Krebs, Vermeulen, Denton, & Carpendale, 1994; Skoe & Diessner, 1994). Using a
meta-analytic approach, Jaee and Hyde (2000) examined 113 studies and drew the
conclusion that men’s and women’s dierences in the two above-mentioned moral
orientations (i.e., justice and care) were small and often statistically non-significant. This
meta-analysis specifically showed that males were more likely than females to have a justice
orientation to moral judgement, while females were more likely than males to have a care
orientation; however, eect sizes for the sex dierences were found to be small (0.19 < | d|
< 0.28; see Jaee & Hyde, 2000).
More recent empirical research suggests that men have a more “groupish” cognitive
orientation in moral judgements (van Vugt, Hogan, & Kaiser, 2008), possibly enabled and
SEX DIFFERENCES IN MORALITY 13
supported by neurohormonal systems and developmental socialization processes (Eagly &
Wood, 2013; Wood & Eagly, 2013). Some scholars have argued that men tend to view their
selves as independent, whereas women tend to view their selves as interdependent (Cross,
Hardin, & Gercek-Swing, 2011). Thus, it stands to reason to expect adult males to score
higher on group-oriented moral values such as Loyalty and Authority. In addition, research
on sex dierences in sexual attitudes and disgust sensitivity, two constructs that are
strongly related to Purity (Horberg, Oveis, Keltner, & Cohen, 2009), suggests that women
have higher Purity concerns than do men (see Al-Shawaf, Lewis, & Buss, 2018). For
example, compared with men, women are more disgust sensitive (Inbar, Pizarro, Knobe, &
Bloom, 2009), tend to have more restrictive attitudes toward sexual promiscuity (Petersen
& Hyde, 2010; Schmitt, 2003), and engage in fewer “unrestricted” sociosexual behaviors
(e.g., greater number of sex partners). Although considerable debate exists about the
magnitude, consistency, or cause of these dierences (for a review, see Eagly & Wood,
2013; Simpson, Wilson, & Winterheld, 2004), these well-replicated findings in the emotion
literature led us to predict that women would score higher than men on Purity.
Consistent with prior research on sex dierences in moral judgements, we expect
women to score higher on Care, while men would score higher on Fairness based on the
Kohlberg-Gilligan argument with respect to gendered morality. Men, based on their
emphasis on group orientation and social hierarchy are predicted to value Loyalty and
Authority more than women. Finally, based on the literature on sex dierences in sexual
preferences and disgust sensitivity, we hypothesize that women will score higher than men
on Purity. In what follows, we briefly review the role of culture in moral judgements and
how dierent theoretical frameworks account for culturally-variable sex dierences.
Cultural Dierences in Moral Judgement
Most influential theories in moral psychology identify a vital role for cultural
transmission of shared values in predicting moral cognition as well as moral behavior
SEX DIFFERENCES IN MORALITY 14
(Jensen, 2015). Multifaceted psychological measurement of morality has opened the door to
studying cross-cultural similarities and dierences in dimensions of morality across a variety
of content domains. Some domains such as honesty are consistently endorsed as morally
important across cultural contexts (Smith, Smith, & Christopher, 2007), whereas others
such as religious values are substantially dierent across cultures (Saucier et al., 2014).
Studying cultural variations in morality helps us understand not only which behaviors
individuals find morally relevant, but also the extent to which their personal values will be
reflected in their attitudes about social issues in dierent societies. In addition,
cross-cultural research in morality can uncover how women and men dier in terms of their
moral judgements embedded across cultures. In other words, such studies are well-suited to
examine the complex relationships between culture, gender, and moral judgements.
Studies using multifaceted measurement of moral values have shown that while
Western, Educated, Industrialized, Rich, and Democratic (WEIRD; Henrich, Heine, &
Norenzayan, 2010) cultures are generally more inclined to endorse moral codes emphasizing
individual rights and independence, non-WEIRD cultures tend to more strongly moralize
duty-based communal obligations and spiritual purity (Buchtel et al., 2015; Guerra &
Giner-Sorolla, 2010; Sachdeva, Singh, & Medin, 2011). Cultures might be sensitive to the
same types of social oenses, but view them as religious violations rather than moral
violations (Sachdeva et al., 2011). In turn, individuals in autonomy-endorsing cultures view
personal actions such as sexual behaviors as a matter of individual rights, whereas those in
community-endorsing cultures are more likely to see them as a collective moral concern
(Vauclair & Fischer, 2011).
Some studies have examined the role of culture in the magnitude of sex dierences in
morally relevant cognitions, emotions, and behaviors. The most well-known program of
research in this area is concerned with the link between gender (in)equality and the
magnitude of sex dierences across cultures. Schwartz and Rubel (2005) showed that
national indices of gender equality were associated with sex dierences in values, such that
SEX DIFFERENCES IN MORALITY 15
the greater gender equality in a country, the larger the sex dierences (men higher) in
power values and the larger the sex dierences (women higher) in benevolence values. In
other words, in countries where women and men enjoy the same rights and opportunities
across all or most sectors of the society, sex dierences in values become larger. In a recent
study, Falk and Hermle (2018) examined the relationship between sex dierences in six
morally relevant preferences (i.e., altruism, trust, positive reciprocity, negative reciprocity,
risk taking, and patience) across 76 cultures. These authors found that higher levels of
economic development and gender egalitarianism are associated with stronger gender
dierentiation in these preferences. Specifically, in more gender-equal cultures men and
women were more dierent in their preferences ( r = .56). These authors highlighted the
key role of availability of material and social resources, as well as gender-equal access to
resources, in facilitating the expression of sex-dierentiated preferences. In line with these
studies, we predict that global sex dierences in moral judgements varies across cultures,
and that gender (in)equality is associated with culture-wide sex dierences in moral
foundations across cultures. We next consider how the three key theoretical perspectives in
the field — evolutionary psychology, cultural evolution, and social role theory — explain
these findings.
Theories of Culturally-Variable Sex Dierences
Three theoretical frameworks have provided explanations as to why and how sex
dierences vary across cultures: (1) evolutionary psychology (see Buss, 1989; Schmitt,
2015), (2) cultural evolution (see Boyd, Richerson, & Henrich, 2011; Mesoudi, Whiten, &
Laland, 2006), and (3) social role theory (Eagly & Wood, 1999; Wood & Eagly, 2012).
Although researchers have compared (a subset of) these theories — especially evolutionary
psychology vs. social role theory — as competing theories of culturally-variable sex
dierences, they do not always yield conflicting predictions, and share some meta-theoretic
assumptions. Crucially, all three frameworks position themselves broadly as “evolutionary”
SEX DIFFERENCES IN MORALITY 16
theories. For example, Buss (1995) states that “evolutionary psychologists [...] develop
hypotheses about the psychological mechanisms that have evolved in humans” (p. 4),
Mesoudi et al. (2006) suggest that “human culture exhibits key Darwinian evolutionary
properties” (p. 329), and Wood and Eagly (2012) argue that “[their] theory builds on
assumptions about evolutionary pressures on human psychology” (p. 102).
Evolutionary psychology focuses primarily on psychological adaptations, i.e.,
mechanisms of the mind that have evolved to solve recurrent adaptive problems of survival
or reproduction. Such adaptations are in contrast to physiological adaptations, which occur
in the body as a consequence of the environment (see Buss, 2019). Cultural evolution is
characterized as a Darwinian process comprising the selective retention of favourable
culturally transmitted variants, as well as a variety of non-selective processes, such as
“drift” (see Mesoudi et al., 2006). The theory of cultural evolution is distinct from
evolutionary psychology (e.g., Barkow, Cosmides, & Tooby, 1995), which deals with
biologically evolved features of the human mind shaped by genetic (rather than cultural)
inheritance. Finally, social role theory is a social psychological theory that pertains to sex
dierences and similarities in social behavior. The key principle of this theory is that sex
dierences and similarities arise primarily from the distribution of men and women into
social roles within their society. The recent positioning of social role theory within a larger
framework, known as the biosocial construction of sex dierences and similarities, is
“compatible with some prominent evolutionary theories, especially human behavioral
ecology’s recognition that varying socioenvironmental factors shape the costs and benefits
that men and women associate with dierent behaviors (Laland, Odling-Smee, & Myles,
2010; Richerson & Boyd, 2008)” (Wood & Eagly, 2012, p. 103).
With respect to culturally-variable sex dierences, cultural evolutionary theory does
not make specific predictions, but evolutionary psychology and social role theory have
opposite predictions in some domains. Evolutionary psychologists argue that the sexes
dier in the domains in which women and men have faced dierent adaptive problems in
SEX DIFFERENCES IN MORALITY 17
their evolutionary history. In this framework, gender-egalitarian cultures provide an
environment in which women and men are freer to express their evolved predispositions
(Buss, 1995; Schmitt, Realo, Voracek, & Allik, 2008). Cultural evolutionary theory, while
not making strong predictions about how sex dierences change across cultures, suggests
that to the extent that humans evolved cultural forms dependent on individual compliance
with norms, and developed the genetic predisposition to punish norm violators, the
fitness-enhancing value of prosocial tendencies were enhanced (a case of gene-culture
co-evolution). Thus, cultural evolutionary theory would suggest that although biological
sex provides, through genetic inheritance, an “initial guess” with regard to moral
judgements, women and men (as a cultural species) can learn the importance of these moral
values from others after these values have been practiced in a particular ecology across
generations (see Boyd et al., 2011). In opposition to evolutionary psychology research on
sex dierences, and informed by cultural evolutionary theory, Wood and Eagly (2012)
argue that proximal biological and social psychological processes dynamically create sex
dierences through shared beliefs within a society. Gender stereotypes and norms follow
from people’s observations of women and men in their social roles. Since occupational roles
provide important observations, the theory does not predict a domain-general relationship
between gender equality and sex dierences in psychological variables; however, it predicts
convergence of the sexes in gender-equal societies on those variables that are plausibly
consequences of greater gender parity in the division of labor (e.g., mate preferences for
status/resources) (Eagly, Nater, Miller, Kaufmann, & Sczesny, 2019; Eagly & Wood, 1999;
Zentner & Eagly, 2015; also see Zhang, Lee, DeBruine, & Jones, 2019).
Overview of Present Studies
The present research aims to examine sex dierences in moral judgements using a
cultural-nesting perspective, as well as the relationship between cultural (i.e.,
individualism, cultural tightness, English proficiency, WEIRDness), socio-economic (i.e.,
SEX DIFFERENCES IN MORALITY 18
Gini coecient, human development, life satisfaction), gender-related factors (i.e., gender
inequality, gender development, gender gap, and sex ratio), and the magnitude of sex
dierences in moral judgements across cultures. Relying on the MFT framework of moral
pluralism, we investigate culturally-variable sex dierences in Care, Fairness, Loyalty,
Authority, and Purity. A multivariate measure of eect size is also utilized to better
understand the link between global sex dierences in moral judgements and country-level
indices (see Del Giudice, 2019).
Study 1 relies on a large, online sample. In order to address the limitations of online
samples in Study 1 (see Yeager et al., 2011), we use a distinct cross-cultural sample in
Study 2, replicating these findings by secondary analyses of data from 19 countries. In
both studies, we conduct multi-level modeling, treating sex dierences nested across
cultures. In our analyses and theoretical inference, we consider evolutionary psychology
research (e.g., Buss & Schmitt, 2011), cultural evolutionary theory (e.g., Boyd et al., 2011),
and social role theory (e.g., Wood & Eagly, 2012) with respect to sex dierences and
similarities in moral judgements. Furthermore, we address the methodological issues with
previously used approaches in the assessment of culturally-variable sex dierences in moral
judgements as well as replicating and extending them in two high-powered studies.
Study 1
In this Study, we examine sex dierences in the moral foundations, using a large
international data set from 67 countries, incorporating cultural nesting into our statistical
analysis. First, we provide the magnitude and the direction of sex dierences in moral
foundations, as well as multivariate sex dierences across foundations in each culture, and
examine their country-level bivariate association with country-level indices of cultural,
socio-economic, and gender-related factors. Five sets of multi-level models (MLMs) were
SEX DIFFERENCES IN MORALITY 19
conducted to test sex dierences in moral judgements nested in cultures. The hypotheses
1
,
specified in the review of the literature, can be summarized as follows:
Hypothesis 1: Women score higher on Care across cultures.
Hypothesis 2: Men score higher on Fairness across cultures.
Hypothesis 3: Men score higher on Loyalty across cultures.
Hypothesis 4: Men score higher on Authority across cultures.
Hypothesis 5: Women score higher on Purity across cultures.
Hypothesis 6: The magnitude of sex dierences in moral foundations vary across
cultures.
Hypothesis 7: Gender equality predicts the magnitude of sex dierences in moral
foundations across cultures.
Methods
Participants. The data for the current study were collected on an online research
platform (YourMorals.org) for research on moral and political psychology. Participants
were not monetarily compensated, but were given feedback on their moral scores compared
with the larger community. Participants voluntarily visited the website and completed an
array of surveys. A total of 392,617 individuals responded to a set of surveys. For the
purposes of the current study, we pre-processed the data, removing those who did not
1
We pre-registered Hypotheses 1–7 and examination of country-level indices of cultural, socioeconomic,
and gender variables with relation to sex dierences in moral foundations prior to data analysis. However,
our current analyses slightly deviate from the pre-registration, due to performance of additional analyses
and incorporating additional variables in our culture-level analysis, as suggested by the reviewers. The
pre-registration will remain publicly available at
https://osf.io/w3f74/?view_only=4d4ce5ac469240b6b7646dc7cbbbd82d.
SEX DIFFERENCES IN MORALITY 20
complete the Moral Foundations Questionnaire (MFQ; Graham et al., 2009), which is the
central measure of the study. Further, we removed those who did not report their sex. We
kept data from countries for which we had at least 100 participants. Overall, the MFQ
responses and self-reported sex from 336,691 participants from 67 countries were retained
for statistical analysis. The median sample size was 439 per country. Across the countries,
the mean percentage of female participants in samples was 38% (Mdn = 38%, SD = 7%).
Measures. On an individual-level, we collected self-report data on sex and moral
foundations. On the country-level, we collected data on cultural, socioeconomic and gender
variables (see below).
Moral Foundations Questionnaire. All participants in the current sample
completed the MFQ (Graham et al., 2011). Generally, MFQ may be considered the most
widely used measure of moral foundations with adequate reliability and validity in dierent
world regions (Graham et al., 2011). The MFQ consists of two 15-item sections, namely
“Relevance” and “judgements.” The first section was designed to measure the relevance
individuals ascribe to each of the foundations (sample item: “Whether or not someone
conformed to the traditions of society”). Items on the “Relevance” section are rated along
a 6-point Likert-type scale ranging from 0 (Not at all relevant)to5(Extremely relevant).
The second section consists of contextualized items that are designed to gauge actual moral
judgements related to the five moral foundations (sample item: “When the government
makes laws, the number one principle should be ensuring that everyone is treated fairly”).
Items on the “judgements” section are rated along a 6-point Likert-type scale ranging from
0(Strongly disagree)to5(Strongly agree). So, each moral foundation is measured by
averaging 6 items, with possible range of 0 to 6. Across countries, the mean Cronbach’s – s
were .69 (SD = 0.04), .63 (SD = 0.05), .72 (SD = 0.03), .72 (SD = 0.04), and .81 (SD =
0.03) for Care, Fairness, Loyalty, Authority, and Purity, respectively (see Table 1).
Cultural Variables. We collected data on countries’ level of individualism,
English proficiency, cultural looseness, and WEIRD cultural distance.
SEX DIFFERENCES IN MORALITY 21
Individualism. Hofstede and colleagues (for a review see Hofstede & Bond, 1984)
conducted several comprehensive studies of how workplace values are shaped by culture. In
this cultural model of values (Hofstede, 1980, p. 24), culture is regarded as “the collective
programming of the human mind that distinguishes the members of one human group from
those of another.” Hofstede (2010) provided scores on the six value dimensions for 76
countries, partly based on replications and extensions of their main study. It is important
to note that scores on these dimensions are only meaningful when cultures are compared
with each other, hence, individual-level inference is not appropriate. We compiled
country-level data for individualism from Hofstede and colleagues’ online data set. In this
framework, individualism is defined as a preference for a loosely-knit social framework in
which individuals are expected to take care of only themselves and their immediate families,
whereas collectivism represents a preference for a tightly-knit framework in society in which
individuals can expect their relatives or members of a particular ingroup to look after them
in exchange for unquestioning loyalty. Except for one country (Afghanistan), we were able
to collect data on individualism for all cultures in our sample (M = 44.06, SD = 23.77).
English proficiency. As mentioned before, in the current study, the MFQ (Graham
et al., 2011) was administered in English. Although it would have been ideal to measure
participants’ English proficiency using standardized tests, it was practically impossible as
many of these tests take a long time to complete. In order to compensate for that, we used
three country-level indexes of English proficiency (English Proficiency Index [EPI; see
www.ef.edu/epi/], Test of English as a Foreign Language [TOEFL; see
www.ets.org/s/toefl/pdf/94227_unlweb.pdf], and International English Language
Testing System [IELTS; see www.ielts.org]) and averaged their standardized scores
(Cronbach’s – = .88) to achieve an internally consistent measure of country-level English
proficiency.
Cultural looseness. Cultural tightness and looseness (CTL) taps into variance in
norms, values, and behavior. The idea of CTL was first suggested during 1960s, and
SEX DIFFERENCES IN MORALITY 22
developed theoretically since then (Gelfand et al., 2011). If a culture is characterized by
pervasive norms and sanctioning of deviance from norms, it is a tight culture. In a tight
culture, people’s values, norms, and behavior are like one another. Thus, cultural tightness
can be conceptualized as homogeneity in values, norms, and behaviors. We collected
country-level indices of combined CTL from Uz (2015), where larger indices indicate more
loose cultures.
WEIRD cultural distance. The psychological sciences have traditionally over-relied
on samples from Western, Educated, Industrialized, Rich, and Democratic (WEIRD)
nations (Henrich et al., 2010). Muthukrishna et al. (2018) recently developed and validated
country-level cultural distance from the United States as a point of comparison. These
authors’ measure of “WEIRDness” is a robust indicator of cultural distance, grounded in
cultural evolutionary theory. We managed to retrieve 46 available indices for this study,
and transformed all to Z scores.
Socioeconomic Variables. We collected data on human development, overall life
satisfaction, and economic inequality (Gini coecient). Since this study uses an online
sample, we also collected countries’ access to internet (www.internetworldstats.com)to
check the ease and breadth of access to internet in these 67 countries (see Table 1).
Human development. The Human Development Index (HDI) is a summary measure
of achievements in three key dimensions of human development: (1) a long and healthy life,
(2) access to knowledge, and (3) a decent standard of living. The HDI is the geometric
mean of normalized indices for each of the three dimensions. The HDI integrates
country-level life expectancy at birth, expected years of schooling, and gross national
income per capita. A country scores higher HDI when the lifespan is higher, the education
level is higher, and the GDP per capita is higher. Data were collected from United Nations
Development Programme (2016).
Overall life satisfaction. Average response to the Gallup World Poll question,
“Please imagine a ladder, with steps numbered from zero at the bottom to ten at the top.
SEX DIFFERENCES IN MORALITY 23
Suppose we say that the top of the ladder represents the best possible life for you, and the
bottom of the ladder represents the worst possible life for you. On which step of the ladder
would you say you personally feel you stand at this time, assuming that the higher the step
the better you feel about your life, and the lower the step the worse you feel about it?
Which step comes closest to the way you feel?” were gathered by the United Nations. We
collected country-level data from United Nations Development Programme (2016).
Gini coecient. To assess country-level economic inequality, we relied on the Gini
coecient which is a measure of the deviation of the distribution of income among
individuals or households within a country from a perfectly equal distribution. A value of 0
represents absolute equality, whereas a value of 100 absolute inequality. The Gini
coecient measures the inequality among values of a frequency distribution (for example,
levels of income). A Gini coecient of zero expresses perfect equality, where all values are
the same (for example, where everyone has the same income). A Gini coecient of 1 (or
100%) expresses maximal inequality. Given the normalization of both the cumulative
population and the cumulative share of income used to calculate the Gini coecient, the
measure is not overly sensitive to the specifics of the income distribution, but rather only
on how incomes vary relative to the other members of a population. Data were collected
from United Nations Development Programme (2016).
Gender Variables. We collected data on population gender inequality, gender
development, gender gap, and sex ratio.
Gender inequality. The Gender Inequality Index (GII) reflects gender-based
disadvantage in three dimensions (i.e., reproductive health, empowerment, and the labor
market) for as many countries as data of reasonable quality allow. It shows the loss in
potential human development due to inequality between female and male achievements in
these dimensions. It ranges from 0, where women and men fare equally, to 1, where one
gender fares as poorly as possible in all measured dimensions. The GII is computed using
the association-sensitive inequality measure suggested by Seth (2009), which suggests that
SEX DIFFERENCES IN MORALITY 24
the index is based on the general mean of general means of dierent orders – the first
aggregation is by a geometric mean across dimensions; these means, calculated separately
for women and men, are then aggregated using a harmonic mean unction across sexes. We
collected nation-level GIIs from United Nations Development Programme (2016).
Gender development. The Gender Development Index (GDI) measures gender
inequalities in achievement in three basic dimensions of human development: health
(measured by female and male life expectancy at birth), education (measured by female
and male expected years of schooling for children, and female and male mean years of
schooling for adults ages 25 years and older), and relative economic resources (measured by
female and male estimated earned income). We collected nation-level GDIs from United
Nations Development Programme (2016).
Gender Gap. The Gender Gap Index (GGI), published by the World Economic
Forum (see http://reports.weforum.org/global-gender-gap-report-2018/
measuring-the-global-gender-gap/), assesses gender equity in access to resources and
opportunities. This composite index comprises four factors addressing the domains of
economics, education, politics, and health. Each factor contributes equally to a nation’s
gender equity assessment, and the component gender ratios are weighted within each factor
to account for their diering variability (Else-Quest & Grabe, 2012).
Sex ratio. The number of men for each woman in a total population represents the
population-level sex ratio of a society. Publicly available data on national sex ratios in 2016
were retrieved from Central Intelligence Agency (2017) available at https://
www.cia.gov/library/publications/the-world-factbook/fields/2018.html.
Procedure and Data Analysis
Before examining sex dierences in moral judgements across cultures, we aimed to
establish cross-cultural measurement invariance. We used a novel method for
multiple-group confirmatory factor analysis (CFA) to establish measurement invariance in
SEX DIFFERENCES IN MORALITY 25
a large number of groups, referred to as the “alignment” method (Asparouhov & Muthén,
2014). This approach has been developed to address common diculties encountered when
tests for multi-group measurement invariance are based on traditional CFA methods (e.g.,
determining the baseline model) and the number of groups is large. Generally, the central
dierence between the traditional CFA approaches and the alignment method is that the
alignment method did not require specified equality restrictions on the factor loadings and
item intercepts across groups. Our measurement invariance analysis in the current study
relies on the Item-Response Theory application of the “alignment” approach to evaluate
measurement (non)invariance in a large number of groups such as those in the present data
(B. Muthén & Asparouhov, 2014). Our comprehensive results on measurement invariance
analysis are presented in Appendix 1.
Because individuals in a certain country share common influences, the assumption of
independence of observations, required for ordinary regression analysis, is violated. For
such nested structures, multi-level statistical techniques help to distinguish between eects
(here, sex dierences) at the country-level and those at the population-level. Multi-level
models account for the clustered nature of data by directly modeling the clustering with
random coecients (Laird & Ware, 1982). Regression coecients in such models consist of
two types of eects: (1) fixed eect and (2) random eect. Fixed eects are analogous to
regression coecients, and the fixed eect of our predictor (here, sex) represent its
averaged relationship with the outcome (here, moral foundation scores) across countries
(Raudenbush & Bryk, 2002). Random eects capture how much the relation between the
predictor and the outcome diers from the fixed eect estimate (i.e., the average).
Moreover, a multilevel analysis can determine whether the explanatory variable at the
group level serves as a moderator of individual-level relationships (Hox, 2000). Here, we
test country-level gender inequality as country-level moderator in sex dierences across
cultures.
In Study 1, we use frequentist multi-level modeling (MLM) to examine the eect of
SEX DIFFERENCES IN MORALITY 26
sex on moral foundations across cultures. This modeling approach was particularly suited
to our research question because it considered fixed eects of sex but also random eects
(i.e., country specific) of sex. We conducted the analyses using the lme4 package (version
1.1; Bates, Mächler, Bolker, & Walker, 2015) in R programming language (version 3.4.2; R
Core Team, 2017). We report fixed eect estimates with 95% confidence intervals (95%
CI). Visualization of model summaries were generated using the sjPlot R package (version
2.5.0; Lüdecke, 2016). Sex was dummy coded as 0 (female) and 1 (male). We tested our
hypotheses with the following steps: First, we estimated moral foundation scores based on
country (Model0). Second, we estimated moral foundation scores based on sex as a fixed
parameter, the sex dierences were not allowed to vary in across countries, resulting in the
average sex dierence ( Model1). Third, we estimated a random slope model allowing
countries to vary in sex dierences ( Model2). These steps were repeated for each moral
foundation.
With regard to country-level analyses, we were principally interested in the direction
and magnitude of sex dierences in moral foundations across cultures. Within each culture,
Cohen’s ds were calculated (Table 1). We also calculated Mahalanobis’ D which generalizes
Cohen’s d for two or more correlated variables (see Del Giudice, 2013). D, as a multivariate
measure of sex dierences, is the unsigned standardized distance between the centroids of
the two groups, and has the same basic interpretation as Cohen’s d (Del Giudice, 2009). In
order to correct for upward biases in small samples and downward biases inherent in
unreliable measurement, a disattenuated, bias-corrected measure (D
cu
) was calculated for
each country (Del Giudice, 2019). The interpretation of D is essentially the same as that of
d (Del Giudice, 2013), although D cannot take negative values (reflecting the multivariate
nature of the comparison). Pearson correlations were used to examine the relationship
between country-level variables and sex dierences. For each correlation, a 95%CI was
computed based on 10,000 bootstrap iterations. In addition, we accounted for multiple
comparisons and inflation of Type I error by applying false discovery rate (FDR) correction
SEX DIFFERENCES IN MORALITY 27
(Benjamini & Hochberg, 1995). Considering the moderate correlation between
country-level variables and non-independence between some of them, FDR is an
appropriate method to account for multiple comparisons. The threshold for statistical
significance in FDR is based on the observed distribution of the results, so the threshold for
statistical significance cannot be declared a priori. Here, we report FDR-adjusted p-values.
Results and Discussion
Multi-Level Analyses. Before running the multi-level analyses, we tested both
the item factor loadings and item intercepts for all foundations. Our IRT application of the
Alignment method (B. Muthén & Asparouhov, 2014), provided evidence that moral
foundations, as measured by MFQ, can be meaningfully compared across 67 cultures. Item
loadings and intercepts were shown to be approximately invariant across groups and
non-invariance indexes (Care: 6.8%, Fairness: 23.6%, Loyalty: 10.9%, Authority: 8.0%, and
Purity: 8.0%) were all below the recommended 25% cuto proposed by B. Muthén and
Asparouhov (2014). Comprehensive results for establishing measurement invariance for all
67 cultures are presented in Appendix 1.
In this section, we summarize the results of five MLMs for five moral foundations
across 67 cultures (see Hypotheses 1-7). The multi-level modeling results for Care,
Fairness, Loyalty, Authority, and Purity are presented in Table 2. The intraclass
correlation coecients (ICCs) were .02, .03, .07, .09, and .12, indicating that 2%, 3%, 7%,
9%, and 12% of the variance in Care, Fairness, Loyalty, Authority, and Purity were at the
country-level, respectively. Since ICC quantifies the proportion of variance explained by a
grouping (random) factor in hierarchical data, it can be inferred that 2%, 3%, 7%, 9%, and
12% of the variance in Care, Fairness, Loyalty, Authority, and Purity are attributable to
country membership, before including sex in the model. It can be seen that sex dierences
in Care, Fairness, and Purity are relatively uniform across cultures and country-level gender
inequality does not moderate sex dierences in dierent moral judgements. The magnitude
SEX DIFFERENCES IN MORALITY 28
of sex dierences for Loyalty and Authority are mixed and highly variable across cultures.
Sex dierences were female-favoring for Care (see Figure 1) and statistically
significant, with an estimated dierence of 0.45 scores across countries. We calculated
conditionalR
2
just based on culture (R
2
c
) and then based on culture and sex together (R
2
cs
)
(Nakagawa, Johnson, & Schielzeth, 2017). For Care, culture explained 2.3% of the variance
(R
2
cs
= 11.4%). Taiwan and Japan showed the smallest coecients while Poland and
Russia showed the largest sex eects. The model for Fairness is visualized in Figure 2. All
sex dierences for Fairness were negative (women higher than men; R
2
c
= 3.3%; R
2
cs
=
5.1%). Belgium and Japan showed the smallest coecients while Sweden and Germany
showed the largest sex eects. The model for Loyalty is visualized in Figure 3. As can be
seen, most sex coecients were non-significant ( R
2
c
= 6.5%; R
2
cs
= 6.8%). The United
States and Australia showed the largest (positive) coecients while China and Vietnam
showed the largest (negative) eects. The model for Authority is visualized in Figure 4
where most sex dierences were statistically non-significant ( R
2
c
= 8.8%; R
2
cs
= 8.8%).
Finland and Poland showed the largest (positive) coecients. Philippines and Nigeria
showed the largest (negative) eects in magnitude. The model for Purity is visualized in
Figure 5 where all sex coecients were negative in direction, indicating a uniform
female-favoring sex dierence ( R
2
c
= 11.6%; R
2
cs
= 11.7%). France and Iceland showed the
smallest sex dierences, whereas Singapore and Kenya showed the largest dierences.
Above-mentioned sex dierences in moral foundations provide support for Hypotheses
1 and 5, but not Hypotheses 2-4. Results suggested that the sex dierences varied across
countries as Model2 (with random slopes) fitted the data better than Model1 (without
random slopes) for Care (p < .001), Fairness (p = .002), Loyalty (p < .001), Authority (p
< .001), but not for Purity (p = .062). Therefore, Hypothesis 6 was supported for four
foundations. This indicates higher universality for sex dierences in Purity and larger
between-country variation in sex dierences in other foundations
2
.
2
Some of the items in MFQ are substantially dierent from others in the same subscale, possibly obscuring
SEX DIFFERENCES IN MORALITY 29
Country-Level Analyses. We examined the correlation coecients between
country-level sex dierences and mean scores of men and women for all foundations
3
.We
focused on the eect sizes (Pearson’s r), and identified suggestive correlations (i.e., p < .05;
Benjamin et al., 2018). Care’s sex-dierence eect was positively correlated with women’s
(r = .49, p < .001) and inversely correlated with men’s (r = -.48, p < .001) average scores.
Fairness’s sex dierence was correlated with women’s ( r = .35, p < .01) and men’s (r =
-.29, p = .02) average scores. Loyalty’s sex dierence was correlated with women’s ( r =
.49, p < .001) average scores, but not men’s (r = .004, p = .98). Authority’s sex dierence
was significantly correlated with women’s (r = .46, p = .001) average scores, but not men’s
(r = .07, p = .56). Finally, Purity’s sex dierence was correlated with women’s ( r = .39, p
= .001) average scores, but not men’s (r = .08, p = .53). Using the Fisher’s r-to-z
transformation, we calculated z values to assess the significance of the dierence between
mentioned pairs of correlations (between men and women). Results suggested that the
correlations for Care (z = 5.99, p < .001), Fairness (z = 3.76, p < .001), and Loyalty (z =
3.01, p = .003) were dierent from each other; however, those of Authority ( z = 2.42, p =
.02) and Purity (z = 1.88, p = .06) were not. These correlational results indicate that the
source of sex dierences in Care, Fairness, and Loyalty may be more attributable to
variation of women’s and men’s scores, but more so to women’s. Moreover, sex dierences
in binding values (i.e., Loyalty, Authority, and Purity) are only correlated with variation in
sex dierences in particular items. Such dierences might be, in part, responsible for relatively low internal
consistency coecients in some subscales shown in Table 1. In order to examine potentially masked
item-level sex dierences, we conducted 30 distinct multi-level analyses for MFQ items, presented in
Appendix 2. Details of the item-analysis along with the code we used are available in Appendix 2, but
generally, the results were in line with foundation-level analyses.
3
We conducted these analyses for countries with 50% or higher access to internet based on internet
penetration estimates provided by https://www.internetworldstats.com (see Table 1). The results did
not change substantially since most countries had high access to internet (M = 71.5%, Mdn = 74.8%, SD
= 19.8%).
SEX DIFFERENCES IN MORALITY 30
women’s average scores across cultures.
Further, we calculated two other composite sex-dierence indexes: General Sex
Dierence Index (GSDI) and Mahalanobis’ D. For calculating GSDI, we used the average
of sex dierentiation on five moral foundations (see Equation 1, where d represents
univariate eect size for each foundation). But GSDI has limitations, if women score higher
on one foundation and men score higher on the second one with a similar magnitude, GSDI
would return zero as a measure of general sex dierence. In order to address this issue, we
calculated Mahalanobis’ D (and its 95%CI based on 10,000 bootstrap iterations), which
estimates the size of global (i.e., multivariate) sex dierences (see Equation 2, where D
represents multivariate eect size, m
M
andm
F
represent vestors of male/female means, and
S represents pooled covariance matrix). As expected, the correlation coecient between
these two sex-dierence variables (GSDI and D) was high, r (N = 67) = .61, p < .001.
Since D can overestimate sex dierences in small samples and underestimate them when
using unreliable measurements, we corrected for both biases by calculating disattenuated,
bias-corrected dierence, known as D
cu
(Del Giudice, 2019). Across 67 cultures, D
cu
was
large in size, M = 0.76, Mdn = 0.76, SD = 0.21.
GSDI =
d
C
+d
F
+d
L
+d
A
+d
P
5
(1)
D =
Ò
(m
M
≠ m
F
)
T
S
≠ 1
(m
M
≠ m
F
) (2)
We examined the correlation coecients between cultural, socioeconomic,
gender-related variables, and sex dierences in moral foundations, GSDI, and D
cu
(see
Table 3). For each correlation, 95%CI based on 10,000 bootstrap iterations were computed.
The results of this analysis are summarized in Table 3. Global sex dierences in moral
judgements (D
cu
) were larger in more individualist (r = .35, 95%CI [.17, .52],
FDR-adjusted p = .002), WEIRD (r = .32, 95%CI [.07, .53], FDR-adjusted p = .035),
English-proficient (r = .27, 95%CI [.08, .45], FDR-adjusted p = .023), and gender-equal
SEX DIFFERENCES IN MORALITY 31
(higher gender parity; r = .27, 95%CI [.09, .46], FDR-adjusted p = .020) cultures. In terms
of sex dierences in specific moral foundations, Care’s sex-dierence eect size was larger
in more individualist (r = .30, 95%CI [.11, .46], FDR-adjusted p = .020), English-proficient
(r = .29, 95%CI [.11, .45], FDR-adjusted p = .012), WEIRD (r = .34, 95%CI [.10, .57],
FDR-adjusted p = .025), gender-equal (higher gender parity; r = .29, 95%CI [.10, .47],
FDR-adjusted p = .015), and female-biased (in terms of sex ration in the population; r =
-.22, 95%CI [-.44, -.11], FDR-adjusted p = .030) cultures. Sex dierences in other moral
foundations were not strongly associated with culture-level indices (see Table 3).
Study 2
We designed Study 2 to replicate our results using a large, distinct data set collected
using translated versions of the MFQ in 19 countries. Notably, this data set is completely
independent of the source of data in Study 1. Specifically, Study 1 had two caveats: (a) the
MFQ was administered in English across cultures, and (b) the data were collected on the
internet using a crowd-sourcing website. In Study 1, all participants completed an English
version of the survey which can cause cultural equivalence issues (an English sentence can
have dierent meanings for people from dierent linguistic backgrounds) as well as
confound actual sex dierences. For example, those who access and complete the MFQ in
English, in a non-Western non-English speaking country are more likely to be somewhat
fluent in English and to be closer to Western cultures since language is a very important
component of cultural learning and the process of learning English as a second language
can expose those individuals to Western values (Imai, Kanero, & Masuda, 2016).
Moreover, completing a task in English can prime participants with a “Western mindset”
as language can activate dierent mindsets (Lee, Oyserman, & Bond, 2010; Oyserman,
2017). In addition, all participants in Study 1 completed the measures on a website
dedicated to moral and political psychological research, which raises concerns about
potential selection biases (Yeager et al., 2011).
SEX DIFFERENCES IN MORALITY 32
In Study 2, we replicate the findings in Study 1 in an independent data set, using
Bayesian inference. The primary research goal was to infer plausible parameter dierences
in moral foundations across culture. We also aimed to take into account the information
from Study 1 to strengthen our inference in Study 2. Hence, we used Bayesian estimation
(see McElreath, 2016), replicating and complementing our frequentist modeling in Study
1
4
. Bayesian inference oers researchers a number of practical benefits for our research
questions (see Wagenmakers, Morey, & Lee, 2016), including (1) intuitive probability
statements about the magnitude of sex dierences, (2) the ability to meaningfully compare
the relative credibility of dierent estimates, (3) incorporate our prior findings about sex
dierences across cultures, and (4) a posterior distribution describing the relative
plausibility that dierent population-level sex dierences could have produced the observed
data set. Bayesian estimation allows us to infer the degree to which dierent potential
pooled and country-level sex dierences in moral foundations could have plausibly
produced the existing cross-cultural data. Here, we synthesize Study 1’s results into our
Bayesian multi-level modeling in Study 2 to get more precise inferences of country-level
eects and cross-country variations in sex dierences in moral foundations. Our
hypotheses
5
, based on the preceding findings in Study 1, regarding multi-level analyses can
be summarized as follows (see Table 6):
Hypothesis 8: Women score higher on Care across cultures.
Hypothesis 9: Women score higher on Fairness across cultures.
Hypothesis 10: Women score higher on Purity across cultures.
4
We also conducted all these analyses in Study 2 using frequentist multi-level models, as in Study 1,
available on Appendix 3 for comparison purposes. Notably, the results are convergent.
5
We pre-registered Hypotheses 8–11 based on Study 1’s results. However, our analyses deviate from the
pre-registration, due to performance of new analyses and incorporating additional variables in our
culture-level analysis, as suggested by the reviewers. The pre-registration will remain publicly available at
https://osf.io/u7tse/?view_only=bbfa5b1809b443f1a45ae7b7b451dee8.
SEX DIFFERENCES IN MORALITY 33
Hypothesis 11: The magnitude of sex dierences in moral foundations vary across
cultures.
Methods
Participants. We conducted a secondary analysis on data from 11,969 participants
in 19 countries: Australia (n = 1,115; Crone, Bode, Murawski, & Laham, 2018), Belgium
(n = 493; Quintelier, Ishii, Weeden, Kurzban, & Braeckman, 2013), China (n = 393; Ji &
Janicke, 2018), France (n = 213; Métayer & Pahlavan, 2014), Hungary (n = 403; Hadarics
& Kende, 2017), Iran (n = 501; Atari & Dehghani, 2018), Japan (n = 534; Konishi, Oe,
Shimizu, Tanaka, & Ohtsubo, 2017), South Korea (n = 478; Kim, Kang, & Yun, 2012),
Latvia (n = 264; Dimdins, Sandgren, & Montgomery, 2016), Mongolia (n = 444; Sychev,
Bespalov, Prudnikova, & Vlasov, 2016), the Netherlands (n = 285; Quintelier et al., 2013),
Poland (n = 1,702; Jarmakowski-Kostrzanowski & Jarmakowska-Kostrzanowska, 2016),
Russia (n = 468; Sychev et al., 2016), Serbia (n = 240; MeedoviÊ & PetroviÊ, 2016;
Pecujlija, Misic-Pavkov, & Popovic, 2015), Spain (n = 240; Rodríguez-Ferreiro & Barberia,
2017), Sweden (n = 2,068; Jansson & Dorrepaal, 2015, Nilsson & Erlandsson, 2015),
Turkey (n = 1,465; Yilmaz, Harma, Bahçekapili, & Cesur, 2016), United Kingdom (n =
273; van Leeuwen, Dukes, Tybur, & Park, 2017), and United States (n = 390; Amazon
Mechanical Turk). The median sample size was 444. Across the countries, the mean
percentage of female participants in samples was 63% (Mdn = 62%, SD = 10%).
Participants in each of the above samples completed the MFQ-30 and self-reported their
biological sex. In dierent sites, dierent accompanying questionnaires were completed by
the participants as well as the MFQ.
Measures. All participants completed a local-language version of the MFQ-30 in 19
countries. These translations, and their reliability and validity, have been reported in detail
in previous reports. Country-level indices were retrieved from sources cited in Study 1.
Procedure and Data Analysis. The current cross-cultural data set is
hierarchically structured because individuals are nested within countries, as in Study 1. A
SEX DIFFERENCES IN MORALITY 34
Bayesian model typically describes not only our uncertainty in the value of an outcome
variable, conditional on some unknown parameter(s), but also our a priori uncertainty
about the parameter(s) themselves. The aim is to update the beliefs about the parameters
(e.g., the coecients in a regression model) using our model and data.
Since priors are an important part of Bayesian inference, it might be useful to
delineate few points about them in the current study. To fit a Bayesian (multi-level)
model, we need to specify a prior distribution on each parameter; these priors are in fact
our prior knowledge about the possible values that these parameters can have. It is
possible to specify completely “uninformative” priors, such as flat priors, but these are far
from ideal since they concentrate too much probability mass outside of reasonable posterior
values. Specifying uninformative priors can have the consequence that without suciently
large data sets, such priors will dominate in determining the posterior distribution
(Gelman, 2006). Yet, this is usually the case in many psychological studies, and can be
useful particularly in exploratory works where prior knowledge is not available or
controversial. Priors that give some minimal amount of information can improve inference
and are called regularizing or “weakly informative” priors (see Gelman, Jakulin, Pittau, &
Su, 2008). An “informative” prior expresses specific information about parameters. The
use of “informative” prior distributions explicitly acknowledges that the analysis is based
on more than the immediate data in hand and includes a considered judgement concerning
plausible values of the parameters based on researcher’s information. These informative
priors could come from actual prior experiments (the posterior from previous experiments)
or from meta-analyses (Vasishth, Chen, Li, & Guo, 2013), or from expert judgements. Such
a use of priors could be a powerful tool for incorporating prior knowledge into a new
analysis. Here, we used informative priors based on Study 1, taking into account our
previous findings in the new Bayesian analysis.
The results of a Bayesian analysis are posterior distributions, that is, distributions
showing the relative probabilities of each possible value of the parameters, conditional on
SEX DIFFERENCES IN MORALITY 35
the data, the prior distribution, and the model. Every parameter of the model (fixed
eects, random eects, shape of the distribution) will have a posterior distribution. We
used the rstanarm package (version 2.17.4; Gabry & Goodrich, 2017) in R to run
multi-level models and draw samples from the posteriors that we can use for inference. We
also report Credible Intervals for the parameters. A Credible Interval (CrI) indicates the
range within which we can be certain with a particular probability that the “true value” of
a parameter lies. It is common to interpret the evidence as strong if zero lies outside the
95% CrI (Kruschke, Aguinis, & Joo, 2012). Convergence was assessed visually using trace
plots, and through
ˆ
R statistic (with
ˆ
R< 1.05 suggesting convergence; Gelman, Carlin,
Stern, & Rubin, 2013). Our informative priors were based on our multi-level models in
Study 1. In the Bayesian two-level models, the eect of sex was allowed to vary across
cultures. Based on our findings in Study 1, we used informative priors for intercepts and
coecients, and weakly informative priors for their covariance matrix. We also report
posterior predictive checking (PPC) which is the process of simulating data based on the
fitted model and comparing the simulations to the observed data set in order to look for
substantial discrepancies.
With regard to country-level analysis (n=19),wereportBayesfactors(BFs). Bayes
factors provide a numerical value that quantifies how well a hypothesis predicts the
empirical data relative to a competing hypothesis. The Bayes factor provides several
advantages, particularly in that it allows the researcher to make a statement about the
alternative hypothesis, rather than just the null hypothesis. In addition, it provides a
clearer estimate of the amount of evidence present in the data. Bayes factors between 1
and 3 were taken to indicate positive, but weak evidence, BFs between 3 and 10 were taken
to indicate strong evidence, and BFs above 10 were taken to indicate very strong and
decisive evidence. Finally, 95% Credible Intervals (95% CrI) were also reported.
SEX DIFFERENCES IN MORALITY 36
Results and Discussion
Multi-Level Analyses. In this part, we report results of five Bayesian MLMs for
five moral foundations across 19 countries (see Hypotheses 8-11). In multi-level analyses,
the intercept priors were set to a normal distribution based on estimates found in Study 1.
In order to ensure convergence of the Monte Carlo chains, we used 4000 iterations. As
suggested by Gelman et al. (2013), we visually checked the trace plots for all estimates and
confirmed convergence in all chains. For the two-level model, the trace plots of the MCMC
chains (not shown here) exhibited good mixing and showed no convergence problems.
PPCs based on 100 generated data for each foundation are presented in Figure 7. Table 4
shows all parameter estimates from the two-level models. With the these models, we can
more precisely describe how the “average country” would show sex dierences in moral
concerns, as well as how countries tend to dier from that average. Evidence for (or
against) all hypotheses are presented in Table 6. As can be seen, we successfully replicated
three female-favoring eects in Care, Fairness, and Purity, thus Hypotheses 8-10 were
supported. The magnitude of sex dierences in moral foundations did vary across cultures,
hence supporting Hypothesis 11.
Country-Level Analyses. Across 19 countries, women consistently reported
higher scores on Care, Fairness, and Purity. Yet, sex dierences in Loyalty and Authority
were mixed in direction and smaller in magnitude. Specifically, the distribution of
sex-dierence eect sizes suggested that women scored higher on Care ( Md = 0.40, SD =
0.19), Fairness (Md = 0.20, SD = 0.13), and Purity (Md = 0.10, SD = 0.15). Results of
sex dierences in Loyalty ( Md = -0.01, SD = 0.12) and Authority (Md = -0.04, SD = 0.17)
suggested highly culturally variable and negligible eect sizes. The global sex dierence
measure (D
cu
) was large in size, Mdn = 0.84, SD = 0.53.
Bayesian inferential statistics of the correlations (and their 95%CrI) between
country-level cultural, socio-economic, gender factors, and sex dierences in moral
foundations are presented in Table 5. Global sex dierences in moral judgements ( D
cu
)
SEX DIFFERENCES IN MORALITY 37
were larger in cultures with higher levels of individualism (r = .59, 95% CrI [.14, .81], BF
10
= 6.26), cultural looseness (r = .68, 95% CrI [.24, .86], BF
10
= 15.44), English proficiency
(r = .75, 95% CrI [.40, .89], BF
10
= 159.42), economic equality (r = -.55, 95% CrI [-.79,
-.10], BF
10
= 4.14), human development (r = .53, 95% CrI [.08, .77], BF
10
= 3.55), life
satisfaction (r = .66, 95% CrI [.26, .84], BF
10
= 23.66), and gender parity (r = .64, 95%
CrI [.22, .83], BF
10
= 15.86). The associations between global sex dierences in moral
judgements and individualism, English proficiency, and gender parity fully replicate Study
1’s findings. Sex dierences in specific moral foundations were not as strongly correlated
with culture-level indices (see Table 5).
General Discussion
Given the pressing need for more conclusive empirical study of sex dierences in moral
judgements, we examined women’s and men’s moral judgements using a high-powered
design, and also investigated country-level correlates of sex dierences in moral judgements
in two consecutive studies. The current research is the first large-scale, cross-cultural
investigation to empirically test multivariate sex dierences in moral judgements nested
within cultures. In Study 1, we examined the role of sex in moral judgements in 67 cultures
using a large, online sample. Further, in our country-level analysis, we examined the role of
country-level cultural, socio-economic, and gender-related indices in the magnitude of sex
dierences in moral foundations across cultures. In Study 2, we replicated these findings
across 19 countries, using a completely independent data set from locally administered,
translated versions of the MFQ. Further, country-level results in Study 2 successfully
replicated the country-level eects found in Study 1 on an exploratory basis.
At the broadest level, Study 1 had three major findings: (1) three moral foundations
of Care, Fairness, and Purity show systematic sex dierences across cultures, with women
scoring higher in all three cases; (2) in more collectivist, non-WEIRD, and male-biased
(higher sex ratio) cultures, sex dierences in Care become smaller; and (3) sex dierences
SEX DIFFERENCES IN MORALITY 38
in Loyalty and Authority are quite variable across cultures. Relying on multivariate sex
dierences (i.e., Mahalanobis’ D, see Del Giudice, 2019) in moral judgements, the present
multivariate eect sizes were found to be substantially larger than previously estimated sex
dierences in moral judgements (e.g., Jaee & Hyde, 2000; Schwartz & Rubel, 2005) and
the median eect size in individual dierences research (Gignac & Szodorai, 2016). These
multivariate eect sizes of sex dierences were substantially larger in individualist,
English-proficient, and gender-equal nations.
Study 2 largely replicated our findings in Study 1. In particular, (1) women scored
reliably higher than men on Care, Fairness, and Purity; (2) sex dierences in Care and
Purity were substantially smaller in collectivist and male-biased (higher sex ratio) cultures;
and (3) sex dierences in Loyalty and Authority are quite variable across cultures. These
replicated findings support the notion that in more egalitarian Western (or Westernized)
cultures, women and men diverge in their Care concerns; and that in societies where the
number of men for each woman is higher, sex dierences in morality (particularly Care)
drop substantially (Schwartz & Rubel-Lifschitz, 2009).
Implications for Moral Psychology
The current research has at least three pragmatic implications for moral psychology.
First, it is noteworthy that Care and Purity are largely sex-dierentiated across cultures.
Women, regardless of culture, score higher than men on care/harm concerns which is
aligned with findings of Schwartz and Rubel (2005) on cultural and sex dierences in
benevolence. Evolutionarily-informed research suggests that emotional connectedness and
caring for kin likely began in the context of parental care long before Homo Sapiens
evolved (MacLean, 1985). Purity (and disgust) also has a strong evolutionary history in
our species. Many scholars theorize that disgust sensitivity has been shaped by the
adaptive challenges imposed by pathogens over evolutionary history (Oaten, Stevenson, &
Case, 2009). Despite individual and cultural variation in specific elicitors and responses to
SEX DIFFERENCES IN MORALITY 39
disgust, the human disgust system exhibits universal features that resemble homologous
systems in other animals (Curtis, De Barra, & Aunger, 2011). Disgust research has
demonstrated a robust sex dierence: women have substantially higher levels of disgust
sensitivity than men (e.g., Al-Shawaf et al., 2018). This well-established eect holds for
both self-report and behavioral data (Rozin, Haidt, McCauley, Dunlop, & Ashmore, 1999)
and for both trait-level and state-level responses to disgusting stimuli (see Al-Shawaf et al.,
2018; Sparks, Fessler, Chan, Ashokkumar, & Holbrook, 2018).
The second implication pertains to the cultural nesting of (sex dierences in) moral
values. There is a relatively well-developed literature on sex dierences in personality traits
in dierent cultures, but such a literature is only emerging in moral psychology. For
example, research on sex dierences in personal values has shown that women report
higher levels of benevolence and universalism values, whereas men report higher levels of
power, achievement, and hedonism values (Schwartz & Rubel-Lifschitz, 2009).
Consistently, women tend to be more prosocial and less negatively reciprocal than men
(Falk & Hermle, 2018). Schwartz and Rubel-Lifschitz (2009) reported that both women
and men value benevolence more in nations with more gender egalitarianism. However, the
eects of gender egalitarianism on women were stronger, leading to larger sex dierences in
nations with more egalitarian gender roles, gender socialization, and sociopolitical gender
equity. Similar findings were observed for universalism, power, achievement and hedonism.
According to Schwartz and Rubel-Lifschitz (2009, p. 171), gender egalitarianism in a
culture tends to “permit both sexes to pursue more freely the values they inherently care
about more.” These conclusions mirror findings of Charles and Bradley (2009) and Falk
and Hermle (2018) who documented that in more gender-egalitarian countries individuals
value self-expression, thus women and men “independently express gender-specific
preferences” (Falk & Hermle, 2018, p. 5).
The third implication of the present work for moral psychology pertains to the
measurement of moral judgements. The MFQ is the most widely used self-report tool for
SEX DIFFERENCES IN MORALITY 40
assessment of moral foundations, but there is room for improvement of this measurement
tool. For example, internal consistency coecients (see Appendix I & II) in some cases did
not reach the conventional thresholds. It is also impossible to tease apart what kind of
Fairness MFQ measures in dierent countries (equity vs. equality). There is also a dearth
of research on validity and reliability of the MFQ in non-WEIRD cultures. Although our
psychometric analyses provided some evidence for MFQ’s cross-cultural measurement
invariance, future research can fully address the mentioned measurement issues, hence
improving our understanding of moral foundations across cultures. Furthermore, examining
and appending other “foundations” to MFT is an active line of inquiry. For instance, in the
original conception of the MFT, concerns about liberty (or freedom) were not theorized,
but Iyer, Koleva, Graham, Ditto, and Haidt (2012) collected data on libertarians and
developed a questionnaire for measurement of libertarian values in the style of the MFQ. It
can be a good next step to expand the theoretical lens of the MFT, develop
psychometrically robust measures, and examine potential sex dierences across cultures
using novel measures of moral judgement.
Implications for the Origin of Sex Dierences
Across present studies, women systematically scored higher in Care, Fairness, and
Purity; however, sex dierences in Loyalty and Authority were small in size and quite
variable across cultural contexts. These culturally-variable sex dierences in moral
foundations have implications for the origin of sex dierences in psychological variables.
First, the magnitude of sex dierences, operationalized by multivariate (or global)
dierence known as Mahalanobis’ D (Del Giudice, 2019), was larger than previously
thought, typically relying on univariate eect size, Cohen’s d (e.g., Jaee & Hyde, 2000;
Schwartz & Rubel, 2005). Second, these eects are considerably variable across cultural
contexts, thus mono-cultural studies in research on sex dierences can be misleading. For
example, by looking at sex dierences in Loyalty in the U.S. vs. China, one would reach
SEX DIFFERENCES IN MORALITY 41
opposite conclusions. Third, these findings can be used to empirically compare (and refine)
theoretical perspectives on culturally-variable sex dierences, hence contributing to a
cumulative science of psychology of gender.
Women’s higher emphasis on Care and Purity judgements may be related to their
parental care systems and disgust sensitivity, extensively researched in evolutionary
psychology (Al-Shawaf et al., 2018; de Waal, 1996). Gender role theory would also expect
uniformity of sex dierences in care (Wood & Eagly, 2002, 2012) as women continue to
occupy more nurturing social roles (e.g., taking care of young children) and people may
infer that women are higher on Care. However, our findings regarding sex dierences in
Loyalty and Authority (i.e., negligible in size and highly variable across cultures) are not
fully consistent with evolutionary psychology’s traditional account of men’s emphasis on
“groupish” traits, desire for status, ingroup loyalty, and leadership (Boehm, 2009; van Vugt
et al., 2008). In case of Loyalty and Authority (which show large cultural variability in sex
dierences, from men scoring higher than women, to no dierence, to women scoring higher
than men), cultural evolution can be the key driving force which accounts for the diversity
of cultural norms among populations. Cultural evolution is typically “faster” than
biological evolution and can be spread in a population in very few generations. It has been
suggested that the legal and political systems that govern societies are themselves
outcomes of cultural evolution (Ayala, 2010; Norenzayan et al., 2016), as it has eventuated
over human history. Future research can examine how cultural evolutionary variables (e.g.,
history of violent conflict, kinship network, exposure to religion) can aect moral
judgements and their sex dierences across cultures.
Cultural variation of sex dierences based on cultural, socio-economic, and
gender-related variables further supports social role theorists’ argument that “biology or
culture alone cannot account for human sex dierences and similarities” (Wood & Eagly,
2013). With regard to the relationship between gender equality and sex dierences, Wood
and Eagly (2012) argued that these correlations may depend on the comparison standards
SEX DIFFERENCES IN MORALITY 42
(or reference groups) against which participants compare themselves. Specifically,
individuals may compare themselves against same-sex individuals in gender-unequal
societies (because of higher gender segregation) which can disguise sex dierences in such
contexts. In more gender-egalitarian societies, gender segregation is lower, hence it is more
likely that individuals compare themselves with the broader population consisting of both
sexes, hence increasing sex dierences in measures without a clear comparison group (e.g.,
MFQ; Graham et al., 2011). In cases where there is a clear comparison group (e.g.,
heterosexual mate preferences, promiscuity), higher country-level gender equality is
associated with smaller sex dierences in such variables (Eagly & Wood, 2013; Zentner &
Eagly, 2015). Our data cannot directly test this prediction, however, it is highly encouraged
for future research to account for reference of social comparisons in cross-cultural research
on sex dierences in morality. Notably, social role theory has explicitly incorporated
cultural evolutionary components into the theory (see Eagly & Wood, 2017), advocating
that “biological characteristics aect the ecient performance of many activities in society,
they underlie central tendencies in the division of labor” (Wood & Eagly, 2012, p. 56).
Thus, cultural evolutionary theory can provide a unifying theoretical framework for
understanding culturally-variable sex dierences in moral judgements, taking into account
genetic dispositions (as “initial guesses” of moral judgements) and cultural contexts (which
can enable and support genetic dispositions) (Boyd et al., 2011).
Current findings suggest that the “origin” of sex dierences in complex phenomena
such as morality is more complicated than simply “choosing” evolutionary psychology,
cultural evolution, or social role theory. Although cultural evolution theorists have
provided interesting insights about the interaction of cultures and genes and their
co-evolution, more empirical research is warranted to shed light on actual origin of sex
dierences in moral judgements (e.g., cultural transmission of morality to women and
men). However, one general, but accurate proposition is that sexes do dier in some key
moral systems and these dierences depend on ecological and cultural contexts. Consistent
SEX DIFFERENCES IN MORALITY 43
with Falk and Hermle (2018), we found that more developed and gender-equal societies
(e.g., Iceland, Norway, Sweden) have more access to resources which facilitates
gender-specific preferences. Indeed, empirical findings can be substantially influenced by
what is being measured and how we define constructs of interest. More nuanced conceptual
definitions of moral foundations, better measurement tools, and representative sampling
are good next steps to more reliably understand how women and men judge behaviors in
the social world as good/virtuous or evil/immoral.
Concluding Remarks
Are women and men dierent in their moral judgements? Do sex dierences in
morality vary across cultures? Previous research on culturally-variable sex dierences in
morality has been relatively sparse, and the few cross-cultural studies that do exist have
drawn small samples with inadequate statistical power to capture cross-sex and
cross-cultural dierences in moral judgements. Our two studies were designed to address
many of these shortcomings. We collected individual-level data using the most widely used
operationalization of Moral Foundations Theory (i.e., Care, Fairness, Loyalty, Authority,
and Purity), and collected country-level data from independent international organizations.
We tested hypotheses using both frequentist (Study 1) and Bayesian (Study 2) multi-level
models to draw a comprehensive picture of sex dierences in five moral foundations in a
large number of cultures. We found that women scored higher than men on Care, Fairness,
and Purity across cultures. The sex dierences in these moral judgements seem to be
replicable and robust across cultures. On the other hand, sex dierences in Loyalty and
Authority were quite variable across cultures. Finally, global sex dierences in moral
judgements were particularly larger in more individualist, English-proficient, and
gender-equal cultures. A refined integration of cross-cultural evolutionary psychology (see
Apicella & Barrett, 2016), cultural evolutionary theory (see Mesoudi et al., 2006), and
social role theory (see Wood & Eagly, 2012) can best characterize and explain
SEX DIFFERENCES IN MORALITY 44
culturally-variable sex dierences in the moral domain. Methodologically, our frequentist
Study 1, complemented by probabilistic statements generated using informative priors in
Bayesian analyses in Study 2 can be regarded as a new systematic model for investigating
and replicating sex dierences in psychological attributes across cultures.
SEX DIFFERENCES IN MORALITY 45
References
Al-Shawaf, L., Lewis, D. M., & Buss, D. M. (2018). Sex dierences in disgust: Why are
women more easily disgusted than men? Emotion Review, 10, 149–160. doi:
10.1177/1754073917709940
Apicella, C. L., & Barrett, H. C. (2016). Cross-cultural evolutionary psychology. Current
Opinion in Psychology, 7,92–97.
Asparouhov, T., & Muthén, B. (2014). Multiple-group factor analysis alignment.
Structural Equation Modeling: A Multidisciplinary Journal, 21, 495–508. doi:
10.1080/10705511.2014.919210
Atari, M., & Dehghani, M. (2018). Moral foundations theory in Iran. Manuscript in
preparation.
Ayala, F. J. (2010). The dierence of being human: Morality. Proceedings of the National
Academy of Sciences, 107, 9015–9022. doi: 10.1073/pnas.0914616107
Barkow, J. H., Cosmides, L., & Tooby, J. (1995). The adapted mind: Evolutionary
psychology and the generation of culture. Oxford University Press, USA.
Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015). Fitting linear mixed-eects models
using lme4. Journal of Statistical Software, 67, 1-48. doi: 10.18637/jss.v067.i01
Baumeister, R. F. (2010). Is there anything good about men?: How cultures flourish by
exploiting men. New York, NY: Oxford University Press.
Benjamin, D. J., Berger, J. O., Johannesson, M., Nosek, B. A., Wagenmakers, E.-J., Berk,
R., ... others (2018). Redefine statistical significance. Nature Human Behaviour, 2,
6–10. doi: 10.1038/s41562-017-0189-z
Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: A practical
and powerful approach to multiple testing. Journal of the Royal Statistical Society:
Series B, 57, 289–300. doi: 10.1111/j.2517-6161.1995.tb02031.x
Boehm, C. (2009). Hierarchy in the forest: The evolution of egalitarian behavior.
Cambridge, MA: Harvard University Press.
SEX DIFFERENCES IN MORALITY 46
Boyd, R., Richerson, P. J., & Henrich, J. (2011). The cultural niche: Why social learning
is essential for human adaptation. Proceedings of the National Academy of Sciences,
108, 10918–10925. doi: 10.1073/pnas.1100290108
Brown, D. E. (1991). Human universals. Philadelphia, PA: Temple University Press.
Buchtel, E. E., Guan, Y., Peng, Q., Su, Y., Sang, B., Chen, S. X., & Bond, M. H. (2015).
Immorality east and west: Are immoral behaviors especially harmful, or especially
uncivilized? Personality and Social Psychology Bulletin, 41, 1382–1394. doi:
10.1177/0146167215595606
Buss, D. M. (1989). Sex dierences in human mate preferences: Evolutionary hypotheses
tested in 37 cultures. Behavioral and Brain Sciences, 12, 1–14. doi:
10.1017/s0140525x00023992
Buss, D. M. (1995). Evolutionary psychology: A new paradigm for psychological science.
Psychological Inquiry, 6,1–30.
Buss, D. M. (2019). Evolutionary psychology: The new science of the mind (6th ed.). New
York, NY: Routledge.
Buss, D. M., & Schmitt, D. P. (2011). Evolutionary psychology and feminism. Sex Roles,
64, 768–787. doi: 10.1007/s11199-011-9987-3
Byrne, B. M., & van de Vijve, F. J. (2017). The maximum likelihood alignment approach
to testing for approximate measurement invariance: A paradigmatic cross-cultural
application. Psicothema, 29, 539–551. doi: 10.7334/psicothema2017.178
Central Intelligence Agency. (2017). The world factbook. Retrieved from https://
www.cia.gov/library/publications/the-world-factbook/fields/2018.html
Charles, M., & Bradley, K. (2009). Indulging our gendered selves? sex segregation by field
of study in 44 countries. American Journal of Sociology, 114, 924–976. doi:
10.1086/595942
SEX DIFFERENCES IN MORALITY 47
Chen, F. F. (2007). Sensitivity of goodness of fit indexes to lack of measurement
invariance. Structural Equation Modeling: A Multidisciplinary Journal, 14,464–504.
doi: 10.1080/10705510701301834
Christov-Moore, L., Simpson, E. A., Coudé, G., Grigaityte, K., Iacoboni, M., & Ferrari,
P. F. (2014). Empathy: Gender eects in brain and behavior. Neuroscience &
Biobehavioral Reviews, 46, 604–627. doi: 10.1016/j.neubiorev.2014.09.001
Crone, D. L., Bode, S., Murawski, C., & Laham, S. M. (2018). The socio-moral image
database (smid): A novel stimulus set for the study of social, moral and aective
processes. PloS One, 13, e0190954. doi: 10.1371/journal.pone.0190954
Cross, S. E., Hardin, E. E., & Gercek-Swing, B. (2011). The what, how, why, and where of
self-construal. Personality and Social Psychology Review, 15, 142–179. doi:
10.1177/1088868310373752
Curtis, V., De Barra, M., & Aunger, R. (2011). Disgust as an adaptive system for disease
avoidance behaviour. Philosophical Transactions of the Royal Society of London B:
Biological Sciences, 366, 389–401. doi: 10.1098/rstb.2010.0117
Del Giudice, M. (2009). On the real magnitude of psychological sex dierences.
Evolutionary Psychology, 7,147470490900700209.
Del Giudice, M. (2013). Multivariate misgivings: Is D a valid measure of group and sex
dierences? Evolutionary Psychology, 11,147470491301100511.
Del Giudice, M. (2019). Measuring sex dierences and similarities. In D. P. VanderLaan &
W. I. Wong (Eds.), Gender and sexuality development: Contemporary theory and
research. New York, NY: Springer.
de Waal, F. B. M. (1996). Good natured: The origins of right and wrong in humans and
other animals. Cambridge, MA: Harvard University Press.
SEX DIFFERENCES IN MORALITY 48
Dimdins, G., Sandgren, M., & Montgomery, H. (2016). Psychological variables underlying
political orientations in an old and a new democracy: A comparative study between
sweden and latvia. Scandinavian Journal of Psychology, 57, 437–445. doi:
10.1111/sjop.12314
Eagly, A. H., Nater, C., Miller, D. I., Kaufmann, M., & Sczesny, S. (2019). Gender
stereotypes have changed: A cross-temporal meta-analysis of us public opinion polls
from 1946 to 2018. American Psychologist. doi: 10.1037/amp0000494
Eagly, A. H., & Wood, W. (1999). The origins of sex dierences in human behavior:
Evolved dispositions versus social roles. American Psychologist, 54, 408–423. doi:
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(3),
340–357.
Eagly, A. H., & Wood, W. (2017). Gender identity: Nature and nurture working together.
Evolutionary Studies in Imaginative Culture, 1, 59–62. doi: 10.26613/esic/1.1.10
Else-Quest, N. M., & Grabe, S. (2012). The political is personal: Measurement and
application of nation-level indicators of gender equity in psychological research.
Psychology of Women Quarterly, 36,131–144.
Falk, A., & Hermle, J. (2018). Relationship of gender dierences in preferences to
economic development and gender equality. Science, 362, eaas9899. doi:
10.1126/science.aas9899
Fiske, A. P. (1992). The four elementary forms of sociality: framework for a unified theory
of social relations. Psychological Review, 99, 689–723. doi: 10.1037/0033-295X.99.4
Gabry, J., & Goodrich, B. (2017). rstanarm: Bayesian applied regression modeling via
stan. r package version 2.15.3.. Retrieved from http://mc-stan.org/rstanarm/
SEX DIFFERENCES IN MORALITY 49
Gelfand, M. J., Raver, J. L., Nishii, L., Leslie, L. M., Lun, J., Lim, B. C., ... Yamaguchi,
S. (2011). Dierences between tight and loose cultures: A 33-nation study. Science,
332, 1100–1104. doi: 10.1126/science.1197754
Gelman, A. (2006). Prior distributions for variance parameters in hierarchical models
(comment on article by browne and draper). Bayesian Analysis, 1, 515–534. doi:
10.1214/06-ba117a
Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2013). Bayesian data analysis (3rd
ed.). Boca Raton, FL: Chapman and Hall/CRC.
Gelman, A., Jakulin, A., Pittau, M. G., & Su, Y.-S. (2008). A weakly informative default
prior distribution for logistic and other regression models. The Annals of Applied
Statistics, 2, 1360–1383. doi: 10.1214/08-aoas191
Gignac, G. E., & Szodorai, E. T. (2016). Eect size guidelines for individual dierences
researchers. Personality and Individual Dierences , 102,74–78.
Gilligan, C. (1982). In a dierent voice . Harvard University Press, Cambridge.
Gilligan, C., & Attanucci, J. (1988). Two moral orientations: Gender dierences and
similarities. Merrill-Palmer Quarterly, 34, 223–237. doi: 10.2307/23086381
Graham, J., Haidt, J., Koleva, S., Motyl, M., Iyer, R., Wojcik, S. P., & Ditto, P. H. (2013).
Moral foundations theory: The pragmatic validity of moral pluralism. Advances in
Experimental Social Psychology, 47, 55–130. doi:
10.1016/B978-0-12-407236-7.00002-4
Graham, J., Haidt, J., & Nosek, B. A. (2009). Liberals and conservatives rely on dierent
sets of moral foundations. Journal of Personality and Social Psychology, 96,
1029–1046. doi: 10.1037/a0015141
Graham, J., Nosek, B. A., & Haidt, J. (2012). The moral stereotypes of liberals and
conservatives: Exaggeration of dierences across the political spectrum. PloS one,
7(12), e50092.
SEX DIFFERENCES IN MORALITY 50
Graham, J., Nosek, B. A., Haidt, J., Iyer, R., Koleva, S., & Ditto, P. H. (2011). Mapping
the moral domain. Journal of Personality and Social Psychology, 101, 366–385. doi:
10.1037/a0021847
Gray, K., Waytz, A., & Young, L. (2012). The moral dyad: A fundamental template
unifying moral judgment. Psychological Inquiry, 23, 206–215. doi:
10.1080/1047840X.2012.686247
Grijalva, E., Newman, D. A., Tay, L., Donnellan, M. B., Harms, P. D., Robins, R. W., &
Yan, T. (2015). Gender dierences in narcissism: A meta-analytic review.
Psychological Bulletin, 141, 261–310. doi: 10.1037/a0038231
Guerra, V. M., & Giner-Sorolla, R. (2010). The community, autonomy, and divinity scale
(cads): A new tool for the cross-cultural study of morality. Journal of Cross-Cultural
Psychology, 41, 35–50. doi: 10.1177/0022022109348919
Hadarics, M., & Kende, A. (2017). A closer look at intergroup threat within the dual
process model framework: The mediating role of moral foundations. Psychological
Thought, 10, 167–177. doi: 10.5964/psyct.v10i1.210
Haidt, J., & Graham, J. (2007). When morality opposes justice: Conservatives have moral
intuitions that liberals may not recognize. Social Justice Research, 20, 98–116. doi:
10.1007/s11211-007-0034-z
Haidt, J., & Joseph, C. (2004). Intuitive ethics: How innately prepared intuitions generate
culturally variable virtues. Daedalus, 133, 55–66. doi: 10.1162/0011526042365555
Henrich, J., Heine, S. J., & Norenzayan, A. (2010). Most people are not weird. Nature,
466, 29. doi: 10.1038/466029a
Hofstede, G. (1980). Culture and organizations. International Studies of Management &
Organization, 10, 15–41. doi: 10.1080/00208825.1980.11656300
Hofstede, G. (2010). National cultural dimensions. Retrieved from
https://www.hofstede-insights.com/country-comparison/
SEX DIFFERENCES IN MORALITY 51
Hofstede, G., & Bond, M. H. (1984). Hofstede’s culture dimensions: An independent
validation using rokeach’s value survey. Journal of Cross-Cultural Psychology, 15,
417–433. doi: 10.1177/0022002184015004003
Horberg, E. J., Oveis, C., Keltner, D., & Cohen, A. B. (2009). Disgust and the
moralization of purity. Journal of Personality and Social Psychology, 97,963–976.
doi: 10.1037/a0017423
Hox, J. J. (2000). Multilevel analyses of grouped and longitudinal data. In T. D. Little,
K. U. Schnabel, & J. Baumert (Eds.), Modeling longitudinal and multilevel data (pp.
14–30). New Jersey, NJ: Lawrence Erlbaum Associates.
Hu, L.-t., & Bentler, P. M. (1999). Cuto criteria for fit indexes in covariance structure
analysis: Conventional criteria versus new alternatives. Structural Equation
Modeling: A Multidisciplinary Journal, 6, 1–55. doi: 10.1080/10705519909540118
Imai, M., Kanero, J., & Masuda, T. (2016). The relation between language, culture, and
thought. Current Opinion in Psychology, 8, 70–77. doi: 10.1016/j.copsyc.2015.10.011
Inbar, Y., Pizarro, D. A., Knobe, J., & Bloom, P. (2009). Disgust sensitivity predicts
intuitive disapproval of gays. Emotion, 9, 435–439. doi: 10.1037/a0015960
Iyer, R., Koleva, S., Graham, J., Ditto, P., & Haidt, J. (2012). Understanding libertarian
morality: The psychological dispositions of self-identified libertarians. PLoS One, 7,
e42366. doi: 10.1371/journal.pone.0042366
Jaee, S., & Hyde, J. S. (2000). Gender dierences in moral orientation: A meta-analysis.
Psychological Bulletin, 126, 703–726. doi: 10.1037/0033-2909.126.5.703
Jansson, J., & Dorrepaal, E. (2015). Personal norms for dealing with climate change:
results from a survey using moral foundations theory. Sustainable Development, 23,
381–395. doi: 10.1002/sd.1598
SEX DIFFERENCES IN MORALITY 52
Jarmakowski-Kostrzanowski, T. A., & Jarmakowska-Kostrzanowska, L. (2016). Polska
adaptacja kwestionariusza kodów moralnych (mfq-pl). Psychologia Spoleczna, 11,
289–508.
Jensen, L. A. (2015). Moral development in a global world: Research from a
cultural-developmental perspective. Cambridge, United Kingdom: Cambridge
University Press.
Ji, Q., & Janicke, S. H. (2018). Culture, political orientation, and moral foundations: A
comparison between china and the us. China Media Research, 14,62-75.
Johnston, D. K. (1988). Adolescents’ solutions to dilemmas in fables: Two moral
orientations–two problem solving strategies. In C. Gilligan, J. V. Ward, J. M. Taylor,
& B. Bardige (Eds.), Mapping the moral domain: A contribution to psychological
theory and education (pp. 49–69). Cambridge, MA: Harvard University Press.
Kim, K. R., Kang, J.-S., & Yun, S. (2012). Moral intuitions and political orientation:
Similarities and dierences between south korea and the united states. Psychological
Reports, 111, 173–185. doi: 10.2466/17.09.21.PR0.111.4.173-185
Kohlberg, L. (1969). Stage and sequence: The cognitive-developmental approach to
socialization. In D. A. Goslin (Ed.), Handbook of socialization theory and research
(pp. 347–480). Chicago, IL: Rand McNally.
Kohlberg, L. (1981). Essays on moral development vol. 1 the philosophy of moral
development. San Francisco, CA: Harper Harper & Row.
Konishi, N., Oe, T., Shimizu, H., Tanaka, K., & Ohtsubo, Y. (2017). Perceived shared
condemnation intensifies punitive moral emotions. Scientific Reports, 7, 7289. doi:
10.1038/s41598-017-07916-z
Krebs, D. L., Vermeulen, S. C., Denton, K. L., & Carpendale, J. I. (1994). Gender and
perspective dierences in moral judgement and moral orientation. Journal of Moral
Education, 23, 17–26. doi: 10.1080/0305724940230102
SEX DIFFERENCES IN MORALITY 53
Kruschke, J. K. (2010). What to believe: Bayesian methods for data analysis. Trends in
Cognitive Sciences, 14, 293–300. doi: 10.1016/j.tics.2010.05.001
Kruschke, J. K., Aguinis, H., & Joo, H. (2012). The time has come: Bayesian methods for
data analysis in the organizational sciences. Organizational Research Methods, 15,
722–752. doi: 10.1177/1094428112457829
Laird, N. M., & Ware, J. H. (1982). Random-eects models for longitudinal data.
Biometrics, 963–974. doi: 10.2307/2529876
Laland, K. N., Odling-Smee, J., & Myles, S. (2010). How culture shaped the human
genome: bringing genetics and the human sciences together. Nature Reviews
Genetics, 11(2), 137.
Lee, S. W., Oyserman, D., & Bond, M. H. (2010). Am i doing better than you? that
depends on whether you ask me in english or chinese: Self-enhancement eects of
language as a cultural mindset prime. Journal of Experimental Social Psychology, 46,
785–791.
Lüdecke, D. (2016). sjplot: Data visualization for statistics in social science.. Retrieved
from http://CRAN.R-project.org/package!sjPlot
MacLean, P. D. (1985). Brain evolution relating to family, play, and the separation call.
Archives of General Psychiatry, 42, 405–417. doi:
10.1001/archpsyc.1985.01790270095011
McAdams, D. P., Albaugh, M., Farber, E., Daniels, J., Logan, R. L., & Olson, B. (2008).
Family metaphors and moral intuitions: How conservatives and liberals narrate their
lives. Journal of Personality and Social Psychology, 95, 978–990. doi:
10.1037/a0012650
McElreath, R. (2016). Statistical rethinking: A bayesian course with examples in r and
stan. Boca Raton, FL: Chapman & Hall/CRC Press.
SEX DIFFERENCES IN MORALITY 54
MeedoviÊ, J., & PetroviÊ, B. (2016). Can there be an immoral morality?: Dark
personality traits as predictors of moral foundations. Psihologija, 49, 185–197. doi:
10.2298/PSI1602185M
Mesoudi, A., Whiten, A., & Laland, K. N. (2006). Towards a unified science of cultural
evolution. Behavioral and brain sciences, 29(4), 329–347.
Métayer, S., & Pahlavan, F. (2014). Validation de l’adaptation française du questionnaire
des principes moraux fondateurs. Revue Internationale de Psychologie Sociale, 27,
79–107.
Munck, I., Barber, C., & Torney-Purta, J. (2018). Measurement invariance in comparing
attitudes toward immigrants among youth across europe in 1999 and 2009: The
alignment method applied to iea cived and iccs. Sociological Methods & Research, 47,
687–728. doi: 10.1177/0049124117729691
Muthén, B., & Asparouhov, T. (2014). IRT studies of many groups: The alignment
method. Frontiers in Psychology, 5, 978. doi: 10.3389/fpsyg.2014.00978
Muthén, L., & Muthén, B. (2017). 1998–2017 mplus user’s guide. (Los Angeles, CA:
Muthén & Muthén)
Muthukrishna, M., Bell, A., Henrich, J., Curtin, C., Gedranovich, A., McInerney, J., &
Thue, B. (2018). Beyond WEIRD psychology: Measuring and mapping scales of
cultural and psychological distance. SSRN Electronic Journal. Retrieved from
https://doi.org/10.2139%2Fssrn.3259613 doi: 10.2139/ssrn.3259613
Nakagawa, S., Johnson, P. C., & Schielzeth, H. (2017). The coecient of determination r 2
and intra-class correlation coecient from generalized linear mixed-eects models
revisited and expanded. Journal of the Royal Society Interface, 14, 20170213. doi:
10.1098/rsif.2017.0213
Narvaez, D., Gleason, T., Mitchell, C., & Bentley, J. (1999). Moral theme comprehension
in children. Journal of Educational Psychology, 91, 477–487. doi:
10.1037/0022-0663.91.3.477
SEX DIFFERENCES IN MORALITY 55
Nilsson, A., & Erlandsson, A. (2015). The moral foundations taxonomy: Structural
validity and relation to political ideology in sweden. Personality and Individual
Dierences , 76, 28–32. doi: 10.1016/j.paid.2014.11.049
Norenzayan, A., Shari, A. F., Gervais, W. M., Willard, A. K., McNamara, R. A.,
Slingerland, E., & Henrich, J. (2016). The cultural evolution of prosocial religions.
Behavioral and brain sciences, 39, e1. doi: 10.1017/S0140525X14001356
Oaten, M., Stevenson, R. J., & Case, T. I. (2009). Disgust as a disease-avoidance
mechanism. Psychological Bulletin, 135, 303–377. doi: 10.1037/a0014823
Oyserman, D. (2017). Culture three ways: Culture and subcultures within countries.
Annual Review of Psychology, 68, 435–463. doi:
10.1146/annurev-psych-122414-033617
Pecujlija, M., Misic-Pavkov, G., & Popovic, M. (2015). Personality and blood types
revisited: Case of morality. Neuroethics, 8, 171–176. doi: 10.1007/s12152-014-9220-5
Petersen, J. L., & Hyde, J. S. (2010). A meta-analytic review of research on gender
dierences in sexuality, 1993–2007. Psychological Bulletin, 136, 21–38. doi:
10.1037/a0017504
Purzycki, B. G., Pisor, A. C., Apicella, C., Atkinson, Q., Cohen, E., Henrich, J., ... others
(2018). The cognitive and cultural foundations of moral behavior. Evolution and
Human Behavior, 39(5), 490–501.
Quintelier, K. J., Ishii, K., Weeden, J., Kurzban, R., & Braeckman, J. (2013). Individual
dierences in reproductive strategy are related to views about recreational drug use
in belgium, the netherlands, and japan. Human Nature, 24, 196–217. doi:
10.1007/s12110-013-9165-0
R Core Team. (2017). R: A language and environment for statistical computing. r
foundation for statistical computing, version 3.5.1. Retrieved from
https://www.R-project.org/.
SEX DIFFERENCES IN MORALITY 56
Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and
data analysis methods (2nd ed.). Thousand Oaks, CA: Sage Publication.
Richerson, P. J., & Boyd, R. (2008). Not by genes alone: How culture transformed human
evolution. University of Chicago press.
Rodríguez-Ferreiro, J., & Barberia, I. (2017). The moral foundations of illusory correlation.
PloS One, 12, e0185758. doi: 10.1371/journal.pone.0185758
Rozin, P., Haidt, J., McCauley, C., Dunlop, L., & Ashmore, M. (1999). Individual
dierences in disgust sensitivity: Comparisons and evaluations of paper-and-pencil
versus behavioral measures. Journal of Research in Personality, 33, 330–351. doi:
10.1006/jrpe.1999.2251
Sachdeva, S., Singh, P., & Medin, D. (2011). Culture and the quest for universal principles
in moral reasoning. International Journal of Psychology, 46, 161–176. doi:
10.1080/00207594.2011.568486
Saucier, G., Kenner, J., Iurino, K., Malham, P. B., Chen, Z., Thalmayer, A. G., ...
Altschul, C. (2014). Cross-cultural dierences in a global “survey of world views”.
Journal of Cross-Cultural Psychology, 46, 53–70. doi: 10.1177/0022022114551791
Schmitt, D. P. (2003). Universal sex dierences in the desire for sexual variety: Tests from
52 nations, 6 continents, and 13 islands. Journal of Personality and Social
Psychology, 85, 85–104. doi: 10.1037/0022-3514.85.1.85
Schmitt, D. P. (2015). The evolution of culturally-variable sex dierences: Men and women
are not always dierent, but when they are... it appears not to result from patriarchy
or sex role socialization. In V. A. Weekes-Shackelford & T. K. Shackelford (Eds.),
The evolution of sexuality (pp. 221–256). New York, NY: Springer.
Schmitt, D. P., Realo, A., Voracek, M., & Allik, J. (2008). Why can’t a man be more like a
woman? sex dierences in big five personality traits across 55 cultures. Journal of
Personality and Social Psychology, 94, 168–182. doi: 10.1037/0022-3514.94.1.168
SEX DIFFERENCES IN MORALITY 57
Schwartz, S. H., & Bilsky, W. (1990). Toward a theory of the universal content and
structure of values: Extensions and cross-cultural replications. Journal of Personality
and Social Psychology, 58, 878–891. doi: 10.1037/0022-3514.58.5.878
Schwartz, S. H., & Rubel, T. (2005). Sex dierences in value priorities: cross-cultural and
multimethod studies. Journal of Personality and Social Psychology, 89,1010–1028.
doi: 10.1037/0022-3514.89.6.1010
Schwartz, S. H., & Rubel-Lifschitz, T. (2009). Cross-national variation in the size of sex
dierences in values: Eects of gender equality. Journal of Personality and Social
Psychology, 97, 171–187. doi: 10.1037/a0015546
Seth, S. (2009). Inequality, interactions, and human development. Journal of Human
Development and Capabilities, 10, 375–396. doi: 10.1080/19452820903048878
Shweder, R. A., Much, N., Mahapatra, M., & Park, L. (1997). The “big three” of morality
(autonomy, community and divinity) and the “big three” explanations of suering. In
A. Brandt & P. Rozin (Eds.), Morality and health (pp. 119–169). Florence, KY:
Taylor & Frances/Routledge.
Simpson, J. A., Wilson, C. L., & Winterheld, H. A. (2004). Sociosexuality and romantic
relationships. In J. H. Harvey, A. Wenzel, & S. Sprecher (Eds.), Handbook of
sexuality in close relationships (pp. 87–112). Mahwah, NJ: Erlbaum.
Sivan, J., Curry, O. S., & Van Lissa, C. J. (2018). Excavating the foundations: Cognitive
adaptations for multiple moral domains. Evolutionary Psychological Science,1–12.
doi: 10.1007/s40806-018-0154-8
Skoe, E. E., & Diessner, R. (1994). Ethic of care, justice, identity, and gender: An
extension and replication. Merrill-Palmer Quarterly, 40, 272–289. doi:
10.1023/A:1020224512888
Smith, K. D., Smith, S. T., & Christopher, J. C. (2007). What defines the good person?
cross-cultural comparisons of experts’ models with lay prototypes. Journal of
Cross-Cultural Psychology, 38, 333–360. doi: 10.1177/0022022107300279
SEX DIFFERENCES IN MORALITY 58
Sparks, A. M., Fessler, D. M., Chan, K. Q., Ashokkumar, A., & Holbrook, C. (2018).
Disgust as a mechanism for decision making under risk: Illuminating sex dierences
and individual risk-taking correlates of disgust propensity. Emotion. (Advance online
publication) doi: 10.1037/emo0000389
Sychev, O., Bespalov, A., Prudnikova, M., & Vlasov, M. (2016). Features of moral
foundations in mongol, german and russian adolescents. Cultural-Historical
Psychology, 12, 85–96. doi: 10.17759/chp.2016120109
Tomasello, M., & Vaish, A. (2013). Origins of human cooperation and morality. Annual
Review of Psychology, 64, 231–255. doi: 10.1146/annurev-psych-113011-143812
United Nations Development Programme. (2016). Human development report. Retrieved
from http://hdr.undp.org/en/
Uz, I. (2015). The index of cultural tightness and looseness among 68 countries. Journal of
Cross-Cultural Psychology, 46, 319–335. doi: 10.1177/0022022114563611
Vandenberg, R. J., & Lance, C. E. (2000). A review and synthesis of the measurement
invariance literature: Suggestions, practices, and recommendations for organizational
research. Organizational Research Methods, 3, 4–70. doi: 10.1177/109442810031002
van Leeuwen, F., Dukes, A., Tybur, J. M., & Park, J. H. (2017). Disgust sensitivity relates
to moral foundations independent of political ideology. Evolutionary Behavioral
Sciences, 11, 92–98. doi: 10.1037/ebs0000075
van Vugt, M., Hogan, R., & Kaiser, R. B. (2008). Leadership, followership, and evolution:
Some lessons from the past. American Psychologist, 63, 182–196. doi:
10.1037/0003-066X.63.3.182
Vasishth, S., Chen, Z., Li, Q., & Guo, G. (2013). Processing chinese relative clauses:
Evidence for the subject-relative advantage. PloS One, 8, e77006. doi:
10.1371/journal.pone.0077006
SEX DIFFERENCES IN MORALITY 59
Vauclair, C.-M., & Fischer, R. (2011). Do cultural values predict individuals’ moral
attitudes? a cross-cultural multilevel approach. European Journal of Social
Psychology, 41, 645–657. doi: 10.1002/ejsp.794
Wagenmakers, E.-J., Morey, R. D., & Lee, M. D. (2016). Bayesian benefits for the
pragmatic researcher. Current Directions in Psychological Science, 25, 169–176. doi:
10.1177/0963721416643289
Wood, W., & Eagly, A. H. (2002). A cross-cultural analysis of the behavior of women and
men: Implications for the origins of sex dierences. Psychological Bulletin, 128,
699–727. doi: 10.1037/0033-2909.128.5.699
Wood, W., & Eagly, A. H. (2012). Biosocial construction of sex dierences and similarities
in behavior. Advances in Experimental Social Psychology, 46, 55–123. doi:
10.1016/B978-0-12-394281-4.00002-7
Wood, W., & Eagly, A. H. (2013). Biology or culture alone cannot account for human sex
dierences and similarities. Psychological Inquiry, 24(3), 241–247.
Yeager, D. S., Krosnick, J. A., Chang, L., Javitz, H. S., Levendusky, M. S., Simpser, A., &
Wang, R. (2011). Comparing the accuracy of rdd telephone surveys and internet
surveys conducted with probability and non-probability samples. Public Opinion
Quarterly, 75,709–747.
Yilmaz, O., Harma, M., Bahçekapili, H. G., & Cesur, S. (2016). Validation of the moral
foundations questionnaire in turkey and its relation to cultural schemas of
individualism and collectivism. Personality and Individual Dierences , 99,149–154.
doi: 10.1016/j.paid.2016.04.090
Yoon, M., & Lai, M. H. (2018). Testing factorial invariance with unbalanced samples.
Structural Equation Modeling: A Multidisciplinary Journal, 25, 201–213. doi:
10.1080/10705511.2017.1387859
SEX DIFFERENCES IN MORALITY 60
Zentner, M., & Eagly, A. H. (2015). A sociocultural framework for understanding partner
preferences of women and men: Integration of concepts and evidence. European
Review of Social Psychology, 26,328–373.
Zhang, L., Lee, A. J., DeBruine, L. M., & Jones, B. C. (2019). Are sex dierences in
preferences for physical attractiveness and good earning capacity in potential mates
smaller in countries with greater gender equality? Evolutionary Psychology, 17,
1474704919852921.
MEASUREMENT INVARIANCE 61
Supplemental Material
Appendix 1: Measurement invariance using maximum likelihood alignment method
Mohammad Atari
University of Southern California
MEASUREMENT INVARIANCE 62
Abstract
Most of the empirical research on sex dierences and cultural variations in morality has
relied on within-culture analyses, or small cross-cultural data, comparing few cultures. To
further broaden the scientific understanding of sex dierences in morality, the current
research relies on two international samples to provide the first large-scale examination of
sex dierences in moral judgements nested within cultures. Using a sample from 67
countries (Study 1; n = 336,691), we found culturally-variable sex dierences in moral
judgements, as conceptualized by the Moral Foundations Theory. Women consistently
scored higher than men on Care, Fairness, and Purity. In contrast, sex dierences in
Loyalty and Authority were negligible and highly variable across cultures. We also
examined country-level sex dierences in moral judgements in relation to cultural,
socio-economic, and gender-equality indicators. In Study 2 (19 countries; n = 11,969), we
largely replicated these results using Bayesian multi-level modeling in a distinct sample.
Specifically, women consistently showed higher concerns for Care, Fairness, and Purity in
their moral judgements than did men. We discuss the implications of these findings on the
ongoing debate about the origin of sex dierences and cultural variations in moral
judgements as well as theoretical and pragmatic implications for moral psychology.
Keywords: morality, gender, sex dierences, culture, Bayesian inference.
MEASUREMENT INVARIANCE 63
Appendix 1: Measurement invariance using maximum likelihood alignment method
Introduction
Asparouhov and Muthén (2014) presented a novel method for multiple-group
confirmatory factor analysis (CFA) to establish measurement invariance in a large number
of groups, referred to as the “alignment” method. This approach has been developed to
address common diculties encountered when tests for multi-group measurement
invariance are based on traditional CFA methods (e.g., determining the baseline model)
and the number of groups is large, especially when sample sizes are unbalanced (see Yoon
& Lai, 2018). Generally, the central dierence between the traditional CFA approaches and
the alignment method is that the alignment method does not require specified equality
restrictions of both the factor loadings and item intercepts across groups. The alignment
method begins with a common configural model with no consideration of group baseline
models, and then automates the closeness of the factor loading estimates in the process of
establishing the most optimal measurement invariance pattern, which substantially
simplifies establishing measurement invariance in large groups.
Indeed, a major strength of the alignment method is that it automates and
substantially simplifies tests for measurement invariance across a large number of groups.
Byrne and van de Vijve (2017) suggested that the alignment method can be particularly
advantageous when the groups represent countries wherein noninvariance is expected to be
large due to cultural dierences as existing methods for establishing invariance are not
practical for handling such complex data. Recent research has shown that the alignment
approach to establishing measurement invariance is quite feasible, even when the number of
groups is large as 92 countries (Munck, Barber, & Torney-Purta, 2018).
MEASUREMENT INVARIANCE 64
Alignment analysis follows a sequence of steps. First, a starting set of invariant
groups is identified. This procedure involves the assessment of every factor loading and
item intercept parameter in the model. That is, given P Parameters and G Groups, there
will be P◊ G factor loading and P◊ G item intercepts. Then, for each of these
parameters, a set of groups is identified for which the parameter is noninvariant. Based on
the conduct of pairwise tests for each pair of groups, two groups are then connected if the
p-value obtained by this comparison is larger than .01 (Asparouhov & Muthén, 2014).
Second, from these comparisons, the largest connected set for this parameter is determined
and then serves as the starting set of groups. Third, the starting set is then modified such
that: (a) the average parameter for the current invariance set is computed, and (b) for each
group in this set, a test of significance is conducted to compare the parameter value for
each group with the average value computed for the current invariance set. A new group is
added to the invariant set if the p-value is larger than .001; if the p-value is smaller than
.001, the group is removed from the invariant set. Finally, this process is repeated until the
invariant set stabilizes. That is, no groups are either added to, or removed from the
invariant set.
To account for the categorical nature of the items (i.e., 6-point scales), in the present
Appendix, we apply an Item-Response Theory (IRT, with the robust diagonally weighted
least square estimation, or WLSMV) approach to the alignment method (B. Muthén &
Asparouhov, 2014) to examine cross-country measurement invariance in Care, Fairness,
Loyalty, Authority, and Purity. As suggested by B. Muthén and Asparouhov (2014), this is
a particularly powerful and appropriate method to establsih approximate measurement
invariance since we expect the noninvariance to be potentially due to cultural dierences.
We conduct five independent analyses for dierent dimensions of the MFQ (Graham et al.,
2009), as discussed in the main document.
MEASUREMENT INVARIANCE 65
Methods
As mentioned in the main document, the present data were collected on an online
research platform for research on moral and political psychology. A total of 392,617
individuals responded to a set of surveys. For the purposes of the current analysis, we
pre-processed the data, removing those who did not complete the MFQ (Graham et al.,
2009), arriving at five data sets for Care (6 items), Fairness (6 items), Loyalty (6 items),
Authority (6 items), and Purity (6 items). According to our pre-registration, we kept data
from countries for each we have at least 100 participants. Overall, the MFQ responses and
self-reported sex from 336,691 participants from 67 countries were retained for the
alignment analysis (see Table 7).
All analyses were conducted using the Mplus 8.2 (L. Muthén & Muthén, 2017). We
used the maximum likelihood procedure as suggested by Byrne and van de Vijve (2017)
and Asparouhov and Muthén (2014). We relied on 25% cutpoint proposed by B. Muthén
and Asparouhov (2014) in providing a reasonable rule of thumb for determining the
trustworthiness of latent mean estimates derived from alignment results.
Since MFQ items are rated on a 6-point Likert scale, rather than just one intercept
for each item, there are five “thresholds” for each item to be evaluated for approximate
invariance across groups. We report the results for all items’ thresholds and average the
five thresholds invariance per item. With regard to loadings, we report all loadings for all
items. Finally we compare our results to the the 25% cutpoint proposed by (B. Muthén &
Asparouhov, 2014) for determining the trustworthiness of latent mean estimates derived
from alignment analysis and meaningfulness of the comparisons.
MEASUREMENT INVARIANCE 66
In order to establish measurement invariance across sexes, we conducted 67
Multi-Group CFA for Care, Fairness, Loyalty, Authority, and Purity. In each run, we
estimated fit indices for three models: (1) configural invariance (across groups, the pattern
of fixed and free parameters is equivalent); (2) Weak factorial invariance (across groups,
corresponding factor loadings are fixed); and (3) Strong factorial invariance (across groups,
corresponding loadings and intercepts are fixed).
Multiple indices were examined to determine model fit. First, the chi-squared
statistic (‰ 2
) was calculated following conventions. However, given its sensitivity to sample
size, is becomes almost always significant and is, therefore, not reported. Second, the
comparative fit index (CFI) acoss models was calculated and reported. Finally, the root
mean square error of approximation (RMSEA) was calculated in all models (see Chen,
2007). We compared the “weak invariance model” to the configural model ( CFI and
RMSEA) and calculated the p values and applied Bonferroni correction for multiple
comparisons. Then, we compared “strong invariance model” to the weak one ( CFI and
RMSEA), calculated p values, and applied Bonferroni correction.
Factorial invariance is essential to provide meaningful comparisons of scores across
sexes. Therefore, we tested three levels of measurement invariance sequentially (configural,
weak, and strong invariance), where each level introduces more equality constraints across
groups. Configural invariance implies that the pattern of fixed and free factor loadings are
equivalent. Weak factorial invariance examines the equivalence of factor loadings (i.e., all
items assess the latent construct in the same way across sexes). Strong factorial invariance
examines the equality of latent means, implying that any dierences in means on the scales
are due to true dierences in means across sexes. Configural invariance is supported if the
same unconstrained factor structure simultaneously fit for the split groups yields a good fit
(RMSEA < 0.10, CFI > 0.90; Hu & Bentler, 1999). After testing configural invariance, we
examined weak/metric invariance. The fit of the restricted model (equal factor loadings
across groups) and the free model (Vandenberg & Lance, 2000).
MEASUREMENT INVARIANCE 67
Results
Care invariance using the alignment method
As can be seen in Table 8 for the item thresholds, item 1 was, on average,
non-invariant in 2.6 countries (averaging the five thresholds). Item 2 was non-invariant in
3.2 countries. Item 3 was non-invariant in 8.6 countries. Item 4 was non-invariant in 16.2
countries. Item 5 was non-invariant in 16.2 countries. Finally, item 6 was non-invariant in
14.8 countries.
As can be seen in Table 9, item 1’s loading was non-invariant in 1 country. Item 2’s
loading was non-invariant in 1 country. Item 3’s loading was non-invariant in 1 country.
Item 4’s loading was invariant was invariant across all countries. Item 5’s loading was
non-invariant in 2 countries. Finally, item 6’s loading was invariant in all groups.
Across all parameters in intercepts and loadings for Care, 6.8% of the parameters
were non-invariant which is well below the 25% cuto point proposed by (B. Muthén &
Asparouhov, 2014). Therefore, mean scores of Care can be meaningfully compared across
groups.
Fairness invariance using the alignment method
As can be seen in Table 10 for the item thresholds, item 1 was, on average,
non-invariant in 23.6 countries (averaging the five thresholds). Item 2 was non-invariant in
24.2 countries. Item 3 was non-invariant in 25.8 countries. Item 4 was non-invariant in 24.6
countries. Item 5 was non-invariant in 29.2 countries. Finally, item 6 was non-invariant in
33.2 countries.
As can be seen in Table 11, item 1’s loading was non-invariant in 5 countries. Item
2’s loading was non-invariant in 5 countries. Item 3’s loading was non-invariant in 2
countries. Item 4’s loading was invariant was non-invariant in 5 countries. Item 5’s loading
was non-invariant in 6 countries. Finally, item 6’s loading was non-invariant in 6 countries.
MEASUREMENT INVARIANCE 68
Across all parameters in intercepts and loadings for Fairness, 23.6% of the parameters
were non-invariant which is below the 25% cuto point proposed by (B. Muthén &
Asparouhov, 2014). Therefore, mean scores of Fairness can be meaningfully compared
across groups.
Loyalty invariance using the alignment method
As can be seen in Table 12 for the item thresholds, item 1 was, on average,
non-invariant in 14.0 countries (averaging the five thresholds). Item 2 was non-invariant in
4.6 countries. Item 3 was non-invariant in 9.4 countries. Item 4 was non-invariant in 20.8
countries. Item 5 was non-invariant in 14.2 countries. Finally, item 6 was non-invariant in
14.6 countries.
As can be seen in Table 9, item 1’s loading was invariant across all countries. Item 2’s
loading was non-invariant in 3 countries. Item 3’s loading was non-invariant in 3 countries.
Item 4’s loading was invariant was non-invariant in 2 countries. Item 5’s loading was
invariant in all countries. Finally, item 6’s loading was non-invariant in 2 countries.
Across all parameters in intercepts and loadings for Loyalty, 10.9% of the parameters
were non-invariant which is well below the 25% cuto point proposed by (B. Muthén &
Asparouhov, 2014). Therefore, mean scores of Loyalty can be meaningfully compared
across groups.
Authority invariance using the alignment method
As can be seen in Table 14 for the item thresholds, item 1 was, on average,
non-invariant in 5.4 countries (averaging the five thresholds). Item 2 was non-invariant in
7.6 countries. Item 3 was non-invariant in 4.2 countries. Item 4 was non-invariant in 13.2
countries. Item 5 was non-invariant in 16.2 countries. Finally, item 6 was non-invariant in
11.4 countries.
MEASUREMENT INVARIANCE 69
As can be seen in Table 15, item 1’s loading was invariant across all countries. Item
2’s loading was non-invariant in 1 country. Item 3’s loading was non-invariant in 1 country.
Item 4’s loading was invariant was non-invariant in 1 country. Item 5’s loading was
invariant across all countries. Finally, item 6’s loading was non-invariant in 3 countries.
Across all parameters in intercepts and loadings for Authority, 8.0% of the
parameters were non-invariant which is well below the 25% cuto point proposed by
(B. Muthén & Asparouhov, 2014). Therefore, mean scores of Authority can be
meaningfully compared across groups.
Purity invariance using the alignment method
As can be seen in Table 16 for the item thresholds, item 1 was, on average,
non-invariant in 6.2 countries (averaging the five thresholds). Item 2 was non-invariant in
8.4 countries. Item 3 was non-invariant in 10.2 countries. Item 4 was non-invariant in 7.2
countries. Item 5 was non-invariant in 4.6 countries. Finally, item 6 was non-invariant in
11.8 countries.
As can be seen in Table 17, item 1’s loading was invariant across all countries. Item
2’s loading was non-invariant in 4 countries. Item 3’s loading was non-invariant in 5
countries. Item 4’s loading was invariant was non-invariant in 4 countries. Item 5’s loading
was non-invariant in 1 country. Finally, item 6’s loading was non-invariant in 2 countries.
Across all parameters in intercepts and loadings for Purity, 8.0% of the parameters
were non-invariant which is well below the 25% cuto point proposed by (B. Muthén &
Asparouhov, 2014). Therefore, mean scores of Purity can be meaningfully compared across
groups.
MEASUREMENT INVARIANCE 70
Measurement Invariance across Sexes
Measurement invariance results for Care are presented in Table 22. After applying
correction for multiple comparisons, 66 countries showed weak invariance (98.5%). The
only country that did not manifest measurement invariance was the US, which can be
attributed the extremely large sample from this country. CFI ranged from -0.05 to 0.02
(M = -0.01) which suggested all countries had acceptable levels of weak invariance.
RMSEA ranged from -0.03 to 0.04 (M = -0.01). With regard to strong invariance, 55.2%
of the countries showed strong invariance. CFI for strong invariance ranged between
-0.11 to 0.01 (M = -0.04). RMSEA ranged from -0.03 to 0.06 (M = 0.01). Thus, in terms
of CFI and RMSEA, most countries had acceptable indices for establishing strong
invariance.
Measurement invariance results for Fairness are presented in Table 21. After applying
correction for multiple comparisons, 64 countries showed weak invariance (95.5%). The
only countries that did not manifest measurement invariance were the US, Venezuela, and
Israel. CFI ranged from -0.20 to 0.02 (M = -0.02) which suggested that most countries
had acceptable levels of weak invariance. RMSEA ranged from -0.03 to 0.06 (M = 0.00).
With regard to strong invariance, 92.5% of the countries showed strong invariance. CFI
for strong invariance ranged between -0.06 to 0.05 (M = -0.01). RMSEA ranged from
-0.02 to 0.02 (M = -0.01). Thus, in terms of CFI and RMSEA, most countries had
acceptable indices for establishing strong invariance.
MEASUREMENT INVARIANCE 71
Measurement invariance results for Loyalty are presented in Table 20. After applying
correction for multiple comparisons, 66 countries showed weak invariance (98.5%). The
only country that did not manifest measurement invariance was the US, which can be
attributable to extremely large sample size. CFI ranged from -0.08 to 0.01 (M = -0.01)
which suggested that most countries had acceptable levels of weak invariance. RMSEA
ranged from -0.02 to 0.02 (M = -0.01). With regard to strong invariance, 85.6% of the
countries showed strong invariance. CFI for strong invariance ranged between -0.05 to
0.03 (M = -0.01). RMSEA ranged from -0.02 to 0.00 (M = -0.01). Thus, in terms of
CFI and RMSEA, most countries had acceptable indices for establishing strong
invariance.
Measurement invariance results for Authority are presented in Table 19. After
applying correction for multiple comparisons, 65 countries showed weak invariance (97.0%).
The only two countries that did not manifest measurement invariance were the US and
Canada, which can be attributable to extremely large sample size. CFI ranged from -0.04
to 0.02 (M = 0.00) which suggested that most countries had acceptable levels of weak
invariance. RMSEA ranged from -0.03 to 0.02 (M = -0.01). With regard to strong
invariance, 65.7% of the countries showed strong invariance. CFI for strong invariance
ranged between -0.10 to 0.01 (M = -0.03). RMSEA ranged from -0.01 to 0.06 (M =
0.01). Thus, in terms of CFI and RMSEA, most countries had acceptable indices for
establishing strong invariance.
SEX DIFFERENCES IN MORALITY 72
Measurement invariance results for Purity are presented in Table 18. After applying
correction for multiple comparisons, 65 countries showed weak invariance (98.5%). The
only country that did not manifest measurement invariance was the US, which can be
attributable to extremely large sample size. CFI ranged from -0.03 to 0.02 (M = 0.00)
which suggested that most countries had acceptable levels of weak invariance. RMSEA
ranged from -0.03 to 0.01 (M = -0.01). With regard to strong invariance, 80.6% of the
countries showed strong invariance. CFI for strong invariance ranged between -0.08 to
0.02 (M = -0.01). RMSEA ranged from -0.02 to 0.04 (M = -0.01). Thus, in terms of
CFI and RMSEA, most countries had acceptable indices for establishing strong
invariance.
SEX DIFFERENCES IN MORALITY 73
Discussion
Comparison of latent means of psychological constructs across a large number of
groups is challenging, especially when these groups are cross-cultural in nature and
represent dierent countries. A long-standing prerequisite for meaningful interpretation of
these comparisons is that the scale is equivalently measuring the construct of interest
across all groups. In other words, testing of both the item factor loadings and item
intercepts have shown to be invariant across groups. The most appropriate method for
establishing measurement invariance in large cross-cultural studies is the alignment
optimization (Asparouhov & Muthén, 2014). Here, using an IRT application of the
alignment method (B. Muthén & Asparouhov, 2014), we provided evidence that moral
foundations, as measured by MFQ, can be meaningfully compared across 67 cultures. Item
loadiongs and intercepts were shown to be approximately invariant across groups and
non-invariance indexes (Care: 6.8%, Fairness: 23.6%, Loyalty: 10.9%, Authority: 8.0%, and
Purity: 8.0%) were all below the recommended 25% cuto proposed by B. Muthén and
Asparouhov (2014). In addition, we established weak to strong measurement invariance
across sexes. Specifically, in most countries, weak measurement invariance was established
in most countries (Care: 98.5%, Fairness: 95.5%, Loyalty: 98.5%, Authority: 97.5%, and
Purity: 98.5%), meaning that factor loadings were equivalent in men and women in these
countries. In addition, strong measurement invariance was established in the majority of
countries (Care: 55.2%, Fairness: 92.5%, Loyalty: 85.6%, Authority: 65.7%, and Purity:
80.6%), indicating that not only the leadings are equivalent, but also intercepts are
equivalent in most countries.
Appendix 2: Item-Level Multi-Level Analysis: Codes and Visualizations
Appendix 2: Item-level multi-level analysis
Care
Item: MFQ_emotionally
Whether or not someone suered emotionally
m0_emotionally <- lmer(MFQ_emotionally ~ (1 | country), data = YMdata2,
REML = FALSE)
stargazer(m0_emotionally, type = "text")
===============================================
Dependent variable:
---------------------------
MFQ_emotionally
-----------------------------------------------
Constant 3.322***
(0.019)
-----------------------------------------------
Observations 310,695
Log Likelihood -496,208.900
Akaike Inf. Crit. 992,423.800
Bayesian Inf. Crit. 992,455.800
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
m1_emotionally <- lmer(MFQ_emotionally ~ gender + (1 | country), data = YMdata2,
REML = FALSE)
stargazer(m1_emotionally, type = "text")
===============================================
Dependent variable:
---------------------------
MFQ_emotionally
-----------------------------------------------
gender -0.498***
(0.004)
Constant 3.631***
(0.020)
-----------------------------------------------
Observations 310,646
Log Likelihood -489,390.300
Akaike Inf. Crit. 978,788.600
1
SEX DIFFERENCES IN MORALITY 74
Bayesian Inf. Crit. 978,831.200
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
m2_emotionally <- lmer(MFQ_emotionally ~ gender + (gender | country), data = YMdata2,
REML = FALSE)
stargazer(m2_emotionally, type = "text")
===============================================
Dependent variable:
---------------------------
MFQ_emotionally
-----------------------------------------------
gender -0.439***
(0.023)
Constant 3.598***
(0.025)
-----------------------------------------------
Observations 310,646
Log Likelihood -489,354.800
Akaike Inf. Crit. 978,721.600
Bayesian Inf. Crit. 978,785.500
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
sjobject<-sjp.lmer(m2_emotionally, type = "coef",
sort.est = "gender",
show.values = F,
facet.grid = F,
vline.color = "blue",
color = "black",
title = c("Random effect of gender on the item",
"Random effect of gender on the item"),
axis.lim = c(-1,1),
prnt.plot = FALSE)
sjobject$plot.list[[2]]
2
SEX DIFFERENCES IN MORALITY 75
Sweden
Romania
Poland
Denmark
Russian Federation
Finland
Bulgaria
Israel
Turkey
Spain
Kenya
Slovakia
Germany
Nigeria
Canada
Lithuania
Chile
Australia
Greece
Slovenia
USA − United States of America
France
Venezuela
Norway
Ukraine
Croatia (Hrvatska)
Guatemala
Brazil
Egypt
Iceland
Afghanistan
United Kingdom
Italy
Belgium
Czech Republic
India
Portugal
Latvia
Mexico
Serbia and Montenegro
Pakistan
Ireland
Argentina
Switzerland
Philippines
Peru
United Arab Emirates
Netherlands
New Zealand
Hong Kong
Thailand
Austria
South Africa
Colombia
Puerto Rico
Singapore
Saudi Arabia
Indonesia
Albania
Hungary
Vietnam
Iran
Malaysia
Korea (south)
China
Taiwan
Japan
−1.0 −0.5 0.0 0.5 1.0
Random effect of gender on the item
stargazer(anova(m2_emotionally, m1_emotionally),type = "text",align = TRUE,summary = FALSE)
============================================================================================
Df AIC BIC logLik deviance Chisq Chi Df Pr(> Chisq)
--------------------------------------------------------------------------------------------
m1_emotionally 4 978,788.600 978,831.200 -489,390.300 978,780.600
m2_emotionally 6 978,721.600 978,785.500 -489,354.800 978,709.600 70.954 2 0
--------------------------------------------------------------------------------------------
3
SEX DIFFERENCES IN MORALITY 76
Item: MFQ_weak
Whether or not someone cared for someone weak or vulnerable
m0_weak <- lmer(MFQ_weak ~ (1 | country), data = YMdata2,
REML = FALSE)
stargazer(m0_weak, type = "text")
===============================================
Dependent variable:
---------------------------
MFQ_weak
-----------------------------------------------
Constant 3.645***
(0.019)
-----------------------------------------------
Observations 310,610
Log Likelihood -474,602.700
Akaike Inf. Crit. 949,211.400
Bayesian Inf. Crit. 949,243.400
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
m1_weak <- lmer(MFQ_weak ~ gender + (1 | country), data = YMdata2,
REML = FALSE)
stargazer(m1_weak, type = "text")
===============================================
Dependent variable:
---------------------------
MFQ_weak
-----------------------------------------------
gender -0.282***
(0.004)
Constant 3.820***
(0.020)
-----------------------------------------------
Observations 310,561
Log Likelihood -472,072.300
Akaike Inf. Crit. 944,152.500
Bayesian Inf. Crit. 944,195.100
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
m2_weak <- lmer(MFQ_weak ~ gender + (gender | country), data = YMdata2,
REML = FALSE)
stargazer(m2_weak, type = "text")
4
SEX DIFFERENCES IN MORALITY 77
===============================================
Dependent variable:
---------------------------
MFQ_weak
-----------------------------------------------
gender -0.273***
(0.018)
Constant 3.817***
(0.025)
-----------------------------------------------
Observations 310,561
Log Likelihood -472,053.500
Akaike Inf. Crit. 944,119.000
Bayesian Inf. Crit. 944,182.900
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
sjobject<-sjp.lmer(m2_weak, type = "coef",
sort.est = "gender",
show.values = F,
facet.grid = F,
vline.color = "blue",
color = "black",
title = c("Random effect of gender on the item",
"Random effect of gender on the item"),
axis.lim = c(-1,1),
prnt.plot = FALSE)
sjobject$plot.list[[2]]
5
SEX DIFFERENCES IN MORALITY 78
Poland
Russian Federation
Sweden
Finland
Israel
Australia
Romania
Germany
Spain
Colombia
Bulgaria
Slovenia
Croatia (Hrvatska)
Chile
Nigeria
Greece
Brazil
Lithuania
Egypt
Ukraine
Turkey
Pakistan
Kenya
Serbia and Montenegro
Slovakia
Italy
Denmark
France
Argentina
Puerto Rico
Canada
USA − United States of America
Saudi Arabia
New Zealand
United Kingdom
South Africa
Iceland
Switzerland
Venezuela
Czech Republic
Netherlands
Hungary
Peru
Guatemala
Belgium
India
United Arab Emirates
Norway
Austria
Ireland
Mexico
Afghanistan
Portugal
Albania
Hong Kong
Philippines
Vietnam
Iran
Korea (south)
Malaysia
Indonesia
Latvia
Taiwan
Singapore
Thailand
China
Japan
−1.0 −0.5 0.0 0.5 1.0
Random effect of gender on the item
stargazer(anova(m2_weak, m1_weak),type = "text",align = TRUE,summary = FALSE)
=====================================================================================
Df AIC BIC logLik deviance Chisq Chi Df Pr(> Chisq)
-------------------------------------------------------------------------------------
m1_weak 4 944,152.500 944,195.100 -472,072.300 944,144.500
m2_weak 6 944,119.000 944,182.900 -472,053.500 944,107.000 37.468 2 0
-------------------------------------------------------------------------------------
6
SEX DIFFERENCES IN MORALITY 79
Item: MFQ_cruel
Whether or not someone was cruel
m0_cruel <- lmer(MFQ_cruel ~ (1 | country), data = YMdata2,
REML = FALSE)
stargazer(m0_cruel, type = "text")
===============================================
Dependent variable:
---------------------------
MFQ_cruel
-----------------------------------------------
Constant 3.989***
(0.026)
-----------------------------------------------
Observations 310,708
Log Likelihood -438,585.700
Akaike Inf. Crit. 877,177.500
Bayesian Inf. Crit. 877,209.400
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
m1_cruel <- lmer(MFQ_cruel ~ gender + (1 | country), data = YMdata2,
REML = FALSE)
stargazer(m1_cruel, type = "text")
===============================================
Dependent variable:
---------------------------
MFQ_cruel
-----------------------------------------------
gender -0.284***
(0.004)
Constant 4.166***
(0.027)
-----------------------------------------------
Observations 310,659
Log Likelihood -435,375.900
Akaike Inf. Crit. 870,759.800
Bayesian Inf. Crit. 870,802.400
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
m2_cruel <- lmer(MFQ_cruel ~ gender + (gender | country), data = YMdata2,
REML = FALSE)
stargazer(m2_cruel, type = "text")
7
SEX DIFFERENCES IN MORALITY 80
===============================================
Dependent variable:
---------------------------
MFQ_cruel
-----------------------------------------------
gender -0.269***
(0.014)
Constant 4.157***
(0.030)
-----------------------------------------------
Observations 310,659
Log Likelihood -435,367.100
Akaike Inf. Crit. 870,746.300
Bayesian Inf. Crit. 870,810.200
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
sjobject<-sjp.lmer(m2_cruel, type = "coef",
sort.est = "gender",
show.values = F,
facet.grid = F,
vline.color = "blue",
color = "black",
title = c("Random effect of gender on the item",
"Random effect of gender on the item"),
axis.lim = c(-1,1),
prnt.plot = FALSE)
sjobject$plot.list[[2]]
8
SEX DIFFERENCES IN MORALITY 81
Poland
Finland
Romania
Greece
Australia
Germany
Russian Federation
Croatia (Hrvatska)
Israel
Bulgaria
Italy
Lithuania
Slovenia
Venezuela
Chile
Singapore
Serbia and Montenegro
Egypt
Argentina
Mexico
Hungary
USA − United States of America
Afghanistan
Slovakia
France
Canada
Nigeria
Hong Kong
Iceland
Brazil
Ukraine
Spain
Peru
Czech Republic
Sweden
Pakistan
Guatemala
Ireland
Colombia
Philippines
Portugal
Puerto Rico
India
New Zealand
Denmark
Belgium
United Kingdom
Albania
Austria
Iran
Saudi Arabia
Switzerland
United Arab Emirates
South Africa
Latvia
Norway
Turkey
Netherlands
Malaysia
Kenya
Taiwan
Korea (south)
Vietnam
Indonesia
Thailand
China
Japan
−1.0 −0.5 0.0 0.5 1.0
Random effect of gender on the item
stargazer(anova(m2_cruel, m1_cruel),type = "text",align = TRUE,summary = FALSE)
======================================================================================
Df AIC BIC logLik deviance Chisq Chi Df Pr(> Chisq)
--------------------------------------------------------------------------------------
m1_cruel 4 870,759.800 870,802.400 -435,375.900 870,751.800
m2_cruel 6 870,746.300 870,810.200 -435,367.100 870,734.300 17.504 2 0.0002
--------------------------------------------------------------------------------------
9
SEX DIFFERENCES IN MORALITY 82
Item: MFQ_compassion
Compassion for those who are suering is the most crucial virtue
m0_compassion <- lmer(MFQ_compassion ~ (1 | country), data = YMdata2,
REML = FALSE)
stargazer(m0_compassion, type = "text")
===============================================
Dependent variable:
---------------------------
MFQ_compassion
-----------------------------------------------
Constant 3.527***
(0.023)
-----------------------------------------------
Observations 310,615
Log Likelihood -504,090.200
Akaike Inf. Crit. 1,008,186.000
Bayesian Inf. Crit. 1,008,218.000
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
m1_compassion <- lmer(MFQ_compassion ~ gender + (1 | country), data = YMdata2,
REML = FALSE)
stargazer(m1_compassion, type = "text")
===============================================
Dependent variable:
---------------------------
MFQ_compassion
-----------------------------------------------
gender -0.464***
(0.004)
Constant 3.815***
(0.023)
-----------------------------------------------
Observations 310,566
Log Likelihood -498,470.100
Akaike Inf. Crit. 996,948.300
Bayesian Inf. Crit. 996,990.900
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
m2_compassion <- lmer(MFQ_compassion ~ gender + (gender | country), data = YMdata2,
REML = FALSE)
stargazer(m2_compassion, type = "text")
10
SEX DIFFERENCES IN MORALITY 83
===============================================
Dependent variable:
---------------------------
MFQ_compassion
-----------------------------------------------
gender -0.378***
(0.027)
Constant 3.767***
(0.024)
-----------------------------------------------
Observations 310,566
Log Likelihood -498,402.700
Akaike Inf. Crit. 996,817.400
Bayesian Inf. Crit. 996,881.300
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
sjobject<-sjp.lmer(m2_compassion, type = "coef",
sort.est = "gender",
show.values = F,
facet.grid = F,
vline.color = "blue",
color = "black",
title = c("Random effect of gender on the item",
"Random effect of gender on the item"),
axis.lim = c(-1,1),
prnt.plot = FALSE)
sjobject$plot.list[[2]]
11
SEX DIFFERENCES IN MORALITY 84
Romania
Greece
Poland
Lithuania
Czech Republic
Finland
Spain
Russian Federation
Bulgaria
Israel
Australia
Sweden
South Africa
Slovakia
Germany
Chile
Italy
Peru
Colombia
USA − United States of America
Brazil
Slovenia
Egypt
Venezuela
Mexico
Norway
Canada
Hungary
Croatia (Hrvatska)
Denmark
Ukraine
Turkey
United Kingdom
France
Afghanistan
New Zealand
Argentina
Ireland
Switzerland
Puerto Rico
Austria
Guatemala
Indonesia
Iceland
Latvia
Hong Kong
Serbia and Montenegro
Netherlands
United Arab Emirates
Pakistan
Kenya
Portugal
Thailand
Singapore
Belgium
India
Philippines
Korea (south)
Japan
Iran
Albania
Saudi Arabia
Nigeria
Malaysia
China
Taiwan
Vietnam
−1.0 −0.5 0.0 0.5 1.0
Random effect of gender on the item
stargazer(anova(m2_compassion, m1_compassion),type = "text",align = TRUE,summary = FALSE)
============================================================================================
Df AIC BIC logLik deviance Chisq Chi Df Pr(> Chisq)
--------------------------------------------------------------------------------------------
m1_compassion 4 996,948.300 996,990.900 -498,470.100 996,940.300
m2_compassion 6 996,817.400 996,881.300 -498,402.700 996,805.400 134.862 2 0
--------------------------------------------------------------------------------------------
12
SEX DIFFERENCES IN MORALITY 85
Item: MFQ_animal
One of the worst things a person could do is hurt a defenseless animal
m0_animal <- lmer(MFQ_animal ~ (1 | country), data = YMdata2,
REML = FALSE)
stargazer(m0_animal, type = "text")
===============================================
Dependent variable:
---------------------------
MFQ_animal
-----------------------------------------------
Constant 3.379***
(0.036)
-----------------------------------------------
Observations 311,016
Log Likelihood -583,167.700
Akaike Inf. Crit. 1,166,341.000
Bayesian Inf. Crit. 1,166,373.000
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
m1_animal <- lmer(MFQ_animal ~ gender + (1 | country), data = YMdata2,
REML = FALSE)
stargazer(m1_animal, type = "text")
===============================================
Dependent variable:
---------------------------
MFQ_animal
-----------------------------------------------
gender -0.670***
(0.006)
Constant 3.795***
(0.034)
-----------------------------------------------
Observations 310,967
Log Likelihood -576,050.300
Akaike Inf. Crit. 1,152,109.000
Bayesian Inf. Crit. 1,152,151.000
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
m2_animal <- lmer(MFQ_animal ~ gender + (gender | country), data = YMdata2,
REML = FALSE)
stargazer(m2_animal, type = "text")
13
SEX DIFFERENCES IN MORALITY 86
===============================================
Dependent variable:
---------------------------
MFQ_animal
-----------------------------------------------
gender -0.604***
(0.026)
Constant 3.754***
(0.031)
-----------------------------------------------
Observations 310,967
Log Likelihood -576,014.700
Akaike Inf. Crit. 1,152,041.000
Bayesian Inf. Crit. 1,152,105.000
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
sjobject<-sjp.lmer(m2_animal, type = "coef",
sort.est = "gender",
show.values = F,
facet.grid = F,
vline.color = "blue",
color = "black",
title = c("Random effect of gender on the item",
"Random effect of gender on the item"),
axis.lim = c(-2,0.25),
prnt.plot = FALSE)
sjobject$plot.list[[2]]
14
SEX DIFFERENCES IN MORALITY 87
Poland
Norway
Russian Federation
Finland
Germany
Denmark
Lithuania
Slovakia
China
Nigeria
Italy
Czech Republic
Romania
Afghanistan
Switzerland
Indonesia
USA − United States of America
Sweden
Hungary
Australia
Brazil
Malaysia
Kenya
Ukraine
Canada
Slovenia
France
Albania
Austria
Japan
Iceland
Spain
Hong Kong
South Africa
Belgium
Singapore
Korea (south)
New Zealand
Peru
Serbia and Montenegro
Netherlands
Thailand
Argentina
Greece
Israel
Bulgaria
Vietnam
Puerto Rico
Venezuela
United Kingdom
Croatia (Hrvatska)
Philippines
Portugal
Guatemala
Chile
Pakistan
United Arab Emirates
Taiwan
Mexico
Latvia
Iran
Ireland
Colombia
Egypt
Turkey
Saudi Arabia
India
−2.0 −1.5 −1.0 −0.5 0.0
Random effect of gender on the item
stargazer(anova(m2_animal, m1_animal),type = "text",align = TRUE,summary = FALSE)
=============================================================================================
Df AIC BIC logLik deviance Chisq Chi Df Pr(> Chisq)
---------------------------------------------------------------------------------------------
m1_animal 4 1,152,109.000 1,152,151.000 -576,050.300 1,152,101.000
m2_animal 6 1,152,041.000 1,152,105.000 -576,014.700 1,152,029.000 71.279 2 0
---------------------------------------------------------------------------------------------
15
SEX DIFFERENCES IN MORALITY 88
Item: MFQ_kill
It can never be right to kill a human being
m0_kill <- lmer(MFQ_kill ~ (1 | country), data = YMdata2,
REML = FALSE)
stargazer(m0_kill, type = "text")
===============================================
Dependent variable:
---------------------------
MFQ_kill
-----------------------------------------------
Constant 2.937***
(0.036)
-----------------------------------------------
Observations 311,180
Log Likelihood -617,902.300
Akaike Inf. Crit. 1,235,811.000
Bayesian Inf. Crit. 1,235,843.000
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
m1_kill <- lmer(MFQ_kill ~ gender + (1 | country), data = YMdata2,
REML = FALSE)
stargazer(m1_kill, type = "text")
===============================================
Dependent variable:
---------------------------
MFQ_kill
-----------------------------------------------
gender -0.857***
(0.006)
Constant 3.470***
(0.037)
-----------------------------------------------
Observations 311,131
Log Likelihood -608,524.900
Akaike Inf. Crit. 1,217,058.000
Bayesian Inf. Crit. 1,217,100.000
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
m2_kill <- lmer(MFQ_kill ~ gender + (gender | country), data = YMdata2,
REML = FALSE)
stargazer(m2_kill, type = "text")
16
SEX DIFFERENCES IN MORALITY 89
===============================================
Dependent variable:
---------------------------
MFQ_kill
-----------------------------------------------
gender -0.734***
(0.029)
Constant 3.395***
(0.033)
-----------------------------------------------
Observations 311,131
Log Likelihood -608,483.800
Akaike Inf. Crit. 1,216,980.000
Bayesian Inf. Crit. 1,217,044.000
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
sjobject<-sjp.lmer(m2_kill, type = "coef",
sort.est = "gender",
show.values = F,
facet.grid = F,
vline.color = "blue",
color = "black",
title = c("Random effect of gender on the item",
"Random effect of gender on the item"),
axis.lim = c(-2,0.25),
prnt.plot = FALSE)
sjobject$plot.list[[2]]
17
SEX DIFFERENCES IN MORALITY 90
Sweden
Denmark
Poland
Norway
Israel
Finland
Russian Federation
Germany
Ireland
Slovakia
United Kingdom
Australia
USA − United States of America
New Zealand
Czech Republic
Afghanistan
Peru
Puerto Rico
Ukraine
Lithuania
Canada
Albania
Mexico
Iceland
Guatemala
Romania
Saudi Arabia
Japan
Egypt
Bulgaria
Venezuela
South Africa
Hungary
Hong Kong
Singapore
Croatia (Hrvatska)
Italy
Switzerland
Thailand
Nigeria
Chile
Taiwan
Philippines
Spain
Netherlands
Brazil
France
Colombia
Serbia and Montenegro
Latvia
Austria
Kenya
Greece
Argentina
Slovenia
Turkey
Iran
Pakistan
Indonesia
Malaysia
Vietnam
India
Korea (south)
China
United Arab Emirates
Belgium
Portugal
−2.0 −1.5 −1.0 −0.5 0.0
Random effect of gender on the item
stargazer(anova(m2_kill, m1_kill),type = "text",align = TRUE,summary = FALSE)
===========================================================================================
Df AIC BIC logLik deviance Chisq Chi Df Pr(> Chisq)
-------------------------------------------------------------------------------------------
m1_kill 4 1,217,058.000 1,217,100.000 -608,524.900 1,217,050.000
m2_kill 6 1,216,980.000 1,217,044.000 -608,483.800 1,216,968.000 82.143 2 0
-------------------------------------------------------------------------------------------
18
SEX DIFFERENCES IN MORALITY 91
Fairness
Item: MFQ_treated
Whether or not some people were treated dierently than others
m0_treated <- lmer(MFQ_treated ~ (1 | country), data = YMdata2,
REML = FALSE)
stargazer(m0_treated, type = "text")
===============================================
Dependent variable:
---------------------------
MFQ_treated
-----------------------------------------------
Constant 3.624***
(0.021)
-----------------------------------------------
Observations 310,561
Log Likelihood -486,886.300
Akaike Inf. Crit. 973,778.600
Bayesian Inf. Crit. 973,810.600
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
m1_treated <- lmer(MFQ_treated ~ gender + (1 | country), data = YMdata2,
REML = FALSE)
stargazer(m1_treated, type = "text")
===============================================
Dependent variable:
---------------------------
MFQ_treated
-----------------------------------------------
gender -0.392***
(0.004)
Constant 3.868***
(0.022)
-----------------------------------------------
Observations 310,513
Log Likelihood -482,406.200
Akaike Inf. Crit. 964,820.400
Bayesian Inf. Crit. 964,863.000
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
19
SEX DIFFERENCES IN MORALITY 92
m2_treated <- lmer(MFQ_treated ~ gender + (gender | country), data = YMdata2,
REML = FALSE)
stargazer(m2_treated, type = "text")
===============================================
Dependent variable:
---------------------------
MFQ_treated
-----------------------------------------------
gender -0.316***
(0.021)
Constant 3.823***
(0.022)
-----------------------------------------------
Observations 310,513
Log Likelihood -482,365.200
Akaike Inf. Crit. 964,742.400
Bayesian Inf. Crit. 964,806.200
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
sjobject<-sjp.lmer(m2_treated, type = "coef",
sort.est = "gender",
show.values = F,
facet.grid = F,
vline.color = "blue",
color = "black",
title = c("Random effect of gender on the item",
"Random effect of gender on the item"),
axis.lim = c(-1,1),
prnt.plot = FALSE)
sjobject$plot.list[[2]]
20
SEX DIFFERENCES IN MORALITY 93
Russian Federation
Sweden
Poland
Norway
Finland
Romania
Lithuania
Israel
Czech Republic
Afghanistan
Slovakia
Peru
Germany
Argentina
Australia
USA − United States of America
Ukraine
Canada
Spain
France
United Kingdom
Denmark
United Arab Emirates
Italy
Ireland
Bulgaria
Greece
Venezuela
New Zealand
Pakistan
Croatia (Hrvatska)
Chile
Netherlands
Puerto Rico
Hong Kong
Egypt
Thailand
Iceland
Belgium
Guatemala
Brazil
Indonesia
Switzerland
Latvia
Iran
Serbia and Montenegro
South Africa
Korea (south)
Hungary
Mexico
Kenya
Saudi Arabia
Slovenia
Albania
Vietnam
Austria
Philippines
Taiwan
Nigeria
India
China
Colombia
Malaysia
Japan
Singapore
Turkey
Portugal
−1.0 −0.5 0.0 0.5 1.0
Random effect of gender on the item
stargazer(anova(m2_treated, m1_treated),type = "text",align = TRUE,summary = FALSE)
========================================================================================
Df AIC BIC logLik deviance Chisq Chi Df Pr(> Chisq)
----------------------------------------------------------------------------------------
m1_treated 4 964,820.400 964,863.000 -482,406.200 964,812.400
m2_treated 6 964,742.400 964,806.200 -482,365.200 964,730.400 82.037 2 0
----------------------------------------------------------------------------------------
21
SEX DIFFERENCES IN MORALITY 94
Item: MFQ_unfairly
Whether or not someone acted unfairly
m0_unfairly <- lmer(MFQ_unfairly ~ (1 | country), data = YMdata2,
REML = FALSE)
stargazer(m0_unfairly, type = "text")
===============================================
Dependent variable:
---------------------------
MFQ_unfairly
-----------------------------------------------
Constant 3.913***
(0.017)
-----------------------------------------------
Observations 310,504
Log Likelihood -441,485.000
Akaike Inf. Crit. 882,976.000
Bayesian Inf. Crit. 883,007.900
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
m1_unfairly <- lmer(MFQ_unfairly ~ gender + (1 | country), data = YMdata2,
REML = FALSE)
stargazer(m1_unfairly, type = "text")
===============================================
Dependent variable:
---------------------------
MFQ_unfairly
-----------------------------------------------
gender -0.142***
(0.004)
Constant 4.001***
(0.017)
-----------------------------------------------
Observations 310,455
Log Likelihood -440,655.100
Akaike Inf. Crit. 881,318.100
Bayesian Inf. Crit. 881,360.700
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
m2_unfairly <- lmer(MFQ_unfairly ~ gender + (gender | country), data = YMdata2,
REML = FALSE)
stargazer(m2_unfairly, type = "text")
22
SEX DIFFERENCES IN MORALITY 95
===============================================
Dependent variable:
---------------------------
MFQ_unfairly
-----------------------------------------------
gender -0.140***
(0.004)
Constant 4.000***
(0.017)
-----------------------------------------------
Observations 310,455
Log Likelihood -440,655.000
Akaike Inf. Crit. 881,322.100
Bayesian Inf. Crit. 881,385.900
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
sjobject<-sjp.lmer(m2_unfairly, type = "coef",
sort.est = "gender",
show.values = F,
facet.grid = F,
vline.color = "blue",
color = "black",
title = c("Random effect of gender on the item",
"Random effect of gender on the item"),
axis.lim = c(-1,1),
prnt.plot = FALSE)
sjobject$plot.list[[2]]
23
SEX DIFFERENCES IN MORALITY 96
China
Japan
Hong Kong
Norway
Sweden
Afghanistan
Thailand
Vietnam
Latvia
USA − United States of America
Russian Federation
United Arab Emirates
Peru
Albania
Saudi Arabia
Canada
Taiwan
Singapore
Czech Republic
Switzerland
Denmark
Korea (south)
Australia
Austria
Indonesia
Ukraine
France
Slovakia
Lithuania
Germany
New Zealand
Ireland
Poland
Netherlands
Italy
Pakistan
Finland
United Kingdom
Kenya
Malaysia
Israel
Belgium
Iceland
Romania
Guatemala
Serbia and Montenegro
South Africa
Egypt
India
Puerto Rico
Philippines
Argentina
Venezuela
Nigeria
Croatia (Hrvatska)
Colombia
Bulgaria
Spain
Hungary
Mexico
Portugal
Iran
Slovenia
Greece
Chile
Brazil
Turkey
−1.0 −0.5 0.0 0.5 1.0
Random effect of gender on the item
stargazer(anova(m2_unfairly, m1_unfairly),type = "text",align = TRUE,summary = FALSE)
========================================================================================
Df AIC BIC logLik deviance Chisq Chi Df Pr(> Chisq)
----------------------------------------------------------------------------------------
m1_unfairly 4 881,318.100 881,360.700 -440,655.100 881,310.100
m2_unfairly 6 881,322.100 881,385.900 -440,655.000 881,310.100 0.079 2 0.961
----------------------------------------------------------------------------------------
24
SEX DIFFERENCES IN MORALITY 97
Item: MFQ_rights
Whether or not someone was denied his or her rights
m0_rights <- lmer(MFQ_rights ~ (1 | country), data = YMdata2,
REML = FALSE)
stargazer(m0_rights, type = "text")
===============================================
Dependent variable:
---------------------------
MFQ_rights
-----------------------------------------------
Constant 4.059***
(0.027)
-----------------------------------------------
Observations 310,679
Log Likelihood -420,886.200
Akaike Inf. Crit. 841,778.500
Bayesian Inf. Crit. 841,810.400
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
m1_rights <- lmer(MFQ_rights ~ gender + (1 | country), data = YMdata2,
REML = FALSE)
stargazer(m1_rights, type = "text")
===============================================
Dependent variable:
---------------------------
MFQ_rights
-----------------------------------------------
gender -0.126***
(0.003)
Constant 4.137***
(0.028)
-----------------------------------------------
Observations 310,630
Log Likelihood -420,120.100
Akaike Inf. Crit. 840,248.300
Bayesian Inf. Crit. 840,290.900
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
m2_rights <- lmer(MFQ_rights ~ gender + (gender | country), data = YMdata2,
REML = FALSE)
stargazer(m2_rights, type = "text")
25
SEX DIFFERENCES IN MORALITY 98
===============================================
Dependent variable:
---------------------------
MFQ_rights
-----------------------------------------------
gender -0.157***
(0.015)
Constant 4.157***
(0.031)
-----------------------------------------------
Observations 310,630
Log Likelihood -420,061.400
Akaike Inf. Crit. 840,134.800
Bayesian Inf. Crit. 840,198.600
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
sjobject<-sjp.lmer(m2_rights, type = "coef",
sort.est = "gender",
show.values = F,
facet.grid = F,
vline.color = "blue",
color = "black",
title = c("Random effect of gender on the item",
"Random effect of gender on the item"),
axis.lim = c(-1,1),
prnt.plot = FALSE)
sjobject$plot.list[[2]]
26
SEX DIFFERENCES IN MORALITY 99
New Zealand
Australia
United Arab Emirates
Israel
France
Poland
Sweden
Finland
Germany
United Kingdom
Peru
Romania
Afghanistan
Venezuela
Netherlands
Turkey
Russian Federation
Brazil
Spain
Chile
Kenya
Saudi Arabia
Canada
Slovakia
Serbia and Montenegro
Croatia (Hrvatska)
Norway
Denmark
Puerto Rico
Czech Republic
Iceland
Slovenia
Greece
Pakistan
Egypt
Italy
Ireland
Mexico
Portugal
Argentina
Switzerland
Albania
Nigeria
Lithuania
Colombia
Philippines
Guatemala
Singapore
Bulgaria
Hungary
Iran
Indonesia
Belgium
India
USA − United States of America
Taiwan
Korea (south)
Ukraine
Latvia
Hong Kong
South Africa
Malaysia
Thailand
Vietnam
China
Austria
Japan
−1.0 −0.5 0.0 0.5 1.0
Random effect of gender on the item
stargazer(anova(m2_rights, m1_rights),type = "text",align = TRUE,summary = FALSE)
========================================================================================
Df AIC BIC logLik deviance Chisq Chi Df Pr(> Chisq)
----------------------------------------------------------------------------------------
m1_rights 4 840,248.300 840,290.900 -420,120.100 840,240.300
m2_rights 6 840,134.800 840,198.600 -420,061.400 840,122.800 117.529 2 0
----------------------------------------------------------------------------------------
27
SEX DIFFERENCES IN MORALITY 100
Item: MFQ_fairly
When the government makes laws, the number one principle should be ensuring that everyone
is treated fairly
m0_fairly <- lmer(MFQ_fairly ~ (1 | country), data = YMdata2,
REML = FALSE)
stargazer(m0_fairly, type = "text")
===============================================
Dependent variable:
---------------------------
MFQ_fairly
-----------------------------------------------
Constant 4.178***
(0.022)
-----------------------------------------------
Observations 310,794
Log Likelihood -511,550.700
Akaike Inf. Crit. 1,023,107.000
Bayesian Inf. Crit. 1,023,139.000
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
m1_fairly <- lmer(MFQ_fairly ~ gender + (1 | country), data = YMdata2,
REML = FALSE)
stargazer(m1_fairly, type = "text")
===============================================
Dependent variable:
---------------------------
MFQ_fairly
-----------------------------------------------
gender -0.290***
(0.005)
Constant 4.359***
(0.023)
-----------------------------------------------
Observations 310,746
Log Likelihood -509,414.200
Akaike Inf. Crit. 1,018,836.000
Bayesian Inf. Crit. 1,018,879.000
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
28
SEX DIFFERENCES IN MORALITY 101
m2_fairly <- lmer(MFQ_fairly ~ gender + (gender | country), data = YMdata2,
REML = FALSE)
stargazer(m2_fairly, type = "text")
===============================================
Dependent variable:
---------------------------
MFQ_fairly
-----------------------------------------------
gender -0.235***
(0.006)
Constant 4.325***
(0.021)
-----------------------------------------------
Observations 310,746
Log Likelihood -509,403.100
Akaike Inf. Crit. 1,018,818.000
Bayesian Inf. Crit. 1,018,882.000
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
sjobject<-sjp.lmer(m2_fairly, type = "coef",
sort.est = "gender",
show.values = F,
facet.grid = F,
vline.color = "blue",
color = "black",
title = c("Random effect of gender on the item",
"Random effect of gender on the item"),
axis.lim = c(-1,1),
prnt.plot = FALSE)
sjobject$plot.list[[2]]
29
SEX DIFFERENCES IN MORALITY 102
Japan
USA − United States of America
Israel
Hong Kong
Singapore
Afghanistan
Canada
Russian Federation
Albania
Australia
China
New Zealand
United Kingdom
Peru
Sweden
Korea (south)
South Africa
Taiwan
Ukraine
Czech Republic
Norway
Germany
Ireland
Latvia
Denmark
Thailand
Slovakia
Indonesia
Romania
Lithuania
Poland
Switzerland
Hungary
France
Kenya
Belgium
Spain
Netherlands
Colombia
Finland
Austria
United Arab Emirates
Malaysia
Venezuela
Guatemala
Mexico
Brazil
Argentina
Vietnam
Philippines
Chile
Pakistan
Italy
Egypt
Bulgaria
Puerto Rico
Iceland
Saudi Arabia
Iran
Nigeria
India
Serbia and Montenegro
Turkey
Portugal
Croatia (Hrvatska)
Slovenia
Greece
−1.0 −0.5 0.0 0.5 1.0
Random effect of gender on the item
stargazer(anova(m2_fairly, m1_fairly),type = "text",align = TRUE,summary = FALSE)
=============================================================================================
Df AIC BIC logLik deviance Chisq Chi Df Pr(> Chisq)
---------------------------------------------------------------------------------------------
m1_fairly 4 1,018,836.000 1,018,879.000 -509,414.200 1,018,828.000
m2_fairly 6 1,018,818.000 1,018,882.000 -509,403.100 1,018,806.000 22.379 2 0.00001
---------------------------------------------------------------------------------------------
30
SEX DIFFERENCES IN MORALITY 103
Item: MFQ_justice
Justice is the most important requirement for a society
m0_justice <- lmer(MFQ_justice ~ (1 | country), data = YMdata2,
REML = FALSE)
stargazer(m0_justice, type = "text")
===============================================
Dependent variable:
---------------------------
MFQ_justice
-----------------------------------------------
Constant 3.867***
(0.027)
-----------------------------------------------
Observations 310,500
Log Likelihood -493,108.400
Akaike Inf. Crit. 986,222.700
Bayesian Inf. Crit. 986,254.700
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
m1_justice <- lmer(MFQ_justice ~ gender + (1 | country), data = YMdata2,
REML = FALSE)
stargazer(m1_justice, type = "text")
===============================================
Dependent variable:
---------------------------
MFQ_justice
-----------------------------------------------
gender -0.010**
(0.004)
Constant 3.873***
(0.027)
-----------------------------------------------
Observations 310,451
Log Likelihood -493,028.200
Akaike Inf. Crit. 986,064.400
Bayesian Inf. Crit. 986,106.900
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
m2_justice <- lmer(MFQ_justice ~ gender + (gender | country), data = YMdata2,
REML = FALSE)
stargazer(m2_justice, type = "text")
31
SEX DIFFERENCES IN MORALITY 104
===============================================
Dependent variable:
---------------------------
MFQ_justice
-----------------------------------------------
gender -0.089***
(0.015)
Constant 3.922***
(0.030)
-----------------------------------------------
Observations 310,451
Log Likelihood -492,991.500
Akaike Inf. Crit. 985,995.000
Bayesian Inf. Crit. 986,058.900
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
sjobject<-sjp.lmer(m2_justice, type = "coef",
sort.est = "gender",
show.values = F,
facet.grid = F,
vline.color = "blue",
color = "black",
title = c("Random effect of gender on the item",
"Random effect of gender on the item"),
axis.lim = c(-1,1),
prnt.plot = FALSE)
sjobject$plot.list[[2]]
32
SEX DIFFERENCES IN MORALITY 105
Germany
China
Turkey
Croatia (Hrvatska)
Mexico
Egypt
Finland
Greece
Brazil
Puerto Rico
Netherlands
Sweden
Norway
Denmark
Indonesia
Italy
Thailand
United Arab Emirates
Bulgaria
Pakistan
Nigeria
Singapore
Iran
France
Vietnam
Guatemala
Venezuela
Spain
Malaysia
South Africa
Argentina
Portugal
Israel
Kenya
Philippines
Slovenia
Slovakia
Serbia and Montenegro
Ukraine
Hong Kong
Lithuania
Czech Republic
India
Romania
Taiwan
Austria
Ireland
Colombia
Saudi Arabia
Korea (south)
Iceland
Chile
Peru
Japan
Switzerland
Russian Federation
Afghanistan
Albania
Hungary
Australia
United Kingdom
Belgium
Poland
Latvia
New Zealand
USA − United States of America
Canada
−1.0 −0.5 0.0 0.5 1.0
Random effect of gender on the item
stargazer(anova(m2_justice, m1_justice),type = "text",align = TRUE,summary = FALSE)
========================================================================================
Df AIC BIC logLik deviance Chisq Chi Df Pr(> Chisq)
----------------------------------------------------------------------------------------
m1_justice 4 986,064.400 986,106.900 -493,028.200 986,056.400
m2_justice 6 985,995.000 986,058.900 -492,991.500 985,983.000 73.342 2 0
----------------------------------------------------------------------------------------
33
SEX DIFFERENCES IN MORALITY 106
Item: MFQ_rich
I think it’s morally wrong that rich children inherit a lot of money while poor children inherit
nothing
m0_rich <- lmer(MFQ_rich ~ (1 | country), data = YMdata2,
REML = FALSE)
stargazer(m0_rich, type = "text")
===============================================
Dependent variable:
---------------------------
MFQ_rich
-----------------------------------------------
Constant 2.247***
(0.037)
-----------------------------------------------
Observations 311,042
Log Likelihood -593,897.400
Akaike Inf. Crit. 1,187,801.000
Bayesian Inf. Crit. 1,187,833.000
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
m1_rich <- lmer(MFQ_rich ~ gender + (1 | country), data = YMdata2,
REML = FALSE)
stargazer(m1_rich, type = "text")
===============================================
Dependent variable:
---------------------------
MFQ_rich
-----------------------------------------------
gender -0.148***
(0.006)
Constant 2.339***
(0.038)
-----------------------------------------------
Observations 310,993
Log Likelihood -593,484.800
Akaike Inf. Crit. 1,186,978.000
Bayesian Inf. Crit. 1,187,020.000
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
34
SEX DIFFERENCES IN MORALITY 107
m2_rich <- lmer(MFQ_rich ~ gender + (gender | country), data = YMdata2,
REML = FALSE)
stargazer(m2_rich, type = "text")
===============================================
Dependent variable:
---------------------------
MFQ_rich
-----------------------------------------------
gender -0.093***
(0.019)
Constant 2.305***
(0.039)
-----------------------------------------------
Observations 310,993
Log Likelihood -593,481.000
Akaike Inf. Crit. 1,186,974.000
Bayesian Inf. Crit. 1,187,038.000
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
sjobject<-sjp.lmer(m2_rich, type = "coef",
sort.est = "gender",
show.values = F,
facet.grid = F,
vline.color = "blue",
color = "black",
title = c("Random effect of gender on the item",
"Random effect of gender on the item"),
axis.lim = c(-1,1),
prnt.plot = FALSE)
sjobject$plot.list[[2]]
35
SEX DIFFERENCES IN MORALITY 108
Australia
Poland
USA − United States of America
Brazil
India
Sweden
Chile
United Kingdom
Peru
Canada
Croatia (Hrvatska)
Thailand
Netherlands
Czech Republic
Finland
Hong Kong
Slovakia
New Zealand
Ukraine
Indonesia
Slovenia
Iceland
Lithuania
Bulgaria
Germany
Japan
Egypt
Guatemala
Kenya
Malaysia
Pakistan
Israel
Venezuela
Italy
Latvia
Austria
Spain
Romania
Argentina
United Arab Emirates
Saudi Arabia
Mexico
Puerto Rico
Turkey
Ireland
Nigeria
Serbia and Montenegro
South Africa
Iran
Switzerland
Albania
Greece
Colombia
Hungary
Taiwan
Vietnam
Afghanistan
Korea (south)
Norway
Singapore
China
Philippines
Denmark
Portugal
France
Russian Federation
Belgium
−1.0 −0.5 0.0 0.5 1.0
Random effect of gender on the item
stargazer(anova(m2_rich, m1_rich),type = "text",align = TRUE,summary = FALSE)
==========================================================================================
Df AIC BIC logLik deviance Chisq Chi Df Pr(> Chisq)
------------------------------------------------------------------------------------------
m1_rich 4 1,186,978.000 1,187,020.000 -593,484.800 1,186,970.000
m2_rich 6 1,186,974.000 1,187,038.000 -593,481.000 1,186,962.000 7.720 2 0.021
------------------------------------------------------------------------------------------
36
SEX DIFFERENCES IN MORALITY 109
Loyalty
Item: MFQ_lovecountry
Whether or not someone’s action showed love for his or her country
m0_lovecountry <- lmer(MFQ_lovecountry ~ (1 | country), data = YMdata2,
REML = FALSE)
stargazer(m0_lovecountry, type = "text")
===============================================
Dependent variable:
---------------------------
MFQ_lovecountry
-----------------------------------------------
Constant 1.651***
(0.052)
-----------------------------------------------
Observations 310,901
Log Likelihood -539,035.200
Akaike Inf. Crit. 1,078,076.000
Bayesian Inf. Crit. 1,078,108.000
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
m1_lovecountry <- lmer(MFQ_lovecountry ~ gender + (1 | country), data = YMdata2,
REML = FALSE)
stargazer(m1_lovecountry, type = "text")
===============================================
Dependent variable:
---------------------------
MFQ_lovecountry
-----------------------------------------------
gender -0.024***
(0.005)
Constant 1.666***
(0.052)
-----------------------------------------------
Observations 310,852
Log Likelihood -538,926.500
Akaike Inf. Crit. 1,077,861.000
Bayesian Inf. Crit. 1,077,904.000
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
37
SEX DIFFERENCES IN MORALITY 110
m2_lovecountry <- lmer(MFQ_lovecountry ~ gender + (gender | country), data = YMdata2,
REML = FALSE)
stargazer(m2_lovecountry, type = "text")
===============================================
Dependent variable:
---------------------------
MFQ_lovecountry
-----------------------------------------------
gender -0.060***
(0.019)
Constant 1.685***
(0.057)
-----------------------------------------------
Observations 310,852
Log Likelihood -538,912.500
Akaike Inf. Crit. 1,077,837.000
Bayesian Inf. Crit. 1,077,901.000
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
sjobject<-sjp.lmer(m2_lovecountry, type = "coef",
sort.est = "gender",
show.values = F,
facet.grid = F,
vline.color = "blue",
color = "black",
title = c("Random effect of gender on the item",
"Random effect of gender on the item"),
axis.lim = c(-1,1),
prnt.plot = FALSE)
sjobject$plot.list[[2]]
38
SEX DIFFERENCES IN MORALITY 111
China
Mexico
Philippines
Nigeria
Indonesia
Vietnam
Malaysia
Latvia
Kenya
Pakistan
Colombia
Venezuela
Taiwan
Japan
Guatemala
Lithuania
Egypt
Saudi Arabia
Puerto Rico
Peru
United Arab Emirates
Thailand
Korea (south)
Ukraine
Albania
Iran
Russian Federation
Czech Republic
Afghanistan
Bulgaria
Chile
Turkey
Singapore
Romania
Israel
Hungary
Germany
Slovenia
South Africa
New Zealand
Greece
USA − United States of America
Hong Kong
Argentina
Croatia (Hrvatska)
Spain
Switzerland
Italy
Brazil
Slovakia
Denmark
Ireland
Serbia and Montenegro
Canada
Poland
India
Belgium
Australia
United Kingdom
Austria
Norway
Sweden
France
Portugal
Iceland
Finland
Netherlands
−1.0 −0.5 0.0 0.5 1.0
Random effect of gender on the item
stargazer(anova(m2_lovecountry, m1_lovecountry),type = "text",align = TRUE,summary = FALSE)
==================================================================================================
Df AIC BIC logLik deviance Chisq Chi Df Pr(> Chisq)
--------------------------------------------------------------------------------------------------
m1_lovecountry 4 1,077,861.000 1,077,904.000 -538,926.500 1,077,853.000
m2_lovecountry 6 1,077,837.000 1,077,901.000 -538,912.500 1,077,825.000 27.938 2 0.00000
--------------------------------------------------------------------------------------------------
39
SEX DIFFERENCES IN MORALITY 112
Item: MFQ_betray
Whether or not someone did something to betray his or her group
m0_betray <- lmer(MFQ_betray ~ (1 | country), data = YMdata2,
REML = FALSE)
stargazer(m0_betray, type = "text")
===============================================
Dependent variable:
---------------------------
MFQ_betray
-----------------------------------------------
Constant 2.935***
(0.033)
-----------------------------------------------
Observations 310,673
Log Likelihood -527,389.400
Akaike Inf. Crit. 1,054,785.000
Bayesian Inf. Crit. 1,054,817.000
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
m1_betray <- lmer(MFQ_betray ~ gender + (1 | country), data = YMdata2,
REML = FALSE)
stargazer(m1_betray, type = "text")
===============================================
Dependent variable:
---------------------------
MFQ_betray
-----------------------------------------------
gender -0.048***
(0.005)
Constant 2.965***
(0.033)
-----------------------------------------------
Observations 310,624
Log Likelihood -527,256.600
Akaike Inf. Crit. 1,054,521.000
Bayesian Inf. Crit. 1,054,564.000
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
m2_betray <- lmer(MFQ_betray ~ gender + (gender | country), data = YMdata2,
REML = FALSE)
stargazer(m2_betray, type = "text")
40
SEX DIFFERENCES IN MORALITY 113
===============================================
Dependent variable:
---------------------------
MFQ_betray
-----------------------------------------------
gender -0.069***
(0.011)
Constant 2.978***
(0.033)
-----------------------------------------------
Observations 310,624
Log Likelihood -527,255.400
Akaike Inf. Crit. 1,054,523.000
Bayesian Inf. Crit. 1,054,587.000
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
sjobject<-sjp.lmer(m2_betray, type = "coef",
sort.est = "gender",
show.values = F,
facet.grid = F,
vline.color = "blue",
color = "black",
title = c("Random effect of gender on the item",
"Random effect of gender on the item"),
axis.lim = c(-1,1),
prnt.plot = FALSE)
sjobject$plot.list[[2]]
41
SEX DIFFERENCES IN MORALITY 114
China
Mexico
Philippines
Puerto Rico
Vietnam
Chile
Australia
Brazil
Switzerland
Singapore
Nigeria
Peru
Iran
Lithuania
United Kingdom
Kenya
Venezuela
Malaysia
Thailand
Poland
Bulgaria
United Arab Emirates
Guatemala
Hungary
Colombia
Pakistan
Slovakia
Egypt
Sweden
New Zealand
Albania
Latvia
Saudi Arabia
Croatia (Hrvatska)
Romania
Germany
Indonesia
Czech Republic
Argentina
Portugal
Slovenia
Taiwan
India
Ireland
Ukraine
Korea (south)
South Africa
Spain
Serbia and Montenegro
Denmark
Hong Kong
Austria
Norway
Greece
Iceland
France
Israel
Afghanistan
Italy
Netherlands
Japan
Finland
Turkey
Belgium
Russian Federation
USA − United States of America
Canada
−1.0 −0.5 0.0 0.5 1.0
Random effect of gender on the item
stargazer(anova(m2_betray, m1_betray),type = "text",align = TRUE,summary = FALSE)
============================================================================================
Df AIC BIC logLik deviance Chisq Chi Df Pr(> Chisq)
--------------------------------------------------------------------------------------------
m1_betray 4 1,054,521.000 1,054,564.000 -527,256.600 1,054,513.000
m2_betray 6 1,054,523.000 1,054,587.000 -527,255.400 1,054,511.000 2.341 2 0.310
--------------------------------------------------------------------------------------------
42
SEX DIFFERENCES IN MORALITY 115
Item: MFQ_loyalty
Whether or not someone showed a lack of loyalty
m0_loyalty <- lmer(MFQ_loyalty ~ (1 | country), data = YMdata2,
REML = FALSE)
stargazer(m0_loyalty, type = "text")
===============================================
Dependent variable:
---------------------------
MFQ_loyalty
-----------------------------------------------
Constant 2.855***
(0.032)
-----------------------------------------------
Observations 310,640
Log Likelihood -518,599.400
Akaike Inf. Crit. 1,037,205.000
Bayesian Inf. Crit. 1,037,237.000
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
m1_loyalty <- lmer(MFQ_loyalty ~ gender + (1 | country), data = YMdata2,
REML = FALSE)
stargazer(m1_loyalty, type = "text")
===============================================
Dependent variable:
---------------------------
MFQ_loyalty
-----------------------------------------------
gender -0.115***
(0.005)
Constant 2.926***
(0.032)
-----------------------------------------------
Observations 310,591
Log Likelihood -518,217.700
Akaike Inf. Crit. 1,036,443.000
Bayesian Inf. Crit. 1,036,486.000
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
m2_loyalty <- lmer(MFQ_loyalty ~ gender + (gender | country), data = YMdata2,
REML = FALSE)
stargazer(m2_loyalty, type = "text")
43
SEX DIFFERENCES IN MORALITY 116
===============================================
Dependent variable:
---------------------------
MFQ_loyalty
-----------------------------------------------
gender -0.151***
(0.013)
Constant 2.949***
(0.033)
-----------------------------------------------
Observations 310,591
Log Likelihood -518,214.000
Akaike Inf. Crit. 1,036,440.000
Bayesian Inf. Crit. 1,036,504.000
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
sjobject<-sjp.lmer(m2_loyalty, type = "coef",
sort.est = "gender",
show.values = F,
facet.grid = F,
vline.color = "blue",
color = "black",
title = c("Random effect of gender on the item",
"Random effect of gender on the item"),
axis.lim = c(-1,1),
prnt.plot = FALSE)
sjobject$plot.list[[2]]
44
SEX DIFFERENCES IN MORALITY 117
Mexico
Romania
Thailand
Chile
Puerto Rico
Bulgaria
Egypt
Guatemala
Denmark
Vietnam
South Africa
Croatia (Hrvatska)
Nigeria
India
Peru
Pakistan
Colombia
Venezuela
Kenya
Switzerland
Portugal
Brazil
United Arab Emirates
Ireland
Lithuania
Netherlands
Philippines
Malaysia
Sweden
Iran
Albania
Latvia
Australia
Poland
Greece
Germany
Spain
Indonesia
Argentina
Finland
Hong Kong
Hungary
Italy
New Zealand
Saudi Arabia
United Kingdom
Iceland
Singapore
Czech Republic
Slovakia
Serbia and Montenegro
Israel
Belgium
Taiwan
Ukraine
Afghanistan
China
Austria
Slovenia
Norway
Russian Federation
Turkey
France
Korea (south)
Japan
USA − United States of America
Canada
−1.0 −0.5 0.0 0.5 1.0
Random effect of gender on the item
stargazer(anova(m2_loyalty, m1_loyalty),type = "text",align = TRUE,summary = FALSE)
=============================================================================================
Df AIC BIC logLik deviance Chisq Chi Df Pr(> Chisq)
---------------------------------------------------------------------------------------------
m1_loyalty 4 1,036,443.000 1,036,486.000 -518,217.700 1,036,435.000
m2_loyalty 6 1,036,440.000 1,036,504.000 -518,214.000 1,036,428.000 7.383 2 0.025
---------------------------------------------------------------------------------------------
45
SEX DIFFERENCES IN MORALITY 118
Item: MFQ_history
I am proud of my country’s history
m0_history <- lmer(MFQ_history ~ (1 | country), data = YMdata2,
REML = FALSE)
stargazer(m0_history, type = "text")
===============================================
Dependent variable:
---------------------------
MFQ_history
-----------------------------------------------
Constant 2.568***
(0.057)
-----------------------------------------------
Observations 311,014
Log Likelihood -570,689.800
Akaike Inf. Crit. 1,141,386.000
Bayesian Inf. Crit. 1,141,418.000
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
m1_history <- lmer(MFQ_history ~ gender + (1 | country), data = YMdata2,
REML = FALSE)
stargazer(m1_history, type = "text")
===============================================
Dependent variable:
---------------------------
MFQ_history
-----------------------------------------------
gender 0.326***
(0.005)
Constant 2.366***
(0.058)
-----------------------------------------------
Observations 310,965
Log Likelihood -568,811.200
Akaike Inf. Crit. 1,137,630.000
Bayesian Inf. Crit. 1,137,673.000
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
m2_history <- lmer(MFQ_history ~ gender + (gender | country), data = YMdata2,
REML = FALSE)
stargazer(m2_history, type = "text")
46
SEX DIFFERENCES IN MORALITY 119
===============================================
Dependent variable:
---------------------------
MFQ_history
-----------------------------------------------
gender -0.036
(0.032)
Constant 2.590***
(0.064)
-----------------------------------------------
Observations 310,965
Log Likelihood -568,482.400
Akaike Inf. Crit. 1,136,977.000
Bayesian Inf. Crit. 1,137,041.000
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
sjobject<-sjp.lmer(m2_history, type = "coef",
sort.est = "gender",
show.values = F,
facet.grid = F,
vline.color = "blue",
color = "black",
title = c("Random effect of gender on the item",
"Random effect of gender on the item"),
axis.lim = c(-1,1),
prnt.plot = FALSE)
sjobject$plot.list[[2]]
47
SEX DIFFERENCES IN MORALITY 120
China
Singapore
Thailand
Czech Republic
Colombia
Korea (south)
Malaysia
Greece
Pakistan
Ireland
Kenya
India
Romania
Philippines
Bulgaria
Mexico
Iceland
Latvia
Hungary
Nigeria
Lithuania
Egypt
Denmark
Slovenia
Taiwan
Puerto Rico
Vietnam
Hong Kong
Russian Federation
Ukraine
Norway
Iran
Albania
Venezuela
Italy
Peru
Sweden
Brazil
Portugal
Indonesia
Guatemala
Slovakia
Belgium
Poland
Austria
Switzerland
Chile
Japan
United Arab Emirates
South Africa
Spain
Finland
Turkey
Saudi Arabia
New Zealand
France
Afghanistan
Croatia (Hrvatska)
Argentina
Israel
Canada
Netherlands
Germany
Serbia and Montenegro
United Kingdom
USA − United States of America
Australia
−1.0 −0.5 0.0 0.5 1.0
Random effect of gender on the item
stargazer(anova(m2_history, m1_history),type = "text",align = TRUE,summary = FALSE)
===============================================================================================
Df AIC BIC logLik deviance Chisq Chi Df Pr(> Chisq)
-----------------------------------------------------------------------------------------------
m1_history 4 1,137,630.000 1,137,673.000 -568,811.200 1,137,622.000
m2_history 6 1,136,977.000 1,137,041.000 -568,482.400 1,136,965.000 657.492 2 0
-----------------------------------------------------------------------------------------------
48
SEX DIFFERENCES IN MORALITY 121
Item: MFQ_family
People should be loyal to their family members, even when they have done something wrong
m0_family <- lmer(MFQ_family ~ (1 | country), data = YMdata2,
REML = FALSE)
stargazer(m0_family, type = "text")
===============================================
Dependent variable:
---------------------------
MFQ_family
-----------------------------------------------
Constant 2.411***
(0.031)
-----------------------------------------------
Observations 310,914
Log Likelihood -566,353.000
Akaike Inf. Crit. 1,132,712.000
Bayesian Inf. Crit. 1,132,744.000
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
m1_family <- lmer(MFQ_family ~ gender + (1 | country), data = YMdata2,
REML = FALSE)
stargazer(m1_family, type = "text")
===============================================
Dependent variable:
---------------------------
MFQ_family
-----------------------------------------------
gender 0.139***
(0.005)
Constant 2.326***
(0.032)
-----------------------------------------------
Observations 310,865
Log Likelihood -565,924.000
Akaike Inf. Crit. 1,131,856.000
Bayesian Inf. Crit. 1,131,899.000
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
m2_family <- lmer(MFQ_family ~ gender + (gender | country), data = YMdata2,
REML = FALSE)
stargazer(m2_family, type = "text")
49
SEX DIFFERENCES IN MORALITY 122
===============================================
Dependent variable:
---------------------------
MFQ_family
-----------------------------------------------
gender 0.096***
(0.017)
Constant 2.353***
(0.035)
-----------------------------------------------
Observations 310,865
Log Likelihood -565,920.900
Akaike Inf. Crit. 1,131,854.000
Bayesian Inf. Crit. 1,131,918.000
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
sjobject<-sjp.lmer(m2_family, type = "coef",
sort.est = "gender",
show.values = F,
facet.grid = F,
vline.color = "blue",
color = "black",
title = c("Random effect of gender on the item",
"Random effect of gender on the item"),
axis.lim = c(-1,1),
prnt.plot = FALSE)
sjobject$plot.list[[2]]
50
SEX DIFFERENCES IN MORALITY 123
Thailand
Peru
Latvia
Lithuania
Vietnam
Colombia
Ukraine
Saudi Arabia
Kenya
New Zealand
Bulgaria
Hungary
Hong Kong
Switzerland
Egypt
Mexico
Iran
Singapore
Austria
Philippines
Malaysia
Russian Federation
Pakistan
Slovakia
Sweden
Romania
Israel
France
Greece
Nigeria
Chile
Guatemala
Italy
Venezuela
Puerto Rico
Denmark
South Africa
Iceland
Albania
Argentina
Serbia and Montenegro
United Arab Emirates
Indonesia
Finland
India
China
Ireland
Czech Republic
Turkey
Taiwan
Croatia (Hrvatska)
Slovenia
Afghanistan
Spain
Japan
Korea (south)
Norway
Portugal
USA − United States of America
Canada
Poland
Belgium
Germany
Australia
Netherlands
Brazil
United Kingdom
−1.0 −0.5 0.0 0.5 1.0
Random effect of gender on the item
stargazer(anova(m2_family, m1_family),type = "text",align = TRUE,summary = FALSE)
============================================================================================
Df AIC BIC logLik deviance Chisq Chi Df Pr(> Chisq)
--------------------------------------------------------------------------------------------
m1_family 4 1,131,856.000 1,131,899.000 -565,924.000 1,131,848.000
m2_family 6 1,131,854.000 1,131,918.000 -565,920.900 1,131,842.000 6.299 2 0.043
--------------------------------------------------------------------------------------------
51
SEX DIFFERENCES IN MORALITY 124
Item: MFQ_team
It is more important to be a team player than to express oneself
m0_team <- lmer(MFQ_team ~ (1 | country), data = YMdata2,
REML = FALSE)
stargazer(m0_team, type = "text")
===============================================
Dependent variable:
---------------------------
MFQ_team
-----------------------------------------------
Constant 2.000***
(0.035)
-----------------------------------------------
Observations 310,847
Log Likelihood -518,551.200
Akaike Inf. Crit. 1,037,108.000
Bayesian Inf. Crit. 1,037,140.000
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
m1_team <- lmer(MFQ_team ~ gender + (1 | country), data = YMdata2,
REML = FALSE)
stargazer(m1_team, type = "text")
===============================================
Dependent variable:
---------------------------
MFQ_team
-----------------------------------------------
gender 0.075***
(0.005)
Constant 1.953***
(0.035)
-----------------------------------------------
Observations 310,798
Log Likelihood -518,331.100
Akaike Inf. Crit. 1,036,670.000
Bayesian Inf. Crit. 1,036,713.000
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
m2_team <- lmer(MFQ_team ~ gender + (gender | country), data = YMdata2,
REML = FALSE)
stargazer(m2_team, type = "text")
52
SEX DIFFERENCES IN MORALITY 125
===============================================
Dependent variable:
---------------------------
MFQ_team
-----------------------------------------------
gender 0.055***
(0.017)
Constant 1.966***
(0.037)
-----------------------------------------------
Observations 310,798
Log Likelihood -518,324.900
Akaike Inf. Crit. 1,036,662.000
Bayesian Inf. Crit. 1,036,726.000
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
sjobject<-sjp.lmer(m2_team, type = "coef",
sort.est = "gender",
show.values = F,
facet.grid = F,
vline.color = "blue",
color = "black",
title = c("Random effect of gender on the item",
"Random effect of gender on the item"),
axis.lim = c(-1,1),
prnt.plot = FALSE)
sjobject$plot.list[[2]]
53
SEX DIFFERENCES IN MORALITY 126
Sweden
Japan
China
Denmark
New Zealand
Malaysia
Iran
Romania
Taiwan
Puerto Rico
United Kingdom
Vietnam
Singapore
Germany
Colombia
Bulgaria
India
Poland
Norway
Slovenia
Lithuania
Albania
Belgium
Ireland
Iceland
Korea (south)
Switzerland
Australia
Kenya
Finland
Venezuela
Serbia and Montenegro
Chile
Greece
Slovakia
Croatia (Hrvatska)
Austria
Canada
Saudi Arabia
Latvia
Indonesia
Hungary
Mexico
Pakistan
Thailand
Ukraine
Afghanistan
France
Egypt
Peru
Israel
Argentina
USA − United States of America
Turkey
Nigeria
Spain
Italy
South Africa
Czech Republic
Netherlands
Brazil
Hong Kong
Portugal
Philippines
Guatemala
Russian Federation
United Arab Emirates
−1.0 −0.5 0.0 0.5 1.0
Random effect of gender on the item
stargazer(anova(m2_team, m1_team),type = "text",align = TRUE,summary = FALSE)
===========================================================================================
Df AIC BIC logLik deviance Chisq Chi Df Pr(> Chisq)
-------------------------------------------------------------------------------------------
m1_team 4 1,036,670.000 1,036,713.000 -518,331.100 1,036,662.000
m2_team 6 1,036,662.000 1,036,726.000 -518,324.900 1,036,650.000 12.281 2 0.002
-------------------------------------------------------------------------------------------
54
SEX DIFFERENCES IN MORALITY 127
Authority
Item: MFQ_respect
Whether or not someone showed a lack of respect for authority
m0_respect <- lmer(MFQ_respect ~ (1 | country), data = YMdata2,
REML = FALSE)
stargazer(m0_respect, type = "text")
===============================================
Dependent variable:
---------------------------
MFQ_respect
-----------------------------------------------
Constant 1.965***
(0.050)
-----------------------------------------------
Observations 310,855
Log Likelihood -532,800.900
Akaike Inf. Crit. 1,065,608.000
Bayesian Inf. Crit. 1,065,640.000
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
m1_respect <- lmer(MFQ_respect ~ gender + (1 | country), data = YMdata2,
REML = FALSE)
stargazer(m1_respect, type = "text")
===============================================
Dependent variable:
---------------------------
MFQ_respect
-----------------------------------------------
gender -0.283***
(0.005)
Constant 2.141***
(0.049)
-----------------------------------------------
Observations 310,806
Log Likelihood -531,003.200
Akaike Inf. Crit. 1,062,014.000
Bayesian Inf. Crit. 1,062,057.000
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
55
SEX DIFFERENCES IN MORALITY 128
m2_respect <- lmer(MFQ_respect ~ gender + (gender | country), data = YMdata2,
REML = FALSE)
stargazer(m2_respect, type = "text")
===============================================
Dependent variable:
---------------------------
MFQ_respect
-----------------------------------------------
gender -0.217***
(0.015)
Constant 2.099***
(0.051)
-----------------------------------------------
Observations 310,806
Log Likelihood -530,981.900
Akaike Inf. Crit. 1,061,976.000
Bayesian Inf. Crit. 1,062,040.000
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
sjobject<-sjp.lmer(m2_respect, type = "coef",
sort.est = "gender",
show.values = F,
facet.grid = F,
vline.color = "blue",
color = "black",
title = c("Random effect of gender on the item",
"Random effect of gender on the item"),
axis.lim = c(-1,1),
prnt.plot = FALSE)
sjobject$plot.list[[2]]
56
SEX DIFFERENCES IN MORALITY 129
USA − United States of America
Mexico
Philippines
Kenya
Colombia
Puerto Rico
Malaysia
Nigeria
Vietnam
Venezuela
Norway
Thailand
Sweden
Australia
Guatemala
United Kingdom
Switzerland
China
South Africa
Saudi Arabia
Indonesia
Japan
Latvia
Germany
Albania
Denmark
Romania
Ireland
Peru
Italy
Bulgaria
Lithuania
Taiwan
Ukraine
Canada
Pakistan
Israel
Iceland
Hong Kong
Chile
Singapore
Iran
Egypt
New Zealand
Poland
Brazil
Afghanistan
Spain
Austria
Portugal
Slovakia
Turkey
Russian Federation
Hungary
Slovenia
Serbia and Montenegro
Finland
Czech Republic
Croatia (Hrvatska)
Greece
United Arab Emirates
France
Belgium
Korea (south)
Netherlands
Argentina
India
−1.0 −0.5 0.0 0.5 1.0
Random effect of gender on the item
stargazer(anova(m2_respect, m1_respect),type = "text",align = TRUE,summary = FALSE)
==============================================================================================
Df AIC BIC logLik deviance Chisq Chi Df Pr(> Chisq)
----------------------------------------------------------------------------------------------
m1_respect 4 1,062,014.000 1,062,057.000 -531,003.200 1,062,006.000
m2_respect 6 1,061,976.000 1,062,040.000 -530,981.900 1,061,964.000 42.753 2 0
----------------------------------------------------------------------------------------------
57
SEX DIFFERENCES IN MORALITY 130
Item: MFQ_traditions
Whether or not someone conformed to the traditions of society
m0_traditions <- lmer(MFQ_traditions ~ (1 | country), data = YMdata2,
REML = FALSE)
stargazer(m0_traditions, type = "text")
===============================================
Dependent variable:
---------------------------
MFQ_traditions
-----------------------------------------------
Constant 1.542***
(0.032)
-----------------------------------------------
Observations 310,841
Log Likelihood -499,778.100
Akaike Inf. Crit. 999,562.100
Bayesian Inf. Crit. 999,594.100
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
m1_traditions <- lmer(MFQ_traditions ~ gender + (1 | country), data = YMdata2,
REML = FALSE)
stargazer(m1_traditions, type = "text")
===============================================
Dependent variable:
---------------------------
MFQ_traditions
-----------------------------------------------
gender -0.068***
(0.004)
Constant 1.584***
(0.032)
-----------------------------------------------
Observations 310,792
Log Likelihood -499,576.000
Akaike Inf. Crit. 999,159.900
Bayesian Inf. Crit. 999,202.500
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
m2_traditions <- lmer(MFQ_traditions ~ gender + (gender | country), data = YMdata2,
REML = FALSE)
stargazer(m2_traditions, type = "text")
58
SEX DIFFERENCES IN MORALITY 131
===============================================
Dependent variable:
---------------------------
MFQ_traditions
-----------------------------------------------
gender -0.110***
(0.014)
Constant 1.609***
(0.035)
-----------------------------------------------
Observations 310,792
Log Likelihood -499,569.000
Akaike Inf. Crit. 999,150.000
Bayesian Inf. Crit. 999,213.900
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
sjobject<-sjp.lmer(m2_traditions, type = "coef",
sort.est = "gender",
show.values = F,
facet.grid = F,
vline.color = "blue",
color = "black",
title = c("Random effect of gender on the item",
"Random effect of gender on the item"),
axis.lim = c(-1,1),
prnt.plot = FALSE)
sjobject$plot.list[[2]]
59
SEX DIFFERENCES IN MORALITY 132
China
Nigeria
Saudi Arabia
Vietnam
Philippines
Malaysia
Japan
Indonesia
Germany
Singapore
Hong Kong
Guatemala
Kenya
Iran
Thailand
Puerto Rico
Pakistan
Colombia
Mexico
Peru
Latvia
Taiwan
Bulgaria
Venezuela
Switzerland
Korea (south)
Egypt
Lithuania
Greece
Hungary
Austria
Sweden
Slovenia
Albania
United Arab Emirates
Spain
Denmark
Romania
New Zealand
Italy
Afghanistan
Czech Republic
Ireland
Russian Federation
Israel
Netherlands
Croatia (Hrvatska)
Norway
Ukraine
Australia
Brazil
Belgium
Iceland
South Africa
Slovakia
Argentina
Chile
Portugal
Finland
France
India
Serbia and Montenegro
USA − United States of America
United Kingdom
Canada
Poland
Turkey
−1.0 −0.5 0.0 0.5 1.0
Random effect of gender on the item
stargazer(anova(m2_traditions, m1_traditions),type = "text",align = TRUE,summary = FALSE)
===========================================================================================
Df AIC BIC logLik deviance Chisq Chi Df Pr(> Chisq)
-------------------------------------------------------------------------------------------
m1_traditions 4 999,159.900 999,202.500 -499,576.000 999,151.900
m2_traditions 6 999,150.000 999,213.900 -499,569.000 999,138.000 13.921 2 0.001
-------------------------------------------------------------------------------------------
60
SEX DIFFERENCES IN MORALITY 133
Item: MFQ_chaos
Whether or not an action caused chaos or disorder
m0_chaos <- lmer(MFQ_chaos ~ (1 | country), data = YMdata2,
REML = FALSE)
stargazer(m0_chaos, type = "text")
===============================================
Dependent variable:
---------------------------
MFQ_chaos
-----------------------------------------------
Constant 2.961***
(0.030)
-----------------------------------------------
Observations 310,723
Log Likelihood -508,718.600
Akaike Inf. Crit. 1,017,443.000
Bayesian Inf. Crit. 1,017,475.000
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
m1_chaos <- lmer(MFQ_chaos ~ gender + (1 | country), data = YMdata2,
REML = FALSE)
stargazer(m1_chaos, type = "text")
===============================================
Dependent variable:
---------------------------
MFQ_chaos
-----------------------------------------------
gender -0.098***
(0.005)
Constant 3.022***
(0.030)
-----------------------------------------------
Observations 310,674
Log Likelihood -508,401.200
Akaike Inf. Crit. 1,016,810.000
Bayesian Inf. Crit. 1,016,853.000
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
m2_chaos <- lmer(MFQ_chaos ~ gender + (gender | country), data = YMdata2,
REML = FALSE)
stargazer(m2_chaos, type = "text")
61
SEX DIFFERENCES IN MORALITY 134
===============================================
Dependent variable:
---------------------------
MFQ_chaos
-----------------------------------------------
gender -0.094***
(0.006)
Constant 3.018***
(0.033)
-----------------------------------------------
Observations 310,674
Log Likelihood -508,396.600
Akaike Inf. Crit. 1,016,805.000
Bayesian Inf. Crit. 1,016,869.000
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
sjobject<-sjp.lmer(m2_chaos, type = "coef",
sort.est = "gender",
show.values = F,
facet.grid = F,
vline.color = "blue",
color = "black",
title = c("Random effect of gender on the item",
"Random effect of gender on the item"),
axis.lim = c(-1,1),
prnt.plot = FALSE)
sjobject$plot.list[[2]]
62
SEX DIFFERENCES IN MORALITY 135
Nigeria
Philippines
Indonesia
Puerto Rico
Malaysia
United Arab Emirates
Thailand
Peru
Kenya
Singapore
Vietnam
Guatemala
Colombia
Pakistan
Mexico
China
Saudi Arabia
Brazil
South Africa
Venezuela
Hong Kong
Albania
Portugal
USA − United States of America
Romania
Croatia (Hrvatska)
Afghanistan
India
Latvia
Japan
Ukraine
Taiwan
Slovenia
Argentina
Ireland
Lithuania
Serbia and Montenegro
Bulgaria
Korea (south)
Greece
Iran
Canada
Egypt
Russian Federation
Chile
Switzerland
Spain
Australia
Israel
Hungary
Turkey
New Zealand
United Kingdom
France
Sweden
Italy
Poland
Finland
Netherlands
Austria
Germany
Iceland
Denmark
Belgium
Czech Republic
Slovakia
Norway
−1.0 −0.5 0.0 0.5 1.0
Random effect of gender on the item
stargazer(anova(m2_chaos, m1_chaos),type = "text",align = TRUE,summary = FALSE)
===========================================================================================
Df AIC BIC logLik deviance Chisq Chi Df Pr(> Chisq)
-------------------------------------------------------------------------------------------
m1_chaos 4 1,016,810.000 1,016,853.000 -508,401.200 1,016,802.000
m2_chaos 6 1,016,805.000 1,016,869.000 -508,396.600 1,016,793.000 9.243 2 0.010
-------------------------------------------------------------------------------------------
63
SEX DIFFERENCES IN MORALITY 136
Item: MFQ_kidrespect
Respect for authority is something all children need to learn
m0_kidrespect <- lmer(MFQ_kidrespect ~ (1 | country), data = YMdata2,
REML = FALSE)
stargazer(m0_kidrespect, type = "text")
===============================================
Dependent variable:
---------------------------
MFQ_kidrespect
-----------------------------------------------
Constant 2.874***
(0.049)
-----------------------------------------------
Observations 310,900
Log Likelihood -540,109.700
Akaike Inf. Crit. 1,080,225.000
Bayesian Inf. Crit. 1,080,257.000
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
m1_kidrespect <- lmer(MFQ_kidrespect ~ gender + (1 | country), data = YMdata2,
REML = FALSE)
stargazer(m1_kidrespect, type = "text")
===============================================
Dependent variable:
---------------------------
MFQ_kidrespect
-----------------------------------------------
gender -0.193***
(0.005)
Constant 2.994***
(0.049)
-----------------------------------------------
Observations 310,851
Log Likelihood -539,263.800
Akaike Inf. Crit. 1,078,536.000
Bayesian Inf. Crit. 1,078,578.000
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
m2_kidrespect <- lmer(MFQ_kidrespect ~ gender + (gender | country), data = YMdata2,
REML = FALSE)
stargazer(m2_kidrespect, type = "text")
64
SEX DIFFERENCES IN MORALITY 137
===============================================
Dependent variable:
---------------------------
MFQ_kidrespect
-----------------------------------------------
gender -0.170***
(0.015)
Constant 2.980***
(0.050)
-----------------------------------------------
Observations 310,851
Log Likelihood -539,254.800
Akaike Inf. Crit. 1,078,522.000
Bayesian Inf. Crit. 1,078,585.000
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
sjobject<-sjp.lmer(m2_kidrespect, type = "coef",
sort.est = "gender",
show.values = F,
facet.grid = F,
vline.color = "blue",
color = "black",
title = c("Random effect of gender on the item",
"Random effect of gender on the item"),
axis.lim = c(-1,1),
prnt.plot = FALSE)
sjobject$plot.list[[2]]
65
SEX DIFFERENCES IN MORALITY 138
Mexico
Philippines
Germany
USA − United States of America
Thailand
Brazil
Italy
Kenya
Venezuela
Puerto Rico
Malaysia
South Africa
Colombia
Vietnam
Norway
Nigeria
Switzerland
Portugal
Romania
Lithuania
Japan
Hong Kong
Israel
Singapore
China
Saudi Arabia
Iceland
Albania
Bulgaria
Turkey
Guatemala
Austria
Hungary
Czech Republic
Afghanistan
Sweden
Serbia and Montenegro
Korea (south)
Iran
Poland
Taiwan
Chile
Indonesia
Ireland
Pakistan
Peru
Egypt
Denmark
Latvia
United Arab Emirates
Greece
Russian Federation
India
Slovakia
Slovenia
Argentina
Belgium
Australia
Ukraine
Spain
Finland
Netherlands
United Kingdom
Croatia (Hrvatska)
France
New Zealand
Canada
−1.0 −0.5 0.0 0.5 1.0
Random effect of gender on the item
stargazer(anova(m2_kidrespect, m1_kidrespect),type = "text",align = TRUE,summary = FALSE)
=================================================================================================
Df AIC BIC logLik deviance Chisq Chi Df Pr(> Chisq)
-------------------------------------------------------------------------------------------------
m1_kidrespect 4 1,078,536.000 1,078,578.000 -539,263.800 1,078,528.000
m2_kidrespect 6 1,078,522.000 1,078,585.000 -539,254.800 1,078,510.000 18.087 2 0.0001
-------------------------------------------------------------------------------------------------
66
SEX DIFFERENCES IN MORALITY 139
Item: MFQ_sexroles
Men and women each have dierent roles to play in society
m0_sexroles <- lmer(MFQ_sexroles ~ (1 | country), data = YMdata2,
REML = FALSE)
stargazer(m0_sexroles, type = "text")
===============================================
Dependent variable:
---------------------------
MFQ_sexroles
-----------------------------------------------
Constant 2.185***
(0.049)
-----------------------------------------------
Observations 310,965
Log Likelihood -586,784.400
Akaike Inf. Crit. 1,173,575.000
Bayesian Inf. Crit. 1,173,607.000
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
m1_sexroles <- lmer(MFQ_sexroles ~ gender + (1 | country), data = YMdata2,
REML = FALSE)
stargazer(m1_sexroles, type = "text")
===============================================
Dependent variable:
---------------------------
MFQ_sexroles
-----------------------------------------------
gender 0.510***
(0.006)
Constant 1.868***
(0.051)
-----------------------------------------------
Observations 310,916
Log Likelihood -582,752.100
Akaike Inf. Crit. 1,165,512.000
Bayesian Inf. Crit. 1,165,555.000
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
m2_sexroles <- lmer(MFQ_sexroles ~ gender + (gender | country), data = YMdata2,
REML = FALSE)
stargazer(m2_sexroles, type = "text")
67
SEX DIFFERENCES IN MORALITY 140
===============================================
Dependent variable:
---------------------------
MFQ_sexroles
-----------------------------------------------
gender 0.444***
(0.029)
Constant 1.905***
(0.060)
-----------------------------------------------
Observations 310,916
Log Likelihood -582,716.000
Akaike Inf. Crit. 1,165,444.000
Bayesian Inf. Crit. 1,165,508.000
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
sjobject<-sjp.lmer(m2_sexroles, type = "coef",
sort.est = "gender",
show.values = F,
facet.grid = F,
vline.color = "blue",
color = "black",
title = c("Random effect of gender on the item",
"Random effect of gender on the item"),
axis.lim = c(-1,1),
prnt.plot = FALSE)
sjobject$plot.list[[2]]
68
SEX DIFFERENCES IN MORALITY 141
Philippines
China
Kenya
Colombia
Mexico
Singapore
Malaysia
South Africa
Romania
Iran
Indonesia
Nigeria
Japan
Taiwan
Vietnam
Pakistan
Russian Federation
Ukraine
Guatemala
Portugal
Saudi Arabia
Slovenia
Hungary
Korea (south)
Venezuela
Lithuania
Albania
Slovakia
Thailand
Brazil
Afghanistan
Ireland
Austria
Bulgaria
Greece
New Zealand
Serbia and Montenegro
India
Croatia (Hrvatska)
Hong Kong
Latvia
Egypt
Turkey
Argentina
USA − United States of America
United Kingdom
Switzerland
Sweden
Belgium
Germany
Czech Republic
Iceland
Peru
Australia
United Arab Emirates
Canada
Chile
Spain
Netherlands
Italy
Puerto Rico
Denmark
France
Norway
Israel
Finland
Poland
−1.0 −0.5 0.0 0.5 1.0
Random effect of gender on the item
stargazer(anova(m2_sexroles, m1_sexroles),type = "text",align = TRUE,summary = FALSE)
===============================================================================================
Df AIC BIC logLik deviance Chisq Chi Df Pr(> Chisq)
-----------------------------------------------------------------------------------------------
m1_sexroles 4 1,165,512.000 1,165,555.000 -582,752.100 1,165,504.000
m2_sexroles 6 1,165,444.000 1,165,508.000 -582,716.000 1,165,432.000 72.201 2 0
-----------------------------------------------------------------------------------------------
69
SEX DIFFERENCES IN MORALITY 142
Item: MFQ_soldier
If I were a soldier and disagreed with my commanding ocer’s orders, I would obey anyway
because that is my duty
m0_soldier <- lmer(MFQ_soldier ~ (1 | country), data = YMdata2,
REML = FALSE)
stargazer(m0_soldier, type = "text")
===============================================
Dependent variable:
---------------------------
MFQ_soldier
-----------------------------------------------
Constant 2.138***
(0.040)
-----------------------------------------------
Observations 310,931
Log Likelihood -576,641.400
Akaike Inf. Crit. 1,153,289.000
Bayesian Inf. Crit. 1,153,321.000
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
m1_soldier <- lmer(MFQ_soldier ~ gender + (1 | country), data = YMdata2,
REML = FALSE)
stargazer(m1_soldier, type = "text")
===============================================
Dependent variable:
---------------------------
MFQ_soldier
-----------------------------------------------
gender 0.068***
(0.006)
Constant 2.096***
(0.040)
-----------------------------------------------
Observations 310,882
Log Likelihood -576,471.300
Akaike Inf. Crit. 1,152,951.000
Bayesian Inf. Crit. 1,152,993.000
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
70
SEX DIFFERENCES IN MORALITY 143
m2_soldier <- lmer(MFQ_soldier ~ gender + (gender | country), data = YMdata2,
REML = FALSE)
stargazer(m2_soldier, type = "text")
===============================================
Dependent variable:
---------------------------
MFQ_soldier
-----------------------------------------------
gender 0.112***
(0.017)
Constant 2.067***
(0.044)
-----------------------------------------------
Observations 310,882
Log Likelihood -576,449.700
Akaike Inf. Crit. 1,152,911.000
Bayesian Inf. Crit. 1,152,975.000
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
sjobject<-sjp.lmer(m2_soldier, type = "coef",
sort.est = "gender",
show.values = F,
facet.grid = F,
vline.color = "blue",
color = "black",
title = c("Random effect of gender on the item",
"Random effect of gender on the item"),
axis.lim = c(-1,1),
prnt.plot = FALSE)
sjobject$plot.list[[2]]
71
SEX DIFFERENCES IN MORALITY 144
Singapore
China
Philippines
Thailand
USA − United States of America
Pakistan
Hong Kong
Mexico
Nigeria
Japan
Kenya
Albania
Taiwan
Vietnam
Afghanistan
Latvia
United Arab Emirates
Puerto Rico
Norway
Malaysia
Saudi Arabia
Peru
Ukraine
Korea (south)
Lithuania
Indonesia
Romania
Russian Federation
Israel
Iran
Egypt
Colombia
Slovenia
Denmark
United Kingdom
Guatemala
Netherlands
Ireland
Turkey
Czech Republic
Switzerland
Bulgaria
Finland
Poland
Germany
Portugal
Belgium
Sweden
South Africa
Argentina
India
Greece
Australia
Hungary
Iceland
New Zealand
Spain
Austria
Slovakia
Italy
Venezuela
France
Brazil
Croatia (Hrvatska)
Serbia and Montenegro
Chile
Canada
−1.0 −0.5 0.0 0.5 1.0
Random effect of gender on the item
stargazer(anova(m2_soldier, m1_soldier),type = "text",align = TRUE,summary = FALSE)
==============================================================================================
Df AIC BIC logLik deviance Chisq Chi Df Pr(> Chisq)
----------------------------------------------------------------------------------------------
m1_soldier 4 1,152,951.000 1,152,993.000 -576,471.300 1,152,943.000
m2_soldier 6 1,152,911.000 1,152,975.000 -576,449.700 1,152,899.000 43.349 2 0
----------------------------------------------------------------------------------------------
72
SEX DIFFERENCES IN MORALITY 145
Purity
Item: MFQ_decency
Whether or not someone violated standards of purity and decency
m0_decency <- lmer(MFQ_decency ~ (1 | country), data = YMdata2,
REML = FALSE)
stargazer(m0_decency, type = "text")
===============================================
Dependent variable:
---------------------------
MFQ_decency
-----------------------------------------------
Constant 2.141***
(0.051)
-----------------------------------------------
Observations 310,715
Log Likelihood -550,651.300
Akaike Inf. Crit. 1,101,309.000
Bayesian Inf. Crit. 1,101,341.000
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
m1_decency <- lmer(MFQ_decency ~ gender + (1 | country), data = YMdata2,
REML = FALSE)
stargazer(m1_decency, type = "text")
===============================================
Dependent variable:
---------------------------
MFQ_decency
-----------------------------------------------
gender -0.193***
(0.005)
Constant 2.261***
(0.050)
-----------------------------------------------
Observations 310,666
Log Likelihood -549,851.900
Akaike Inf. Crit. 1,099,712.000
Bayesian Inf. Crit. 1,099,754.000
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
73
SEX DIFFERENCES IN MORALITY 146
m2_decency <- lmer(MFQ_decency ~ gender + (gender | country), data = YMdata2,
REML = FALSE)
stargazer(m2_decency, type = "text")
===============================================
Dependent variable:
---------------------------
MFQ_decency
-----------------------------------------------
gender -0.211***
(0.015)
Constant 2.271***
(0.053)
-----------------------------------------------
Observations 310,666
Log Likelihood -549,848.200
Akaike Inf. Crit. 1,099,708.000
Bayesian Inf. Crit. 1,099,772.000
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
sjobject<-sjp.lmer(m2_decency, type = "coef",
sort.est = "gender",
show.values = F,
facet.grid = F,
vline.color = "blue",
color = "black",
title = c("Random effect of gender on the item",
"Random effect of gender on the item"),
axis.lim = c(-1,1),
prnt.plot = FALSE)
sjobject$plot.list[[2]]
74
SEX DIFFERENCES IN MORALITY 147
Singapore
Vietnam
Philippines
Kenya
Nigeria
China
Saudi Arabia
Guatemala
Thailand
United Arab Emirates
Indonesia
Malaysia
Pakistan
Egypt
United Kingdom
South Africa
Taiwan
Japan
Peru
Albania
Iran
Afghanistan
Puerto Rico
Korea (south)
New Zealand
Ireland
Latvia
Turkey
Hong Kong
Colombia
Ukraine
Canada
Lithuania
Australia
Mexico
Venezuela
Netherlands
India
Romania
Bulgaria
Switzerland
Greece
Sweden
Croatia (Hrvatska)
Belgium
Russian Federation
USA − United States of America
Austria
Slovakia
Israel
Italy
Hungary
Chile
Denmark
Germany
Czech Republic
Portugal
France
Norway
Argentina
Slovenia
Spain
Serbia and Montenegro
Iceland
Finland
Poland
Brazil
−1.0 −0.5 0.0 0.5 1.0
Random effect of gender on the item
stargazer(anova(m2_decency, m1_decency),type = "text",align = TRUE,summary = FALSE)
=============================================================================================
Df AIC BIC logLik deviance Chisq Chi Df Pr(> Chisq)
---------------------------------------------------------------------------------------------
m1_decency 4 1,099,712.000 1,099,754.000 -549,851.900 1,099,704.000
m2_decency 6 1,099,708.000 1,099,772.000 -549,848.200 1,099,696.000 7.427 2 0.024
---------------------------------------------------------------------------------------------
75
SEX DIFFERENCES IN MORALITY 148
Item: MFQ_disgusting
Whether or not someone did something disgusting
m0_disgusting <- lmer(MFQ_disgusting ~ (1 | country), data = YMdata2,
REML = FALSE)
stargazer(m0_disgusting, type = "text")
===============================================
Dependent variable:
---------------------------
MFQ_disgusting
-----------------------------------------------
Constant 2.240***
(0.031)
-----------------------------------------------
Observations 310,672
Log Likelihood -533,610.300
Akaike Inf. Crit. 1,067,227.000
Bayesian Inf. Crit. 1,067,259.000
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
m1_disgusting <- lmer(MFQ_disgusting ~ gender + (1 | country), data = YMdata2,
REML = FALSE)
stargazer(m1_disgusting, type = "text")
===============================================
Dependent variable:
---------------------------
MFQ_disgusting
-----------------------------------------------
gender -0.329***
(0.005)
Constant 2.444***
(0.029)
-----------------------------------------------
Observations 310,624
Log Likelihood -531,240.500
Akaike Inf. Crit. 1,062,489.000
Bayesian Inf. Crit. 1,062,532.000
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
m2_disgusting <- lmer(MFQ_disgusting ~ gender + (gender | country), data = YMdata2,
REML = FALSE)
stargazer(m2_disgusting, type = "text")
76
SEX DIFFERENCES IN MORALITY 149
===============================================
Dependent variable:
---------------------------
MFQ_disgusting
-----------------------------------------------
gender -0.304***
(0.009)
Constant 2.431***
(0.024)
-----------------------------------------------
Observations 310,624
Log Likelihood -531,228.000
Akaike Inf. Crit. 1,062,468.000
Bayesian Inf. Crit. 1,062,532.000
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
sjobject<-sjp.lmer(m2_disgusting, type = "coef",
sort.est = "gender",
show.values = F,
facet.grid = F,
vline.color = "blue",
color = "black",
title = c("Random effect of gender on the item",
"Random effect of gender on the item"),
axis.lim = c(-1,1),
prnt.plot = FALSE)
sjobject$plot.list[[2]]
77
SEX DIFFERENCES IN MORALITY 150
Finland
Denmark
Sweden
Norway
Austria
Romania
Germany
Slovakia
Hungary
Czech Republic
United Kingdom
Ireland
Poland
Canada
Australia
Lithuania
Chile
France
Netherlands
Greece
Israel
Italy
Argentina
USA − United States of America
Afghanistan
Iceland
Switzerland
Brazil
New Zealand
Mexico
Spain
Latvia
Albania
Turkey
Venezuela
Peru
Slovenia
Hong Kong
Belgium
Egypt
Malaysia
Serbia and Montenegro
Colombia
South Africa
Thailand
Portugal
Croatia (Hrvatska)
Taiwan
Kenya
Puerto Rico
Singapore
Russian Federation
Bulgaria
Iran
United Arab Emirates
Saudi Arabia
Ukraine
Indonesia
Korea (south)
Guatemala
Japan
India
Nigeria
Pakistan
Vietnam
Philippines
China
−1.0 −0.5 0.0 0.5 1.0
Random effect of gender on the item
stargazer(anova(m2_disgusting, m1_disgusting),type = "text",align = TRUE,summary = FALSE)
=================================================================================================
Df AIC BIC logLik deviance Chisq Chi Df Pr(> Chisq)
-------------------------------------------------------------------------------------------------
m1_disgusting 4 1,062,489.000 1,062,532.000 -531,240.500 1,062,481.000
m2_disgusting 6 1,062,468.000 1,062,532.000 -531,228.000 1,062,456.000 24.879 2 0.00000
-------------------------------------------------------------------------------------------------
78
SEX DIFFERENCES IN MORALITY 151
Item: MFQ_god
Whether or not someone acted in a way that God would approve of
m0_god <- lmer(MFQ_god ~ (1 | country), data = YMdata2,
REML = FALSE)
stargazer(m0_god, type = "text")
===============================================
Dependent variable:
---------------------------
MFQ_god
-----------------------------------------------
Constant 1.141***
(0.070)
-----------------------------------------------
Observations 310,830
Log Likelihood -603,182.500
Akaike Inf. Crit. 1,206,371.000
Bayesian Inf. Crit. 1,206,403.000
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
m1_god <- lmer(MFQ_god ~ gender + (1 | country), data = YMdata2,
REML = FALSE)
stargazer(m1_god, type = "text")
===============================================
Dependent variable:
---------------------------
MFQ_god
-----------------------------------------------
gender -0.176***
(0.006)
Constant 1.251***
(0.069)
-----------------------------------------------
Observations 310,781
Log Likelihood -602,675.600
Akaike Inf. Crit. 1,205,359.000
Bayesian Inf. Crit. 1,205,402.000
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
m2_god <- lmer(MFQ_god ~ gender + (gender | country), data = YMdata2,
REML = FALSE)
stargazer(m2_god, type = "text")
79
SEX DIFFERENCES IN MORALITY 152
===============================================
Dependent variable:
---------------------------
MFQ_god
-----------------------------------------------
gender -0.153***
(0.009)
Constant 1.236***
(0.073)
-----------------------------------------------
Observations 310,781
Log Likelihood -602,665.600
Akaike Inf. Crit. 1,205,343.000
Bayesian Inf. Crit. 1,205,407.000
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
sjobject<-sjp.lmer(m2_god, type = "coef",
sort.est = "gender",
show.values = F,
facet.grid = F,
vline.color = "blue",
color = "black",
title = c("Random effect of gender on the item",
"Random effect of gender on the item"),
axis.lim = c(-1,1),
prnt.plot = FALSE)
sjobject$plot.list[[2]]
80
SEX DIFFERENCES IN MORALITY 153
Nigeria
Saudi Arabia
Philippines
Guatemala
Kenya
United Arab Emirates
Indonesia
Pakistan
Puerto Rico
Egypt
Peru
Albania
Malaysia
Vietnam
Korea (south)
USA − United States of America
Singapore
Afghanistan
China
Mexico
Iran
Venezuela
South Africa
Thailand
Colombia
Japan
Taiwan
Hong Kong
Ukraine
India
Slovakia
Chile
Romania
Lithuania
Argentina
Hungary
Canada
Latvia
Brazil
Turkey
New Zealand
Poland
Israel
Russian Federation
Spain
Croatia (Hrvatska)
Greece
Bulgaria
Switzerland
Czech Republic
Italy
Australia
Germany
Ireland
Slovenia
United Kingdom
Austria
France
Portugal
Serbia and Montenegro
Netherlands
Belgium
Denmark
Finland
Sweden
Iceland
Norway
−1.0 −0.5 0.0 0.5 1.0
Random effect of gender on the item
stargazer(anova(m2_god, m1_god),type = "text",align = TRUE,summary = FALSE)
==========================================================================================
Df AIC BIC logLik deviance Chisq Chi Df Pr(> Chisq)
------------------------------------------------------------------------------------------
m1_god 4 1,205,359.000 1,205,402.000 -602,675.600 1,205,351.000
m2_god 6 1,205,343.000 1,205,407.000 -602,665.600 1,205,331.000 19.988 2 0.00005
------------------------------------------------------------------------------------------
81
SEX DIFFERENCES IN MORALITY 154
Item: MFQ_harmlessdg
People should not do things that are disgusting, even if no one is harmed
m0_harmlessdg <- lmer(MFQ_harmlessdg ~ (1 | country), data = YMdata2,
REML = FALSE)
stargazer(m0_harmlessdg, type = "text")
===============================================
Dependent variable:
---------------------------
MFQ_harmlessdg
-----------------------------------------------
Constant 1.855***
(0.056)
-----------------------------------------------
Observations 310,814
Log Likelihood -568,038.000
Akaike Inf. Crit. 1,136,082.000
Bayesian Inf. Crit. 1,136,114.000
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
m1_harmlessdg <- lmer(MFQ_harmlessdg ~ gender + (1 | country), data = YMdata2,
REML = FALSE)
stargazer(m1_harmlessdg, type = "text")
===============================================
Dependent variable:
---------------------------
MFQ_harmlessdg
-----------------------------------------------
gender -0.294***
(0.005)
Constant 2.038***
(0.055)
-----------------------------------------------
Observations 310,765
Log Likelihood -566,477.500
Akaike Inf. Crit. 1,132,963.000
Bayesian Inf. Crit. 1,133,006.000
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
m2_harmlessdg <- lmer(MFQ_harmlessdg ~ gender + (gender | country), data = YMdata2,
REML = FALSE)
stargazer(m2_harmlessdg, type = "text")
82
SEX DIFFERENCES IN MORALITY 155
===============================================
Dependent variable:
---------------------------
MFQ_harmlessdg
-----------------------------------------------
gender -0.331***
(0.020)
Constant 2.059***
(0.056)
-----------------------------------------------
Observations 310,765
Log Likelihood -566,473.800
Akaike Inf. Crit. 1,132,960.000
Bayesian Inf. Crit. 1,133,023.000
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
sjobject<-sjp.lmer(m2_harmlessdg, type = "coef",
sort.est = "gender",
show.values = F,
facet.grid = F,
vline.color = "blue",
color = "black",
title = c("Random effect of gender on the item",
"Random effect of gender on the item"),
axis.lim = c(-1,1),
prnt.plot = FALSE)
sjobject$plot.list[[2]]
83
SEX DIFFERENCES IN MORALITY 156
Japan
South Africa
China
Singapore
Germany
Malaysia
Colombia
Philippines
Bulgaria
Denmark
Lithuania
Kenya
Poland
Ireland
Venezuela
Iran
Norway
Hungary
Austria
Puerto Rico
Taiwan
Nigeria
Mexico
Pakistan
Spain
Indonesia
Latvia
Belgium
Greece
Netherlands
Croatia (Hrvatska)
Romania
Slovakia
Switzerland
Sweden
Peru
Vietnam
Ukraine
Guatemala
Australia
Slovenia
France
Egypt
Serbia and Montenegro
Albania
Chile
Saudi Arabia
Hong Kong
Russian Federation
Czech Republic
United Kingdom
Turkey
Thailand
Finland
USA − United States of America
Canada
Argentina
New Zealand
Afghanistan
Iceland
United Arab Emirates
Brazil
Korea (south)
Italy
Portugal
Israel
India
−1.0 −0.5 0.0 0.5 1.0
Random effect of gender on the item
stargazer(anova(m2_harmlessdg, m1_harmlessdg),type = "text",align = TRUE,summary = FALSE)
================================================================================================
Df AIC BIC logLik deviance Chisq Chi Df Pr(> Chisq)
------------------------------------------------------------------------------------------------
m1_harmlessdg 4 1,132,963.000 1,133,006.000 -566,477.500 1,132,955.000
m2_harmlessdg 6 1,132,960.000 1,133,023.000 -566,473.800 1,132,948.000 7.438 2 0.024
------------------------------------------------------------------------------------------------
84
SEX DIFFERENCES IN MORALITY 157
Item: MFQ_unnatural
I would call some acts wrong on the grounds that they are unnatural
m0_unnatural <- lmer(MFQ_unnatural ~ (1 | country), data = YMdata2,
REML = FALSE)
stargazer(m0_unnatural, type = "text")
===============================================
Dependent variable:
---------------------------
MFQ_unnatural
-----------------------------------------------
Constant 1.498***
(0.048)
-----------------------------------------------
Observations 310,577
Log Likelihood -569,257.000
Akaike Inf. Crit. 1,138,520.000
Bayesian Inf. Crit. 1,138,552.000
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
m1_unnatural <- lmer(MFQ_unnatural ~ gender + (1 | country), data = YMdata2,
REML = FALSE)
stargazer(m1_unnatural, type = "text")
===============================================
Dependent variable:
---------------------------
MFQ_unnatural
-----------------------------------------------
gender -0.072***
(0.005)
Constant 1.543***
(0.047)
-----------------------------------------------
Observations 310,528
Log Likelihood -569,078.700
Akaike Inf. Crit. 1,138,165.000
Bayesian Inf. Crit. 1,138,208.000
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
m2_unnatural <- lmer(MFQ_unnatural ~ gender + (gender | country), data = YMdata2,
REML = FALSE)
stargazer(m2_unnatural, type = "text")
85
SEX DIFFERENCES IN MORALITY 158
===============================================
Dependent variable:
---------------------------
MFQ_unnatural
-----------------------------------------------
gender -0.130***
(0.018)
Constant 1.578***
(0.050)
-----------------------------------------------
Observations 310,528
Log Likelihood -569,070.700
Akaike Inf. Crit. 1,138,153.000
Bayesian Inf. Crit. 1,138,217.000
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
sjobject<-sjp.lmer(m2_unnatural, type = "coef",
sort.est = "gender",
show.values = F,
facet.grid = F,
vline.color = "blue",
color = "black",
title = c("Random effect of gender on the item",
"Random effect of gender on the item"),
axis.lim = c(-1,1),
prnt.plot = FALSE)
sjobject$plot.list[[2]]
86
SEX DIFFERENCES IN MORALITY 159
Singapore
Kenya
Japan
Malaysia
China
Vietnam
Latvia
Puerto Rico
Pakistan
Saudi Arabia
Colombia
South Africa
Nigeria
United Arab Emirates
Taiwan
Peru
Guatemala
Philippines
Venezuela
Ukraine
Germany
Indonesia
Mexico
Iran
Thailand
Lithuania
Greece
Sweden
Australia
Hong Kong
Afghanistan
Korea (south)
Switzerland
Romania
Egypt
Norway
Albania
Austria
Brazil
Ireland
Bulgaria
Croatia (Hrvatska)
Finland
Slovakia
Russian Federation
Netherlands
Chile
Czech Republic
Serbia and Montenegro
Argentina
Denmark
Slovenia
United Kingdom
Hungary
Turkey
Spain
Iceland
Israel
New Zealand
France
Belgium
Italy
USA − United States of America
Canada
Portugal
India
Poland
−1.0 −0.5 0.0 0.5 1.0
Random effect of gender on the item
stargazer(anova(m2_unnatural, m1_unnatural),type = "text",align = TRUE,summary = FALSE)
================================================================================================
Df AIC BIC logLik deviance Chisq Chi Df Pr(> Chisq)
------------------------------------------------------------------------------------------------
m1_unnatural 4 1,138,165.000 1,138,208.000 -569,078.700 1,138,157.000
m2_unnatural 6 1,138,153.000 1,138,217.000 -569,070.700 1,138,141.000 15.860 2 0.0004
------------------------------------------------------------------------------------------------
87
SEX DIFFERENCES IN MORALITY 160
Item: MFQ_chastity
Chastity is an important and valuable virtue
m0_chastity <- lmer(MFQ_chastity ~ (1 | country), data = YMdata2,
REML = FALSE)
stargazer(m0_chastity, type = "text")
===============================================
Dependent variable:
---------------------------
MFQ_chastity
-----------------------------------------------
Constant 1.645***
(0.070)
-----------------------------------------------
Observations 310,746
Log Likelihood -593,809.500
Akaike Inf. Crit. 1,187,625.000
Bayesian Inf. Crit. 1,187,657.000
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
m1_chastity <- lmer(MFQ_chastity ~ gender + (1 | country), data = YMdata2,
REML = FALSE)
stargazer(m1_chastity, type = "text")
===============================================
Dependent variable:
---------------------------
MFQ_chastity
-----------------------------------------------
gender 0.030***
(0.006)
Constant 1.627***
(0.070)
-----------------------------------------------
Observations 310,697
Log Likelihood -593,700.000
Akaike Inf. Crit. 1,187,408.000
Bayesian Inf. Crit. 1,187,451.000
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
m2_chastity <- lmer(MFQ_chastity ~ gender + (gender | country), data = YMdata2,
REML = FALSE)
stargazer(m2_chastity, type = "text")
88
SEX DIFFERENCES IN MORALITY 161
===============================================
Dependent variable:
---------------------------
MFQ_chastity
-----------------------------------------------
gender -0.031
(0.026)
Constant 1.660***
(0.079)
-----------------------------------------------
Observations 310,697
Log Likelihood -593,674.300
Akaike Inf. Crit. 1,187,361.000
Bayesian Inf. Crit. 1,187,424.000
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
sjobject<-sjp.lmer(m2_chastity, type = "coef",
sort.est = "gender",
show.values = F,
facet.grid = F,
vline.color = "blue",
color = "black",
title = c("Random effect of gender on the item",
"Random effect of gender on the item"),
axis.lim = c(-1,1),
prnt.plot = FALSE)
sjobject$plot.list[[2]]
89
SEX DIFFERENCES IN MORALITY 162
Kenya
Singapore
Philippines
Pakistan
Vietnam
Malaysia
Japan
China
Saudi Arabia
Taiwan
Guatemala
Indonesia
Nigeria
Iran
Thailand
Mexico
Egypt
Peru
Albania
Hong Kong
Russian Federation
Korea (south)
United Arab Emirates
Puerto Rico
South Africa
Latvia
Switzerland
Lithuania
Croatia (Hrvatska)
Ukraine
Hungary
Afghanistan
Bulgaria
Austria
Slovakia
India
New Zealand
Romania
Italy
Venezuela
Colombia
Turkey
Spain
Germany
USA − United States of America
Israel
Netherlands
Czech Republic
Serbia and Montenegro
Argentina
United Kingdom
Ireland
Norway
Slovenia
Greece
Chile
Canada
Belgium
Denmark
Australia
Finland
Iceland
Brazil
Portugal
Sweden
France
Poland
−1.0 −0.5 0.0 0.5 1.0
Random effect of gender on the item
stargazer(anova(m2_chastity, m1_chastity),type = "text",align = TRUE,summary = FALSE)
===============================================================================================
Df AIC BIC logLik deviance Chisq Chi Df Pr(> Chisq)
-----------------------------------------------------------------------------------------------
m1_chastity 4 1,187,408.000 1,187,451.000 -593,700.000 1,187,400.000
m2_chastity 6 1,187,361.000 1,187,424.000 -593,674.300 1,187,349.000 51.402 2 0
-----------------------------------------------------------------------------------------------
90
SEX DIFFERENCES IN MORALITY 163
FREQUENTIST MLM 164
Appendix 3: Frequentist Multi-Level Modeling of Sex Dierences across 19 Cultures
(Study 2)
Mohammad Atari
University of Southern California
FREQUENTIST MLM 165
Abstract
Most of the empirical research on sex dierences and cultural variations in morality has
relied on within-culture analyses, or small cross-cultural data, comparing few cultures. To
further broaden the scientific understanding of sex dierences in morality, the current
research relies on two international samples to provide the first large-scale examination of
sex dierences in moral judgements nested within cultures. Using a sample from 67
countries (Study 1; n = 336,691), we found culturally-variable sex dierences in moral
judgements, as conceptualized by the Moral Foundations Theory. Women consistently
scored higher than men on Care, Fairness, and Purity. In contrast, sex dierences in
Loyalty and Authority were negligible and highly variable across cultures. We also
examined country-level sex dierences in moral judgements in relation to cultural,
socio-economic, and gender-equality indicators. In Study 2 (19 countries; n = 11,969), we
largely replicated these results using Bayesian multi-level modeling in a distinct sample.
Specifically, women consistently showed higher concerns for Care, Fairness, and Purity in
their moral judgements than did men. We discuss the implications of these findings on the
ongoing debate about the origin of sex dierences and cultural variations in moral
judgements as well as theoretical and pragmatic implications for moral psychology.
Keywords: morality, gender, sex dierences, culture, Bayesian inference.
FREQUENTIST MLM 166
Appendix 3: Frequentist Multi-Level Modeling of Sex Dierences across 19 Cultures
(Study 2)
Introduction
Here, we supplement our Study 2’s Bayesian analysis (see the main document) using
their frequentist multi-level counterparts, for comparison purposes (also see Gelman et al.,
2013). We use multi-level models to help distinguish between sex dierences across
cultures. Multi-level models account for the clustered nature of data by directly modeling
the clustering with random coecients (Laird & Ware, 1982). Here, we use frequentist
multi-level modeling (MLM) to examine the eect of sex on moral foundations across 19
cultures (see Study 2 for more details).
Methods
As mentioned in the main document, we conducted a secondary analysis on data
from 11,969 participants in 19 countries: Australia (n = 1,115; Crone et al., 2018), Belgium
(n = 493; Quintelier et al., 2013), China (n = 393; Ji & Janicke, 2018), France (n=213;
Métayer & Pahlavan, 2014), Hungary (n = 403; Hadarics & Kende, 2017), Iran (n=501;
Atari & Dehghani, 2018), Japan (n = 534; Konishi et al., 2017), South Korea (n=478;
Kim et al., 2012), Latvia (n = 264; Dimdins et al., 2016), Mongolia (n = 444; Sychev et
al., 2016), the Netherlands (n = 285; Quintelier et al., 2013), Poland (n = 1,702;
Jarmakowski-Kostrzanowski & Jarmakowska-Kostrzanowska, 2016), Russia (n=468;
Sychev et al., 2016), Serbia (n = 240; MeedoviÊ & PetroviÊ, 2016; Pecujlija et al., 2015),
Spain (n = 240; Rodríguez-Ferreiro & Barberia, 2017), Sweden (n = 2,068; Jansson &
Dorrepaal, 2015, Nilsson & Erlandsson, 2015), Turkey (n = 1,465; Yilmaz et al., 2016),
United Kingdom (n = 273; van Leeuwen et al., 2017), and United States (n=390;
Amazon Mechanical Turk). The median sample size was 444. Participants in each of the
above samples completed the MFQ-30 and self-reported their biological sex.
FREQUENTIST MLM 167
We ran MLMs using the lme4 package (version 1.1; Bates et al., 2015) in R
programming language (version 3.6.1; R Core Team, 2017). We report fixed eect
estimates with 95% confidence intervals (95% CI). Sex was dummy coded as 0 (female) and
1(male). First, we estimated moral foundation scores based on country (Model 0). Second,
we estimated moral foundation scores based on sex as a fixed parameter, the sex dierences
were not allowed to vary in across countries, resulting in the average sex dierence ( Model
1). Third, we estimated a random slope model allowing countries to vary in sex dierences
(Model 2). These steps were repeated for each moral foundation.
Results
Care
The multi-level model for Care indicated that women (intercept = 3.63, SE = 0.06)
scored higher than men (B =≠ 0.27, SE = 0.03, t = -8.59, p < .001). Intraclass
Correlation Coecient (ICC) was .099 and the conditional R
2
was 12.5%. The results of
the three models are presented in Table 23.
Fairness
The multi-level model for Fairness indicated that women (intercept = 3.57, SE =
0.06) scored higher than men (B =≠ 0.15, SE = 0.02, t = -9.50, p < .001). The model’s
ICC was .130 and the conditional R
2
was 14.0%. The results of the three models are
presented in Table 24.
Loyalty
The multi-level model for Loyalty indicated that men (intercept = 2.76, SE = 0.08)
scored higher than women (B=0.05, SE = 0.02, t = 2.42, p = .030). The model’s ICC
was .129 and the conditional R
2
was 13.0%. The results of the three models are presented
in Table 25.
SEX DIFFERENCES IN MORALITY 168
Authority
The multi-level model for Authority indicated that men (intercept = 2.69, SE =
0.05) scored higher than women, but this eect was not significant ( B=0.03, SE = 0.03, t
= 1.24, p = .233). The model’s ICC was .047 and the conditionalR
2
was 4.8%. The results
of the three models are presented in Table 26.
Purity
The multi-level model for Purity indicated that women (intercept = 2.60, SE = 0.11)
scored higher than men (B =≠ 0.11, SE = 0.03, t = -3.30, p = .003). The model’s ICC
was .190 and the conditional R
2
was 19.2%. The results of the three models are presented
in Table 27.
Discussion
The present results show that across cultures, women systematically score higher than
men on Care, Fairness, and Purity, whereas sex dierences in Loyalty and Authority were
substantially smaller in size. These findings are fully in line with Study 1’s findings (see the
main document) using a distinct sample. In addition, these frequentist models’ results are
in full agreement with our Bayesian models in the main document, demonstrating stable
female-favoring sex dierences in Care, Fairness, and Purity. Therefore, our findings using
Bayesian framework in Study 2 are not an artifact of sampling issues in Study 1 (see
Yeager et al., 2011) or our weakly informed priors in Bayesian models (see Kruschke, 2010).
SEX DIFFERENCES IN MORALITY 169
Table 1
Sex Dierences in Moral Foundations in 67 Countries (Study 1)
No. Country Authority d Fairness d Care d Loyalty d Purity d Care – Fairness – Loyalty – Authority – Purity – Dcu DD Lower D Upper N Internet Access (%)
1 Afghanistan -0.13 0.24 0.58 -0.20 0.14 0.73 0.63 0.69 0.76 0.83 0.95 0.72 0.41 0.90 317 17.60
2 Albania -0.06 -0.10 0.18 -0.07 0.01 0.78 0.81 0.74 0.73 0.85 0.00 0.32 0.07 0.42 113 65.80
3 Argentina -0.14 0.28 0.51 -0.06 -0.04 0.60 0.62 0.73 0.71 0.83 0.73 0.57 0.32 0.73 382 78.60
4 Australia -0.02 0.32 0.67 -0.09 0.15 0.72 0.66 0.73 0.74 0.83 1.01 0.73 0.68 0.78 6367 85.90
5 Austria 0.02 0.01 0.38 0.02 0.35 0.70 0.57 0.69 0.74 0.81 0.77 0.64 0.37 0.81 380 84.60
6 Belgium -0.10 0.02 0.33 -0.05 0.13 0.70 0.67 0.68 0.69 0.79 0.77 0.53 0.32 0.68 652 82.20
7 Brazil 0.02 0.26 0.51 -0.02 0.09 0.73 0.66 0.69 0.74 0.81 0.74 0.58 0.46 0.67 1607 70.20
8 Bulgaria 0.07 0.23 0.66 0.32 0.26 0.78 0.70 0.73 0.69 0.79 1.02 0.80 0.46 1.00 199 53.10
9 Canada -0.06 0.23 0.59 -0.06 0.15 0.70 0.63 0.70 0.73 0.82 0.90 0.65 0.61 0.68 15127 94.70
10 Chile -0.15 0.33 0.51 0.14 0.08 0.68 0.60 0.74 0.80 0.84 0.92 0.74 0.38 0.97 262 66.70
11 China 0.12 0.19 0.37 0.15 0.29 0.64 0.59 0.72 0.69 0.76 0.50 0.42 0.29 0.51 1563 58.40
12 Colombia 0.18 0.01 0.46 0.27 0.30 0.66 0.66 0.76 0.78 0.86 0.76 0.61 0.33 0.80 321 63.20
13 Croatia -0.13 0.40 0.57 0.02 0.24 0.74 0.64 0.74 0.77 0.80 0.74 0.69 0.42 0.88 314 70.90
14 Czech Republic -0.12 0.26 0.47 0.09 0.02 0.72 0.68 0.71 0.68 0.84 0.62 0.59 0.30 0.78 349 74.10
15 Denmark 0.03 0.20 0.65 0.15 0.22 0.69 0.64 0.72 0.70 0.81 0.97 0.80 0.57 0.95 594 94.60
16 Egypt -0.21 0.29 0.60 0.10 0.04 0.68 0.62 0.74 0.74 0.77 0.68 0.67 0.25 0.89 131 48.70
17 Finland -0.15 0.36 0.74 -0.05 0.14 0.75 0.69 0.74 0.72 0.83 0.93 0.81 0.66 0.93 1043 91.50
18 France -0.12 0.26 0.53 -0.05 0.08 0.67 0.69 0.68 0.72 0.79 0.69 0.57 0.46 0.66 1514 92.60
19 Germany 0.05 0.35 0.66 -0.03 0.27 0.72 0.66 0.68 0.74 0.81 0.99 0.78 0.70 0.85 2792 96.20
20 Greece -0.12 0.23 0.66 0.04 0.12 0.73 0.59 0.74 0.69 0.82 0.95 0.77 0.51 0.93 342 59.90
21 Guatemala -0.01 0.24 0.61 -0.17 0.29 0.76 0.62 0.72 0.70 0.82 0.92 0.79 0.41 1.01 185 19.70
22 Hong Kong 0.04 0.24 0.52 0.00 0.16 0.72 0.69 0.70 0.66 0.76 0.72 0.56 0.38 0.68 722 80.50
23 Hungary -0.03 0.07 0.45 0.14 0.21 0.69 0.58 0.73 0.73 0.83 0.57 0.52 0.20 0.70 262 72.60
24 Iceland 0.06 0.21 0.52 0.10 -0.08 0.73 0.61 0.74 0.66 0.82 0.76 0.60 0.25 0.80 188 96.50
25 India -0.09 0.16 0.36 0.02 0.05 0.70 0.58 0.73 0.72 0.82 0.63 0.44 0.34 0.51 2526 40.90
26 Indonesia 0.04 0.20 0.36 -0.03 0.11 0.62 0.58 0.69 0.66 0.81 0.54 0.41 0.13 0.56 314 53.70
27 Iran 0.27 -0.03 0.21 0.14 0.19 0.68 0.61 0.75 0.77 0.79 0.41 0.39 0.08 0.53 190 76.00
28 Ireland 0.02 0.24 0.47 0.07 0.20 0.68 0.66 0.68 0.71 0.83 0.65 0.54 0.40 0.64 1213 78.30
29 Israel -0.15 0.37 0.68 -0.13 0.00 0.71 0.66 0.71 0.67 0.78 0.97 0.78 0.54 0.94 492 78.50
30 Italy 0.00 0.26 0.58 -0.01 0.11 0.69 0.64 0.73 0.72 0.83 0.80 0.65 0.46 0.77 708 92.90
31 Japan 0.11 0.05 0.25 0.01 0.43 0.64 0.62 0.65 0.67 0.79 0.80 0.54 0.33 0.68 548 93.30
32 Kenya 0.41 0.24 0.58 0.30 0.45 0.68 0.49 0.71 0.76 0.86 0.64 0.65 0.22 0.85 130 83.00
33 Korea (South) -0.09 0.10 0.39 0.01 0.10 0.61 0.57 0.73 0.74 0.80 0.54 0.43 0.23 0.56 637 92.30
34 Latvia -0.06 0.04 0.25 0.05 0.17 0.63 0.62 0.78 0.72 0.80 0.65 0.39 0.11 0.54 189 75.20
35 Lithuania 0.22 0.50 0.97 0.67 0.43 0.77 0.64 0.74 0.79 0.82 1.19 1.10 0.59 1.38 119 82.60
36 Malaysia 0.24 0.10 0.33 0.25 0.44 0.68 0.58 0.71 0.73 0.86 0.61 0.52 0.26 0.68 388 67.50
37 Mexico 0.21 0.22 0.51 0.20 0.27 0.70 0.67 0.72 0.72 0.84 0.68 0.56 0.40 0.69 807 65.00
38 Netherlands -0.08 0.25 0.43 -0.11 0.20 0.66 0.63 0.69 0.72 0.80 0.70 0.58 0.46 0.68 1364 94.00
39 New Zealand -0.03 0.26 0.60 0.05 0.18 0.66 0.61 0.72 0.75 0.85 0.78 0.62 0.52 0.71 1925 86.60
40 Nigeria -0.01 0.10 0.59 0.01 -0.00 0.63 0.41 0.74 0.80 0.86 0.97 0.67 0.24 0.94 132 55.50
41 Norway 0.05 0.26 0.55 0.02 0.25 0.68 0.66 0.74 0.72 0.82 0.89 0.70 0.52 0.84 830 95.00
42 Pakistan 0.13 0.21 0.38 0.25 0.29 0.66 0.67 0.75 0.70 0.85 0.63 0.52 0.20 0.70 245 22.20
43 Peru -0.19 0.43 0.59 -0.04 0.16 0.67 0.70 0.70 0.68 0.80 0.87 0.74 0.47 0.93 301 41.70
44 Philippines 0.25 0.11 0.43 0.08 0.25 0.64 0.63 0.74 0.79 0.81 0.81 0.52 0.31 0.67 587 62.90
45 Poland -0.11 0.41 0.85 0.01 0.04 0.78 0.67 0.73 0.74 0.81 1.00 0.86 0.73 0.97 1191 65.00
46 Portugal -0.07 0.04 0.26 -0.14 -0.04 0.68 0.66 0.72 0.73 0.81 0.43 0.36 0.14 0.49 439 62.10
47 Puerto Rico -0.03 0.25 0.53 0.17 0.27 0.69 0.57 0.72 0.73 0.84 0.70 0.60 0.32 0.77 281 73.90
48 Romania 0.14 0.30 0.75 0.18 0.26 0.76 0.66 0.70 0.73 0.85 0.93 0.79 0.60 0.93 646 49.80
49 Russian Federation -0.04 0.25 0.81 -0.02 0.14 0.71 0.67 0.72 0.73 0.84 1.25 0.88 0.66 1.03 530 76.10
50 Saudi Arabia 0.01 0.11 0.34 -0.09 0.04 0.63 0.69 0.76 0.69 0.82 0.56 0.52 0.14 0.71 215 88.60
51 Serbia 0.14 0.16 0.36 -0.03 0.08 0.76 0.63 0.71 0.66 0.79 0.36 0.42 0.09 0.57 166 57.00
52 Singapore 0.12 0.15 0.47 0.11 0.36 0.70 0.63 0.71 0.72 0.82 0.68 0.56 0.44 0.66 1397 82.00
53 Slovakia -0.27 0.56 0.79 0.09 0.09 0.74 0.66 0.76 0.69 0.78 1.00 0.96 0.36 1.25 140 79.00
54 Slovenia -0.08 0.17 0.57 -0.04 -0.00 0.78 0.66 0.71 0.65 0.79 0.75 0.66 0.36 0.82 254 72.70
55 South Africa 0.07 0.19 0.56 0.05 0.26 0.64 0.59 0.68 0.70 0.85 0.89 0.63 0.48 0.75 901 53.10
56 Spain -0.10 0.29 0.67 -0.10 0.18 0.66 0.66 0.69 0.72 0.82 1.16 0.78 0.61 0.91 685 74.80
57 Sweden 0.01 0.33 0.64 0.09 0.16 0.69 0.65 0.76 0.77 0.82 0.95 0.76 0.64 0.85 1857 94.80
58 Switzerland 0.07 0.18 0.47 0.13 0.25 0.72 0.64 0.72 0.71 0.79 0.66 0.55 0.32 0.69 586 86.70
59 Taiwan 0.06 0.11 0.12 0.08 0.31 0.61 0.63 0.70 0.75 0.80 0.49 0.40 0.15 0.55 333 84.00
60 Thailand 0.10 0.25 0.40 0.13 0.19 0.60 0.55 0.70 0.69 0.75 0.49 0.43 0.21 0.57 482 34.90
61 Turkey 0.05 0.17 0.52 -0.06 0.08 0.70 0.60 0.72 0.74 0.76 0.83 0.58 0.34 0.75 441 46.30
62 Ukraine -0.04 0.30 0.65 0.06 0.29 0.72 0.66 0.67 0.73 0.84 0.76 0.72 0.32 0.94 169 41.80
63 United Arab Emirates -0.28 0.39 0.34 -0.21 -0.02 0.68 0.54 0.73 0.69 0.83 0.65 0.56 0.27 0.74 320 96.90
64 United Kingdom -0.03 0.26 0.55 -0.06 0.17 0.68 0.66 0.70 0.74 0.82 0.84 0.62 0.58 0.65 13285 94.70
65 USA 0.02 0.26 0.65 -0.08 0.15 0.70 0.64 0.72 0.75 0.84 1.05 0.69 0.68 0.70 262583 95.60
66 Venezuela 0.20 0.33 0.57 0.19 0.35 0.73 0.59 0.77 0.76 0.81 0.62 0.66 0.27 0.85 167 61.50
67 Vietnam 0.12 -0.07 0.14 0.24 0.35 0.63 0.60 0.59 0.66 0.76 0.54 0.43 0.10 0.60 195 66.30
SEX DIFFERENCES IN MORALITY 170
Table 2
Results of Multi-Level Modeling (Study 1)
Moral foundation Model Random eects Fixed eects
Intercept variance (Country) Slope variance (Sex) Estimate (Intercept) Standard Error Estimate (Sex) Standard Error
Care 0 0.017 3.47** 0.02
1 0.019 3.78** 0.02 -0.51** 0.003
2 0.02 0.015 3.75** 0.02 -0.45** 0.02
Fairness 0 0.018 3.65** 0.02
1 0.02 3.76** 0.02 -0.19** 0.003
2 0.02 0.002 3.76** 0.02 -0.17** 0.01
Loyalty 0 0.056 2.40** 0.03
1 0.057 2.37** 0.03 0.06** 0.003
2 0.066 0.004 2.42** 0.03 -0.03* 0.01
Authority 0 0.083 2.28** 0.04
1 0.082 2.28** 0.04 -0.01** 0.003
2 0.096 0.003 2.28** 0.04 -0.003 0.01
Purity 0 0.168 1.75** 0.05
1 0.162 1.86** 0.05 -0.17** 0.004
2 0.178 0.003 1.87** 0.05 -0.20** 0.01
*p < .05, **p < .01. Note. GII = Gender Inequality Index.
SEX DIFFERENCES IN MORALITY 171
Table 3
Correlations between Sex Dierences in Moral Foundations and Country-Level Indicators (Study 1)
Sex dierences Cultural variables Socioeconomic variables Gender variables
Individualism (n = 66) Looseness (n = 48) English (n = 67) WEIRD distance (n = 46) Gini coecient (n = 56) Human development (n = 64) Life satisfaction (n = 64) Gender inequality (n = 62) Gender development (n = 66) Gender gap (n = 63) Sex ratio (n = 67)
Care d .30* [.11,.46] .16 [-.11,.45] .29* [.11,.45] -.34* [-.57,-.10] -.03 [-.22,.16] .12 [-.06,.29] .16 [-.06,.37] -.15 [-.35,.06] .31 [.05,.53] .30* [.11,.47] -.22* [-.44,-.11]
Fairness d .17 [-.04,.37] .21 [-.08,.49] .14 [-.05,.32] -.32 [-.55,-.07] -.06 [-.29,.20] .13 [-.03,.29] .21 [.00,.41] -.08 [-.28,.13] .17 [.02,.33] .19 [-.01,.38] .09 [-.35,.29]
Loyalty d -.18 [-.43,.05] -.14 [-.36,.08] -.04 [-.28,.19] .37* [.11,.61] .12 [-.11,.37] -.13 [-.42,.15] -.15 [-.41,.12] .13 [-.16,.45] -.14 [-.39,.18] -.02 [-.27,.18] -.27 [-.48,.08]
Authority d -.21 [-.38,-.02] -.18 [-.44,.09] -.07 [-.24,.14] .24 [-.14,.59] .32 [.06,.54] -.23 [-.48,.03] -.13 [-.36,.11] .25 [-.05,.51] -.13 [-.35,.10] -.04 [-.28,.21] -.22 [-.51,.25]
Purity d -.12 [-.30,.08] .08 [-.16,.32] .01 [-.19,.21] .13 [-.19,.46] .21 [-.06,.44] -.10 [-.35,.16] .05 [-.28,.18] .12 [-.14,.36] -.24 [-.47,.11] -.01 [-.26,.24] -.24 [-.42,.01]
GSDI .01 [-.20,.21] .06 [-.21,.36] .13 [-.08,.33] -.02 [-.25,.22] .17 [-.05,.39] -.06 [-.30,.15] .01 [-.23,.27] .08 [-.17,.35] .01 [-.20,.27] .15 [-.05,.32] -.29* [-.49,-.04]
Dcu .35* [.17,.52] .24 [-.02,.46] .27* [.08,.45] -.32* [-.53,-.07] .04 [-.19,.22] .15 [-.05,.35] .23 [-.01,.46] -.21 [-.42,-.01] .20 [.04,.37] .27* [.09,.46] -.15 [-.42,-.05]
*FDR-adjusted p < .05
SEX DIFFERENCES IN MORALITY 172
Table 4
Results of Bayesian Multi-Level Modeling (Study 2)
Care
Parameter
ˆ
R ESS Mean SD MCSE 95% CrI
Intercept 1.01 1499 3.78 0.05 0.001 3.68, 3.89
Slope (sex) 1 2646 -0.33 0.03 0.001 -0.41, -0.27
Error SD 1 8000 0.73 0 0 0.72, 0.74
Between-country SD intercept 1 1795 0.09 0.04 0.001 0.05, 0.19
Correlation Intercept-Slope 1 2237 -0.02 0.02 0 -0.06, 0.00
Between-country SD Slope 1 2458 0.02 0.01 0 0.01, 0.05
Fairness
Intercept 1 1386 3.72 0.05 0.001 3.62, 3.82
Slope (sex) 1 5838 -0.17 0.02 0 -0.20, -0.13
Error SD 1 8000 0.69 0 0 0.68, 0.70
Between-country SD intercept 1 1501 0.11 0.04 0.001 0.05, 0.21
Correlation Intercept-Slope 1 3849 -0.01 0.01 0 -0.03, 0.00
Between-country SD Slope 1 3114 0 0 0 0.00, 0.01
Loyalty
Intercept 1 1945 2.64 0.07 0.002 2.49, 2.77
Slope (sex) 1 10267 0.03 0.02 0 0.02, 0.08
Error SD 1 16000 0.82 0.01 0 0.81, 0.83
Between-country SD intercept 1 2752 0.14 0.06 0.001 0.07, 0.28
Correlation Intercept-Slope 1 9146 -0.01 0.01 0 -0.04, 0.01
Between-country SD Slope 1 6165 0.01 0 0 0.00, 0.02
Authority
Intercept 1 1420 2.63 0.05 0.001 2.60, 2.72
Slope (sex) 1 4152 0.02 0.02 0 -0.03, 0.07
Error SD 1 8000 0.87 0.01 0 0.86, 0.88
Between-country SD intercept 1 1905 0.05 0.02 0.001 0.02, 0.11
Correlation Intercept-Slope 1 4165 -0.01 0.01 0 -0.03, 0.01
Between-country SD Slope 1 2527 0.01 0.01 0 0.00, 0.03
Purity
Intercept 1 1150 2.35 0.12 0.003 2.10, 2.56
Slope (sex) 1 6025 -0.17 0.03 0 -0.23, -0.10
Error SD 1 8000 0.98 0.01 0 0.96, 0.99
Between-country SD intercept 1 1490 0.32 0.13 0.003 0.15, 0.66
Correlation Intercept-Slope 1 3355 0.01 0.03 0 -0.03, 0.08
Between-country SD Slope 1 4682 0.02 0.01 0 0.01, 0.06
SEX DIFFERENCES IN MORALITY 173
Table 5
Correlations between Sex Dierences in Moral Foundations and Country-Level Indicators (Study 2)
Sex dierences Statistic Cultural variables Socioeconomic variables Gender variables
Individualism (n = 18) Looseness (n = 16) English (n = 19) WEIRD distance (n = 16) Gini coecient ( n = 18) Human development (n = 19) Life satisfaction (n = 19) Gender inequality (n = 19) Gender development (n = 19) Gender gap (n = 19) Sex ratio (n=19)
Caredr .46 .07 .54 -.16 -.04 .19 .30 -.01 .33 .26 -.36
BF10 1.67 0.31 4.14 0.36 0.29 0.37 0.57 0.28 0.69 0.49 0.84
Fairnessdr .03 -.24 .12 .06 .20 -.28 -.13 .24 .33 .06 -.60
BF10 0.29 0.44 0.31 0.32 0.39 0.52 0.32 0.44 0.70 0.29 9.02
Loyaltydr -.24 .09 -.22 .04 .13 -.46 -.31 .22 .14 0.24 -.04
BF10 0.45 0.32 0.41 0.31 0.32 1.74 0.61 0.43 0.33 0.44 0.28
Authoritydr .05 .17 -.09 .00 .12 -.11 -.08 .16 .00 .24 -.03
BF10 0.29 0.36 0.30 0.31 0.32 0.31 0.30 0.34 0.28 0.44 0.28
Puritydr .42 .39 .29 -.38 -.09 .20 .19 -.13 .40 .51 -.48
BF10 1.15 0.83 0.56 0.83 0.31 0.38 0.38 0.32 1.05 2.89 2.02
GSDI r .29 .17 .27 -.19 .09 -.10 .04 .14 .40 .45 -.50
BF10 0.54 0.37 0.49 0.39 0.30 0.30 0.28 0.32 1.09 1.55 2.48
Dcu r .59 .68 .75 -.26 -.55 .53 .66 -.46 .34 .64 -.16
BF10 6.26 15.44 159.42 0.48 4.14 3.55 23.66 1.70 0.72 15.86 0.35
Note. All Bayes Factors are based on two-tailed priors. GSDI = General Sex-Dierence Index, D
cu
= Disattenuated, bias-corrected,
multivariate standardized dierence between women and men. .
SEX DIFFERENCES IN MORALITY 174
Table 6
Evidence for (or Against) All Hypotheses
No. Study A Priori Hypothesis Result
1 Study 1 Women score higher on Care across cultures. Supported
2 Study 1 Men score higher on Fairness across cultures. Not Supported
3 Study 1 Men score higher on Loyalty across cultures. Not Supported
4 Study 1 Men score higher on Authority across cultures. Not Supported
5 Study 1 Women score higher on Purity across cultures. Supported
6 Study 1 The magnitude of sex dierences in moral foundations vary across cultures. Supported
7 Study 1 Gender equality predicts the magnitude of sex dierences in moral foundations across cultures. Supported
8 Study 2 Women score higher on Care across cultures. Supported
9 Study 2 Women score higher on Fairness across cultures. Supported
10 Study 2 Women score higher on Purity across cultures. Supported
11 Study 2 The magnitude of sex dierences in moral foundations vary across cultures. Supported
SEX DIFFERENCES IN MORALITY 175
Country number Country Sample size
1 Switzerland 586
2 USA 262629
3 Australia 6369
4 Ireland 1213
5Kenya 130
6 United Kingdom 13286
7 Indonesia 314
8 India 2526
9 Canada 15127
10 Guatemala 185
11 Nigeria 132
12 United Arab Emirates 320
13 Vietnam 195
14 Russian Federation 530
15 Netherlands 1365
16 Sweden 1857
17 Spain 686
18 Denmark 595
19 Serbia and Montenegro 166
20 China 1564
21 Malaysia 388
22 Germany 2792
23 New Zealand 1925
24 Afghanistan 317
25 Romania 646
26 Albania 113
27 Turkey 441
28 Poland 1191
29 Belgium 652
30 Argentina 382
31 South Africa 902
32 Chile 262
33 Austria 380
34 Venezuela 167
35 Italy 708
36 Korea (south) 637
37 Thailand 482
38 Greece 342
39 Egypt 131
40 Ukraine 169
41 France 1515
42 Israel 492
43 Singapore 1397
44 Hungary 262
45 Philippines 587
46 Croatia 314
47 Hong Kong 723
48 Taiwan 333
49 Brazil 1607
50 Mexico 807
51 Colombia 321
52 Japan 548
53 Norway 830
54 Portugal 439
55 Finland 1043
56 Czech Republic 349
57 Iran 190
58 Iceland 188
59 Slovakia 140
60 Pakistan 245
61 Saudi Arabia 215
62 Peru 302
63 Bulgaria 199
64 Slovenia 254
65 Lithuania 119
66 Puerto Rico 281
67 Latvia 189
Table 7
Countries and their sample size
SEX DIFFERENCES IN MORALITY 176
Threshold Country number
Y1$1 58 (65) 4 28 32 64 26 (25) 9 21 40 63 67 49 38 57 56 15 51 11 36 19 39 1 48 2 61 45 6 3 55 10 5 66 30 33 60 20 16 62 18 29 52 23 44 13 22 59 7 37 12 31 41 46 17 14 27 24 53 42 50 43 8 54 35 47 34
Y1$2 58 (65) 4 28 32 (64) 26 (25) 9 21 40 63 67 49 38 57 56 15 51 11 36 19 39 1 48 2 61 (45) 6 3 55 10 5 66 30 33 60 20 16 62 18 29 52 23 44 13 22 59 7 37 12 31 41 46 17 14 27 24 53 42 50 43 8 54 35 47 34
Y1$3 58 (65) 4 28 32 (64) 26 (25) 9 21 40 63 67 49 38 57 56 15 51 11 36 19 39 1 48 2 61 45 6 3 55 10 5 66 30 33 60 20 16 62 18 29 52 23 44 13 22 59 7 37 12 31 41 46 17 14 27 24 53 42 50 43 8 54 35 47 34
Y1$4 58 65 4 28 32 (64) 26 25 9 21 40 63 67 49 38 57 56 15 51 11 36 19 39 1 48 2 61 45 6 3 55 10 5 66 30 33 60 20 16 62 18 29 52 23 44 13 22 59 7 37 12 31 41 46 17 14 27 24 53 42 50 43 8 54 35 47 34
Y1$5 58 65 4 28 32 (64) 26 25 9 21 40 63 67 49 38 (57) 56 15 51 11 36 19 39 1 48 2 61 45 6 3 55 10 5 66 30 33 60 20 16 62 (18) 29 52 23 44 13 22 59 7 37 12 31 41 46 17 14 27 24 53 42 50 43 8 54 35 47 34
Y2$1 58 65 4 28 (32) 64 26 25 9 21 (40) 63 67 49 38 57 56 15 51 11 36 19 39 1 48 2 61 45 6 3 55 10 5 66 30 33 60 20 16 62 18 29 52 23 44 13 22 59 7 37 12 31 41 46 17 14 27 24 (53) 42 50 43 8 54 35 47 34
Y2$2 58 65 4 28 32 64 26 25 9 21 40 63 67 49 38 57 56 15 51 11 36 19 39 1 48 2 61 45 6 3 55 10 5 66 30 33 60 20 16 62 18 29 52 23 44 13 22 59 7 37 12 31 41 46 17 14 27 24 53 42 50 43 8 54 35 47 34
Y2$3 58 (65) 4 28 32 64 26 25 9 21 40 63 67 49 38 57 56 15 51 11 36 19 39 1 48 2 61 45 6 3 55 10 5 66 30 33 (60) 20 16 62 18 29 (52) 23 44 13 22 59 7 37 12 31 41 46 17 14 27 24 53 42 50 43 8 54 35 47 34
Y2$4 58 65 4 28 32 64 26 25 9 21 40 63 67 49 38 (57) 56 15 51 11 36 (19) 39 1 48 2 61 45 6 3 55 10 5 66 30 33 (60) 20 16 62 (18) 29 (52) 23 44 13 (22) 59 7 37 12 31 41 46 (17) 14 27 24 53 42 50 43 8 54 35 47 34
Y2$5 58 (65) 4 28 32 64 26 25 9 21 40 63 67 49 (38) 57 56 15 51 11 36 19 39 1 48 2 61 45 6 3 55 10 5 66 30 33 60 20 16 62 18 29 (52) 23 44 13 22 59 7 37 12 31 41 46 17 14 27 24 53 42 50 43 8 54 35 47 34
Y3$1 58 (65) 4 28 32 64 26 (25) 9 21 40 63 67 49 38 57 56 15 51 11 36 19 39 1 48 2 61 45 6 3 55 10 5 66 30 (33) 60 20 16 62 18 29 52 23 44 13 22 59 7 37 12 31 41 46 17 14 27 24 53 42 50 43 8 54 35 47 34
Y3$2 58 (65) (4) 28 (32) (64) 26 25 (9) 21 40 63 67 49 38 57 56 15 51 11 36 19 39 1 48 2 61 45 6 3 55 10 5 66 30 33 (60) 20 16 62 18 29 52 23 44 13 22 59 7 37 12 31 41 46 17 14 27 24 53 42 50 43 8 54 35 47 34
Y3$3 58 (65) (4) 28 (32) (64) 26 25 (9) 21 40 63 67 49 38 57 56 15 51 (11) 36 19 (39) 1 48 2 61 45 6 3 55 10 5 66 30 33 (60) (20) 16 62 18 29 52 23 44 13 22 59 (7) 37 12 31 41 46 17 14 27 24 53 42 50 43 8 54 35 47 34
Y3$4 58 65 4 28 (32) 64 26 (25) 9 21 40 (63) (67) 49 38 57 56 15 51 (11) 36 19 39 1 48 2 61 45 6 3 55 10 5 66 30 (33) (60) 20 16 62 18 29 (52) 23 (44) 13 (22) (59) 7 37 12 (31) 41 46 17 14 (27) 24 53 42 50 43 8 54 35 (47) 34
Y3$5 58 65 4 28 32 64 26 (25) 9 21 40 (63) (67) 49 38 57 56 15 51 (11) 36 19 39 1 48 2 61 (45) 6 3 55 10 5 66 30 (33) (60) 20 16 62 18 29 (52) 23 44 13 (22) 59 (7) 37 12 31 41 46 17 14 27 24 53 42 50 43 8 54 35 47 34
Y4$1 58 (65) 4 28 32 64 26 25 9 21 40 63 67 49 38 57 56 15 51 11 36 19 39 1 48 2 61 45 6 3 55 10 5 66 (30) 33 60 20 16 62 18 29 52 23 44 13 22 59 7 37 12 31 41 46 17 14 27 24 53 42 50 43 8 54 35 47 34
Y4$2 58 (65) 4 28 32 64 26 25 9 21 40 63 67 49 38 57 (56) 15 51 11 36 19 39 1 48 2 61 45 6 3 55 10 5 66 (30) 33 60 20 16 62 18 29 52 23 44 13 22 59 7 37 12 31 41 (46) 17 14 27 24 53 42 50 43 8 54 35 47 34
Y4$3 58 (65) 4 28 32 64 26 (25) 9 21 40 63 67 49 38 57 (56) 15 51 11 36 19 39 1 48 2 61 45 6 3 55 10 5 66 (30) 33 60 20 16 62 18 29 (52) 23 44 13 22 59 7 37 12 31 41 (46) 17 14 27 24 53 42 50 43 8 54 35 47 34
Y4$4 58 (65) 4 28 32 64 26 (25) (9) 21 40 63 67 49 38 57 (56) 15 51 11 36 (19) 39 1 48 2 61 45 6 3 55 10 5 66 30 33 60 20 16 62 18 29 52 23 44 13 22 59 7 37 12 31 41 (46) 17 14 27 24 53 42 50 43 8 54 35 47 34
Y4$5 58 65 4 28 32 64 (26) (25) 9 21 40 63 67 49 38 57 56 15 51 11 36 (19) 39 1 48 2 61 45 6 3 55 10 5 66 30 33 60 20 16 62 18 29 52 23 44 13 22 59 7 37 12 31 41 46 17 14 27 24 53 (42) (50) 43 8 54 35 47 34
Y5$1 58 65 (4) 28 32 (64) 26 (25) 9 21 40 63 67 49 38 (57) 56 (15) 51 11 36 (19) 39 1 48 2 61 45 6 3 55 10 5 66 30 (33) 60 20 16 62 (18) 29 (52) 23 44 13 22 59 7 37 12 31 (41) 46 17 14 27 24 53 42 50 43 8 54 35 47 34
Y5$2 58 65 4 28 32 (64) 26 (25) 9 (21) 40 63 67 49 (38) (57) 56 (15) 51 11 36 (19) 39 1 48 2 (61) 45 6 3 (55) 10 5 66 30 (33) 60 20 16 62 (18) 29 52 23 44 13 (22) 59 7 37 12 31 (41) 46 17 14 (27) 24 53 42 50 43 8 54 35 47 34
Y5$3 (58) (65) 4 (28) 32 (64) 26 (25) (9) 21 (40) 63 67 49 (38) (57) (56) (15) 51 11 36 (19) 39 1 48 2 (61) 45 (6) 3 55 10 (5) 66 (30) 33 60 20 16 62 (18) 29 52 23 44 13 22 59 7 37 12 31 (41) 46 (17) 14 (27) 24 53 42 50 43 8 54 35 47 34
Y5$4 58 65 4 28 32 (64) 26 (25) 9 21 40 63 67 49 (38) (57) (56) (15) 51 (11) 36 (19) 39 1 48 2 (61) 45 (6) 3 (55) 10 (5) 66 30 33 60 (20) 16 62 (18) 29 52 23 44 13 22 59 7 37 (12) 31 (41) 46 17 14 27 24 53 42 50 43 8 54 35 (47) (34)
Y5$5 (58) 65 4 28 32 (64) 26 (25) 9 21 40 63 67 49 (38) (57) (56) (15) 51 11 36 (19) 39 1 48 2 (61) 45 (6) 3 (55) 10 (5) 66 30 33 60 (20) 16 62 (18) 29 52 23 44 13 22 59 7 37 (12) (31) 41 46 (17) 14 27 24 53 42 (50) 43 8 54 35 (47) 34
Y6$1 58 (65) (4) (28) 32 (64) 26 25 (9) 21 40 63 67 (49) 38 (57) 56 15 51 11 36 19 (39) (1) 48 2 61 45 6 3 (55) 10 5 66 30 33 60 20 16 62 18 (29) 52 23 44 13 22 59 (7) 37 12 31 (41) 46 17 (14) 27 24 53 42 50 43 8 54 35 47 34
Y6$2 58 (65) (4) (28) 32 (64) 26 25 (9) 21 40 63 67 49 38 (57) 56 15 51 11 36 19 (39) 1 48 2 61 45 6 3 (55) 10 5 66 30 (33) 60 20 16 62 18 (29) 52 23 44 13 22 59 7 37 12 31 (41) 46 (17) (14) 27 24 53 42 50 43 8 54 35 47 34
Y6$3 58 (65) (4) (28) 32 (64) 26 25 (9) 21 40 63 67 49 38 (57) 56 15 51 11 36 19 (39) (1) 48 (2) 61 45 6 3 (55) 10 5 66 30 (33) 60 20 16 62 18 (29) (52) 23 44 13 22 59 7 37 12 31 (41) 46 (17) (14) 27 24 53 42 50 43 8 54 35 47 34
Y6$4 58 (65) (4) (28) 32 (64) 26 25 (9) 21 40 63 67 49 38 (57) 56 15 51 11 36 19 (39) (1) (48) 2 61 45 6 3 (55) 10 5 66 30 (33) 60 20 16 62 (18) (29) (52) 23 44 13 22 59 7 37 12 31 41 46 (17) (14) 27 24 53 42 50 43 8 54 35 47 34
Y6$5 58 (65) (4) (28) 32 (64) 26 25 (9) 21 40 63 67 49 38 57 56 15 51 11 36 19 (39) (1) (48) 2 61 45 6 3 55 10 5 66 (30) (33) 60 20 16 62 (18) (29) (52) 23 44 13 22 59 (7) 37 12 31 41 46 (17) 14 27 24 53 42 50 43 8 54 35 47 34
Table 8
Approximate measurement invariance (noninvariance) of the intercepts for the Care items. Non-invariant parameters are
parenthesized. Y indicates item numbers and $ indicates thresholds. See Table 7 for a description of country numbers
SEX DIFFERENCES IN MORALITY 177
Item Country
Y1 58 (65) 4 28 32 64 26 25 9 21 40 63 67 49 38 57 56 15 51 11 36 19 39 1 48 2 61 45 6 3 55 10 5 66 30 33 60 20 16 62 18 29 52 23 44 13 22 59 7 37 12 31 41 46 17 14 27 24 53 42 50 43 8 54 35 47 34
Y2 58 (65) 4 28 32 64 26 25 9 21 40 63 67 49 38 57 56 15 51 11 36 19 39 1 48 2 61 45 6 3 55 10 5 66 30 33 60 20 16 62 18 29 52 23 44 13 22 59 7 37 12 31 41 46 17 14 27 24 53 42 50 43 8 54 35 47 34
Y3 58 65 4 28 (32) 64 26 25 9 21 40 63 67 49 38 57 56 15 51 11 36 19 39 1 48 2 61 45 6 3 55 10 5 66 30 33 60 20 16 62 18 29 52 23 44 13 22 59 7 37 12 31 41 46 17 14 27 24 53 42 50 43 8 54 35 47 34
Y4 58 65 4 28 32 64 26 25 9 21 40 63 67 49 38 57 56 15 51 11 36 19 39 1 48 2 61 45 6 3 55 10 5 66 30 33 60 20 16 62 18 29 52 23 44 13 22 59 7 37 12 31 41 46 17 14 27 24 53 42 50 43 8 54 35 47 34
Y5 58 65 4 28 32 (64) 26 25 9 21 40 63 67 49 38 (57) 56 15 51 11 36 19 39 1 48 2 61 45 6 3 55 10 5 66 30 33 60 20 16 62 18 29 52 23 44 13 22 59 7 37 12 31 41 46 17 14 27 24 53 42 50 43 8 54 35 47 34
Y6 58 65 4 28 32 64 26 25 9 21 40 63 67 49 38 57 56 15 51 11 36 19 39 1 48 2 61 45 6 3 55 10 5 66 30 33 60 20 16 62 18 29 52 23 44 13 22 59 7 37 12 31 41 46 17 14 27 24 53 42 50 43 8 54 35 47 34
Table 9
Approximate measurement invariance (noninvariance) of the loadings for the Care items. Non-invariant parameters are
parenthesized. See Table 7 for a description of country numbers
SEX DIFFERENCES IN MORALITY 178
Threshold Country number
Y1$1 58 (65) 4 28 32 64 26 25 9 21 40 63 67 49 38 57 56 15 51 11 36 19 39 1 48 2 (61) 45 6 3 55 10 5 (66) (30) (33) (60) (20) (16) (62) 18 (29) (52) 23 (44) (13) (22) (59) (7) (37) (12) (31) 41 (46) (17) (14) (27) (24) (53) 42 50 (43) (8) 54 (35) (47) (34)
Y1$2 58 (65) 4 28 32 64 26 25 9 21 40 63 67 49 38 57 56 (15) 51 11 36 19 39 1 48 2 (61) 45 6 3 55 10 5 66 (30) (33) 60 (20) (16) (62) (18) (29) (52) (23) 44 (13) (22) (59) (7) 37 12 (31) (41) 46 (17) (14) (27) (24) (53) (42) 50 (43) (8) (54) (35) 47 34
Y1$3 58 (65) 4 28 32 64 26 25 9 21 40 63 67 49 38 57 56 15 51 11 36 19 39 1 48 2 (61) 45 6 (3) 55 10 5 66 (30) 33 60 (20) (16) (62) (18) (29) (52) (23) (44) 13 (22) (59) (7) 37 (12) (31) (41) 46 (17) (14) (27) (24) 53 42 50 (43) (8) 54 (35) (47) (34)
Y1$4 58 (65) 4 28 32 64 26 25 (9) 21 40 63 67 49 38 57 56 15 51 (11) 36 19 39 1 48 2 61 45 6 3 55 10 5 66 30 (33) (60) 20 16 (62) 18 (29) 52 (23) 44 13 (22) 59 7 37 12 31 (41) 46 17 14 27 24 53 42 (50) 43 8 54 35 47 34
Y1$5 58 (65) 4 28 32 64 26 25 (9) 21 40 63 67 49 38 57 56 15 51 11 36 19 39 1 48 2 61 45 6 3 55 10 5 (66) 30 (33) (60) (20) 16 62 18 (29) (52) (23) (44) (13) (22) 59 (7) 37 (12) (31) 41 (46) (17) (14) 27 24 (53) 42 (50) (43) 8 54 35 (47) 34
Y2$1 58 (65) 4 28 (32) 64 26 25 9 21 (40) 63 67 49 38 57 56 15 51 11 36 19 39 1 48 2 61 45 6 3 55 10 5 66 (30) (33) (60) 20 (16) (62) 18 (29) (52) (23) (44) 13 (22) (59) 7 37 (12) (31) (41) (46) 17 (14) 27 (24) (53) 42 (50) (43) (8) (54) (35) (47) (34)
Y2$2 58 65 4 28 32 64 26 (25) 9 21 (40) 63 67 49 38 (57) 56 15 51 11 36 19 39 1 48 2 (61) 45 6 3 55 10 5 66 (30) (33) (60) (20) (16) 62 (18) (29) (52) 23 (44) 13 22 (59) 7 37 12 (31) (41) (46) 17 (14) 27 (24) (53) (42) (50) (43) 8 54 (35) (47) (34)
Y2$3 58 65 4 28 32 64 26 25 9 21 40 63 67 49 38 57 56 15 51 11 36 19 39 (1) 48 2 61 45 6 3 55 10 5 66 (30) (33) (60) 20 16 (62) (18) (29) (52) (23) 44 13 22 (59) (7) 37 12 (31) (41) 46 (17) (14) 27 (24) (53) (42) (50) (43) (8) 54 (35) 47 (34)
Y2$4 58 65 4 28 32 (64) 26 25 9 21 40 63 67 49 38 57 56 15 51 11 36 19 39 1 48 2 61 45 6 3 (55) 10 5 66 (30) (33) (60) (20) 16 62 (18) (29) 52 (23) 44 13 (22) (59) (7) 37 12 (31) 41 46 (17) (14) 27 24 (53) 42 (50) (43) 8 54 35 47 34
Y2$5 58 65 4 28 32 64 26 25 9 21 40 63 67 49 38 57 56 15 51 11 36 19 39 1 48 2 61 45 6 3 (55) 10 (5) 66 (30) (33) (60) (20) 16 62 (18) 29 (52) (23) 44 13 (22) (59) (7) (37) (12) 31 (41) (46) (17) 14 (27) (24) (53) (42) (50) (43) 8 54 (35) (47) (34)
Y3$1 58 (65) 4 28 (32) 64 26 25 9 21 (40) (63) (67) 49 38 57 56 15 51 (11) 36 19 39 1 48 2 (61) 45 6 (3) 55 10 (5) 66 (30) (33) (60) (20) (16) (62) (18) (29) (52) 23 44 13 (22) (59) (7) 37 (12) 31 (41) 46 17 (14) (27) (24) 53 (42) (50) (43) (8) (54) (35) (47) 34
Y3$2 58 (65) 4 28 32 64 (26) 25 (9) 21 40 63 (67) 49 38 57 56 15 51 (11) 36 19 39 1 48 2 (61) 45 6 3 55 10 5 66 30 (33) (60) (20) (16) 62 18 (29) (52) (23) 44 13 (22) (59) (7) 37 (12) (31) 41 46 17 14 (27) (24) 53 (42) (50) (43) 8 (54) (35) 47 34
Y3$3 58 (65) 4 28 32 64 26 25 (9) 21 40 63 (67) 49 38 57 56 15 51 (11) 36 19 39 1 48 2 (61) 45 6 3 55 10 5 (66) (30) (33) (60) (20) 16 (62) (18) (29) (52) 23 44 13 (22) (59) (7) 37 (12) (31) 41 46 (17) 14 (27) (24) 53 42 (50) 43 8 54 35 47 (34)
Y3$4 58 (65) 4 28 32 (64) (26) 25 (9) 21 40 63 (67) 49 38 57 56 15 51 (11) (36) 19 39 1 48 2 (61) 45 6 3 55 10 5 (66) (30) (33) (60) (20) 16 (62) (18) (29) (52) (23) 44 13 (22) (59) 7 (37) 12 (31) 41 46 (17) 14 (27) 24 53 42 (50) 43 8 54 35 47 (34)
Y3$5 58 (65) (4) 28 32 64 26 25 (9) 21 40 63 67 49 (38) (57) 56 15 51 (11) 36 19 39 1 (48) 2 61 45 6 3 55 10 5 66 30 (33) (60) 20 (16) 62 18 29 (52) 23 44 13 (22) (59) (7) (37) (12) (31) 41 46 17 (14) 27 24 (53) 42 50 (43) 8 54 35 47 (34)
Y4$1 58 (65) 4 28 32 64 26 25 9 21 40 63 67 49 38 57 56 15 (51) 11 36 19 39 1 48 2 61 45 6 3 55 10 5 (66) 30 (33) 60 20 (16) (62) 18 (29) 52 (23) 44 (13) (22) 59 7 37 (12) (31) (41) 46 (17) 14 27 (24) 53 (42) (50) 43 (8) (54) (35) (47) (34)
Y4$2 58 (65) 4 28 32 (64) 26 25 9 21 40 63 67 49 38 57 56 15 51 11 36 19 39 1 48 2 61 45 6 3 55 10 5 66 (30) (33) (60) 20 16 (62) (18) (29) 52 23 (44) (13) (22) (59) 7 (37) (12) (31) (41) (46) (17) (14) 27 (24) 53 42 50 43 (8) (54) (35) 47 34
Y4$3 58 (65) (4) 28 32 (64) 26 25 (9) 21 40 63 67 49 38 57 56 15 51 11 36 19 39 1 48 2 61 45 6 3 55 10 5 66 (30) (33) (60) (20) 16 (62) 18 (29) (52) 23 (44) (13) (22) (59) 7 37 12 (31) (41) (46) (17) (14) 27 (24) (53) 42 50 (43) (8) (54) (35) 47 (34)
Y4$4 58 (65) (4) 28 32 (64) 26 25 (9) 21 40 63 67 49 38 57 56 15 51 11 36 19 (39) 1 48 2 61 45 6 3 55 10 5 66 30 (33) 60 (20) 16 62 18 (29) (52) (23) 44 13 (22) (59) (7) (37) (12) (31) (41) (46) 17 (14) 27 24 (53) 42 50 (43) 8 54 35 47 (34)
Y4$5 58 65 4 28 32 64 26 (25) 9 21 40 (63) 67 49 38 (57) 56 15 51 (11) (36) 19 39 1 (48) 2 61 45 6 3 (55) 10 5 66 (30) 33 (60) (20) (16) 62 (18) (29) (52) (23) (44) 13 (22) (59) (7) (37) 12 31 (41) 46 (17) (14) 27 24 (53) (42) (50) 43 (8) 54 (35) 47 (34)
Y5$1 58 65 (4) 28 32 (64) 26 25 (9) 21 40 63 67 49 38 57 56 15 51 11 36 19 39 1 48 2 61 45 6 (3) 55 (10) 5 66 (30) (33) (60) 20 (16) (62) (18) (29) (52) (23) (44) 13 (22) 59 (7) (37) (12) (31) (41) (46) (17) (14) 27 (24) 53 42 (50) (43) 8 (54) (35) (47) (34)
Y5$2 58 (65) (4) (28) 32 (64) 26 25 (9) 21 40 63 67 49 (38) (57) 56 (15) 51 11 36 19 (39) 1 48 2 61 45 (6) 3 (55) 10 (5) 66 30 (33) 60 20 (16) 62 18 (29) 52 (23) (44) 13 22 (59) (7) 37 (12) (31) (41) (46) (17) (14) 27 24 (53) 42 50 43 (8) 54 (35) (47) (34)
Y5$3 58 (65) (4) (28) 32 (64) 26 25 (9) 21 40 63 67 49 (38) (57) 56 15 51 11 36 19 (39) 1 48 2 61 45 (6) 3 55 10 (5) 66 30 (33) (60) 20 (16) (62) (18) (29) (52) (23) (44) 13 22 (59) (7) 37 12 (31) (41) (46) (17) (14) 27 24 53 (42) 50 43 (8) 54 35 (47) 34
Y5$4 58 (65) (4) (28) 32 (64) 26 (25) (9) 21 40 63 67 49 (38) (57) 56 15 51 (11) 36 19 (39) 1 48 2 61 45 6 3 55 10 5 66 30 33 (60) (20) (16) 62 18 (29) (52) (23) 44 13 (22) (59) (7) 37 12 (31) (41) 46 (17) 14 27 24 (53) (42) (50) (43) 8 54 (35) 47 (34)
Y5$5 58 65 4 28 (32) 64 26 (25) (9) 21 40 63 67 49 38 57 56 15 51 (11) 36 19 (39) 1 (48) 2 61 45 6 3 (55) 10 5 66 (30) (33) (60) (20) (16) (62) (18) 29 (52) 23 (44) 13 (22) (59) 7 (37) 12 (31) (41) 46 17 (14) 27 24 (53) (42) (50) (43) 8 54 (35) (47) 34
Y6$1 58 (65) (4) 28 (32) (64) 26 (25) 9 (21) 40 63 67 (49) 38 (57) 56 (15) 51 11 36 19 39 1 (48) 2 61 (45) 6 3 (55) (10) (5) (66) (30) (33) (60) 20 (16) (62) (18) (29) (52) (23) (44) (13) (22) (59) (7) (37) (12) (31) (41) 46 (17) (14) 27 (24) (53) 42 (50) (43) (8) 54 (35) (47) (34)
Y6$2 58 (65) 4 28 32 (64) 26 (25) 9 (21) 40 63 67 (49) 38 (57) 56 (15) 51 (11) 36 19 39 1 (48) 2 61 (45) 6 3 (55) 10 5 (66) 30 (33) 60 20 (16) (62) (18) (29) (52) (23) (44) (13) (22) (59) (7) (37) (12) 31 (41) 46 (17) (14) 27 (24) (53) 42 (50) (43) (8) 54 (35) 47 (34)
Y6$3 58 (65) 4 28 32 (64) (26) (25) 9 (21) 40 63 67 (49) 38 57 56 15 51 (11) 36 (19) 39 1 (48) 2 61 (45) 6 3 (55) 10 (5) (66) 30 33 (60) 20 (16) (62) (18) (29) (52) (23) (44) (13) 22 (59) (7) (37) (12) 31 41 46 17 (14) 27 24 (53) 42 (50) (43) (8) 54 (35) 47 (34)
Y6$4 58 (65) (4) 28 32 (64) 26 25 9 (21) 40 63 67 49 38 57 56 15 51 (11) 36 (19) 39 1 48 2 (61) 45 6 3 55 10 (5) (66) 30 (33) (60) 20 16 (62) (18) (29) (52) (23) (44) 13 22 59 7 (37) (12) 31 (41) 46 17 (14) 27 24 (53) 42 (50) (43) (8) 54 (35) 47 (34)
Y6$5 58 (65) 4 28 32 (64) 26 25 9 21 40 63 67 49 38 57 56 15 51 (11) 36 (19) 39 1 48 2 (61) 45 6 3 55 10 (5) (66) (30) 33 (60) (20) 16 62 (18) (29) (52) (23) (44) 13 (22) (59) 7 (37) (12) 31 (41) 46 (17) (14) 27 24 (53) 42 (50) (43) 8 54 (35) 47 (34)
Table 10
Approximate measurement invariance (noninvariance) of the intercepts for the Fairness items. Non-invariant parameters are
parenthesized. Y indicates item numbers and $ indicates thresholds. See Table 7 for a description of country numbers
SEX DIFFERENCES IN MORALITY 179
Item Country
Y1 58 (65) 4 28 32 64 26 25 9 21 40 63 67 49 38 57 56 15 51 11 36 19 39 1 48 2 61 45 6 3 55 10 5 66 30 33 60 20 16 62 18 (29) 52 23 44 13 22 59 (7) 37 (12) 31 41 46 17 14 27 (24) 53 42 50 43 8 54 35 47 34
Y2 58 65 4 28 32 (64) 26 25 9 21 40 63 67 49 38 57 56 15 51 (11) 36 19 39 1 48 2 61 45 6 3 55 10 5 66 30 (33) 60 20 16 62 18 29 52 23 44 (13) 22 59 7 37 12 31 41 46 17 14 27 (24) 53 42 50 43 8 54 35 47 34
Y3 58 (65) 4 28 32 64 26 25 9 21 40 63 67 49 38 57 56 15 51 11 36 19 39 1 48 2 61 45 6 3 55 10 5 66 30 33 60 20 16 62 18 29 52 23 44 13 22 59 7 37 12 31 41 46 17 14 27 (24) 53 42 50 43 8 54 35 47 34
Y4 58 65 4 28 32 64 26 (25) 9 21 40 63 67 49 38 57 56 15 51 11 36 19 39 1 48 2 61 45 6 3 55 10 5 66 30 33 60 20 16 62 18 29 (52) 23 44 13 22 (59) 7 37 12 (31) 41 46 17 14 27 (24) 53 42 50 43 8 54 35 47 34
Y5 58 (65) (4) 28 32 (64) 26 25 (9) 21 40 63 67 49 38 57 56 15 51 11 36 19 39 1 48 2 61 45 6 3 55 10 5 66 30 33 60 20 16 62 18 29 52 23 44 13 22 59 (7) 37 12 31 41 46 17 14 27 (24) 53 42 50 43 8 54 35 47 34
Y6 58 65 4 28 32 (64) 26 (25) 9 21 (40) 63 67 49 38 57 56 15 51 (11) 36 19 39 1 48 2 61 45 6 3 55 10 5 66 30 33 60 20 (16) 62 18 29 52 23 44 13 22 59 7 37 12 31 41 46 17 14 27 (24) 53 42 50 43 8 54 35 47 34
Table 11
Approximate measurement invariance (noninvariance) of the loadings for the Fairness items. Non-invariant parameters are
parenthesized. See Table 7 for a description of country numbers
SEX DIFFERENCES IN MORALITY 180
Threshold Country number
Y1$1 (58) (65) 4 (28) 32 (64) 26 (25) 9 21 40 63 67 49 38 (57) 56 15 51 (11) 36 (19) (39) 1 48 2 61 45 (6) 3 55 10 5 66 30 (33) 60 20 16 62 18 29 52 23 44 13 22 59 (7) 37 12 31 41 46 17 14 27 24 53 42 50 43 8 54 35 47 34
Y1$2 (58) (65) (4) (28) 32 (64) 26 25 (9) 21 40 63 67 49 (38) (57) 56 (15) 51 (11) 36 (19) (39) 1 48 2 61 45 (6) 3 55 10 (5) 66 30 (33) 60 20 16 62 (18) (29) 52 23 (44) 13 22 59 (7) 37 12 31 (41) (46) (17) 14 27 24 53 42 50 43 8 54 35 47 34
Y1$3 (58) (65) (4) (28) 32 (64) 26 25 (9) 21 40 63 67 49 (38) (57) 56 (15) 51 (11) 36 (19) (39) 1 48 2 61 45 (6) 3 55 10 (5) 66 30 33 60 20 16 62 (18) 29 (52) 23 44 13 22 59 (7) 37 12 31 (41) 46 (17) 14 27 24 53 42 50 43 8 54 35 47 34
Y1$4 58 (65) 4 28 32 (64) 26 (25) 9 21 40 63 (67) 49 38 (57) 56 15 51 (11) 36 (19) 39 (1) (48) 2 61 45 6 3 55 10 5 66 30 33 60 20 16 62 18 29 52 23 44 13 22 59 7 (37) 12 31 41 46 17 14 27 24 53 42 50 (43) 8 54 35 47 34
Y1$5 58 (65) 4 28 32 64 26 (25) 9 21 40 63 (67) 49 38 57 56 15 51 (11) 36 19 39 1 48 2 61 45 6 3 55 10 5 66 30 33 60 20 16 62 18 29 52 23 44 13 22 59 7 (37) (12) 31 41 46 17 14 27 24 53 42 50 43 8 54 35 47 34
Y2$1 58 (65) 4 28 32 64 26 25 9 21 40 (63) 67 49 38 57 56 15 51 11 36 19 39 1 48 2 61 45 6 3 55 10 5 66 30 33 60 20 (16) 62 18 29 52 23 44 13 22 59 7 37 12 31 41 46 17 14 (27) 24 53 42 (50) 43 8 54 35 47 34
Y2$2 58 (65) 4 28 32 64 26 (25) 9 21 40 63 67 49 38 57 56 15 51 11 36 19 39 1 48 2 61 45 6 3 55 10 5 66 30 33 60 20 16 62 18 29 52 23 44 13 22 59 7 37 12 31 41 46 17 14 (27) 24 53 (42) 50 43 8 54 35 47 34
Y2$3 58 (65) 4 28 32 64 26 25 9 21 40 63 67 49 (38) 57 56 15 51 11 36 (19) 39 1 48 2 61 45 6 3 55 10 5 66 30 33 60 20 16 62 18 29 52 23 44 13 22 59 7 37 12 31 41 46 17 14 27 24 53 42 50 43 8 54 35 47 34
Y2$4 58 (65) (4) 28 32 (64) 26 25 (9) 21 40 63 67 49 38 57 56 15 51 (11) 36 19 (39) 1 48 2 61 45 6 3 55 10 5 66 30 33 60 20 16 62 18 29 52 23 44 (13) 22 59 7 37 12 31 41 46 17 (14) 27 24 53 42 50 43 8 (54) 35 47 34
Y2$5 58 65 4 28 32 64 26 25 9 21 40 63 67 49 38 57 56 15 51 11 36 19 39 1 48 2 61 45 6 3 55 10 5 66 30 33 60 20 16 62 18 29 52 23 44 13 22 59 7 37 12 31 41 46 17 (14) 27 24 53 42 50 43 8 54 (35) 47 34
Y3$1 58 65 4 28 32 64 26 (25) 9 21 40 (63) 67 (49) 38 57 56 (15) 51 11 36 19 39 1 48 2 61 45 6 3 55 10 5 66 30 (33) 60 (20) (16) 62 18 29 52 23 44 13 22 59 7 37 12 31 41 46 17 14 27 24 53 (42) (50) 43 8 54 (35) 47 34
Y3$2 58 65 (4) 28 32 (64) 26 (25) 9 21 40 (63) 67 (49) 38 57 56 15 51 11 36 19 39 1 48 2 61 45 6 3 55 10 5 66 30 (33) 60 (20) 16 62 18 29 52 23 44 13 22 (59) 7 37 12 (31) 41 46 17 14 (27) 24 53 42 (50) 43 8 54 35 47 34
Y3$3 58 65 (4) 28 32 (64) 26 (25) (9) 21 40 63 67 (49) 38 57 (56) 15 51 11 (36) 19 (39) 1 (48) 2 61 45 6 3 55 10 5 66 (30) (33) 60 20 16 62 (18) 29 52 23 44 13 22 59 7 37 12 (31) 41 46 17 14 27 24 53 42 (50) 43 8 54 35 47 34
Y3$4 58 (65) 4 28 32 64 26 (25) 9 21 40 63 67 49 38 57 56 15 51 (11) 36 19 39 1 48 2 61 (45) 6 3 55 (10) 5 66 30 (33) 60 20 16 62 18 29 (52) 23 44 (13) 22 59 7 37 12 31 41 46 17 14 27 24 53 42 50 43 8 54 35 47 34
Y3$5 58 (65) 4 28 32 64 26 25 9 21 40 63 67 49 38 57 56 15 51 (11) 36 19 (39) 1 48 2 61 45 6 3 55 (10) 5 66 30 33 60 20 16 62 18 29 52 23 44 13 22 59 7 37 12 31 41 46 17 14 27 24 53 42 50 43 8 54 35 47 34
Y4$1 (58) 65 (4) 28 32 (64) 26 (25) (9) (21) 40 63 67 49 38 (57) 56 15 51 (11) (36) (19) 39 1 (48) 2 61 45 (6) (3) (55) 10 (5) (66) 30 33 60 20 16 62 18 29 (52) 23 44 13 22 59 (7) (37) (12) 31 (41) 46 (17) 14 27 24 53 42 50 43 8 54 35 47 34
Y4$2 58 65 (4) (28) 32 64 26 (25) (9) (21) (40) 63 67 49 38 (57) 56 15 51 (11) (36) (19) 39 1 (48) 2 61 45 (6) (3) (55) 10 (5) (66) 30 33 60 20 16 62 18 29 (52) 23 44 13 22 59 (7) (37) (12) 31 (41) 46 (17) 14 27 24 53 42 50 43 8 54 35 47 34
Y4$3 58 (65) (4) (28) 32 64 26 (25) (9) (21) 40 63 67 49 38 (57) 56 15 51 (11) (36) (19) 39 1 (48) 2 61 45 (6) (3) (55) 10 (5) 66 30 33 60 20 16 62 18 29 (52) (23) 44 13 22 59 (7) (37) (12) 31 (41) (46) (17) 14 27 (24) 53 (42) 50 43 8 54 35 47 34
Y4$4 58 (65) (4) (28) 32 64 26 (25) (9) (21) 40 63 67 49 38 57 56 15 51 (11) (36) (19) 39 1 (48) 2 61 45 (6) (3) (55) 10 (5) 66 30 33 60 20 16 62 18 (29) (52) 23 44 13 22 59 (7) 37 (12) 31 (41) 46 (17) 14 27 24 (53) 42 50 43 8 54 35 47 34
Y4$5 58 (65) 4 (28) (32) (64) 26 (25) (9) 21 40 63 67 49 38 57 56 15 51 (11) 36 (19) 39 1 (48) 2 61 45 6 3 55 10 5 66 30 33 60 (20) 16 62 18 (29) (52) 23 44 13 22 59 (7) 37 12 31 (41) 46 17 14 27 24 53 42 50 43 8 54 35 47 34
Y5$1 58 65 4 28 32 64 (26) (25) 9 (21) (40) 63 67 49 38 57 56 15 51 11 36 19 39 1 48 2 61 (45) 6 (3) 55 10 5 (66) (30) 33 60 20 16 62 (18) 29 52 23 (44) 13 22 59 (7) (37) (12) 31 41 (46) 17 14 27 24 53 42 50 43 8 54 35 (47) 34
Y5$2 58 (65) (4) 28 32 64 26 (25) 9 (21) 40 63 67 49 38 57 56 15 51 11 36 19 39 1 (48) 2 61 (45) 6 (3) 55 10 5 (66) (30) 33 60 20 16 62 (18) 29 52 23 (44) 13 22 59 (7) (37) (12) 31 41 (46) 17 14 27 24 53 42 50 43 8 54 35 (47) 34
Y5$3 58 (65) (4) (28) 32 (64) 26 (25) (9) 21 40 63 67 (49) 38 (57) 56 (15) 51 11 36 (19) (39) 1 48 2 61 (45) 6 3 (55) 10 5 66 (30) 33 60 20 16 62 18 29 52 (23) 44 13 22 (59) (7) (37) 12 31 (41) (46) 17 14 27 24 53 42 50 43 8 54 35 (47) 34
Y5$4 58 65 4 28 32 64 26 (25) 9 21 40 63 67 49 (38) 57 56 15 51 11 36 19 39 1 48 2 61 (45) 6 (3) 55 10 5 66 (30) (33) 60 20 16 62 (18) 29 (52) 23 44 13 22 (59) (7) (37) (12) 31 41 (46) 17 14 27 24 53 42 50 43 8 54 35 (47) 34
Y5$5 58 (65) 4 28 32 64 26 25 9 21 40 63 67 49 38 57 56 15 51 11 36 19 39 1 48 (2) 61 (45) 6 3 (55) 10 5 66 30 33 60 20 16 62 18 29 52 23 44 13 22 59 (7) 37 12 31 41 46 17 14 27 24 53 42 50 43 8 54 35 47 34
Y6$1 58 65 4 28 32 64 26 25 (9) 21 40 63 67 49 38 57 56 15 51 (11) (36) (19) 39 1 48 2 61 45 6 3 (55) 10 5 66 30 (33) 60 20 16 62 18 29 (52) 23 44 13 (22) 59 (7) (37) 12 31 41 46 (17) 14 27 24 53 42 50 43 8 54 35 (47) 34
Y6$2 (58) 65 4 28 32 64 (26) 25 (9) 21 40 63 (67) 49 38 (57) 56 (15) 51 (11) (36) (19) 39 1 48 2 61 45 6 3 (55) 10 5 66 30 (33) (60) 20 16 62 18 29 (52) 23 44 13 (22) (59) (7) 37 12 (31) (41) 46 (17) 14 27 24 53 42 50 43 8 54 35 47 34
Y6$3 58 (65) 4 28 32 64 (26) (25) (9) 21 40 63 (67) 49 38 (57) 56 (15) 51 (11) (36) (19) 39 1 48 2 61 45 6 3 (55) 10 5 66 30 (33) (60) 20 16 62 18 29 (52) 23 44 13 (22) (59) 7 37 12 (31) (41) 46 (17) 14 27 24 53 42 50 43 8 54 35 47 34
Y6$4 58 (65) (4) (28) 32 (64) 26 25 (9) 21 40 63 67 49 38 (57) 56 15 51 (11) 36 (19) (39) 1 48 2 61 45 6 3 (55) 10 5 66 30 (33) (60) 20 16 62 18 29 52 23 44 13 22 59 7 37 12 31 41 46 17 14 27 24 53 42 50 43 8 54 35 47 34
Y6$5 58 65 (4) 28 (32) (64) 26 (25) 9 (21) (40) 63 67 49 38 57 56 15 51 (11) 36 (19) 39 1 48 2 (61) 45 6 3 55 10 5 66 30 33 (60) 20 16 62 18 29 52 23 44 13 22 59 7 37 12 31 41 46 17 14 27 24 53 42 50 43 8 54 (35) 47 34
Table 12
Approximate measurement invariance (noninvariance) of the intercepts for the Loyalty items. Non-invariant parameters are
parenthesized. Y indicates item numbers and $ indicates thresholds. See Table 7 for a description of country numbers
SEX DIFFERENCES IN MORALITY 181
Item Country
Y1 58 65 4 28 32 64 26 25 9 21 40 63 67 49 38 57 56 15 51 11 36 19 39 1 48 2 61 45 6 3 55 10 5 66 30 33 60 20 16 62 18 29 52 23 44 13 22 59 7 37 12 31 41 46 17 14 27 24 53 42 50 43 8 54 35 47 34
Y2 58 (65) 4 28 32 64 26 25 9 21 40 (63) 67 49 38 57 56 15 51 11 36 19 39 1 48 2 61 45 6 3 55 10 5 66 30 33 60 20 16 62 18 29 52 23 44 13 22 59 7 37 12 31 41 46 17 14 27 24 53 42 50 43 8 54 35 47 (34)
Y3 58 65 4 28 32 64 26 25 9 21 40 (63) 67 49 38 57 56 15 51 11 36 19 39 1 48 2 61 45 6 3 55 10 5 66 30 33 60 (20) 16 62 18 29 52 23 44 13 22 59 7 37 12 31 41 46 17 14 27 24 53 42 50 43 8 54 35 47 (34)
Y4 58 (65) 4 28 32 64 26 25 (9) 21 40 63 67 49 38 57 56 15 51 11 36 19 39 1 48 2 61 45 6 3 55 10 5 66 30 33 60 20 16 62 18 29 52 23 44 13 22 59 7 37 12 31 41 46 17 14 27 24 53 42 50 43 8 54 35 47 34
Y5 58 65 4 28 32 64 26 25 9 21 40 63 67 49 38 57 56 15 51 11 36 19 39 1 48 2 61 45 6 3 55 10 5 66 30 33 60 20 16 62 18 29 52 23 44 13 22 59 7 37 12 31 41 46 17 14 27 24 53 42 50 43 8 54 35 47 34
Y6 58 (65) 4 28 32 64 26 25 9 21 40 63 67 49 38 57 56 15 51 11 36 (19) 39 1 48 2 61 45 6 3 55 10 5 66 30 33 60 20 16 62 18 29 52 23 44 13 22 59 7 37 12 31 41 46 17 14 27 24 53 42 50 43 8 54 35 47 34
Table 13
Approximate measurement invariance (noninvariance) of the loadings for the Loyalty items. Non-invariant parameters are
parenthesized. See Table 7 for a description of country numbers
SEX DIFFERENCES IN MORALITY 182
Threshold Country number
Y1$1 58 65 4 28 32 (64) 26 (25) 9 21 40 63 67 (49) (38) 57 56 15 51 11 36 19 39 1 48 2 61 45 6 3 55 10 5 66 30 33 60 20 16 62 18 29 52 23 (44) 13 22 59 7 37 12 31 41 46 17 14 27 24 53 42 (50) 43 8 54 35 47 34
Y1$2 58 65 4 28 32 (64) 26 (25) (9) 21 40 63 67 (49) (38) 57 56 15 51 11 36 19 39 1 48 2 61 45 6 3 55 10 5 66 30 33 60 20 16 (62) 18 29 52 23 44 13 22 59 (7) 37 12 31 41 46 17 14 27 24 53 (42) 50 43 8 54 35 47 34
Y1$3 58 65 4 28 32 (64) 26 25 (9) (21) 40 63 67 (49) (38) 57 (56) 15 51 11 36 (19) 39 1 48 2 61 45 6 3 55 10 5 66 30 33 60 20 16 62 18 29 52 23 44 13 22 59 7 37 12 31 41 46 17 14 27 24 53 42 50 43 8 54 35 47 34
Y1$4 58 65 4 28 32 (64) 26 25 9 21 40 63 67 49 38 57 (56) 15 51 11 36 19 39 1 48 2 61 45 6 3 55 10 5 66 30 33 60 20 16 62 18 29 52 23 44 13 22 59 7 37 12 31 41 46 17 14 27 24 53 42 50 43 8 54 35 47 34
Y1$5 58 65 4 28 32 64 26 25 9 21 40 (63) 67 49 38 57 56 15 (51) 11 36 19 39 1 48 2 61 45 6 3 55 10 5 66 30 33 60 20 16 62 18 29 52 23 44 13 22 59 7 37 12 31 41 46 17 14 27 24 53 42 (50) 43 8 (54) 35 47 34
Y2$1 58 (65) 4 28 32 64 26 (25) 9 21 40 63 67 49 (38) 57 56 15 51 11 36 (19) 39 1 48 2 61 45 6 3 55 10 5 66 30 (33) 60 20 16 62 18 29 52 23 44 13 22 59 (7) (37) 12 31 41 46 17 14 27 24 53 42 (50) 43 8 54 35 47 34
Y2$2 58 (65) 4 28 32 64 26 (25) 9 21 40 63 (67) 49 38 57 56 15 51 (11) 36 (19) 39 1 48 2 61 45 6 3 (55) 10 5 66 30 (33) 60 20 16 62 18 29 52 23 44 13 22 59 (7) (37) 12 (31) 41 (46) 17 14 27 24 53 42 50 43 8 54 35 47 34
Y2$3 58 (65) 4 28 32 64 26 25 9 21 40 63 (67) 49 38 57 56 15 51 (11) 36 (19) 39 1 48 2 61 45 6 3 55 10 5 66 30 33 60 20 16 62 18 29 52 23 44 13 22 59 7 37 12 (31) 41 46 17 14 27 24 53 42 50 43 8 54 35 47 34
Y2$4 58 (65) (4) 28 32 (64) 26 25 (9) 21 40 63 67 49 38 57 56 15 51 11 36 19 (39) 1 48 2 61 45 6 3 55 10 5 66 30 33 60 20 16 62 18 29 52 23 44 13 22 59 7 37 12 (31) 41 46 17 14 27 24 53 42 (50) 43 8 54 35 47 34
Y2$5 58 65 4 28 32 64 (26) 25 9 21 40 63 67 49 38 57 56 15 (51) 11 36 19 39 1 48 2 61 45 6 3 55 10 (5) 66 30 33 60 20 16 (62) 18 29 52 23 44 13 22 59 7 37 12 31 41 46 17 14 27 24 (53) 42 (50) 43 8 54 (35) 47 34
Y3$1 58 (65) 4 28 32 64 26 25 (9) 21 (40) 63 67 49 38 57 56 15 51 11 36 19 39 1 48 2 61 45 6 3 55 10 5 66 30 33 60 20 16 62 18 29 52 23 44 13 22 59 7 37 12 31 41 46 17 14 27 24 53 42 50 43 8 54 35 47 34
Y3$2 58 (65) 4 28 32 64 26 (25) 9 21 40 63 67 49 38 57 56 15 51 11 36 19 39 1 48 2 61 45 6 3 55 10 5 66 30 33 60 20 16 62 18 29 52 23 44 13 22 59 7 37 12 31 41 46 17 14 27 24 53 42 50 43 8 54 35 47 34
Y3$3 58 (65) 4 28 32 64 26 25 9 21 40 63 67 49 38 57 56 15 51 11 36 19 39 1 48 2 61 45 6 3 55 10 5 66 30 33 60 20 16 62 18 29 52 23 44 (13) 22 59 (7) 37 12 31 41 46 17 14 27 24 53 42 50 43 8 54 35 47 34
Y3$4 58 (65) 4 28 32 64 (26) 25 9 21 40 63 67 49 38 (57) 56 15 51 11 36 19 39 1 (48) 2 61 45 6 3 (55) 10 5 66 30 33 60 20 16 62 18 29 52 23 44 13 22 59 (7) 37 12 31 41 46 17 14 27 24 53 42 50 43 8 54 35 47 34
Y3$5 58 (65) 4 (28) 32 64 (26) 25 9 21 40 63 67 49 (38) 57 56 15 51 11 36 19 39 1 (48) 2 61 45 6 3 (55) 10 5 66 30 33 60 20 16 62 18 29 52 23 44 13 22 59 (7) 37 12 31 41 46 17 14 27 24 53 42 50 43 8 54 35 47 34
Y4$1 58 (65) 4 28 32 64 26 (25) 9 21 40 63 67 (49) 38 57 56 15 51 (11) 36 19 39 1 (48) 2 61 45 6 3 55 10 5 66 30 (33) 60 20 (16) 62 18 29 52 23 44 13 (22) 59 7 37 12 31 41 46 17 14 27 24 53 42 50 43 8 54 35 47 34
Y4$2 58 (65) 4 28 32 64 26 (25) 9 21 40 63 67 (49) 38 57 56 15 51 (11) 36 19 39 1 (48) 2 (61) 45 6 3 55 10 5 66 30 (33) (60) 20 (16) 62 18 29 52 23 44 (13) (22) (59) 7 37 12 (31) 41 46 17 14 (27) 24 53 (42) 50 43 8 54 35 47 34
Y4$3 58 (65) 4 28 32 64 (26) (25) 9 21 40 63 67 (49) 38 57 56 15 51 (11) (36) 19 39 1 (48) 2 (61) (45) 6 3 55 10 5 66 30 (33) (60) 20 (16) 62 18 29 (52) 23 44 (13) (22) (59) 7 37 12 (31) 41 46 17 14 (27) 24 53 (42) 50 43 8 54 35 47 34
Y4$4 58 (65) 4 28 32 (64) 26 (25) (9) 21 40 63 67 (49) 38 57 (56) 15 51 (11) 36 19 39 1 (48) 2 (61) (45) 6 3 (55) 10 5 66 30 (33) 60 (20) 16 62 (18) 29 (52) 23 44 13 (22) (59) 7 37 12 (31) 41 46 17 14 27 24 53 (42) 50 43 8 54 35 47 34
Y4$5 58 (65) 4 28 32 64 26 (25) 9 21 40 63 67 49 38 57 56 15 51 (11) 36 19 39 1 48 2 61 (45) 6 3 55 10 5 66 30 33 60 20 16 62 (18) 29 52 23 44 13 22 59 7 37 12 31 41 46 17 14 27 24 53 42 50 43 8 54 35 47 34
Y5$1 58 (65) 4 28 32 64 26 25 (9) 21 40 63 67 49 38 (57) (56) 15 51 (11) 36 (19) 39 1 48 2 61 45 6 (3) 55 10 5 (66) 30 33 60 20 16 62 (18) 29 (52) 23 44 13 22 59 (7) (37) (12) 31 41 (46) 17 14 27 24 53 42 50 43 8 54 35 (47) 34
Y5$2 58 65 (4) (28) 32 64 26 (25) 9 21 40 63 67 49 (38) (57) (56) 15 51 (11) (36) (19) (39) 1 48 2 61 45 6 (3) (55) 10 5 66 30 33 60 20 16 62 (18) 29 (52) (23) 44 13 22 59 (7) 37 12 (31) 41 46 17 (14) 27 24 53 42 (50) 43 8 54 35 47 34
Y5$3 58 65 (4) (28) 32 (64) 26 (25) 9 21 40 63 67 (49) (38) (57) (56) 15 51 (11) (36) (19) (39) 1 (48) 2 61 45 6 (3) (55) 10 5 66 30 33 60 20 16 62 (18) 29 (52) (23) 44 13 22 59 (7) 37 12 (31) 41 46 17 (14) 27 24 53 42 (50) 43 8 54 35 47 34
Y5$4 58 65 4 (28) (32) 64 26 (25) 9 21 40 63 67 (49) (38) 57 56 15 51 (11) (36) 19 39 1 (48) 2 61 45 6 3 (55) 10 5 66 30 33 60 (20) 16 (62) 18 29 (52) 23 44 13 22 59 7 37 12 31 41 46 17 (14) 27 24 (53) 42 (50) 43 8 54 35 47 34
Y5$5 58 65 4 (28) 32 64 26 (25) 9 21 40 63 67 49 38 57 56 15 51 (11) (36) 19 39 1 (48) 2 61 (45) 6 3 (55) 10 5 66 30 33 60 20 16 62 18 29 52 23 44 13 22 59 7 37 12 31 41 46 17 (14) 27 24 (53) 42 (50) 43 8 54 35 47 34
Y6$1 58 (65) 4 28 32 64 26 (25) 9 21 40 63 67 49 38 57 56 15 51 (11) 36 19 39 1 48 2 61 45 6 (3) (55) (10) 5 (66) 30 33 60 20 16 62 (18) 29 (52) 23 44 13 22 59 7 (37) (12) 31 41 46 17 14 27 24 53 42 50 43 8 54 35 (47) 34
Y6$2 (58) (65) 4 28 32 (64) 26 (25) 9 (21) 40 63 67 49 38 57 56 15 51 (11) 36 (19) 39 1 48 2 61 45 6 3 (55) (10) 5 (66) 30 (33) 60 20 16 62 (18) 29 (52) 23 44 13 22 59 7 (37) (12) 31 41 46 17 14 27 24 53 42 50 43 8 54 35 47 34
Y6$3 (58) (65) 4 28 32 (64) 26 (25) 9 (21) 40 63 67 49 38 (57) 56 15 51 (11) 36 (19) 39 1 48 2 61 45 6 3 55 (10) 5 (66) 30 (33) 60 20 16 62 18 29 (52) 23 44 13 22 59 7 (37) (12) 31 41 46 17 14 27 24 53 42 50 43 8 54 35 47 34
Y6$4 58 (65) 4 28 32 (64) 26 (25) (9) 21 40 63 67 (49) 38 (57) 56 (15) 51 (11) 36 19 39 1 48 2 61 45 6 3 55 10 5 66 30 (33) 60 20 16 62 18 (29) 52 23 44 13 22 59 7 37 12 31 41 46 17 14 27 24 53 42 50 43 8 54 35 47 34
Y6$5 58 (65) 4 28 32 (64) 26 (25) (9) 21 40 63 67 (49) 38 57 56 15 51 (11) 36 19 39 1 48 2 61 45 6 3 55 10 5 66 30 33 60 20 16 62 18 29 52 23 44 13 22 59 7 37 12 31 41 46 17 14 27 24 53 42 50 43 8 54 35 47 34
Table 14
Approximate measurement invariance (noninvariance) of the intercepts for the Authority items. Non-invariant parameters are
parenthesized. Y indicates item numbers and $ indicates thresholds. See Table 7 for a description of country numbers
SEX DIFFERENCES IN MORALITY 183
Item Country
Y1 58 65 4 28 32 64 26 25 9 21 40 63 67 49 38 57 56 15 51 11 36 19 39 1 48 2 61 45 6 3 55 10 5 66 30 33 60 20 16 62 18 29 52 23 44 13 22 59 7 37 12 31 41 46 17 14 27 24 53 42 50 43 8 54 35 47 34
Y2 58 (65) 4 28 32 64 26 25 9 21 40 63 67 49 38 57 56 15 51 11 36 19 39 1 48 2 61 45 6 3 55 10 5 66 30 33 60 20 16 62 18 29 52 23 44 13 22 59 7 37 12 31 41 46 17 14 27 24 53 42 50 43 8 54 35 47 34
Y3 58 (65) 4 28 32 64 26 25 9 21 40 63 67 49 38 57 56 15 51 11 36 19 39 1 48 2 61 45 6 3 55 10 5 66 30 33 60 20 16 62 18 29 52 23 44 13 22 59 7 37 12 31 41 46 17 14 27 24 53 42 50 43 8 54 35 47 34
Y4 58 (65) 4 28 32 64 26 25 9 21 40 63 67 49 38 57 56 15 51 11 36 19 39 1 48 2 61 45 6 3 55 10 5 66 30 33 60 20 16 62 18 29 52 23 44 13 22 59 7 37 12 31 41 46 17 14 27 24 53 42 50 43 8 54 35 47 34
Y5 58 65 4 28 32 64 26 25 9 21 40 63 67 49 38 57 56 15 51 11 36 19 39 1 48 2 61 45 6 3 55 10 5 66 30 33 60 20 16 62 18 29 52 23 44 13 22 59 7 37 12 31 41 46 17 14 27 24 53 42 50 43 8 54 35 47 34
Y6 58 (65) 4 28 32 (64) 26 25 9 21 40 63 67 49 38 57 56 15 51 (11) 36 19 39 1 48 2 61 45 6 3 55 10 5 66 30 33 60 20 16 62 18 29 52 23 44 13 22 59 7 37 12 31 41 46 17 14 27 24 53 42 50 43 8 54 35 47 34
Table 15
Approximate measurement invariance (noninvariance) of the loadings for the Authority items. Non-invariant parameters are
parenthesized. See Table 7 for a description of country numbers
SEX DIFFERENCES IN MORALITY 184
Threshold Country number
Y1$1 58 (65) (4) 28 32 (64) 26 (25) 9 21 40 63 67 49 38 57 56 15 (51) (11) 36 19 39 1 48 2 61 45 6 3 55 10 5 66 30 33 60 20 16 62 18 29 52 23 44 13 22 59 7 37 12 (31) 41 46 17 14 27 24 53 42 (50) 43 8 54 35 47 34
Y1$2 58 (65) (4) 28 32 (64) 26 25 (9) 21 40 63 67 49 (38) 57 56 15 (51) 11 36 19 (39) 1 48 2 61 (45) 6 3 55 10 5 66 30 33 60 20 16 62 18 29 (52) 23 44 13 22 59 7 37 12 31 41 46 17 14 27 24 53 42 50 43 8 54 35 47 34
Y1$3 58 65 (4) 28 32 (64) 26 25 9 21 40 63 67 49 38 57 56 15 (51) (11) 36 19 39 1 48 2 61 (45) 6 3 55 10 5 66 30 33 60 20 16 62 18 29 52 23 44 13 22 59 7 (37) 12 31 41 46 17 14 27 24 53 42 50 43 8 54 35 47 34
Y1$4 58 65 4 28 32 (64) 26 (25) 9 21 40 63 67 49 38 57 56 15 51 11 36 19 39 1 48 2 61 (45) 6 3 55 10 5 66 30 33 60 20 16 62 18 29 52 23 44 13 22 59 7 37 12 31 41 46 (17) 14 27 24 53 42 50 43 8 54 35 47 34
Y1$5 58 65 4 28 32 (64) 26 (25) (9) 21 40 63 67 49 38 57 56 15 51 11 36 19 39 1 48 2 61 45 6 3 55 10 5 66 30 33 60 20 16 62 18 29 52 23 44 13 22 59 7 37 12 31 41 46 17 14 27 24 (53) 42 50 43 8 54 35 47 34
Y2$1 58 (65) (4) 28 (32) (64) 26 25 9 21 40 63 67 49 38 57 56 15 51 11 36 19 39 1 48 2 61 45 6 3 55 10 5 66 30 33 60 20 (16) 62 18 29 (52) 23 44 13 22 59 7 37 12 31 41 46 17 14 27 24 53 42 (50) 43 8 54 35 47 34
Y2$2 58 (65) 4 28 32 (64) (26) 25 (9) 21 40 63 67 49 38 57 56 15 51 11 36 19 39 (1) (48) 2 61 45 6 3 55 10 5 66 30 33 (60) 20 (16) 62 18 29 (52) 23 44 13 (22) 59 7 37 12 31 41 46 17 14 27 24 53 42 (50) (43) 8 54 35 47 34
Y2$3 58 (65) 4 28 (32) (64) 26 25 (9) 21 40 63 67 49 38 57 56 15 51 11 36 19 39 (1) 48 2 61 45 (6) 3 55 10 5 66 30 33 (60) 20 16 62 18 29 (52) 23 44 13 (22) 59 7 37 12 31 41 (46) 17 14 27 (24) 53 42 50 43 8 54 35 47 34
Y2$4 58 (65) 4 28 32 64 26 (25) 9 21 40 63 67 (49) 38 57 (56) 15 51 11 36 19 39 1 48 2 61 45 (6) 3 55 10 5 66 30 33 60 20 16 62 (18) 29 (52) 23 44 13 22 59 (7) 37 12 31 41 (46) 17 14 27 24 53 42 50 43 8 54 35 47 34
Y2$5 58 (65) 4 28 32 64 26 25 9 21 40 63 67 49 38 57 56 15 51 11 36 19 39 1 48 2 61 45 6 3 55 10 5 66 30 33 60 20 16 62 (18) 29 52 23 44 13 22 59 (7) 37 12 31 41 46 17 14 27 24 53 42 50 43 8 54 35 47 34
Y3$1 58 (65) (4) (28) 32 (64) (26) (25) 9 (21) 40 (63) 67 49 (38) (57) 56 15 51 11 36 19 39 (1) 48 2 61 45 (6) 3 55 10 5 66 30 33 60 20 16 62 18 29 52 23 (44) 13 22 59 7 (37) 12 31 (41) 46 17 14 27 24 53 42 50 (43) 8 54 35 (47) 34
Y3$2 58 (65) (4) 28 32 64 (26) (25) (9) (21) 40 (63) 67 49 (38) (57) 56 15 51 11 36 (19) 39 (1) 48 (2) 61 45 6 3 55 10 5 66 30 33 60 20 16 62 18 29 52 23 (44) 13 22 59 (7) (37) 12 31 41 46 17 14 27 24 53 42 (50) (43) 8 54 35 (47) 34
Y3$3 58 (65) 4 28 32 64 (26) (25) 9 21 40 63 67 49 38 57 56 15 51 11 36 19 39 (1) 48 2 61 45 6 3 55 10 5 66 30 33 60 20 16 62 18 29 52 23 (44) 13 22 59 7 (37) 12 31 41 46 17 14 27 24 53 42 50 (43) 8 54 35 (47) 34
Y3$4 58 (65) 4 28 32 64 26 (25) 9 21 40 63 67 49 38 57 56 15 51 (11) 36 19 39 1 48 2 61 45 6 3 (55) 10 5 66 30 33 60 20 16 62 18 29 52 23 44 13 22 59 7 37 12 31 41 46 17 14 27 24 53 42 50 43 8 54 35 47 34
Y3$5 58 (65) 4 28 32 (64) 26 (25) 9 21 40 63 67 49 38 57 56 15 51 (11) 36 19 39 1 48 2 61 45 6 3 55 10 5 66 30 33 60 20 16 62 18 29 52 23 44 13 22 59 7 37 12 31 41 46 17 14 27 24 53 42 50 43 8 54 35 47 34
Y4$1 58 (65) 4 28 32 64 26 25 9 21 40 63 67 49 38 57 56 15 51 11 36 19 39 1 48 2 61 45 6 3 55 10 5 66 30 33 60 20 16 62 18 29 52 23 (44) 13 22 59 (7) 37 12 31 41 46 17 14 27 24 53 42 50 43 8 54 35 47 34
Y4$2 58 (65) 4 28 32 (64) 26 25 9 21 40 63 67 49 38 57 56 15 51 (11) 36 19 39 1 48 2 61 45 6 3 55 10 5 66 30 33 60 20 16 62 (18) 29 52 23 44 13 (22) 59 7 37 12 31 41 46 17 14 27 24 53 42 50 (43) 8 54 35 47 34
Y4$3 58 (65) (4) (28) 32 (64) 26 25 (9) 21 40 63 67 49 38 57 56 15 51 11 36 19 (39) 1 48 2 61 45 6 3 (55) 10 5 66 30 33 60 20 16 62 (18) 29 52 23 44 13 22 59 7 37 12 31 41 46 (17) 14 27 24 53 42 50 43 8 54 35 47 34
Y4$4 58 (65) (4) 28 32 (64) 26 (25) (9) 21 40 63 67 49 38 57 (56) 15 51 11 36 19 (39) 1 48 2 61 45 6 3 55 10 5 66 30 33 60 20 16 62 (18) 29 (52) 23 44 13 22 59 7 37 12 31 41 46 17 14 27 24 53 42 50 43 8 54 35 47 34
Y4$5 58 (65) (4) 28 32 (64) 26 (25) 9 21 40 63 67 49 38 57 (56) 15 51 11 36 19 (39) 1 48 2 61 45 6 3 55 10 5 66 30 33 60 20 16 62 (18) 29 52 23 44 13 22 59 (7) 37 12 31 41 (46) 17 14 27 24 53 42 50 43 8 54 35 47 34
Y5$1 58 (65) 4 28 32 (64) 26 (25) 9 21 40 63 67 49 38 57 56 15 51 (11) 36 19 39 1 48 2 61 45 6 3 55 10 5 66 30 33 60 20 (16) 62 18 (29) 52 23 44 13 22 59 7 37 12 31 41 46 17 14 27 24 53 42 50 43 8 54 35 47 34
Y5$2 58 (65) 4 28 32 (64) 26 (25) 9 21 40 63 67 49 (38) 57 56 15 51 (11) 36 19 39 1 48 2 61 45 (6) 3 55 10 5 66 30 33 (60) 20 16 62 18 (29) 52 23 44 13 (22) 59 7 37 12 31 41 46 17 14 27 24 53 42 50 (43) 8 54 35 47 34
Y5$3 58 65 4 28 32 (64) 26 (25) 9 21 40 63 67 49 38 57 56 15 51 11 36 19 39 1 48 2 61 45 6 3 55 10 5 66 30 33 60 20 16 62 18 29 52 23 44 13 22 59 7 37 12 31 41 46 17 14 27 24 53 42 50 43 8 54 35 47 34
Y5$4 58 (65) 4 28 32 64 26 25 9 21 40 63 67 49 38 57 56 15 51 11 36 19 39 1 48 2 61 45 6 3 55 10 5 66 30 33 60 20 16 62 18 29 52 23 44 13 22 59 7 37 12 31 41 46 17 14 27 24 53 42 50 43 8 54 35 47 34
Y5$5 58 (65) 4 28 32 64 26 25 9 21 40 63 67 49 38 57 56 15 51 (11) 36 19 39 1 48 2 61 45 6 3 55 10 5 (66) 30 33 60 20 16 62 18 29 (52) 23 44 13 22 59 7 37 12 31 41 46 17 14 27 24 53 42 50 43 8 54 35 47 34
Y6$1 (58) (65) 4 28 32 64 26 25 9 21 40 63 67 49 (38) (57) 56 15 51 (11) 36 19 (39) 1 48 2 (61) (45) 6 3 55 10 5 (66) (30) 33 60 20 16 62 (18) 29 (52) 23 44 13 22 59 (7) 37 (12) 31 41 (46) (17) 14 27 24 (53) 42 50 43 8 54 35 47 (34)
Y6$2 (58) (65) 4 28 32 64 (26) 25 9 21 40 (63) 67 49 38 57 56 15 51 (11) (36) (19) 39 1 48 2 (61) (45) 6 3 55 10 5 66 (30) (33) 60 20 16 62 (18) 29 (52) (23) 44 (13) (22) 59 (7) 37 12 (31) 41 (46) (17) (14) 27 24 53 42 50 43 8 54 35 47 (34)
Y6$3 (58) (65) 4 28 32 64 (26) 25 9 21 40 63 67 49 38 57 56 15 51 (11) (36) (19) 39 1 48 2 61 (45) 6 3 55 10 5 66 (30) 33 60 20 16 62 18 29 (52) 23 44 13 (22) 59 (7) 37 12 31 41 (46) (17) (14) 27 24 53 42 50 43 8 54 35 47 34
Y6$4 58 (65) (4) 28 32 64 26 25 9 21 40 63 67 49 38 57 56 15 51 (11) 36 (19) 39 1 48 2 61 45 6 3 55 10 5 66 30 33 60 20 16 62 18 29 (52) 23 44 13 22 59 7 37 12 31 41 46 17 14 27 24 53 42 50 43 8 54 35 47 34
Y6$5 58 65 4 28 32 64 26 25 9 21 40 63 67 49 38 57 56 15 51 11 36 19 39 1 48 2 61 45 6 3 55 10 5 66 30 33 60 20 16 62 18 29 52 23 44 13 22 59 7 37 12 31 41 46 17 14 27 24 53 42 50 43 8 54 35 47 34
Table 16
Approximate measurement invariance (noninvariance) of the intercepts for the Purity items. Non-invariant parameters are
parenthesized. Y indicates item numbers and $ indicates thresholds. See Table 7 for a description of country numbers
SEX DIFFERENCES IN MORALITY 185
Item Country
Y1 58 65 4 28 32 64 26 25 9 21 40 63 67 49 38 57 56 15 51 11 36 19 39 1 48 2 61 45 6 3 55 10 5 66 30 33 60 20 16 62 18 29 52 23 44 13 22 59 7 37 12 31 41 46 17 14 27 24 53 42 50 43 8 54 35 47 34
Y2 58 (65) (4) 28 32 (64) 26 25 9 21 40 63 67 49 38 57 56 15 51 11 36 19 39 1 48 2 61 45 6 (3) 55 10 5 66 30 33 60 20 16 62 18 29 52 23 44 13 22 59 7 37 12 31 41 46 17 14 27 24 53 42 50 43 8 54 35 47 34
Y3 58 (65) (4) 28 32 (64) 26 25 9 21 40 63 67 49 38 (57) 56 15 51 (11) 36 19 39 1 48 2 61 45 6 3 55 10 5 66 30 33 60 20 16 62 18 29 52 23 44 13 22 59 7 37 12 31 41 46 17 14 27 24 53 42 50 43 8 54 35 47 34
Y4 58 (65) 4 28 32 (64) 26 25 9 21 40 63 67 49 38 57 (56) 15 51 11 36 19 39 1 48 2 61 45 6 3 55 10 5 66 30 33 60 20 16 62 18 29 52 23 44 13 22 59 (7) 37 12 31 41 46 17 14 27 24 53 42 50 43 8 54 35 47 34
Y5 58 65 4 28 32 64 26 25 9 21 40 63 67 49 38 57 56 15 51 11 36 19 39 1 48 2 61 45 6 3 55 10 5 66 30 33 60 20 16 62 18 29 52 23 (44) 13 22 59 7 37 12 31 41 46 17 14 27 24 53 42 50 43 8 54 35 47 34
Y6 58 (65) 4 28 32 (64) 26 25 9 21 40 63 67 49 38 57 56 15 51 11 36 19 39 1 48 2 61 45 6 3 55 10 5 66 30 33 60 20 16 62 18 29 52 23 44 13 22 59 7 37 12 31 41 46 17 14 27 24 53 42 50 43 8 54 35 47 34
Table 17
Approximate measurement invariance (noninvariance) of the loadings for the Purity items. Non-invariant parameters are
parenthesized. See Table 7 for a description of country numbers
SEX DIFFERENCES IN MORALITY 186
Country configural RMSEA configral CFI weak RMSEA weak CFI strong RMSEA strong CFI weak invariance p strong invariance p
1 Switzerland 0.1228 0.9123 0.1127 0.9056 0.0999 0.9097 0.0644 0.8861
2 USA 0.1604 0.9001 0.1427 0.8989 0.1358 0.8885 0.0000 0.0000
3 Australia 0.1488 0.9056 0.1316 0.9056 0.1290 0.8897 0.4227 0.0000
4 Ireland 0.1438 0.9077 0.1265 0.9087 0.1232 0.8946 0.7219 0.0000
5 Kenya 0.1636 0.9102 0.1392 0.9169 0.1262 0.9168 0.6803 0.4124
6 United Kingdom 0.1491 0.8941 0.1321 0.8938 0.1259 0.8827 0.0354 0.0000
7 Indonesia 0.1732 0.8601 0.1512 0.8639 0.1375 0.8628 0.6931 0.3538
8 India 0.1206 0.9285 0.1078 0.9269 0.0992 0.9246 0.0445 0.0155
9 Canada 0.1484 0.8985 0.1317 0.8979 0.1284 0.8818 0.0009 0.0000
10 Guatemala 0.1942 0.8319 0.1729 0.8296 0.1553 0.8327 0.3282 0.5606
11 Nigeria 0.1513 0.9267 0.1313 0.9294 0.1431 0.8979 0.5367 0.0090
12 United Arab Emirates 0.1326 0.9224 0.1130 0.9281 0.1034 0.9267 0.8825 0.3229
13 Vietnam 0.1098 0.9067 0.0871 0.9250 0.0764 0.9299 0.9351 0.5475
14 Russian Federation 0.0612 0.9830 0.0535 0.9834 0.0475 0.9840 0.4631 0.4939
15 Netherlands 0.1269 0.9097 0.1127 0.9089 0.1094 0.8955 0.2675 0.0000
16 Sweden 0.1313 0.9223 0.1197 0.9175 0.1224 0.8950 0.0007 0.0000
17 Spain 0.1573 0.8877 0.1384 0.8889 0.1321 0.8767 0.6210 0.0015
18 Denmark 0.1407 0.9020 0.1262 0.8993 0.1213 0.8868 0.1778 0.0042
19 Serbia and Montenegro 0.2041 0.7958 0.1736 0.8112 0.1598 0.8053 0.9738 0.2525
20 China 0.1112 0.9130 0.0990 0.9118 0.0913 0.9087 0.2064 0.0590
21 Malaysia 0.1775 0.8853 0.1675 0.8694 0.1540 0.8656 0.0037 0.1535
22 Germany 0.1554 0.8777 0.1375 0.8777 0.1313 0.8642 0.4325 0.0000
23 New Zealand 0.1380 0.9247 0.1235 0.9230 0.1205 0.9107 0.0392 0.0000
24 Afghanistan 0.1676 0.8807 0.1457 0.8848 0.1346 0.8803 0.7449 0.1843
25 Romania 0.1476 0.9161 0.1291 0.9179 0.1216 0.9115 0.7749 0.0183
26 Albania 0.1581 0.9080 0.1320 0.9180 0.1376 0.8916 0.7612 0.0453
27 Turkey 0.1674 0.8194 0.1491 0.8171 0.1375 0.8107 0.2836 0.1394
28 Poland 0.1299 0.9143 0.1155 0.9135 0.1351 0.8559 0.2644 0.0000
29 Belgium 0.1367 0.8953 0.1202 0.8965 0.1179 0.8789 0.5763 0.0006
30 Argentina 0.1336 0.9254 0.1171 0.9267 0.1090 0.9228 0.5442 0.1594
31 South Africa 0.1556 0.9067 0.1358 0.9092 0.1287 0.9008 0.9977 0.0007
32 Chile 0.1031 0.9553 0.1051 0.9406 0.0935 0.9428 0.0346 0.5582
33 Austria 0.1072 0.9367 0.0955 0.9358 0.0834 0.9404 0.3545 0.7819
34 Venezuela 0.1027 0.9437 0.0818 0.9544 0.0743 0.9541 0.8130 0.4069
35 Italy 0.1415 0.9088 0.1259 0.9077 0.1160 0.9047 0.2650 0.1205
36 Korea (south) 0.1287 0.9101 0.1182 0.9031 0.1086 0.9004 0.0413 0.1794
37 Thailand 0.1316 0.8717 0.1285 0.8438 0.1241 0.8225 0.0016 0.0069
38 Greece 0.0980 0.9552 0.0982 0.9425 0.0980 0.9302 0.0340 0.0373
39 Egypt 0.1609 0.8494 0.1425 0.8491 0.1222 0.8648 0.4106 0.8276
40 Ukraine 0.1155 0.9426 0.1083 0.9356 0.1034 0.9285 0.2097 0.2075
41 France 0.1458 0.8790 0.1335 0.8704 0.1299 0.8505 0.0003 0.0000
42 Israel 0.1204 0.9149 0.1071 0.9140 0.0962 0.9155 0.3517 0.5431
43 Singapore 0.1416 0.9054 0.1253 0.9054 0.1142 0.9043 0.4094 0.1756
44 Hungary 0.1249 0.9326 0.1089 0.9346 0.1078 0.9220 0.5400 0.0488
45 Philippines 0.1457 0.8960 0.1276 0.8981 0.1165 0.8966 0.6859 0.2751
46 Croatia 0.1547 0.8783 0.1337 0.8838 0.1239 0.8786 0.8172 0.1788
47 Hong Kong 0.1170 0.9071 0.1035 0.9070 0.0964 0.9019 0.4146 0.0941
48 Taiwan 0.0823 0.9588 0.0767 0.9543 0.0658 0.9591 0.2282 0.7019
49 Brazil 0.1563 0.8809 0.1384 0.8808 0.1298 0.8724 0.3858 0.0000
50 Mexico 0.1477 0.9114 0.1310 0.9109 0.1200 0.9091 0.3297 0.1576
51 Colombia 0.1336 0.9361 0.1400 0.9103 0.1364 0.8963 0.0003 0.0104
52 Japan 0.1126 0.9226 0.1031 0.9171 0.0952 0.9139 0.1127 0.2021
53 Norway 0.1269 0.9237 0.1126 0.9232 0.1103 0.9102 0.3394 0.0003
54 Portugal 0.1676 0.8649 0.1535 0.8551 0.1442 0.8444 0.0325 0.0243
55 Finland 0.1400 0.9136 0.1238 0.9137 0.1236 0.8952 0.4405 0.0000
56 Czech Republic 0.1142 0.9448 0.0988 0.9471 0.0906 0.9459 0.6293 0.3270
57 Iran 0.0556 0.9818 0.0604 0.9726 0.0369 0.9876 0.2048 0.9242
58 Iceland 0.1516 0.8898 0.1294 0.8974 0.1122 0.9060 0.7705 0.8175
59 Slovakia 0.1695 0.8346 0.1441 0.8474 0.1372 0.8315 0.7539 0.1600
60 Pakistan 0.1466 0.9190 0.1504 0.8911 0.1365 0.8908 0.0028 0.3976
61 Saudi Arabia 0.0992 0.9561 0.0844 0.9593 0.0835 0.9516 0.5903 0.1546
62 Peru 0.1303 0.9086 0.1192 0.9022 0.1089 0.9008 0.1566 0.3390
63 Bulgaria 0.1554 0.8627 0.1409 0.8557 0.1446 0.8150 0.2222 0.0052
64 Slovenia 0.1343 0.8980 0.1152 0.9040 0.1091 0.8952 0.7298 0.1440
65 Lithuania 0.1252 0.9265 0.1044 0.9348 0.1424 0.8523 0.6423 0.0007
66 Puerto Rico 0.1782 0.8802 0.1576 0.8802 0.1419 0.8819 0.4209 0.5558
67 Latvia 0.1020 0.9420 0.0925 0.9390 0.1100 0.8951 0.3230 0.0041
Table 18
Results of Measurement Invariance for Purity in Women and Men across 67 countries.
RMSEA = Root Mean Square Error of Approximation; CFI = Comparative Fit Index;
Bonferroni-corrected p threshold = 0.0001
SEX DIFFERENCES IN MORALITY 187
Country configural RMSEA configral CFI weak RMSEA weak CFI strong RMSEA strong CFI weak invariance p strong invariance p
1 Switzerland 0.0879 0.9372 0.0862 0.9229 0.1027 0.8668 0.0208 0.0000
2 USA 0.0810 0.9575 0.0734 0.9555 0.0930 0.9130 0.0000 0.0000
3 Australia 0.0757 0.9600 0.0676 0.9593 0.0891 0.9138 0.0631 0.0000
4 Ireland 0.0687 0.9611 0.0668 0.9530 0.0805 0.9168 0.0130 0.0000
5 Kenya 0.0919 0.9491 0.0704 0.9619 0.0579 0.9686 0.7038 0.5579
6 United Kingdom 0.0725 0.9627 0.0649 0.9619 0.0838 0.9227 0.0032 0.0000
7 Indonesia 0.1274 0.8474 0.1107 0.8528 0.1055 0.8373 0.6105 0.1070
8 India 0.0691 0.9603 0.0615 0.9599 0.0732 0.9306 0.2952 0.0000
9 Canada 0.0769 0.9561 0.0694 0.9543 0.0907 0.9052 0.0000 0.0000
10 Guatemala 0.1269 0.8774 0.1204 0.8590 0.1089 0.8597 0.1171 0.4330
11 Nigeria 0.0815 0.9689 0.0999 0.9403 0.1250 0.8861 0.0454 0.0046
12 United Arab Emirates 0.1294 0.8502 0.1164 0.8451 0.1226 0.7907 0.2635 0.0010
13 Vietnam 0.1100 0.8662 0.0940 0.8751 0.0860 0.8726 0.5863 0.3740
14 Russian Federation 0.0818 0.9525 0.0682 0.9577 0.0704 0.9451 0.8596 0.0301
15 Netherlands 0.0500 0.9806 0.0529 0.9723 0.0721 0.9372 0.0052 0.0000
16 Sweden 0.0887 0.9531 0.0785 0.9531 0.0956 0.9153 0.4006 0.0000
17 Spain 0.0944 0.9301 0.0889 0.9208 0.0976 0.8836 0.0414 0.0000
18 Denmark 0.0546 0.9739 0.0469 0.9754 0.0754 0.9226 0.5263 0.0000
19 Serbia and Montenegro 0.1771 0.7443 0.1590 0.7367 0.1487 0.7196 0.2809 0.1659
20 China 0.0717 0.9512 0.0640 0.9502 0.0636 0.9402 0.2840 0.0025
21 Malaysia 0.0940 0.9246 0.0824 0.9259 0.0846 0.9049 0.4738 0.0327
22 Germany 0.0687 0.9665 0.0611 0.9660 0.0883 0.9137 0.2727 0.0000
23 New Zealand 0.0678 0.9690 0.0607 0.9683 0.0805 0.9320 0.2430 0.0000
24 Afghanistan 0.0912 0.9468 0.0906 0.9330 0.0973 0.9059 0.0701 0.0095
25 Romania 0.0494 0.9816 0.0477 0.9781 0.0457 0.9756 0.1918 0.2441
26 Albania 0.0948 0.9297 0.0698 0.9513 0.0990 0.8808 0.7680 0.0238
27 Turkey 0.1185 0.9050 0.1050 0.9047 0.1129 0.8659 0.3996 0.0002
28 Poland 0.0731 0.9636 0.0641 0.9642 0.1094 0.8732 0.5137 0.0000
29 Belgium 0.0653 0.9627 0.0542 0.9672 0.0628 0.9463 0.8080 0.0034
30 Argentina 0.0744 0.9546 0.0700 0.9486 0.0657 0.9450 0.2023 0.2709
31 South Africa 0.0668 0.9623 0.0627 0.9576 0.0664 0.9420 0.1044 0.0023
32 Chile 0.0711 0.9752 0.0606 0.9770 0.0905 0.9375 0.5126 0.0007
33 Austria 0.1008 0.9325 0.0926 0.9272 0.0933 0.9101 0.1877 0.0249
34 Venezuela 0.0247 0.9957 0.0000 1.0000 0.0639 0.9556 0.5542 0.0166
35 Italy 0.0570 0.9751 0.0466 0.9788 0.0841 0.9157 0.8073 0.0000
36 Korea (south) 0.0890 0.9461 0.0767 0.9488 0.0809 0.9308 0.7042 0.0024
37 Thailand 0.0592 0.9682 0.0552 0.9647 0.0811 0.9072 0.2597 0.0000
38 Greece 0.0771 0.9479 0.0629 0.9557 0.0789 0.9152 0.7517 0.0042
39 Egypt 0.0277 0.9935 0.0000 1.0000 0.0618 0.9501 0.8330 0.0198
40 Ukraine 0.1381 0.8702 0.1308 0.8513 0.1278 0.8272 0.1236 0.0862
41 France 0.0851 0.9443 0.0782 0.9399 0.0936 0.8952 0.0355 0.0000
42 Israel 0.0899 0.9204 0.0795 0.9204 0.1067 0.8255 0.4158 0.0000
43 Singapore 0.0891 0.9377 0.0797 0.9363 0.0770 0.9276 0.2167 0.0032
44 Hungary 0.0714 0.9632 0.0693 0.9557 0.0770 0.9333 0.2079 0.0420
45 Philippines 0.1035 0.9425 0.1043 0.9253 0.0940 0.9262 0.0018 0.5106
46 Croatia 0.0730 0.9685 0.0661 0.9670 0.0772 0.9451 0.3451 0.0141
47 Hong Kong 0.0976 0.9105 0.0889 0.9051 0.0951 0.8678 0.1354 0.0000
48 Taiwan 0.1101 0.9231 0.1041 0.9122 0.0975 0.9062 0.0928 0.1874
49 Brazil 0.0728 0.9636 0.0680 0.9594 0.0830 0.9262 0.0244 0.0000
50 Mexico 0.0728 0.9607 0.0690 0.9549 0.0734 0.9379 0.0704 0.0012
51 Colombia 0.0738 0.9690 0.0579 0.9756 0.0449 0.9821 0.8458 0.8436
52 Japan 0.0910 0.9155 0.0791 0.9186 0.0749 0.9110 0.5937 0.1436
53 Norway 0.0692 0.9648 0.0652 0.9602 0.0938 0.8997 0.1003 0.0000
54 Portugal 0.0793 0.9521 0.0683 0.9546 0.0721 0.9384 0.5713 0.0285
55 Finland 0.0830 0.9474 0.0741 0.9465 0.1134 0.8473 0.3081 0.0000
56 Czech Republic 0.0748 0.9501 0.0781 0.9304 0.0906 0.8862 0.0471 0.0019
57 Iran 0.0582 0.9800 0.0664 0.9668 0.0676 0.9581 0.1454 0.2120
58 Iceland 0.1032 0.8895 0.0913 0.8894 0.0978 0.8456 0.4147 0.0431
59 Slovakia 0.0916 0.9355 0.0919 0.9170 0.0959 0.8901 0.1883 0.1262
60 Pakistan 0.0749 0.9525 0.0755 0.9384 0.0804 0.9149 0.1613 0.0804
61 Saudi Arabia 0.1296 0.8602 0.1137 0.8624 0.1346 0.7656 0.4768 0.0001
62 Peru 0.0962 0.9077 0.0909 0.8948 0.1013 0.8407 0.1432 0.0023
63 Bulgaria 0.0679 0.9587 0.0676 0.9476 0.0979 0.8661 0.2216 0.0015
64 Slovenia 0.0699 0.9462 0.0549 0.9576 0.0479 0.9608 0.7279 0.4946
65 Lithuania 0.1039 0.9456 0.1192 0.9085 0.1145 0.8972 0.0402 0.2190
66 Puerto Rico 0.1052 0.9351 0.1079 0.9127 0.1219 0.8643 0.0162 0.0002
67 Latvia 0.0746 0.9590 0.0507 0.9758 0.0909 0.9054 0.9141 0.0014
Table 19
Results of Measurement Invariance for Authority in Women and Men across 67 countries.
RMSEA = Root Mean Square Error of Approximation; CFI = Comparative Fit Index;
Bonferroni-corrected p threshold = 0.0001
SEX DIFFERENCES IN MORALITY 188
Country configural RMSEA configral CFI weak RMSEA weak CFI strong RMSEA strong CFI weak invariance p strong invariance p
1 Switzerland 0.1304 0.8675 0.1138 0.8711 0.1052 0.8659 0.7287 0.1480
2 USA 0.1504 0.8368 0.1332 0.8365 0.1285 0.8148 0.0000 0.0000
3 Australia 0.1444 0.8515 0.1284 0.8499 0.1298 0.8131 0.0052 0.0000
4 Ireland 0.1170 0.8727 0.1068 0.8645 0.0997 0.8560 0.0181 0.0164
5 Kenya 0.0797 0.9461 0.0669 0.9514 0.0639 0.9461 0.4927 0.3483
6 United Kingdom 0.1359 0.8466 0.1202 0.8466 0.1162 0.8256 0.4385 0.0000
7 Indonesia 0.1677 0.7672 0.1507 0.7600 0.1433 0.7356 0.2099 0.0317
8 India 0.1473 0.8487 0.1301 0.8492 0.1212 0.8408 0.5972 0.0000
9 Canada 0.1227 0.8727 0.1090 0.8717 0.1030 0.8605 0.0010 0.0000
10 Guatemala 0.1705 0.8104 0.1579 0.7922 0.1481 0.7775 0.0993 0.1322
11 Nigeria 0.0891 0.9497 0.0900 0.9344 0.0821 0.9335 0.1857 0.3966
12 United Arab Emirates 0.1499 0.8225 0.1360 0.8132 0.1344 0.7778 0.1606 0.0064
13 Vietnam 0.1324 0.7534 0.1171 0.7534 0.0984 0.7881 0.4153 0.9446
14 Russian Federation 0.1368 0.8533 0.1284 0.8347 0.1234 0.8142 0.0108 0.0071
15 Netherlands 0.1106 0.8822 0.0998 0.8774 0.1027 0.8420 0.0647 0.0000
16 Sweden 0.0868 0.9508 0.0805 0.9461 0.0774 0.9393 0.0066 0.0010
17 Spain 0.1503 0.8011 0.1418 0.7739 0.1283 0.7745 0.0005 0.4714
18 Denmark 0.1298 0.8651 0.1157 0.8629 0.1085 0.8533 0.2782 0.0555
19 Serbia and Montenegro 0.1492 0.8377 0.1266 0.8508 0.1236 0.8267 0.7595 0.0948
20 China 0.1156 0.8951 0.1038 0.8919 0.1034 0.8695 0.0707 0.0000
21 Malaysia 0.1487 0.8257 0.1375 0.8095 0.1239 0.8119 0.0528 0.5313
22 Germany 0.1511 0.8078 0.1360 0.8010 0.1289 0.7823 0.0003 0.0000
23 New Zealand 0.1038 0.9189 0.0926 0.9176 0.0854 0.9146 0.1729 0.0449
24 Afghanistan 0.1594 0.7950 0.1429 0.7893 0.1351 0.7706 0.2441 0.0606
25 Romania 0.1415 0.8344 0.1302 0.8209 0.1219 0.8090 0.0201 0.0289
26 Albania 0.1594 0.8552 0.1407 0.8559 0.1358 0.8366 0.4290 0.1538
27 Turkey 0.1771 0.7740 0.1612 0.7607 0.1472 0.7569 0.0449 0.2375
28 Poland 0.1586 0.8248 0.1402 0.8250 0.1305 0.8154 0.4490 0.0036
29 Belgium 0.1643 0.7725 0.1473 0.7663 0.1339 0.7650 0.1113 0.3261
30 Argentina 0.1389 0.8642 0.1267 0.8557 0.1165 0.8516 0.1185 0.2325
31 South Africa 0.1044 0.8988 0.0923 0.8990 0.0881 0.8880 0.4353 0.0167
32 Chile 0.1742 0.7911 0.1542 0.7908 0.1380 0.7961 0.4040 0.6323
33 Austria 0.1448 0.8112 0.1327 0.7973 0.1175 0.8066 0.0832 0.8726
34 Venezuela 0.1200 0.9219 0.1004 0.9301 0.0805 0.9452 0.7059 0.9413
35 Italy 0.1455 0.8531 0.1277 0.8556 0.1157 0.8557 0.7047 0.4226
36 Korea (south) 0.1124 0.8940 0.1061 0.8794 0.1027 0.8625 0.0170 0.0097
37 Thailand 0.1316 0.8451 0.1200 0.8353 0.1145 0.8173 0.0979 0.0249
38 Greece 0.1376 0.8693 0.1226 0.8674 0.1148 0.8584 0.3355 0.1353
39 Egypt 0.1430 0.8634 0.1388 0.8357 0.1306 0.8227 0.0928 0.2151
40 Ukraine 0.1238 0.8321 0.1096 0.8317 0.0967 0.8406 0.4112 0.5615
41 France 0.1359 0.8324 0.1194 0.8347 0.1090 0.8323 0.8656 0.1448
42 Israel 0.1140 0.9043 0.1051 0.8961 0.0967 0.8929 0.1048 0.2508
43 Singapore 0.1456 0.8193 0.1285 0.8204 0.1217 0.8037 0.6045 0.0000
44 Hungary 0.1404 0.8636 0.1433 0.8184 0.1264 0.8280 0.0021 0.8415
45 Philippines 0.1867 0.7722 0.1665 0.7683 0.1553 0.7546 0.1808 0.0142
46 Croatia 0.1137 0.9028 0.1101 0.8834 0.1128 0.8511 0.0413 0.0069
47 Hong Kong 0.1253 0.8525 0.1121 0.8489 0.1042 0.8412 0.2038 0.0800
48 Taiwan 0.1358 0.8358 0.1167 0.8451 0.1065 0.8430 0.8080 0.3451
49 Brazil 0.1684 0.7676 0.1500 0.7645 0.1386 0.7552 0.0792 0.0013
50 Mexico 0.1297 0.8795 0.1173 0.8741 0.1121 0.8602 0.0782 0.0033
51 Colombia 0.1620 0.8520 0.1413 0.8563 0.1339 0.8427 0.6923 0.0470
52 Japan 0.1171 0.8425 0.1057 0.8360 0.1089 0.7882 0.1877 0.0003
53 Norway 0.1094 0.9122 0.0952 0.9150 0.0881 0.9115 0.7846 0.1434
54 Portugal 0.1668 0.8126 0.1478 0.8121 0.1407 0.7927 0.3844 0.0096
55 Finland 0.1242 0.8892 0.1103 0.8884 0.1052 0.8764 0.3119 0.0021
56 Czech Republic 0.1504 0.8197 0.1306 0.8264 0.1300 0.7906 0.7575 0.0035
57 Iran 0.1283 0.8841 0.1106 0.8901 0.1004 0.8897 0.5771 0.4083
58 Iceland 0.1296 0.8905 0.1518 0.8079 0.1498 0.7724 0.0002 0.0205
59 Slovakia 0.1478 0.8746 0.1297 0.8768 0.1107 0.8907 0.4673 0.7913
60 Pakistan 0.1394 0.8716 0.1308 0.8556 0.1212 0.8490 0.0983 0.2391
61 Saudi Arabia 0.1792 0.7995 0.1566 0.8042 0.1378 0.8156 0.6002 0.8810
62 Peru 0.1605 0.7964 0.1410 0.7993 0.1250 0.8077 0.5374 0.8033
63 Bulgaria 0.1508 0.8597 0.1317 0.8633 0.1225 0.8560 0.5434 0.2254
64 Slovenia 0.0853 0.9305 0.0832 0.9153 0.0738 0.9189 0.1382 0.5207
65 Lithuania 0.1801 0.7920 0.1852 0.7189 0.1741 0.6977 0.0085 0.1541
66 Puerto Rico 0.1101 0.9139 0.1034 0.9029 0.0931 0.9043 0.1262 0.4714
67 Latvia 0.1707 0.8333 0.1493 0.8369 0.1372 0.8325 0.5485 0.2865
Table 20
Results of Measurement Invariance for Loyalty in Women and Men across 67 countries.
RMSEA = Root Mean Square Error of Approximation; CFI = Comparative Fit Index;
Bonferroni-corrected p threshold = 0.0001
SEX DIFFERENCES IN MORALITY 189
Country configural RMSEA configral CFI weak RMSEA weak CFI strong RMSEA strong CFI weak invariance p strong invariance p
1 Switzerland 0.0442 0.9804 0.0547 0.9617 0.0497 0.9615 0.0163 0.4051
2 USA 0.0686 0.9474 0.0622 0.9447 0.0637 0.9296 0.0000 0.0000
3 Australia 0.0657 0.9531 0.0583 0.9528 0.0570 0.9451 0.2777 0.0000
4 Ireland 0.0724 0.9474 0.0711 0.9351 0.0659 0.9321 0.0047 0.1611
5 Kenya 0.0712 0.9305 0.1047 0.8079 0.0853 0.8448 0.0184 0.7896
6 United Kingdom 0.0653 0.9552 0.0579 0.9549 0.0544 0.9517 0.1484 0.0000
7 Indonesia 0.0957 0.8875 0.1087 0.8144 0.0954 0.8262 0.0018 0.7426
8 India 0.0944 0.8975 0.0848 0.8943 0.0786 0.8896 0.0651 0.0226
9 Canada 0.0658 0.9482 0.0590 0.9468 0.0608 0.9313 0.0015 0.0000
10 Guatemala 0.0000 1.0000 0.0289 0.9866 0.0262 0.9865 0.1225 0.4152
11 Nigeria 0.0576 0.9520 0.1013 0.8103 0.0801 0.8554 0.0098 0.8787
12 United Arab Emirates 0.0864 0.8899 0.0838 0.8676 0.0753 0.8698 0.1174 0.4624
13 Vietnam 0.1307 0.8126 0.1205 0.7964 0.1036 0.8171 0.1981 0.8423
14 Russian Federation 0.0896 0.9221 0.0782 0.9242 0.0917 0.8733 0.5343 0.0001
15 Netherlands 0.0834 0.9209 0.0732 0.9222 0.0656 0.9239 0.5750 0.6477
16 Sweden 0.1032 0.8864 0.0933 0.8814 0.0871 0.8741 0.0356 0.0087
17 Spain 0.0538 0.9701 0.0571 0.9569 0.0549 0.9515 0.0345 0.1611
18 Denmark 0.0787 0.9303 0.0795 0.9093 0.0766 0.8975 0.0155 0.0749
19 Serbia and Montenegro 0.1909 0.7560 0.1936 0.6794 0.1712 0.6948 0.0013 0.8470
20 China 0.1150 0.8219 0.1005 0.8263 0.0931 0.8186 0.9723 0.0313
21 Malaysia 0.1514 0.7699 0.1301 0.7829 0.1182 0.7821 0.9382 0.3866
22 Germany 0.0709 0.9438 0.0622 0.9446 0.0602 0.9369 0.6717 0.0009
23 New Zealand 0.0700 0.9343 0.0642 0.9294 0.0643 0.9139 0.0553 0.0003
24 Afghanistan 0.0985 0.8880 0.0794 0.9071 0.0864 0.8659 0.9630 0.0180
25 Romania 0.0530 0.9693 0.0459 0.9706 0.0464 0.9635 0.4941 0.1402
26 Albania 0.0242 0.9975 0.0415 0.9906 0.0334 0.9926 0.2683 0.4675
27 Turkey 0.0824 0.9235 0.0747 0.9196 0.0699 0.9143 0.2894 0.2532
28 Poland 0.0901 0.9180 0.0822 0.9128 0.0807 0.8977 0.0834 0.0023
29 Belgium 0.0810 0.9372 0.0756 0.9301 0.0777 0.9101 0.1134 0.0068
30 Argentina 0.0614 0.9510 0.0659 0.9279 0.0719 0.8956 0.0598 0.0251
31 South Africa 0.0850 0.9009 0.0797 0.8886 0.0746 0.8812 0.0392 0.1084
32 Chile 0.0000 1.0000 0.0440 0.9656 0.0645 0.9098 0.0096 0.0224
33 Austria 0.0656 0.9364 0.0589 0.9345 0.0573 0.9244 0.3704 0.2157
34 Venezuela 0.0715 0.9394 0.1319 0.7366 0.1259 0.7077 0.0000 0.1394
35 Italy 0.0766 0.9333 0.0774 0.9129 0.0687 0.9165 0.0097 0.6722
36 Korea (south) 0.0697 0.9279 0.0633 0.9240 0.0615 0.9128 0.2735 0.1157
37 Thailand 0.0720 0.8990 0.0675 0.8864 0.0602 0.8901 0.1857 0.5123
38 Greece 0.1070 0.8426 0.0926 0.8492 0.0803 0.8620 0.5889 0.7655
39 Egypt 0.1298 0.8706 0.1043 0.8934 0.0889 0.9056 0.8767 0.6569
40 Ukraine 0.0832 0.9300 0.0843 0.9081 0.0945 0.8594 0.1644 0.0445
41 France 0.0681 0.9569 0.0607 0.9562 0.0605 0.9469 0.3131 0.0044
42 Israel 0.0909 0.9337 0.1048 0.8875 0.0957 0.8858 0.0001 0.3252
43 Singapore 0.0809 0.9261 0.0752 0.9186 0.0674 0.9202 0.0263 0.6465
44 Hungary 0.0959 0.8862 0.0998 0.8425 0.0904 0.8429 0.0288 0.4240
45 Philippines 0.0980 0.8972 0.0890 0.8916 0.0791 0.8957 0.2015 0.6518
46 Croatia 0.0862 0.9265 0.0850 0.9087 0.0722 0.9197 0.0891 0.8243
47 Hong Kong 0.0787 0.9412 0.0740 0.9335 0.0674 0.9329 0.0798 0.3719
48 Taiwan 0.0947 0.8946 0.0893 0.8803 0.0792 0.8854 0.1484 0.5674
49 Brazil 0.0988 0.9089 0.0876 0.9085 0.0797 0.9077 0.3537 0.2953
50 Mexico 0.0833 0.9334 0.0766 0.9280 0.0732 0.9200 0.1215 0.0620
51 Colombia 0.1004 0.8962 0.0854 0.9040 0.0758 0.9080 0.6902 0.5499
52 Japan 0.0684 0.9435 0.0742 0.9150 0.0752 0.8938 0.0087 0.0260
53 Norway 0.0855 0.9370 0.0732 0.9410 0.0753 0.9240 0.8606 0.0029
54 Portugal 0.1006 0.9146 0.1030 0.8857 0.1002 0.8684 0.0048 0.0330
55 Finland 0.0708 0.9529 0.0632 0.9520 0.0636 0.9408 0.3250 0.0109
56 Czech Republic 0.0774 0.9433 0.0767 0.9289 0.0729 0.9218 0.0999 0.2119
57 Iran 0.1843 0.7363 0.1663 0.7255 0.1461 0.7422 0.2196 0.8656
58 Iceland 0.1067 0.8921 0.0874 0.9074 0.0763 0.9141 0.7719 0.5617
59 Slovakia 0.0000 1.0000 0.0000 1.0000 0.0000 1.0000 0.9767 0.9221
60 Pakistan 0.1265 0.8831 0.1309 0.8402 0.1172 0.8439 0.0090 0.5328
61 Saudi Arabia 0.1837 0.8021 0.1577 0.8137 0.1465 0.8042 0.9152 0.1620
62 Peru 0.0954 0.9191 0.0984 0.8901 0.0954 0.8742 0.0217 0.0915
63 Bulgaria 0.0760 0.9523 0.0547 0.9685 0.0572 0.9580 0.8827 0.2131
64 Slovenia 0.0793 0.9464 0.1004 0.8903 0.0886 0.8960 0.0025 0.6055
65 Lithuania 0.0717 0.9313 0.0838 0.8801 0.0775 0.8749 0.1270 0.3733
66 Puerto Rico 0.0000 1.0000 0.0000 1.0000 0.0000 1.0000 0.7002 0.7191
67 Latvia 0.0656 0.9454 0.0629 0.9359 0.0637 0.9199 0.2909 0.2247
Table 21
Results of Measurement Invariance for Fairness in Women and Men across 67 countries.
RMSEA = Root Mean Square Error of Approximation; CFI = Comparative Fit Index;
Bonferroni-corrected p threshold = 0.0001
SEX DIFFERENCES IN MORALITY 190
Country configural RMSEA configral CFI weak RMSEA weak CFI strong RMSEA strong CFI weak invariance p strong invariance p
1 Switzerland 0.0819 0.9515 0.0701 0.9545 0.0827 0.9229 0.6941 0.0001
2 USA 0.0838 0.9404 0.0752 0.9387 0.0936 0.8842 0.0000 0.0000
3 Australia 0.0817 0.9491 0.0735 0.9473 0.0875 0.9091 0.0053 0.0000
4 Ireland 0.0837 0.9425 0.0828 0.9282 0.0962 0.8819 0.0005 0.0000
5 Kenya 0.0567 0.9694 0.0694 0.9416 0.0818 0.9011 0.1678 0.1065
6 United Kingdom 0.0842 0.9381 0.0748 0.9376 0.0876 0.8958 0.0449 0.0000
7 Indonesia 0.0782 0.9151 0.0861 0.8684 0.1021 0.7750 0.0227 0.0008
8 India 0.0844 0.9394 0.0760 0.9372 0.0762 0.9231 0.0777 0.0000
9 Canada 0.0802 0.9481 0.0712 0.9477 0.0877 0.9036 0.0695 0.0000
10 Guatemala 0.0762 0.9623 0.0730 0.9558 0.0879 0.9219 0.2558 0.0223
11 Nigeria 0.1147 0.8219 0.1157 0.7683 0.1103 0.7438 0.1110 0.2356
12 United Arab Emirates 0.1624 0.7787 0.1414 0.7856 0.1267 0.7903 0.7106 0.6082
13 Vietnam 0.1113 0.8637 0.0907 0.8844 0.0935 0.8503 0.8368 0.0813
14 Russian Federation 0.0793 0.9368 0.0742 0.9293 0.0799 0.9003 0.1500 0.0044
15 Netherlands 0.0583 0.9673 0.0505 0.9687 0.0648 0.9373 0.6322 0.0000
16 Sweden 0.0766 0.9506 0.0676 0.9508 0.0917 0.8898 0.4736 0.0000
17 Spain 0.0690 0.9495 0.0661 0.9408 0.0762 0.9040 0.0912 0.0002
18 Denmark 0.0667 0.9623 0.0598 0.9612 0.1078 0.8467 0.3472 0.0000
19 Serbia and Montenegro 0.0833 0.9535 0.0624 0.9667 0.0541 0.9695 0.7934 0.4887
20 China 0.0790 0.9315 0.0721 0.9272 0.0915 0.8571 0.0731 0.0000
21 Malaysia 0.1012 0.9104 0.0992 0.8899 0.1174 0.8124 0.0371 0.0000
22 Germany 0.0760 0.9545 0.0663 0.9557 0.0882 0.9047 0.9079 0.0000
23 New Zealand 0.0658 0.9564 0.0641 0.9471 0.0872 0.8808 0.0016 0.0000
24 Afghanistan 0.0391 0.9888 0.0140 0.9982 0.0706 0.9434 0.8587 0.0003
25 Romania 0.0831 0.9510 0.0800 0.9420 0.0886 0.9134 0.0419 0.0001
26 Albania 0.0000 1.0000 0.0000 1.0000 0.0000 1.0000 0.5803 0.0689
27 Turkey 0.0934 0.9210 0.0810 0.9241 0.0869 0.8937 0.5744 0.0058
28 Poland 0.0780 0.9599 0.0724 0.9559 0.0951 0.9072 0.0614 0.0000
29 Belgium 0.0662 0.9653 0.0718 0.9478 0.0722 0.9357 0.0049 0.0225
30 Argentina 0.0746 0.9233 0.0621 0.9321 0.0626 0.9161 0.6969 0.1258
31 South Africa 0.0872 0.9193 0.0819 0.9091 0.0995 0.8366 0.0321 0.0000
32 Chile 0.0824 0.9478 0.1017 0.8984 0.0953 0.8914 0.0031 0.2330
33 Austria 0.0718 0.9639 0.0746 0.9502 0.0788 0.9323 0.0513 0.0252
34 Venezuela 0.0189 0.9969 0.0591 0.9610 0.0671 0.9389 0.0580 0.1301
35 Italy 0.0767 0.9433 0.0844 0.9123 0.0959 0.8623 0.0003 0.0000
36 Korea (south) 0.0768 0.9267 0.0736 0.9140 0.0891 0.8468 0.0696 0.0000
37 Thailand 0.0595 0.9518 0.0485 0.9590 0.0784 0.8696 0.7103 0.0000
38 Greece 0.1237 0.8682 0.1177 0.8473 0.1125 0.8301 0.0461 0.0707
39 Egypt 0.1310 0.8366 0.1168 0.8339 0.1132 0.8103 0.3811 0.1760
40 Ukraine 0.0000 1.0000 0.0000 1.0000 0.0000 1.0000 0.3780 0.2456
41 France 0.0703 0.9530 0.0655 0.9477 0.0761 0.9143 0.0386 0.0000
42 Israel 0.1153 0.8961 0.1083 0.8828 0.1090 0.8556 0.0478 0.0033
43 Singapore 0.0872 0.9353 0.0776 0.9346 0.0973 0.8747 0.3089 0.0000
44 Hungary 0.1192 0.8797 0.1016 0.8884 0.1069 0.8495 0.7243 0.0116
45 Philippines 0.0580 0.9623 0.0483 0.9667 0.0701 0.9146 0.6568 0.0001
46 Croatia 0.0872 0.9460 0.0857 0.9334 0.0773 0.9340 0.0898 0.4419
47 Hong Kong 0.0905 0.9303 0.0830 0.9251 0.0908 0.8910 0.1246 0.0000
48 Taiwan 0.1040 0.8990 0.0969 0.8878 0.0929 0.8745 0.1536 0.1243
49 Brazil 0.0599 0.9732 0.0545 0.9716 0.0643 0.9519 0.1758 0.0000
50 Mexico 0.0586 0.9719 0.0611 0.9609 0.0762 0.9260 0.0165 0.0000
51 Colombia 0.1250 0.8667 0.1248 0.8303 0.1188 0.8128 0.0065 0.0665
52 Japan 0.0898 0.9161 0.0886 0.8956 0.1159 0.7826 0.0190 0.0000
53 Norway 0.0976 0.9200 0.0852 0.9222 0.1075 0.8489 0.6542 0.0000
54 Portugal 0.0440 0.9846 0.0386 0.9848 0.0518 0.9667 0.4270 0.0244
55 Finland 0.0792 0.9595 0.0778 0.9501 0.0929 0.9132 0.0045 0.0000
56 Czech Republic 0.1086 0.9181 0.0987 0.9136 0.1001 0.8917 0.2361 0.0162
57 Iran 0.0936 0.9213 0.0759 0.9340 0.0868 0.8949 0.7052 0.0461
58 Iceland 0.0405 0.9893 0.0446 0.9834 0.0776 0.9389 0.2708 0.0089
59 Slovakia 0.0000 1.0000 0.0000 1.0000 0.0408 0.9770 0.4017 0.0072
60 Pakistan 0.0707 0.9510 0.0682 0.9417 0.0544 0.9549 0.2472 0.7550
61 Saudi Arabia 0.0529 0.9693 0.0564 0.9554 0.0675 0.9221 0.2067 0.0680
62 Peru 0.1143 0.8666 0.1094 0.8439 0.1105 0.8061 0.0589 0.0132
63 Bulgaria 0.0465 0.9859 0.0266 0.9941 0.0000 1.0000 0.7072 0.7055
64 Slovenia 0.0727 0.9719 0.0533 0.9807 0.0523 0.9774 0.9001 0.2801
65 Lithuania 0.1363 0.8611 0.1217 0.8585 0.1075 0.8656 0.3787 0.5287
66 Puerto Rico 0.1029 0.9143 0.0960 0.9047 0.1238 0.8068 0.1687 0.0000
67 Latvia 0.0819 0.9158 0.0645 0.9332 0.0568 0.9371 0.7247 0.4778
Table 22
Results of Measurement Invariance for Care in Women and Men across 67 countries.
RMSEA = Root Mean Square Error of Approximation; CFI = Comparative Fit Index;
Bonferroni-corrected p threshold = 0.0001
SEX DIFFERENCES IN MORALITY 191
Table 23
Results of Multi-Level Modeling for Care (Study 2)
Model 0 Model 1 Model 2
(Intercept) 3.54 (0.06)
úúú
3.65 (0.05)
úúú
3.63 (0.06)
úúú
Sex ≠ 0.31 (0.01)
úúú
-0.27 (0.03)
úúú
Num. groups: Country 19 19 19
Var: Country (Intercept) 0.06 0.05 0.06
Var: Residual 0.55 0.53 0.53
Var: Country Sex 0.01
Cov: Country (Intercept) Sex ≠ 0.01
úúú
p< 0.001,
úú
p< 0.01,
ú p< 0.05;Randomeectofsexappearsinbold.
Table 24
Results of Multi-Level Modeling for Fairness (Study 2)
Model 0 Model 1 Model 2
(Intercept) 3.51 (0.06)
úúú
3.57 (0.06)
úúú
3.57 (0.06)
úúú
Sex ≠ 0.16 (0.01)
úúú
-0.15 (0.02)
úúú
Num. groups: Country 19 19 19
Var: Country (Intercept) 0.07 0.07 0.08
Var: Residual 0.48 0.47 0.47
Var: Country Sex 0.00
Cov: Country (Intercept) Sex ≠ 0.01
úúú
p< 0.001,
úú
p< 0.01,
ú p< 0.05;Randomeectofsexappearsinbold.
SEX DIFFERENCES IN MORALITY 192
Table 25
Results of Multi-Level Modeling for Loyalty (Study 2)
Model 0 Model 1 Model 2
(Intercept) 2.78 (0.07)
úúú
2.77 (0.07)
úúú
2.76 (0.08)
úúú
Sex 0.04 (0.02)
úú
0.05 (0.02)
ú Num. groups: Country 19 19 19
Var: Country (Intercept) 0.10 0.10 0.11
Var: Residual 0.67 0.67 0.67
Var: Country Sex 0.00
Cov: Country (Intercept) Sex ≠ 0.01
úúú
p< 0.001,
úú
p< 0.01,
ú p< 0.05;Randomeectofsexappearsinbold.
Table 26
Results of Multi-Level Modeling for Authority (Study 2)
Model 0 Model 1 Model 2
(Intercept) 2.70 (0.04)
úúú
2.68 (0.04)
úúú
2.69 (0.05)
úúú
Sex 0.04 (0.02)
ú 0.03 (0.03)
Num. groups: Country 19 19 19
Var: Country (Intercept) 0.04 0.04 0.04
Var: Residual 0.76 0.76 0.76
Var: Country Sex 0.01
Cov: Country (Intercept) Sex ≠ 0.01
úúú
p< 0.001,
úú
p< 0.01,
ú p< 0.05;Randomeectofsexappearsinbold.
SEX DIFFERENCES IN MORALITY 193
Table 27
Results of Multi-Level Modeling for Purity (Study 2)
Model 0 Model 1 Model 2
(Intercept) 2.56 (0.11)
úúú
2.59 (0.11)
úúú
2.60 (0.11)
úúú
Sex ≠ 0.09 (0.02)
úúú
-0.11 (0.03)
úúú
Num. groups: Country 19 19 19
Var: Country (Intercept) 0.21 0.22 0.22
Var: Residual 0.96 0.96 0.95
Var: Country Sex 0.01
Cov: Country (Intercept) Sex ≠ 0.00
úúú
p< 0.001,
úú
p< 0.01,
ú p< 0.05;Randomeectofsexappearsinbold.
SEX DIFFERENCES IN MORALITY 194
Poland
Russia
Finland
Romania
Sweden
Lithuania
Slovakia
Denmark
Germany
Israel
Australia
Bulgaria
Greece
Norway
USA
Ukraine
Czech Republic
Spain
Italy
Slovenia
Canada
Afghanistan
Brazil
Guatemala
Egypt
Croatia
United Kingdom
Peru
Venezuela
France
Chile
New Zealand
Iceland
Kenya
South Africa
Nigeria
Hong Kong
Switzerland
Mexico
Puerto Rico
Turkey
Hungary
Argentina
Ireland
Colombia
Serbia
Saudi Arabia
Austria
Singapore
Netherlands
Philippines
Pakistan
Albania
Indonesia
Thailand
Belgium
UAE
India
Korea (South)
Latvia
Malaysia
China
Iran
Vietnam
Portugal
Japan
Taiwan
−0.8 −0.4 0.0
Random effect of sex on Harm
Figure 1. Multi-Level Model for Care (Study 1)
SEX DIFFERENCES IN MORALITY 195
Sweden
Germany
Poland
Australia
Finland
Israel
Peru
United Kingdom
Slovakia
Norway
Croatia
UAE
France
Romania
Lithuania
Brazil
Czech Republic
USA
Russia
Spain
Chile
Netherlands
Ukraine
Ireland
New Zealand
Italy
Venezuela
Egypt
Afghanistan
Hong Kong
Argentina
Denmark
Thailand
Bulgaria
Kenya
Guatemala
Canada
Puerto Rico
Greece
Pakistan
Iceland
Indonesia
Mexico
Serbia
Saudi Arabia
Nigeria
Slovenia
Switzerland
South Africa
Turkey
Taiwan
Latvia
Hungary
Albania
Malaysia
China
Iran
Philippines
Vietnam
Colombia
India
Korea (South)
Austria
Portugal
Singapore
Japan
Belgium
−0.8 −0.4 0.0
Random effect of sex on Fairness
Figure 2. Multi-Level Model for Fairness (Study 1)
SEX DIFFERENCES IN MORALITY 196
China
Vietnam
Mexico
Thailand
Lithuania
Kenya
Singapore
Philippines
Malaysia
Puerto Rico
Latvia
Colombia
Bulgaria
Iran
Nigeria
Peru
Pakistan
Egypt
Romania
Venezuela
Saudi Arabia
Chile
Sweden
Taiwan
Switzerland
Denmark
Hungary
Indonesia
Albania
Guatemala
India
Ukraine
Greece
Hong Kong
Korea (South)
Ireland
UAE
New Zealand
Czech Republic
Japan
Slovakia
Poland
South Africa
Norway
Croatia
Slovenia
Iceland
Russia
Italy
Afghanistan
Austria
Portugal
Brazil
Israel
Argentina
Finland
Turkey
Germany
France
Spain
Serbia
Belgium
Canada
United Kingdom
Netherlands
Australia
USA
−0.8 −0.4 0.0
Random effect of sex on Ingroup
Figure 3. Multi-Level Model for Loyalty (Study 1)
SEX DIFFERENCES IN MORALITY 197
Philippines
Nigeria
Kenya
China
Vietnam
Singapore
Saudi Arabia
Mexico
Malaysia
Thailand
Guatemala
Puerto Rico
Indonesia
Colombia
Pakistan
Japan
Peru
UAE
Albania
Hong Kong
Afghanistan
Venezuela
South Africa
USA
Latvia
Taiwan
Romania
Lithuania
Brazil
Iran
Egypt
Korea (South)
Germany
Bulgaria
Ireland
Ukraine
Slovenia
Switzerland
Portugal
Russia
Australia
Italy
New Zealand
Israel
Sweden
Norway
Chile
United Kingdom
Hungary
Denmark
Argentina
Spain
Greece
India
Iceland
Czech Republic
Slovakia
Austria
Croatia
Netherlands
Turkey
Belgium
Canada
France
Serbia
Poland
Finland
−0.8 −0.4 0.0
Random effect of sex on Authority
Figure 4. Multi-Level Model for Authority (Study 1)
SEX DIFFERENCES IN MORALITY 198
Singapore
Kenya
Nigeria
Philippines
Saudi Arabia
Malaysia
China
Vietnam
Pakistan
Japan
Guatemala
South Africa
Indonesia
Puerto Rico
UAE
Taiwan
Iran
Thailand
Peru
Egypt
Mexico
Germany
Colombia
Ukraine
Latvia
Korea (South)
Afghanistan
Hong Kong
Venezuela
Albania
Lithuania
Romania
Bulgaria
Croatia
New Zealand
Russia
Ireland
Switzerland
Hungary
Netherlands
Slovakia
Turkey
Austria
Greece
United Kingdom
Spain
Chile
USA
Serbia
Norway
Australia
Belgium
Czech Republic
Slovenia
Canada
Italy
Denmark
Sweden
Israel
India
Argentina
Brazil
Finland
Portugal
Poland
Iceland
France
−0.8 −0.4 0.0
Random effect of sex on Purity
Figure 5. Multi-Level Model for Purity (Study 1)
SEX DIFFERENCES IN MORALITY 199
Sweden Turkey UK US
Netherlands Poland Russia Serbia Spain
Iran Japan Korea Latvia Mongolia
Australia Belgium China France Hungary
0 1 0 1 0 1 0 1
0 1
0
1
2
3
4
5
0
1
2
3
4
5
0
1
2
3
4
5
0
1
2
3
4
5
Sex
Care
Figure 6. Bayesian Multi-Level Models for Care; 0 = female, 1 = male (Study 2)
SEX DIFFERENCES IN MORALITY 200
0 2 4 6
Care
y
y rep
0 2 4 6
Fairness
y
y rep
0.0 2.5 5.0
Loyalty
y
y rep
0 2 4 6
Authority
y
y rep
−2.5 0.0 2.5 5.0 7.5
Purity
y
y rep
Figure 7. Posterior Predictive Checks for Multi-Level Models (Study 2)
SEX DIFFERENCES IN MORALITY 201
Sweden Turkey UK US
Netherlands Poland Russia Serbia Spain
Iran Japan Korea Latvia Mongolia
Australia Belgium China France Hungary
0 1 0 1 0 1 0 1
0 1
0
1
2
3
4
5
0
1
2
3
4
5
0
1
2
3
4
5
0
1
2
3
4
5
Sex
FAIRNESS
Figure 8. Bayesian Multi-Level Models for Fairness; 0 = female, 1 = male (Study 2)
SEX DIFFERENCES IN MORALITY 202
Sweden Turkey UK US
Netherlands Poland Russia Serbia Spain
Iran Japan Korea Latvia Mongolia
Australia Belgium China France Hungary
0 1 0 1 0 1 0 1
0 1
0
1
2
3
4
5
0
1
2
3
4
5
0
1
2
3
4
5
0
1
2
3
4
5
Sex
Loyalty
Figure 9. Bayesian Multi-Level Models for Loyalty; 0 = female, 1 = male (Study 2)
SEX DIFFERENCES IN MORALITY 203
Sweden Turkey UK US
Netherlands Poland Russia Serbia Spain
Iran Japan Korea Latvia Mongolia
Australia Belgium China France Hungary
0 1 0 1 0 1 0 1
0 1
0
1
2
3
4
5
0
1
2
3
4
5
0
1
2
3
4
5
0
1
2
3
4
5
Sex
Authority
Figure 10. Bayesian Multi-Level Models for Authority; 0 = female, 1 = male (Study 2)
SEX DIFFERENCES IN MORALITY 204
Sweden Turkey UK US
Netherlands Poland Russia Serbia Spain
Iran Japan Korea Latvia Mongolia
Australia Belgium China France Hungary
0 1 0 1 0 1 0 1
0 1
0
1
2
3
4
5
0
1
2
3
4
5
0
1
2
3
4
5
0
1
2
3
4
5
Sex
Purity
Figure 11. Bayesian Multi-Level Models for Purity; 0 = female, 1 = male (Study 2)

Abstract (if available)

Abstract Most of the empirical research on sex differences and cultural variations in morality has relied on within-culture analyses, or small cross-cultural data, comparing few cultures. To further broaden the scientific understanding of sex differences in morality, the current research relies on two international samples to provide the first large-scale examination of sex differences in moral judgements nested within cultures. Using a sample from 67 countries (Study 1

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University of Southern California Dissertations and Theses

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Asset Metadata

Creator Atari, Mohammad (author)

Core Title Sex differences in moral judgements across 67 countries

School College of Letters, Arts and Sciences

Degree Master of Arts

Degree Program Psychology

Publication Date 12/11/2019

Defense Date 12/09/2019

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag Bayesian inference,culture,gender,Morality,OAI-PMH Harvest,sex differences

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Dehghani, Morteza (committee chair), Lai, Hok Chio (committee member), Wood, Wendy (committee member)

Creator Email atari@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c89-252537

Unique identifier UC11673340

Identifier etd-AtariMoham-8045.pdf (filename),usctheses-c89-252537 (legacy record id)

Legacy Identifier etd-AtariMoham-8045.pdf

Dmrecord 252537

Document Type Thesis

Rights Atari, Mohammad

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

Repository Name University of Southern California Digital Library

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