According to the work of Rentfrow and colleagues, personalities are not randomly distributed. Instead, they fit into distinct geographic clusters. Based on where you live, do you agree or disagree with the traits associated with yourself and the residents of your area of the country? Why or why not?
I live in Miami
PERSONALITY PROCESSES AND INDIVIDUAL DIFFERENCES
Divided We Stand: Three Psychological Regions of the United States and
Their Political, Economic, Social, and Health Correlates
Peter J. Rentfrow
University of Cambridge
Samuel D. Gosling
University of Texas at Austin
Markus Jokela
University of Helsinki
David J. Stillwell and Michal Kosinski
University of Cambridge
Jeff Potter
Atof Inc., Cambridge, Massachusetts
There is overwhelming evidence for regional variation across the United States on a range of key
political, economic, social, and health indicators. However, a substantial body of research suggests that
activities in each of these domains are typically influenced by psychological variables, raising the
possibility that psychological forces might be the mediating or causal factors responsible for regional
variation in the key indicators. Thus, the present article examined whether configurations of psycholog-
ical variables, in this case personality traits, can usefully be used to segment the country. Do regions
emerge that can be defined in terms of their characteristic personality profiles? How are those regions
distributed geographically? And are they associated with particular patterns of key political, economic,
social, and health indicators? Results from cluster analyses of 5 independent samples totaling over 1.5
million individuals identified 3 robust psychological profiles: Friendly & Conventional, Relaxed &
Creative, and Temperamental & Uninhibited. The psychological profiles were found to cluster geograph-
ically and displayed unique patterns of associations with key geographical indicators. The findings
demonstrate the value of a geographical perspective in unpacking the connections between microlevel
processes and consequential macrolevel outcomes.
Keywords: Big Five, personality, regional differences, cluster analysis
From the Deep South and the Bible Belt, to the Rustbelt and the
Stroke Belt, America has long been divided into a variety of
distinct regions. These regions have been characterized in terms of
the political, economic, social, and health characteristics that are
shared by neighboring states. Recent research indicates that U.S.
states also differ systematically in terms of their mean personality
scores (e.g., Rentfrow, Gosling, & Potter, 2008). Such findings
raise the possibility of dividing regions, not in terms of traditional
social and economic indicators, but in terms of psychological
characteristics instead. Characterizations of regions based on the
psychological characteristics of the people who live in them are
appealing because psychological factors are likely to be the driving
forces behind the individual-level behaviors that eventually get
expressed in terms of macrolevel social and economic indicators.
Therefore, the present work aimed to determine whether it is
possible to construct a map of the United States based entirely on
psychological characteristics, in this case personality traits. What
would such a map look like? And how would its individual regions
vary in terms of key political, economic, social, and health (PESH)
metrics known to vary geographically within countries?
Regional Variation in PESH Metrics
All behaviors take place in specific physical locations, which is
why geography has long been recognized as necessary for under-
standing human behavior. Indeed, geographical information is a
This article was published Online First October 14, 2013.
Peter J. Rentfrow, Department of Psychology, University of Cambridge,
Cambridge, United Kingdom; Samuel D. Gosling, Department of Psychol-
ogy, University of Texas at Austin; Markus Jokela, Department of Psy-
chology, Institute of Behavioral Sciences, University of Helsinki, Helsinki,
Finland; David J. Stillwell and Michal Kosinski, Department of Psychol-
ogy, University of Cambridge; Jeff Potter, Atof Inc., Cambridge, Massa-
chusetts.
Support for Markus Jokela was provided by the Kone Foundation and
the Academy of Finland (Grant 268388).
Correspondence concerning this article should be addressed to Peter J.
Rentfrow, University of Cambridge, Department of Psychology, Free
School Lane, Cambridge CB2 3RQ, United Kingdom. E-mail: pjr39@cam
.ac.uk
Journal of Personality and Social Psychology, 2013, Vol. 105, No. 6,
central focus of several disciplines, including political science,
economics, sociology, and epidemiology. Political geography ad-
dresses questions about the influence of historical migration pat-
terns and local demography on political regionalism. For example,
Elazar’s (1994) classic work on American political culture identi-
fied three distinct political subcultures—individualistic, moralistic,
and traditionalistic—that emerged as a result of the historical
settlement and migration patterns in specific regions. Other re-
searchers have focused on the impact of ethnic diversity on re-
gional political values (Heppen, 2003; Hero, 1998). According to
Hero (1998), racially homogenous regions (predominantly in the
Midwest and Great Plains) have undifferentiated social structures
and are concerned with community development, whereas racially
heterogeneous regions (East, South, and West Coast) have com-
plex social structures and are concerned with maintaining social
order and economic prosperity.
Geographical research in economics and sociology examines the
spatial organization of various economic and social phenomena.
For example, Florida’s (2002) research on the creative class fo-
cuses on the regional distribution of the workforce employed in the
arts, sciences, media, and technology industries. Regional analyses
of the creative class show that it is concentrated in culturally
diverse urban areas where residents have progressive values and
are tolerant of social differences (predominantly in the Northeast
and West Coast). Research on social capital examines the spatial
distribution of civic engagement, or the degree to which people
feel connected to family, friends, and neighbors. Putnam (2000)
found that social capital varies within the United States, with the South-
east and Mid-Atlantic states showing low levels and the West,
North Central, and Mountain states showing high levels. The
prevalence of social capital within regions appears to be driven by
religiosity, ethnic diversity, and media consumption.
Spatial epidemiology examines regional variations in disease
and mortality. A well-established line of research in this field
concerns the regional distribution of stroke mortality (Borhani,
1965; Lanska, 1993). Health statistics dating back more than 70
years have consistently shown that mortality from stroke and other
forms of cardiovascular disease are disproportionately high
throughout the Southeast and the southern Midwest, or a region
dubbed the Stroke Belt. Although the key factors responsible for
this geographic pattern have remained elusive, there appear to be
some social characteristics common to this region that are influ-
ential. Specifically, racial composition, socioeconomic character-
istics, social norms for health and nutrition, and environmental
features appear to be among the main factors that contribute to the
health and well-being of residents in this region (Glymour, Aven-
daño, & Berkman, 2007).
Taken together, research in the geographical sciences clearly
shows that there are distinct regions within the United States that
are defined in terms of their PESH characteristics. In light of all
this geographically oriented work in the social sciences, the scar-
city of research on geographic variation in psychological attributes
is notable. This neglect is particularly puzzling given that many of
the processes by which demographic factors become expressed in
traditional PESH metrics are likely to be psychological in nature.
Presumably, the phenomena studied by psychologists (e.g., stereo-
typing and prejudice, decision making, social support, susceptibil-
ity to stress) play a key role in mediating the links between such
predictive factors as ethnic diversity, socioeconomic status, and
education and outcomes such as regional prosperity, voting pat-
terns, and disease rates. There are some early hints that psycho-
logical processes may contribute to these regional differences in
PESH outcomes. In particular, evidence is beginning to emerge
from a few studies to suggest there are meaningful psychological
differences across regions of the United States.
Regional Variation in Personality
Research in psychology has only just begun to examine the
interplay between geography and psychological processes. This
interest stems largely from the establishment of the Big Five
framework as an empirically based and widely accepted model for
conceptualizing the structure of personality (Costa & McCrae,
1992; Goldberg, 1992). Results from investigations of cross-
national differences using this framework show geographic vari-
ation in each of the five personality domains (e.g., McCrae, 2001;
McCrae & Terracciano, 2008; Schmitt, Allik, McCrae, & Benet-
Martínez, 2007; Steel & Ones, 2002). For example, there is evi-
dence that members of Asian cultures score low on measures of
Extraversion compared with members of other cultures; members
of Central and South American cultures score comparatively high
on measures of Openness; and members of Southern and Eastern
Europe score higher in Neuroticism compared with members of
other European cultures (Allik & McCrae, 2004; McCrae, Terrac-
ciano, & 79 Members of the Personality Profiles of Culturs Proj-
ect, 2005).
Considerably less attention has been given to regional person-
ality differences within nations. To date, three investigations have
explored the geographical distribution of personality within the
United States (Krug & Kulhavy, 1973; Plaut, Markus, & Lachman,
2002; Rentfrow et al., 2008). Conceptual and methodological
differences between the studies make it hard to systematically
compare the results, but a broad analysis of the general trends
suggests regions do vary in their mean levels of personality traits.
For example, Neuroticism appears to be highest in the Northeast
and Southeast and lowest in the Midwest and West; Openness
appears to be highest in the New England, Middle Atlantic, and
Pacific regions and lower in the Great Plain, Midwest, and south-
eastern states; and Agreeableness is generally high in the Southern
regions and low in the Northeast. The spatial patterns for Extra-
version and Conscientiousness do not appear consistent across
studies.
The regional personality differences observed within the United
States have been shown to be associated with a variety of geo-
graphical indicators. Specifically, using the state-level personality
scores published by Rentfrow and colleagues (2008), studies have
shown that state-level personality scores are related to health and
morbidity (McCann, 2010a, 2010b, 2011b; Pesta, Bertsch, Mc-
Daniel, Mahoney, & Poznanski, 2012; Voracek, 2009), psycho-
logical well-being (McCann, 2011a; Pesta, McDaniel, & Bertsch,
2010; Rentfrow, Mellander, & Florida, 2009), social capital (Rent-
frow, 2010), creative capital (Florida, 2008), income inequality (de
Vries, Gosling, & Potter, 2011), entrepreneurship rates (Ob-
schonka, Schmitt-Rodermund, Silbereisen, Gosling, & Potter,
2013), political values (Rentfrow, Jost, Gosling, & Potter, 2009),
and regional stereotypes (Rogers & Wood, 2010). These findings
suggest that the personality traits that are common in a state are
997PSYCHOLOGICAL REGIONS
linked to an assortment of important indicators that delineate
different regions of the United States.
Thus far, all the research on regional personality differences has
compared states or regions on individual traits. This approach
effectively focuses the prevalence of individuals with specific
personality traits and how the prevalence of individuals with
certain traits relates to PESH characteristics. It is useful because it
provides information about how places compare on particular
psychological attributes, and it reveals the degree to which per-
sonality processes generalize across multiple levels of analysis.
However, a limitation of this approach is that it focuses on traits in
isolation without reference to other psychological characteristics.
An alternative approach that overcomes the limitations of trait-
centered methods is the idiographic perspective, which focuses on
the configuration traits. This perspective has received considerable
attention within personality psychology because it provides a more
holistic depiction of the person compared with the trait-centered
approach. Research from this perspective has identified specific
configurations of traits that commonly occur in the population, are
stable over time, and predict important life outcomes (Asendorpf
& van Aken, 1999; Caspi, 2000; Chapman & Goldberg, 2011;
Hart, Atkins, & Fegley, 2003). At a geographical level, the idio-
graphic perspective allows for investigating whether there are
particular configurations of traits that occur with some regularity
in specific regions and whether those trait configurations are
related to macrolevel outcomes. Characterizing regions on the
basis of the organization of multiple attributes is consistent with
the approaches used to examine regional variation in various
PESH indicators (e.g., the “Middle America” political region com-
prises states with comparatively small minority populations, low
incomes, and conservative values, whereas the “Sunbelt” political
region comprises states with large minority populations, high
incomes, conservative values; Heppen, 2003). Thus, characterizing
regions in terms of configurations of personality traits would seem
to be a useful approach for exploring the psychological topography
of the country, one that provides a more thorough representation of
personality.
The Present Investigation
The aim of the present investigation was to investigate the
viability of characterizing regions of the United States in terms of
their personality attributes and explore the potential utility of these
characterizations. Specifically, in the present investigation, we
asked the following: Are there distinct psychological regions in the
United States? How are the psychological regions geographically
distributed? What are the psychological profiles of the regions?
How are the psychological regions linked to PESH indicators?
In the present investigation, we sought to identify psychological
regions by focusing on the organization of traits within a state.
This approach is optimal for investigating psychological regions
because it allows for identifying groups of states where the con-
figuration of personality traits is similar, as is occasionally done
for characterizations based on PESH indicators. Such groups con-
stitute psychological regions in so far as they represent geograph-
ical areas that can be characterized in terms of a common person-
ality trait profile.
Our regional analyses focused on state-level personality traits,
which are the mean trait scores of respondents who live in the
state. Thus, to say that New York is high in Neuroticism is to say
that the mean level of Neuroticism derived from a sample of New
York residents is high compared with the mean levels of Neurot-
icism derived from samples of residents from other states. The
approach we used effectively examines how the mean personality
traits are arranged within states. So although New York may be
high in Neuroticism compared with other states, New York may be
lower in Neuroticism than it is in Openness, but higher in Neu-
roticism than Extraversion, Agreeableness, and Conscientiousness.
To the extent that there are other states with a personality profile
that is similar to New York’s, those states would constitute a
psychological region that is defined by the personality profile they
share. And states that share a psychological profile that is different,
where Neuroticism is also high but lower than Openness, for
example, would form another psychological region.
The primary objective of the investigation was to map the
psychological topography of the United States. We made no ex-
plicit predictions about the number of psychological regions that
would emerge from the data, the geographical distribution of those
regions, the precise organization of traits within them, or their
PESH characteristics. Results from studies of cross-national per-
sonality indicate that geographical proximity is related to person-
ality profile similarity (e.g., Allik & McCrae, 2004; McCrae et al.,
2005), so we expected states that are close to one another geo-
graphically to, on average, be psychologically similar too. How-
ever, it should be emphasized that we modeled the personality
clusters in the data without reference to geographical information,
and only later examined whether the identified profiles produced
geographically coherent psychological regions, or whether the
personality profiles were distributed in some other pattern (e.g.,
with multiple centers or randomly) across the United States.
Method
Our analyses were based on data from five samples that varied
in their methods, Big Five trait measures, data collection periods,
and recruitment strategies. This multisample approach allowed us
to examine the robustness of the effects by comparing findings
across samples. Information about participants’ state of residence
and their self-reported personalities were collected over a period of
12 years. Large samples were available for the 48 contiguous states
and Washington D.C., so the analyses were restricted to those
states only (i.e., excluding Alaska and Hawaii). Across all five
samples, a total of 1,596,704 individuals participated. We describe
each of the samples in detail below.
Sample 1 (S1)
S1 is the sample used by Rentfrow et al. (2008) in research on
regional personality differences across the United States. The only
difference between the sample used in the present study and that
used previously is that the present investigation did not include
personality scores for Alaska or Hawaii. Data for S1 were col-
lected between December 1999 and January 2005 as part of the
Gosling-Potter Internet Personality Project (for details, see Gos-
ling, Vazire, Srivastava, & Swann, 2004; Rentfrow et al., 2008;
Srivastava, John, Gosling, & Potter, 2003), which hosts a noncom-
mercial, advertisement-free website containing a variety of per-
sonality measures. Respondents could learn about the project
998 RENTFROW ET AL.
through several channels, including search engines or links on such
websites as www.socialpsychology.org. Respondents volunteered
to participate in the study by “clicking” on the personality test icon
and were then presented with a series of questions about their
personality characteristics, demographics, and state of residence.
After submitting their responses, participants received customized
feedback about their personalities.
Participants. Multiple criteria were used to eliminate repeat
responders and duplicate entries (e.g., removal of entries from the
same IP address within a 60-min period). Implementation of the
screening criteria resulted in data for 612,140 respondents (55%
female). The mean age of respondents was 24.73 years (SD �
10.39 years), with 347,940 (57%) respondents between the ages of
18 and 24, 252,936 (41%) between 25 and 54, and 10,284 (2%)
over 55 years old. Of those who indicated, 24,479 respondents
(4.0%) were African American; 38,487 (5%) were Asian; 28,059
(3%) were Hispanic; 488,427 (84%) were White; and 27,279 (4%)
indicated “other.” Summary statistics for the sample are reported
in the first data column of Table 1.
Participants were asked to indicate the state in which they lived
at the time in which they participated in the study. The sample
sizes ranged between 1,536 participants from Wyoming and
71,873 participants from California (M � 12,493; Mdn � 8,368).
The demographic characteristics of the sample were compared
with data from the U.S. Census Bureau (2000) to determine the
representativeness of the sample. Specifically, the percentage of
respondents in each demographic group from the Internet sample
was correlated with the percentage of the population from that
group within each state. The correlation between the number of
respondents in a state and the population of the state was .98,
indicating that no states were over- or underrepresented in the data.
With regard to ethnicity, the correlations for African Americans,
Asians, Hispanics, Whites, and “other” ethnicities were .97, .84,
.93, .91, and .75, respectively. With regard to age, the correlations
for people aged 18 –24, 25–54, and 55 and over were .24, .39, and
.16, respectively. These results show that the ethnic breakdowns
for the sample are fairly representative of the population, but the
age representation is less so.
Personality. The Big Five Inventory was used to assess per-
sonality (BFI; John & Srivastava, 1999). The BFI consists of 44
short statements designed to assess the prototypical traits defining
each of the five-factor personality model dimensions. Using a
5-point Likert-type rating scale with endpoints at 1 (Disagree
strongly) and 5 (Agree strongly), respondents indicated the extent
to which they agreed with each statement. The psychometric
validity of the BFI scales have been demonstrated in earlier re-
search (John & Srivastava, 1999). Analyses of the present data
indicated that the BFI scales were internally reliable at the indi-
vidual level of analysis (�s � .85, .80, .82, .83, and .79, for
Extraversion, Agreeableness, Conscientiousness, Neuroticism, and
Openness, respectively).
Sample 2 (S2)
Data for S2 were collected between February 2005 and August
2009 as part of the Gosling-Potter Internet Personality Project. The
procedure was identical in every respect to the one used with S1
except the data were collected over a different period.
Participants. Multiple criteria were also used to eliminate
repeat responders and duplicate entries from S2. Implementation
of the criteria resulted in data for 507,987 respondents (65%
female). The mean age of respondents was 24.98 years (SD �
11.01 years), with 326,545 (64%) respondents between the ages of
18 and 24, 176,537 (35%) between 25 and 54, and 4,905 (1%) over
55 years old. Of those who indicated, 42,897 respondents (9%)
were African American; 37,001 (7%) were Asian; 44,211 (9%)
were Hispanic; 353,712 (70%) were White; and 25,714 (5%)
indicated “other.” Summary statistics for the sample are reported
in the second data column of Table 1. Participants were asked to
indicate the state in which they lived at the time in which they
participated in the study. The sample sizes ranged between 857
Table 1
Summary of Sample Characteristics
Variable S1 S2 S3 S4 S5
Data collection period 1999–2005 2005–2009 2002–2009 2008–2010 2007–2008
Measure BFI BFI TIPI IPIP TIPI
Recruitment method Self-selected Self-selected Self-selected Self-selected RDD
N 612,140 507,987 145,307 312,568 18,182
% female 55 65 62 62 53
Age
% �25 57 64 69 64 6
% 25–54 41 35 30 35 61
%
54 2 1 1 1 33
Ethnicity
% African American 4 9 4 — 7
% Asian 5 7 6 — 1
% Hispanic 3 9 6 — 6
% White 84 70 79 — 82
% other 4 5 5 — 4
Note. S1 � Sample 1; S2 � Sample 2; S3 � Sample 3; S4 � Sample 4; S5 � Sample 5; BFI � Big Five
Inventory (John & Srivastava, 1999); TIPI � Ten-Item Personality Inventory (Gosling, Rentfrow, & Swann,
2003); IPIP � International Personality Item Pool (Goldberg et al., 2006); RDD � random digit dialing.
Information about participants’ ethnicity was not collected for Sample 4. Dashes indicate that data were
unavailable.
999PSYCHOLOGICAL REGIONS
participants from Wyoming and 54,945 participants from Califor-
nia (M � 10,367; Mdn � 7,213).
The demographic characteristics of the sample were compared
with data from the U.S. Census Bureau (2000) to determine its
representativeness. The correlation between the number of respon-
dents in a state and the population of the state was .99. For
ethnicity, the correlations for African Americans, Asians, Hispan-
ics, Whites, and “other” ethnicities were .94, .71, .95, .94, and .80,
respectively. With respect to age, the correlations for people aged
18 –24, 25–54, and 55 and over were .06, .13, and .18, respectively.
These results indicate that ethnicity is representative of the popu-
lation, but the age is less so.
Personality. The BFI was used to assess personality using the
same 5-point rating scale administered to S1. Analyses of the
present data indicated that the BFI scales were internally reliable at
the individual level of analysis (�s � .86, .79, .83, .83, and .78, for
Extraversion, Agreeableness, Conscientiousness, Neuroticism, and
Openness, respectively).
Sample 3 (S3)
Data for S3 were collected between August 2002 and August
2009 as part of the Rentfrow-Potter Music Preference Project,
which hosts a noncommercial, advertisement-free website with a
music preference and personality survey. Respondents could learn
about the project through several channels, including search en-
gines or links on such websites as www.socialpsychology.org.
Respondents volunteered to participate in the study by “clicking”
on the music test icon and were then presented with a series of
questions about their music preferences, personality characteris-
tics, demographics, and state of residence. After submitting their
responses, participants received customized feedback about their
music preferences.
Participants. This sample comprised 145,307 respondents
(62% female). The mean age was 23.65 years (SD � 11.30 years),
with 100,080 (69%) respondents between the ages of 18 and 24,
43,575 (30%) between 25 and 54, and 1,654 (1%) over 55 years
old. Of those who indicated, 6,260 respondents (4%) were African
American; 8,269 (6%) were Asian; 8,520 (6%) were Hispanic;
113,319 (79%) were White; and 8,045 (5%) indicated “other.”
Summary statistics for the sample are reported in the third data
column of Table 1.
Participants were asked to indicate the state in which they lived
at the time in which they participated in the study. The sample
sizes ranged between 261 participants from Wyoming and 15,690
participants from California (M � 2,965; Mdn � 2,326).
The correlation between the number of respondents in a state
and the population of the state was .98. For ethnicity, the correla-
tions for African Americans, Asians, Hispanics, Whites, and
“other” ethnicities were .95, .87, .97, .90, and .70, respectively.
With respect to age, the correlations for people aged 18 –24,
25–54, and 55 and over were .01, .40, and �.22, respectively.
These results indicate that ethnicity is representative of the popu-
lation but that the distribution of age is less representative.
Personality. The Ten-Item Personality Inventory (TIPI; Gos-
ling, Rentfrow, & Swann, 2003) was used to assess personality.
The TIPI assesses each of the Big Five domains and was designed
to maximize the content validity of the broad domains using only
two bipolar items. Consequently, and consistent with other re-
search, analyses of internal consistency and structural validity
invariably yielded comparatively low internal reliability coeffi-
cients and poor fit statistics. Nonetheless, the TIPI has good
test–retest reliability, and convergent and discriminant validity
with longer measures of the Big Five (Gosling et al., 2003). In the
present sample, respondents were asked to report the degree to
which they agreed with each item using a 7-point rating scale with
endpoints at 1 (Disagree) and 7 (Agree). Analyses of the present
data revealed varying degrees of reliability for each of the two-
item scales (�s � .70, .35, .56, .64, and .40, for Extraversion,
Agreeableness, Conscientiousness, Neuroticism, and Openness,
respectively).
Sample 4 (S4)
Data for S4 were collected between February 2008 and May
2010 using the “MyPersonality” Facebook application. Users of
Facebook are able to create personal profiles in which they can
report their age, state of residence, schools attended, as well as
their leisure interests. Facebook users can enhance their personal
profiles by including additional information about themselves
from a wide variety of Facebook applications. The MyPersonality
application allows users to complete various psychological surveys
(e.g., a measure of the Big Five personality domains), and, if they
choose, users can display the results on their Facebook profile.
When users agree to use the “MyPersonality” application, they are
asked for consent to use their responses to the surveys for research
purposes. Like the other samples, S4 relied on self-selection.
Participants. This sample comprised 312,568 respondents
(62% female). The mean age was 24.58 years (SD � 9.17 years),
with 125,685 (64%) respondents under 25 years old, 70,140 (35%)
between 25 and 54, and 2,070 (1%) over 54 years old. At the time
in which the data were collected, Facebook did not collect infor-
mation about the ethnicity of users, nor did MyPersonality request
such information from respondents. Summary statistics for the
sample are reported in the fourth data column of Table 1.
Information about participants’ state of residence was obtained
from their Facebook profiles. The sample sizes ranged between
483 participants from Wyoming and 33,109 participants from
California (M � 6,379; Mdn � 4,232).
The correlation between the number of respondents in each state
and the Census Bureau’s population estimates of the state was .99.
With respect to age, the correlations for people aged 18 –24,
25–54, and 55 and over were .22, .18, and �.20, respectively.
Personality. Personality was assessed in this sample using a
20-item version of the Revised NEO Personality Inventory (NEO-
PI) developed from the International Personality Item Pool (IPIP;
Goldberg et al., 2006), with four items assessing each personality
domain. Respondents were asked to indicate the accuracy of each
item using a 5-point rating scale with endpoints at 1 (Very Inac-
curate) and 5 (Very Accurate). These short scales are highly
correlated with the 100-item IPIP measure of the NEO-PI—Re-
vised domains (rs � .91, .83, .86, .89, and .77, for Extraversion,
Agreeableness, Conscientiousness, Neuroticism, and Openness,
respectively). Analyses of the present data indicated that each of
the four-item IPIP scales were internally reliable at the individual
level of analysis (�s � .72, .60, .67, .65, and .50, for Extraversion,
Agreeableness, Conscientiousness, Neuroticism, and Openness,
respectively).
1000 RENTFROW ET AL.
Sample 5 (S5)
Data for S5 were collected between December 2007 and No-
vember 2008 as part of the Cooperative Campaign Analysis Proj-
ect (CCAP), a large panel study of attitudes about presidential
candidates, social issues, race, and other political topics leading to
the 2008 U.S. presidential election (Jackman & Vavreck, 2009).
During the third wave of the project, a Big Five personality
measure was administered. The project was conducted over the
Internet by YouGov/Polimetrix and used participant recruitment
methods designed to approximate a random digit-dialing sample.
By design, CCAP was intended to provide a nationally represen-
tative sample of registered voters for analyses of the impending
election. As such, the project was not designed to provide repre-
sentative samples of each individual state. In addition, the so-
called battleground states of presidential elections (e.g., Pennsyl-
vania, Ohio, Florida) were overrepresented in the sample (for more
details, see Jackman & Vavreck, 2009; Vavreck & Rivers, 2008),
resulting in disproportionate representation for some regions. Nev-
ertheless, despite these possible limitations, S5 is the only sample
that did not rely on a self-selection recruitment method and there-
fore provides an important comparison sample.
Participants. The sample comprised 18,182 respondents
(53% female). The mean age was 47.97 years (SD � 15.19 years),
with 1,021 (6%) respondents under the age 25, 11,108 (61%)
between 25 and 54, and 6,053 (33%) over 54 years old. Of those
who indicated, 1,334 respondents (7%) were African American;
180 (1%) were Asian; 1,091 (6%) were Hispanic; 14,969 (82%)
were White; and 608 (4%) indicated “other.” Summary statistics
for the sample are reported in the fifth data column of Table 1.
Members of the YouGov/Polimetrix panels in each state were
recruited to participate. The state sample sizes were small com-
pared with S1 through S4, with 10 regions having fewer than 100
participants (Delaware, D.C., Idaho, Mississippi, Montana, North
Dakota, Rhode Island, South Dakota, Vermont, and Wyoming).
The sample sizes ranged between 29 participants from Wyoming
and 1,876 participants from Florida (M � 382; Mdn � 204).
The correlation between the number of respondents in a state
and the population of the state was .83. For ethnicity, the correla-
tions for African Americans, Asians, Hispanics, Whites, and
“other” ethnicities were .94, .46, .90, .93, and .75, respectively.
With respect to age, the correlations for people aged 18 –24,
25–54, and 55 and over were .30, .29, and .31, respectively. These
results indicate that people of different ethnic and age groups were
reasonably represented in the sample.
Personality. The TIPI (Gosling et al., 2003) was used to
assess personality. In the present sample, respondents were asked
to report the degree to which they agreed with each item using a
7-point rating scale with endpoints at 1 (Strongly Disagree) and 7
(Strongly Agree). As before, the reliability estimates for the two-
item scales were lower than for the longer scales (�s � .62, .36,
.52, .63, and .43, for Extraversion, Agreeableness, Conscientious-
ness, Neuroticism, and Openness, respectively).
Secondary Data
To determine whether the psychological regions were associated
with important PESH outcomes, we gathered data from variety of
secondary sources and developed 11 social indicator indices, a
common approach in studies of regional differences (e.g., Crone &
Clayton-Matthews, 2005; Florida, 2002; McCann, 2008; Richter,
1966). Specifically, we developed three economic indicators (state
wealth, human capital, innovation), four sociological indicators
(social capital, social tolerance, violent crime, residential mobil-
ity), two ideological indicators (political conservatism, religiosity),
and two health indicators (well-being, health behavior). We also
gathered demographic data from the U.S. Census. In gathering
secondary data for the social indicators, we tried to obtain statistics
for 2007–2008, because that was the approximate midpoint for the
collection of personality data. However, in a few instances, some
of the indicators were not available for that period, so we included
data that were collected a few years before or after, as described
below.
Population statistics. Population statistics were obtained
from the U.S. Census Bureau (2000) and included the proportion
of women and non-Whites, and median age.
Political conservatism. To assess regional political prefer-
ences, we gathered voting data for each of the 48 contiguous states
and Washington, D.C. from Dave Leip’s Atlas of U.S. Presidential
Elections, an online database consisting of Presidential election
results obtained from publications by official election agencies
within each state (i.e., Secretary of State offices, State Board of
Election offices, Congressional Quarterly, and the U.S. National
Archives and Records Administration; Leip, 2009). In the present
study, we computed an index of political conservatism by first
standardizing the percentages of votes for George W. Bush in 2004
and for John S. McCain in 2008. The correlation between the two
standardized variables was r � .98. Thus, we computed the mean
of the two standardized variables for the political conservatism
index.
Religiosity. State-level religiosity data were obtained from the
Association of Religion Data Archives (2013). This organization
administers polls and collects other data about religiosity and
church membership in the United States. For the present study,
religiosity was indexed using a marker of adherents to mainline
Protestant denominations. Specifically, we examined rates of ad-
herents to mainline Protestant religions per 1,000 residents in
2000.
Wealth. We gathered data from the U.S. Census Bureau to
create an index of state wealth from four variables: gross regional
product per capita in 2007 (the value of everything that was
produced in a region in a year and reflects the level of productivity
as well as the standard of living in a state), median household
income per capita in 2007 (sum of the amounts reported separately
for wage or salary income including net self-employment income;
interest, dividends, or net rental or royalty income or income from
estates and trusts; social security or railroad retirement income;
Supplemental Security Income; public assistance or welfare pay-
ments; retirement, survivor, or disability pensions; and all other
income), median housing value of owner-occupied housing units
in 2005–2007 (owner-occupied single-family housing units on
fewer than 10 acres without a business or medical office on the
property, derived from the American Community Survey admin-
istered by the U.S. Census, 2005–2007), and proportion of popu-
lation living below poverty for the past 12 months in 2007 (based
on the state household population, and excluding individuals living
in institutions, college dormitories, and other group quarters). To
create an index of state wealth, we reverse keyed the state poverty
variable and standardized all four variables. The interitem reliabil-
1001PSYCHOLOGICAL REGIONS
ity among the standardized variables was � � .78. The state-
wealth index was computed as the average of these four standard-
ized variables.
Human capital. Human capital is a commonly used metric in
economics for gauging the stock of knowledge and skills prevalent
in a region. A state-level index of human capital was created using
four measures of educational attainment (the percentage of the
regional labor force with a bachelor’s degree in 2006 and 2007, or
with an advanced degree in 2006 and 2007, derived from Ameri-
can Community Survey administered by the U.S. Census, 2005–
2007). We first standardized all four variables and computed the
average of the four standardized variables to create the human
capital index. The interitem reliability among the standardized
variables was � � .99.
Innovation. Innovation reflects the degree to which states
invest and contribute to the creation and discovery of new ideas.
The state-level innovation index was measured by the number of
patents produced per capita data from 1977 to 2004 as reported by
the U.S. Patent and Trademark Office; the proportion of working-
aged adults employed in the high-tech industry; and the proportion
of working-aged adults employed in scientific professions, as
reported by the Bureau for Labor Statistics (2008). The interitem
reliability among these three standardized variables was � � .84.
An innovation index was computed by taking the average of the
three standardized variables.
Social capital. Social capital reflects the degree to which state
residents value social relations and community (Putnam, 2000) and
is measured in terms of rates of volunteerism, civic participation,
and social trust. In the present study, we used Putnam’s social
capital index and supplemented it with additional markers of
family relations, including the proportion of state residents who
were (a) married, (b) separated or divorced (from the 2010 Cen-
sus), and (c) the proportion of children living in safe and support-
ive neighborhoods (from the 2003 National Survey of Children’s
Health, conducted by the U.S. Department of Health and Human
Services; Centers for Disease Control and Prevention [CDC],
2003). To compute our index, we reverse keyed the separation and
divorce statistics and then standardized all four variables. The
interitem reliability among the standardized variables was � � .79.
The four standardized variables comprised the social capital index.
Social tolerance. Social tolerance reflects the degree to which
residents are tolerant and accepting of people who are unconven-
tional, live alternative lifestyles, or are from different cultures. It
was operationalized using four population statistics, including
Florida’s (2002) bohemian index (ranking states by the proportion
of working-aged adults who work as professional artists, enter-
tainers, and musicians), the proportion of gay residents (from
Florida, 2002), the proportion of foreign-born residents, and the
proportion of residents 5 years and older who speak a language
other than English at home, both were obtained from the U.S.
Census Bureau (2010). All four variables were standardized and
averaged to create the social tolerance index. The interitem reli-
ability among the standardized variables was � � .93.
Violent crime. Crime statistics were obtained from the Uni-
form Crime Reporting Program at the Federal Bureau of Investi-
gation (2008). In the present study, we examined three indicators
of violent crime: murder, robbery, and aggravated assault per
capita. The interitem reliability among the standardized variables
was � � .90, and the violent crime index was created as an average
of the three standardized variables.
Residential mobility. Statistics for rates of residential mobil-
ity in each state were obtained from the U.S. Census Bureau
(2005–2007) American Community Survey. Four variables were
used to compute the residential mobility index: the proportion of
the population born in their state of residence between 2005 and
2009, the proportion of the population living in the same house
between 2009 and 2010, the proportion of the population living in
the same state but a different house between 2009 and 2010, and
the proportion of the population who lived in a different state
between 2009 and 2010. The first two variables were reverse
keyed to reflect high residential mobility, and all the variables
were standardized. The interitem reliability among the four stan-
dardized variables was � � .81, and the residential mobility index
was created as the mean of the four standardized variables.
Well-being. State-level well-being was indexed using mark-
ers of physical and mental health obtained from the CDC, the
Gallup Organization, and the Substance Abuse and Mental Health
Services Administration (SAMHSA). We gathered mortality sta-
tistics from the CDC for cancer, diabetes, and coronary heart
disease, as well as state-level life expectancy. Death rates for
cancer and diabetes were for 2008, and heart disease death rate and
life expectancy were for 2007 (Miniño, Murphy, Xu, & Kochanek,
2011). State levels of psychological well-being were obtained from
the Gallup-Healthway’s (2008) Well-Being Index. For additional
markers of psychological health, we gathered data from SAMHSA
(2007, 2008) for the proportion of each state’s population reporting
being of excellent health in 2004 and the percentage of residents
who suffered from serious psychological distress or serious mental
illness in 2003–2004. We created the health index by first reverse
keying the death rate variables and the proportion of residents who
suffered serious mental illness problems, and then standardizing
them. The interitem reliability among the standardized variables
was � � .88. Thus, we computed the mean of the seven standard-
ized variables for the well-being index.
Health behavior. The health behavior index reflects the de-
gree to which residents of a state engage in physical exercise, eat
healthily, and do not smoke cigarettes. State-level data on healthy
behavior were obtained from the CDC (2008). Specifically, the
four variables from the Healthy People surveys were used to create
the index: the proportion of the population that is physically active,
consumes five or more pieces of fruit per day, eats vegetables
daily, and smokes cigarettes daily. The last variable was reverse
keyed, and all the variables were standardized. The interitem
reliability was � � .82, and we computed the average of the four
variables to develop the health behavior index.
Results and Discussion
Preliminary Psychometric Analyses
To effectively determine whether there are distinct psycholog-
ical regions in the United States, it was essential that we first
evaluate the reliability and generalizability of the personality data
across samples, measures, states, and levels of analysis. Doing so
ensures that it is appropriate to aggregate personality scores to the
state level and also reduces the possibility of making incorrect
inferences from the data. Thus, before undertaking the focal anal-
1002 RENTFROW ET AL.
yses, four sets of psychometric analyses were performed. First, to
rule out that measurement artifacts drive regional personality vari-
ation, we examined whether there were systematic statewide dif-
ferences in the reliabilities and discriminant validities of the per-
sonality scales. Second, to ensure that the personality scales
assessed the same latent constructs across states, we examined
multiple-group measurement invariance. Third, to gauge the de-
gree of sampling error in the state-level personality means,
random-intercept multilevel regression models were examined.
Finally, to assess the degree of variance in state-level personality
scores, we examined the state-level characteristics of the person-
ality scales using multilevel modeling.
To determine whether there are systematic statewide differences
in how participants used the personality scales, we examined the
reliabilities and discriminant correlations of the individual-level
scales. Specifically, we computed 1,225 individual-level reliabili-
ties for the Big Five scales used in each sample for each individual
state (five scales � five samples � 49 states). The mean reliability
coefficients were generally high, with little variance across states
(across all samples, M � � .69, SD � .03). We next computed the
mean individual-level heterotrait-monomethod correlations for
each sample and state, which resulted in 2,450 discriminant cor-
relations (10 discriminant correlations � five samples � 49
states). The mean absolute discriminant correlations were gener-
ally low, with little variance across states (M discriminant corre-
lation � .17; SD � .02). These results suggest no systematic
statewide differences in scale reliabilities or discriminant correla-
tions.
Second, to test whether the traits measured the same latent
personality dimensions similarly in different states, we examined
the measurement invariance of personality traits across states. This
was accomplished by fitting a series of two-group confirmatory
factor analyses in which the factor structure of each personality
trait in each state was compared against the factor structure in the
second group, which comprised the other 48 states. We examined
both metric invariance (equal factor loadings) and scalar invari-
ance (equal factor loadings and intercepts). Because of the large
sample size and sensitivity of �2 test to sample size, differences in
the comparative fit index (CFI) were used to evaluate invariance,
with model differences of CFI larger than 0.01 considered as
significant deviations from invariance (Cheung & Rensvold,
2002). For two-item scales, reliable factor structures cannot be
computed, so S3 and S5, which used the two-item TIPI scales,
were excluded in this analysis. Of the 735 two-group comparisons
of metric invariance (three samples � five scales � 49 states), 11
of the comparisons (or 1%) indicated significant deviations (two
cases in S1, one in S2, and eight in S4). Among the additional 735
comparisons of scalar invariance, 12 of the comparisons (2%)
indicated significant deviations from invariance (all cases in S4,
eight of them involving Conscientiousness). The details of the
deviating traits and states are presented in Table 1 in the supple-
mental materials. These results suggest no consistent or pro-
nounced deviations from the overall factor structure of the Big
Five traits for any of the 49 states, especially when considering the
fact that a total of 1,470 models were tested.
Third, some of the samples included fewer than 1,000 partici-
pants per state, so using raw sample means in calculations of
state-level mean scores could potentially introduce sampling error
for smaller states. So to generate more robust estimates of state-level
personality scores for each sample, random-intercept multilevel re-
gression models were fitted to predict state means (states as Level
2 units), and state-level personality scores for individual samples
used in subsequent analyses were derived as best linear unbiased
predictions (BLUP; Gelman & Hill, 2007) from the models. In the
total sample with sufficient numbers for all states, the BLUP and
sample mean estimates were virtually identical (M convergent r �
.99; SD � .10). We next examined the correlations between
state-level estimates across the five samples. All the mean conver-
gent correlations were positive and moderate to large in magni-
tude, ranging from .27 for Extraversion to .61 for Openness, with
an average correlation across all traits of .45. The convergent
correlations for all the samples are shown in Table 3 in the
supplemental materials.
Fourth, to examine the degree of variance in personality at the
state level, we examined the spatial autocorrelations and group-
mean reliabilities of the traits. Spatial autocorrelations are com-
monly used in the geographical sciences to measure the degree to
which variables geographically cluster. Across the personality
traits, the total adjacency-based autocorrelations ranged from .21
to .54 in the total sample, suggesting a good degree of spatial
clustering such that neighboring states were more similar in per-
sonality compared with nonneighboring states. To determine
whether the group-level personality means were reliable and that
the groups could be reliably differentiated, we examined the av-
erage group-mean reliabilities. In the total sample, the average
group-mean reliability (i.e., ICC2; Bliese, 2000) across the traits
was high (M ICC2 � .96; SD � .05). Table 2 in the supplemental
materials shows the spatial autocorrelations for both adjacency and
inverse-weight matrices, as well as intraclass correlations for the
personality traits in each sample.
In summary, the results from our analyses of the psychometric
characteristics of the individual-level and state-level personality
scales indicated that there were no clear or consistent statewide
differences in any of the scale properties. Furthermore, the state-
level personality trait scores converged across samples, indicating
that the scores were reliable and generalizable. These findings
offer strong justification for computing state-level personality trait
scores, which were based on the mean of the unit-weighted scale
scores of participants in each state for each sample. So that all the
mean scores were on the same metric, we converted them to
z-scores. This procedure resulted in 1,225 standardized state-level
personality trait scores.
Focal Analyses: Identification of
Psychological Regions
The second wave of analyses focused on identifying psycho-
logical regions. We first performed a series of cluster analyses
on the state-level personality traits from each sample to deter-
mine whether there is a robust set of regional personality
profiles; that is, are there particular configurations of person-
ality traits that are found repeatedly across states? Next, we
mapped the geographical distribution of the personality profiles
identified in the previous step. Finally, we examined the cor-
relations between the psychological regions and the state-level
PESH indicators.
Cluster analyses. We examined psychological regions us-
ing an elaboration of the multistep clustering procedure de-
1003PSYCHOLOGICAL REGIONS
scribed by Chapman and Goldberg (2011) to investigate per-
sonality prototypes at the individual level. In all the following
analyses, the unit of measurement was the state and the per-
sonality variables analyzed were the standardized state-level
personality trait scores from each of the five comparison sam-
ples. First, we determined the optimal cluster structure by
conducting Ward’s hierarchical clustering (using Euclidean dis-
tance) separately within each of the comparison samples. These
analyses were used to derive initial cluster centers and to
identify the optimal number of clusters for each sample. Sec-
ond, we used K-means clustering to achieve clusters with the
highest within-cluster similarity and the greatest between-
cluster variability. This procedure produces a clearer cluster
solution than relying solely on hierarchical clustering (Breck-
enridge, 1989; Chapman & Goldberg, 2011). Third, states from
each sample were assigned to clusters derived from each of the
five comparison samples on the basis of the Euclidean distance
to those cluster centers. This cross-classification technique was
performed a total of 20 times. Finally, we measured cross-
classification agreement using Cohen’s kappa to determine the
generalizability of the clusters.
The results from the hierarchical clustering suggested two to
seven clusters, so we performed the aforementioned multistep
procedure for two to seven cluster solutions. The empirical kappa
distributions for the cross-classifications are shown in Table 2. As
can be seen in the first data row, the mean kappa coefficients
ranged from .44 for the seven-cluster solution to .61 for the
three-cluster solution. The three-cluster solution provided the best
fit compared with the other models and exceeded the threshold for
substantial agreement (i.e., � coefficients � .60; Fleiss, 1981;
Landis & Koch, 1977). These results indicate that the three-cluster
solution provided the most robust and generalizable solution.
The results from the cluster analyses suggest that statewide
variation in personality can be reliably characterized in terms of
three psychological regions. Furthermore, the degree of conver-
gence suggests that these clusters generalize across samples and
methods. Given the consistency across the five samples, we cre-
ated a sixth set of personality scores by computing mean scores for
each personality domain averaging across the five samples (the
Appendix presents these state-level personality estimates con-
verted to T-scores). All subsequent analyses are based on the
clusters derived from the combined samples.
Regional personality profiles. To gain an understanding of
the nature of the regional personality clusters, we plotted the
cluster centers for the three clusters. The means depicted in Figure
1 show the personality profiles for each cluster. As can be seen in
the first set of five bars, the profile for Cluster 1 was marked by
high Extraversion, Agreeableness, and Conscientiousness, and low
Neuroticism and Openness. This configuration of traits portrays a
region of people who are, on average, conventional, friendly,
sociable, compliant, and emotionally stable. On the basis of this
configuration and the especially low Openness score, we labeled
this cluster the Friendly & Conventional profile. As can be seen in
the second set of bars, the profile for Cluster 2 was marked by low
Extraversion, low Agreeableness, average Conscientiousness, very
low Neuroticism, and very high Openness. The image depicted by
this profile represents a region of people who are, on average,
creative and relaxed, reserved, and perhaps somewhat socially
distant. Given this configuration of traits, we labeled this cluster
the Relaxed & Creative profile. The third set of bars for Cluster 3
reveal slightly below-average Extraversion, low Agreeableness,
low Conscientiousness, very high Neuroticism, and slightly above-
average Openness. This configuration represents a region of peo-
ple who are, on average, irritable, impulsive, and quarrelsome.
This pattern of traits led us to label this cluster the Temperamental
& Uninhibited profile. Of course, as with all factor labels, these
profile labels are provided for the sake of communication and give
only a broad sense of the makeup of the profiles, so investigators
are urged to base their understanding on the full configuration of
traits, not the profile labels alone.
Geographical distribution. To examine the geographic dis-
tribution of the three state-level personality profiles, we mapped
the state-level prototypicality scores for each of the three clusters.
Specifically, we first computed profile correlations between each
state’s personality profile and the three sets of cluster centers that
were derived from the combined sample. States with large positive
profile correlations are prototypical of a cluster, whereas states
with negative profile correlations are most different from that
cluster. We then applied the Getis-Ord G� statistic for geographical
clustering analysis (also known as “hotspot analysis”) to locate
geographical concentration or concentrations of high and low
values of profile correlation values. The G� statistic identifies areas
that have high (or low) values and that also have neighboring areas
that have high (or low) values in the profile correlations (Ord &
Getis, 1995). The statistic is interpreted as a z-score, with values
above 1.96 or below �1.96 indicating statistically significant
clustering. This analysis was performed using the spdep package
of R 2.15.1 (Bivand, Pebesma, & Gómez-Rubio, 2008), with
contiguity spatial weight matrix (0 � does not share boundary, 1 �
shares boundary). (The corresponding maps of raw profile corre-
lation values instead of G� statistics are presented in the supple-
mental materials, Figure 1.)
The maps displayed in Figure 2 show the geographical concen-
trations of the personality clusters across the United States. What
is especially striking is that each of the personality clusters formed
a distinctive geographical pattern. Cluster 1 (Friendly & Conven-
tional) comprises states predominantly in the north central Great
Plains and in the South. States in the Mountain, Pacific Coast,
Mid-Atlantic, and New England regions were the least similar to
this particular cluster. States predominantly in the West and some
along the Eastern Seaboard were prototypical of Cluster 2 (Re-
laxed & Creative), whereas most of the states in the Midwest,
Great Plains, and Gulf Coast were most different from this cluster.
Finally, states in New England and the Middle Atlantic were
Table 2
Characteristics of Empirical Kappa Distributions Derived From
the State-Level Personality Traits in Five Samples
prototypical of Cluster 3 (Temperamental & Uninhibited), whereas
states in the Southeast, Great Plains, and Mountain region were not
members of this cluster.
Correlations with PESH indicators. To gain a broader un-
derstanding of the three psychological regions, we examined the
correlations between the state prototypicality scores and the PESH
indicators. The correlations shown in Table 3 reveal a number of
associations between the clusters and indicators.1 In general, the
patterns of relationships appear to be unique to each cluster and
thus highlight the distinctiveness of the regions.
As can be seen in the first data column of Table 3, states in the
Friendly & Conventional region displayed strong negative corre-
lations with all three economic indicators, indicating that this
region is less affluent, has fewer highly educated residents, and is
less innovative compared with states in the other regions. States in
this region also appeared to have higher levels of social capital and
less social tolerance compared with states in other regions. More-
over, Friendly & Conventional states were more politically con-
servative and Protestant compared with other regions. Analyses of
the health indicators suggest that residents of Friendly & Conven-
tional states are less healthy and engage in less health-promoting
behavior compared with people who live in other psychological
regions.
The patterns of relationships displayed in the second data col-
umn of Table 3 show the correlations for the Relaxed & Creative
region. The positive correlation with the proportion of non-White
residents suggests that relaxed and creative states are culturally
and ethnically more diverse than states in other regions. It also
appears that the Relaxed & Creative region is wealthier, has more
highly educated residents, and is more innovative than other re-
gions. The links with the social indicators indicate that levels of
social capital are comparatively low, that residents are socially
tolerant and accepting, and that levels of residential mobility are
comparatively high in this region. States in this region cast fewer
ballots for conservative presidential candidates and had fewer
Protestants compared with states in other regions. The patterns of
correlations with the health indicators indicate that residents of the
Relaxed & Creative region are comparatively healthy and engage
in health-promoting behaviors.
The last data column in Table 3 shows the correlates of the
Temperamental & Uninhibited region. The associations with the
demographic indicators show that this region has larger propor-
tions of women and older adults than states in other regions. Levels
of state wealth were also positively associated with this region,
suggesting that residents of this region were more affluent than
residents of other states. The negative association with residential
mobility suggests that most residents of this region are from their
home state. Residents of this region also appear to be politically
liberal, as evidenced by the negative association with votes for
Republican presidential candidates. There also appear to be
smaller proportions of Protestants in this region.
1 The corresponding associations between social indicators and state-
level personality scores for the individual traits of the Big Five are shown
in Supplementary Figures 2–14. Most of the associations were observed
fairly consistently with similar effect magnitudes across the five studies,
providing evidence for convergent external validity for state-level person-
ality scores determined from the different samples.
Standardized Scores
Cluster 1:
Friendly & Conventional
Profile
Cluster 2:
Relaxed & Creative
Profile
Cluster 3:
Temperamental & Uninhibited
Profile
0.31
-0.40
-0.33
0.30
-0.20
-0.57
0.30
0.02
–
0.80
-0.13
-0.55
1.00
-0.48
0.80
0.30
-1.25
-1.00
–
0.75
–
0.50
–
0.25
0.00
0.25
0.50
0.75
1.00
1.25 E A C N O
Figure 1. Mean Big Five standardized scores by cluster profile. E � Extraversion; A � Agreeableness; C �
Conscientiousness; N � Neuroticism; O � Openness.
1005PSYCHOLOGICAL REGIONS
General Discussion
The aim of the present article was to map the psychological
topography of the United States. To that end, we gathered data
from five large samples that used different measures and sampling
procedures. Analyses of the psychometric properties of the per-
sonality measures revealed no systematic differences between
samples or geographic regions. Results from the clustering analy-
ses converged, indicating that statewide variation in personality
can be characterized in terms of three psychological regions. The
psychological regions comprised states with similar personality
profiles that were geographically close. Each region was uniquely
related to the PESH indicators. Taken together, these results pro-
vide strong evidence that there are robust psychological differ-
ences between regions of the United States that are associated with
important geographical factors.
A Theoretical Framework for Regional
Variation in Personality
In the light of the present findings, we may draw on theory and
research in personality, as well as research on migration, conta-
gion, and social influence, to begin to construct a theoretical
framework for understanding the causes and consequences of
regional variation in personality. The present work provides an
empirical foundation on which to develop such a framework. It
must begin by characterizing the regions and considering the
potential mechanisms that could have shaped them.
Friendly & Conventional region. In many respects, the
Friendly & Conventional region reflects Middle America, or
“Red” states. Not only is this region in the geographical center of
the country, the psychological profile and all the social indicators
betray a region that is marked by conservative social values. The
region is defined by moderately high levels of Extraversion,
Agreeableness, and Conscientiousness, moderately low Neuroti-
cism, and very low Openness. This configuration of traits portrays
the sort of person who is sociable, considerate, dutiful, and tradi-
tional, qualities that are also reflected in the patterns of correlations
with the PESH indicators. This region comprises predominantly
White residents with comparatively low levels of education,
wealth, economic innovation, and social tolerance. Residents of
this region also tend to be politically conservative, religious, and
civically engaged in their communities. The associations with
health indicators suggest that disproportionate numbers of people
who inhabit in this region live unhealthy lifestyles. Taken together,
the characteristics of this psychological region suggest a place
where traditional values, family, and the status quo are important.
What are the mechanisms that might contribute to the emer-
gence of this psychological region? One likely mechanism is
selective migration, or the notion that individuals selectively move
to places that satisfy their needs. Recent research indicates that
high Openness is associated with moving from one’s home state to
a different state (reflecting an interest in and tolerance of novelty),
high Extraversion is associated with relocating within one’s home
state (reflecting high activity coupled with connection to one’s
A. Cluster 1:
Convention
C. Cluster 3: Te
Uninhibite
Friendly &
nal Region
emperamental
&
ed Region
B. Clus
Cre
ster 2: Relaxed &
eative Region
&
Figure 2. Maps of multistate personality clusters. Cluster scores were based on the z-transformed profile
correlations between the state-level personality scores from the combine samples and the cluster centers. The
colored areas are hotspots derived from the Getis-Ord G� statistic.
1006 RENTFROW ET AL.
social group), and high Agreeableness is associated with staying
within one’s hometown (Boneva et al., 1998; Jokela, 2009; Jokela,
Elovainio, Kivimäki, & Keltikangas-Järvinen, 2008). Given the
psychological and PESH characteristics of the Friendly &nd Con-
ventional region, it is conceivable that this region emerged as a
result of individuals choosing to settle in areas near family and
friends. We investigated this hypothesis by examining the propor-
tion of residents in this region who lived in the same state in the
past year, which was one of the variables in the residential mobility
index. Our analysis indicated that a large proportion of residents in
the Friendly & Conventional region lived in the same state the
previous year (r � .30, p � .05), lending support to the idea that
people in this part of the country are more likely to stay close to
home than to move away.
For people with Friendly & Conventional psychological pro-
files, settling near family and friends helps them to preserve and
maintain intimate social relationships that can bring fulfillment
and support throughout life. This notion is consistent with the
comparatively high rates of social capital observed in this region,
as well as the positive associations with political conservatism and
rates of Protestantism. If residents of this region are indeed placing
a particular importance on traditional family values, it might help
explain why the economic indicators are comparatively low in this
part of the country because people might be choosing to stay near
family and friends and thus forgoing educational and career op-
portunities that require relocating.
Relaxed & Creative region. The Relaxed & Creative region
comprises predominantly states along the West Coast, Rocky
Mountains, and Sunbelt. According to the U.S. Census (Ihrke &
Faber, 2012), states in this region are among the most popular
destinations for people with college degrees and for non-Whites.
The psychological profile of this region is marked by low Extra-
version and Agreeableness, very low Neuroticism, and very high
Openness. There are disproportionate numbers of non-White res-
idents in this region, in addition to people who are wealthy,
educated, and economically innovative. Social capital is compar-
atively low here, but tolerance for cultural diversity and alternative
lifestyles is high. This is an area where significant numbers of
people are choosing to settle, as indicated by the positive associ-
ation with residential mobility. It is also a place where residents are
politically liberal, as well as psychologically and physically
healthy. There are fewer mainline Protestants here, too. In general,
the qualities of this region depict a place where open-mindedness,
tolerance, individualism, and happiness are valued.
It is likely that selective migration has also played a crucial role
in the formation of this region. Indeed, the West was the last region
settled in the United States, as migrants traveled from New Eng-
land and the Midwest, through the Great Plains and Rocky Moun-
tains along the Oregon Trail. The trek itself was dangerous and the
living conditions were poor, so only certain types of people would
have chosen to endure such a difficult move. Kitayama and col-
leagues argue that settlers of frontier regions are independent,
dominant, resilient, and adventurous (Kitayama, Conway, Pi-
etromonaco, Park, & Plaut, 2010; Kitayama, Ishii, Imada, Take-
mura, & Ramaswamy, 2006; Varnum & Kitayama, 2011). These
characteristics are directly in line with the Relaxed & Creative
profile. To explore the possibility that frontier settlement might
have contributed to the emergence of this psychological region, we
examined the association between the Relaxed & Creative profile
and the year in which states were founded as an index of frontier
settlement and obtained some support for this hypothesis (r �
�.28, p � .05). This finding suggests that the Relaxed & Creative
psychological profile is more common in frontier regions.
Table 3
Correlations Between State Prototypicality Scores for Three Regional Personality Clusters and
State-Level Indicators
Cluster
State-level indicator
1: Friendly &
Conventional region
2: Relaxed &
Creative region
3: Temperamental &
Uninhibited region
Demographic
Women �.22 �.16 .39�
Non-Whites �.26† .52� �.10
Mdn age �.18 �.17 .44�
Political/Ideological
Votes for Republicans .50� �.35� �.42�
Mainline Protestants .43� �.49� �.24�
Economic
Wealth �.42� .35� .28�
Human capital �.50� .47� .26†
Innovation �.42� .45� .22
Sociological
Social capital .34� �.37� �.14
Social tolerance �.38� .54� .08
Violent crime �.17 .24† .01
Residential mobility .12 .27† �.38�
Health
Well-being �.23 .47� �.06
Health behavior �.46� .56� .15
Note. N � 49.
† p � .10. � p � .05.
1007PSYCHOLOGICAL REGIONS
The link to frontier settlement offers clues about how this
psychological region came about, but why has it persisted? This
region is undergoing more residential mobility than other parts of
the nation because young people, professionals, and immigrants
are choosing to move here to pursue educational and employment
opportunities. Such challenging and exciting opportunities are
likely to attract individuals who have creative psychological pro-
files, which then become expressed in terms of human capital,
wealth, and economic innovation. The open-minded atmosphere
fostered in this region might also attract individuals from different
cultures or people who live alternative lifestyles who sense that
this is a place where social and cultural differences are tolerated
and accepted. Taken together, there appear to be several reasons
why individuals with Relaxed & Creative psychological profiles
would choose to settle in this part of the country.
Another mechanism that could be at work in this region is social
influence, or the notion that the social environment affects peo-
ple’s thoughts, feelings, and behaviors. Research on attitude
change, for example, suggests that individuals’ opinions and be-
liefs are affected by the attitudes and behaviors of the people in
their environment (e.g., Bourgeois & Bowen, 2001; Pettigrew &
Tropp, 2006). Moreover, Openness, which is a central trait in this
profile and the most value-laden trait among the Big Five, has
been shown to change in response to college education and
experience (McCrae, 1996). Thus, given the social diversity and
tolerance in this region, it is conceivable that people’s attitudes
about culture, race, and sexual orientation are influenced by the
attitudes of those around them, which could perpetuate a cli-
mate of open-mindedness.
Temperamental & Uninhibited region. The Temperamental
& Uninhibited region comprises states predominantly in the Mid-
Atlantic and Northeast. This region is made up of the quintessen-
tial Blue states. The psychological profile of the region is defined
by low Extraversion, very low Agreeableness and Conscientious-
ness, very high Neuroticism, and moderately high Openness. This
particular configuration of traits depicts the type of person who is
reserved, aloof, impulsive, irritable, and inquisitive. There are
disproportionate numbers of older adults and women in this region,
in addition to affluent and college-educated individuals. Residen-
tial mobility is low here, and in fact, data from the U.S. Census
(Ihrke & Faber, 2012) indicates that significant numbers of resi-
dents of this region are leaving the area. Residents of this region
also appear to be politically liberal and not mainline Protestants.
Overall, it appears that this psychological region is a place where
residents are passionate, competitive, and liberal.
How did the Temperamental & Uninhibited region emerge?
Compared with other parts of the country, this region is undergo-
ing an exodus of residents, with many people relocating to the
South and Southwest. Recent work by Jokela (2013) suggests that
individuals who desired to and actually did move to another part of
the country were comparatively high in Openness, Conscientious,
and low in Neuroticism, characteristics that are almost entirely
opposite of the Temperamental & Uninhibited profile. These find-
ings make it reasonable to speculate that one mechanism respon-
sible for the psychological profile of this region stems from resi-
dential mobility, or rather immobility. In other words, the
psychological profile common in this region reflects the charac-
teristics of residents who have chosen not to move to another
region.
Another mechanism that could be at work in this region is social
influence. For example, this region has the longest settlement
history, which means it has the oldest traditions and institutions in
the country. It is conceivable that over time, the norms and
institutions established here have shaped the behavior and psycho-
logical characteristics of residents. This idea is consistent with
much theorizing in cultural psychology, which argues that norms
and institutions have top-down effects on personality development
by influencing what people are socialized to believe is right and
wrong, and by shaping life experiences and opportunities (Hofst-
ede & McCrae, 2004). Such processes should be less likely to
occur in frontier regions because norms and values are more
variable (Kitayama et al., 2010). In addition to norms and institu-
tions, social influence could also contribute to the psychological
characteristics of this area by way of emotional contagion. For
example, research indicates that the positive and negative affect of
one’s friends and romantic partners can influence individuals’
levels of happiness and depression (Fowler & Christakis, 2008;
Joiner & Katz, 1999). Considering that the Temperamental &
Uninhibited profile is marked by high Neuroticism, it seems rea-
sonable to speculate that social influence might facilitate the
spread of anxiety and irritability across the region.
Implications of Psychological Regions
for PESH Research
Although PESH researchers may be especially interested in
specific regions of the country that are conservative, prosperous,
helpful, or unhealthy, and, in turn, focus on the key demographic
variables that contribute relevant outcomes in those domains, the
present research provides a new set of variables for researchers to
consider. Indeed, all of the outcomes studied in PESH subjects are
related, if not driven, by the behaviors of the same people (each
and every individual has personal values, a level of productivity,
strategies for coping with stress, etc.), and these behaviors are
affected by personality traits and other psychological variables. So
the psychological approach used in the present investigation offers
the promise of unifying the causal nexus of these varied (and
typically partitioned) outcomes. Specifically, the present research
offers a means for understanding the unit (persons) that unifies the
behavioral outcomes that are typically studied in isolation at geo-
graphical levels of analysis.
The present findings have the potential to inform our under-
standing of theory and research in political science. For example,
the psychological regions resemble, to some degree, the political
regions identified by Elazar (1994). Specifically, 10 of the 11
(91%) primary states located in the individualistic political re-
gion—where government’s sole function is to maintain a healthy
economy—are located in the Temperamental & Uninhibited re-
gion, and seven of the 10 (70%) primary states in the traditional
political region—where government’s sole function is to maintain
the status quo—are located in the Friendly & Conventional region.
The present findings raise interesting questions about the possible
connections between political and psychological regions. The most
common explanations for the American political divide point to
religion, racial diversity, education, or wealth (Bishop, 2008;
Gelman, Shor, Bafumi, & Park, 2007; Heppen, 2003). The present
findings suggest another explanation for the differences, stemming
from the psychological characteristics of residents. In left-leaning
1008 RENTFROW ET AL.
regions, it appears that residents are generally open, reserved, and
socially distant, whereas in right-leaning regions, residents appear
to be friendly, warm, dutiful, and traditional. It is also noteworthy
that so-called Blue states were divided into two separate psycho-
logical regions, suggesting there are distinct psychological profiles
differentiating East Coast from West Coast liberals.
The psychological regions also cast new light on our under-
standing of the geographical distributions of various economic
indicators. Indeed, the core assumptions underlying theories of
creative capital, for example, are based on psychological concepts
(e.g., creativity, openness, prejudice), yet the nature of the disci-
plines within which most of that research has been carried out does
not actually assess psychological constructs. The present research
provides important evidence that links psychological characteris-
tics to such macrolevel processes by suggesting that part of the
reason why certain regions of the United States are economically
vibrant may have to do with the psychological characteristics of
residents. Indeed, the present results and recent research on entre-
preneurial rates (Obschonska et al., 2013) strongly suggest that the
proportion of individuals with personality traits associated with
high Openness and low Neuroticism is an important factor con-
tributing to economic innovation and prosperity.
Research on psychological regions has the potential to inform
our understanding of regional variation in rates of social capital,
crime, and cultural diversity. Typically, variation in such social
indicators is attributed to population structures and demographic
shifts. However, the psychological characteristics of regions would
appear to offer some insight, too. For example, the configuration of
traits represented by the Friendly & Conventional profile appears
to reinforce our understanding of the psychological underpinnings
of social capital by suggesting that communal orientation at a
macro level is based, in part, on the prevalence of traits associated
with sociability, warmth, dutifulness, and conventionality. With
this combination of traits, it is conceivable that social capital might
arise as a result of a preponderance of individuals with Friendly &
Conventional profiles. If so, efforts to foster or create social capital
in places where it is absent could be ineffective because residents
may not value such close social ties.
The psychological regions also have implications for research
on regional health disparities. Indeed, of the 11 states that com-
prise the Stroke Belt, nine (82%) are located in the Friendly &
Conventional region. Given the robust associations between per-
sonality and health outcomes at the individual level of analysis
(e.g., Roberts, Kuncel, Shiner, Caspi, & Goldberg, 2007) and the
present findings, it is conceivable that the psychological charac-
teristics of regions may contribute to healthy lifestyles and lon-
gevity. The Friendly & Conventional region displayed evidence of
poor well-being and health behavior, whereas the Relaxed &
Creative region displayed evidence of good health. How might the
psychological characteristics of regions affect health? One possi-
bility is through additive effects. Specifically, if there were dis-
proportionate numbers of people in a region with a particular
combination of traits, such as low Neuroticism and high Openness,
the health of that region would reflect the fact that there are large
numbers of residents who do not typically overreact to difficult
events and who use effective coping methods. A related possibility
is that the behaviors that are most common in a region create a
psychological atmosphere that fosters health-promoting behaviors,
such as healthy eating, physical exercise, and meditation. So, for
instance, large numbers of people who have a disposition to
engage in physical exercise might help produce an environment
that encourages others to engage in exercise, too. Whatever the
specific pathways are, there would be much gained by integrating
psychological and health perspectives for research on geographical
differences in health and well-being.
Conclusion
The present investigation was designed to extend theory and
research in psychology and the geographical sciences by mapping
the psychological topography of the United States. Characterizing
regions on the basis of the psychological characteristics of resi-
dents is important because psychological factors are likely to be
the force behind the individual-level behaviors that are expressed
on macrolevel PESH metrics. Our approach to addressing this
issue provides an entirely novel perspective on geographical per-
sonality differences. In a sense, our approach challenges the stan-
dard methods of dividing up the country (e.g., on the basis of
economic indicators, voting patterns, cultural stereotypes, or geo-
graphical and physical features) that appear to have become in-
grained in the way people think about the United States. In their
stead, our approach offers a new empirically derived system for
understanding the country that brings psychological characteristics
to the fore, clustering regions for the first time in terms of psy-
chological traits. By showing how these psychological configura-
tions relate to important social indicators, the present work under-
scores the value of this new way of thinking. We believe this
approach offers the promise of unifying the causal nexus that
drives macrolevel behavioral outcomes.
References
Allik, J., & McCrae, R. R. (2004). Toward a geography of personality
traits: Patterns of profiles across 36 cultures. Journal of Cross-Cultural
Psychology, 35, 13–28. doi:10.1177/0022022103260382
Asendorpf, J. B., & van Aken, M. A. G. (1999). Resilient, overcontrolled,
and undercontrolled personality prototypes in childhood: Replicability,
predictive power, and the trait-type issue. Journal of Personality and
Social Psychology, 77, 815– 832. doi:10.1037/0022-3514.77.4.815
Association of Religion Data Archives. (2013). Religious Congregations
and Membership Study, 2000 (state file). Retrieved from http://www
.thearda.com/Archive/Files/Downloads/RCMSST_DL.asp
Bishop, B. (2008). The big sort: Why the clustering of like-minded America
is tearing us apart. New York, NY: Houghton Mifflin.
Bivand, R. S., Pebesma, E. J., & Gómez-Rubio, V. (2008). Applied spatial
data analysis with R. New York, NY: Springer.
Bliese, P. D. (2000). Within-group agreement, non-independence, and
reliability: Implications for data aggregation and analysis. In K. J. Klein
& S. W. Kozlowski (Eds.), Multilevel theory, research, and methods in
organizations (pp. 349 –381). San Francisco, CA: Jossey-Bass.
Boneva, B. S., Frieze, I. H., Ferligoj, A., Jarošová, E., Pauknerová, D., &
Orgocka, A. (1998). Achievement, power, and affiliation motives as
clues to (e)migration desires: A four-countries comparison. European
Psychologist, 3, 247–254. doi:10.1027/1016-9040.3.4.247
Borhani, N. E. (1965). Changes and geographic distribution of mortality
from cerebrovascular disease. American Journal of Public Health, 55,
673– 681. doi:10.2105/AJPH.55.5.673
Bourgeois, M. J., & Bowen, A. (2001). Self-organization of alcohol-related
attitudes and beliefs in a campus housing complex: An initial investigation.
Health Psychology, 20, 434 – 437. doi:10.1037/0278-6133.20.6.434
1009PSYCHOLOGICAL REGIONS
Breckenridge, J. N. (1989). Replicating cluster analysis: Method, consis-
tency, and validity. Multivariate Behavioral Research, 24, 147–161.
doi:10.1207/s15327906mbr2402_1
Caspi, A. (2000). The child is father of the man: Personality continuities
from childhood to adulthood. Journal of Personality and Social Psy-
chology, 78, 158 –172. doi:10.1037/0022-3514.78.1.158
Centers for Disease Control and Prevention. (2003). National Survey of
Children’s Health. Atlanta, GA: Author.
Centers for Disease Control and Prevention. (2008). Behavioral Risk
Factor Surveillance System Survey data. Atlanta, GA: Author.
Chapman, B. P., & Goldberg, L. R. (2011). Replicability and 40-year
predictive power of childhood ARC types. Journal of Personality and
Social Psychology, 101, 593– 606. doi:10.1037/a0024289
Cheung, G. W., & Rensvold, R. B. (2002). Evaluating goodness-of-fit
indexes for testing measurement invariance. Structural Equation Mod-
eling, 9, 233–255. doi:10.1207/S15328007SEM0902_5
Costa, P. T., Jr., & McCrae, R. R. (1992). Revised NEO Personality
Inventory (NEO-PI-R) and NEO Five-Factor Inventory (NEO-FFI) pro-
fessional manual. Odessa, FL: Psychological Assessment Resources.
Crone, T. M., & Clayton-Matthews, A. (2005). Consistent economic in-
dexes for the 50 states. The Review of Economics and Statistics, 87,
593– 603. doi:10.1162/003465305775098242
de Vries, R., Gosling, S. D., & Potter, J. (2011). Income inequality and
personality: Are less equal U.S. states less agreeable? Social Science and
Medicine, 72, 1978 –1985. doi:10.1016/j.socscimed.2011.03.046
Elazar, D. J. (1994). The American mosaic: The impact of space, time, and
culture on American politics. Boulder, CO: Westview Press.
Federal Bureau of Investigation. (2008). Uniform crime reports. Washing-
ton, DC: U.S. Department of Justice.
Fleiss, J. L. (1981). Statistical methods for rates and proportions (2nd ed.).
New York, NY: Wiley.
Florida, R. (2002). The rise of the creative class. New York, NY: Perseus
Books.
Florida, R. (2008). Who’s your city? How the creative economy is making
where to live the most important decision of your life. New York, NY:
Basic Books.
Fowler, J. H., & Christakis, N. A. (2008). Dynamic spread of happiness in
a large social network: Longitudinal analysis over 20 years in the
Framingham Heart Study. British Medical Journal, 337, a2338. doi:
10.1136/bmj.a2338
Gallup-Healthways. (2008). Well-Being Index. Retrieved from http://www
.well-beingindex.com
Gelman, A., & Hill, J. (2007). Data analysis using regression and multi-
level/hierarchical models. Cambridge, England: Cambridge University
Press.
Gelman, A., Shor, B., Bafumi, J., & Park, D. (2007). Rich state, poor state,
red state, blue state: What’s the matter with Connecticut? Quarterly
Journal of Political Science, 2, 345–367. doi:10.1561/100.00006026
Glymour, M. M., Avendaño, M., & Berkman, L. F. (2007). Is the ‘Stroke
Belt’ worn from childhood? Risk of first stroke and state of residence in
childhood and adulthood. Stroke, 38, 2415–2421. doi:10.1161/
STROKEAHA.107.482059
Goldberg, L. R. (1992). The development of markers for the Big-Five
factor structure. Psychological Assessment, 4, 26 – 42. doi:10.1037/
1040-3590.4.1.26
Goldberg, L. R., Johnson, J. A., Eber, H. W., Hogan, R., Ashton, M. C.,
Cloninger, C. R., & Gough, H. G. (2006). The International Personality
Item Pool and the future of public-domain personality measures. Journal
of Research in Personality, 40, 84 –96. doi:10.1016/j.jrp.2005.08.007
Gosling, S. D., Rentfrow, P. J., & Swann, W. B., Jr. (2003). A very brief
measure of the Big Five personality domains. Journal of Research in
Personality, 37, 504 –528. doi:10.1016/S0092-6566(03)00046-1
Gosling, S. D., Vazire, S., Srivastava, S., & John, O. P. (2004). Should we
trust web-based studies? A comparative analysis of six preconceptions
about Internet questionnaires. American Psychologist, 59, 93–104. doi:
10.1037/0003-066X.59.2.93
Hart, D., Atkins, R., & Fegley, S. (2003). Personality and development in
childhood: A person-centered approach. Monographs of the Society for
Research in Child Development, 68, i–122.
Heppen, J. (2003). Racial and social diversity and U.S. presidential election
regions. Professional Geographer, 55, 191–205.
Hero, R. F. (1998). Faces of inequality: Social diversity in American
politics. New York, NY: Oxford University Press.
Hofstede, G., & McCrae, R. R. (2004). Personality and culture revisited:
Linking traits and dimensions of culture. Cross-Cultural Research, 38,
52– 88. doi:10.1177/1069397103259443
Ihrke, D. K., & Faber, C. S. (2012). Geographical mobility: 2005 to 2010
(Current Population Reports, P20-567). Retrieved from http://www
.census.gov/prod/2012pubs/p20-567
Jackman, S., & Vavreck, L. (2009). The Cooperative Campaign Analysis
Project 2007–2008 (Release 1.5). Palo Alto, CA: YouGov/Polimetrix.
John, O. P., & Srivastava, S. (1999). The Big Five Trait taxonomy: History,
measurement, and theoretical perspectives. In L. A. Pervin & O. P. John
(Eds.), Handbook of personality: Theory and research (2nd ed., pp.
102–139). New York, NY: Guilford Press.
Joiner, T. E., Jr., & Katz, J. (1999). Contagion of depressive symptoms and
mood: Meta-analytic review and explanations from cognitive, behav-
ioral, and interpersonal viewpoints. Clinical Psychology: Science &
Practice, 6, 149 –164. doi:10.1093/clipsy.6.2.149
Jokela, M. (2009). Personality predicts migration within and between U.S.
states. Journal of Research in Personality, 43, 79 – 83. doi:10.1016/j.jrp
.2008.09.005
Jokela, M. (2013). Personality and the realization of migration desires. In
P. J. Rentfrow (Ed.), Geographical psychology: Exploring the interac-
tion of environment and behavior (pp. 71– 87). Washington DC: Amer-
ican Psychological Association.
Jokela, M., Elovainio, M., Kivimäki, M., & Keltikangas-Järtvinen, L.
(2008). Temperament and migration patterns in Finland. Psychological
Science, 19, 831– 837. doi:10.1111/j.1467-9280.2008.02164.x
Kitayama, S., Conway, L. G., Pietromonaco, P. R., Park, H., & Plaut, V. C.
(2010). Ethos of independence across regions in the United States: The
production-adoption model of cultural change. American Psychologist,
65, 559 –574. doi:10.1037/a0020277
Kitayama, S., Ishii, K., Imada, T., Takemura, K., & Ramaswamy, J. (2006).
Voluntary settlement and the spirit of independence: Evidence from
Japan’s “Northern Frontier”. Journal of Personality and Social Psychol-
ogy, 91, 369 –384. doi:10.1037/0022-3514.91.3.369
Krug, S. E., & Kulhavy, R. W. (1973). Personality differences across
regions of the United States. Journal of Social Psychology, 91, 73–79.
doi:10.1080/00224545.1973.9922648
Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement
for categorical data. Biometrics, 33, 159 –174. doi:10.2307/2529310
Lanska, D. J. (1993). Geographic distribution of stroke mortality in the
United States: 1939 –1941 to 1979 –1981. Neurology, 43, 1839 –1851.
doi:10.1212/WNL.43.9.1839
Leip, D. (2009). Dave Leip’s atlas of U.S. presidential elections. Retrieved
from http://www.uselectionatlas.org
McCann, S. J. H. (2008). Societal threat, authoritarianism, conservatism,
and U.S. state death penalty sentencing (1977–2004). Journal of Per-
sonality and Social Psychology, 94, 913–923. doi:10.1037/0022-3514
.94.5.913
McCann, S. J. H. (2010a). Subjective well-being, personality, demographic
variables, and American state differences in smoking prevalence. Nico-
tine & Tobacco Research, 12, 895–904. doi:10.1093/ntr/ntq113
McCann, S. J. H. (2010b). Suicide, Big Five personality factors, and
depression at the American state level. Archives of Suicide Research, 14,
368 –374. doi:10.1080/13811118.2010.524070
1010 RENTFROW ET AL.
McCann, S. J. H. (2011a). Emotional health and the Big Five personality
factors at the American state level. Journal of Happiness Studies, 12,
547–560. doi:10.1007/s10902-010-9215-9
McCann, S. J. H. (2011b). Personality and American state differences in
obesity prevalence. Journal of Psychology: Interdisciplinary and Ap-
plied, 145, 419 – 433. doi:10.1080/00223980.2011.584081
McCrae, R. R. (1996). Social consequences of experiential openness. Psycho-
logical Bulletin, 120, 323–337. doi:10.1037/0033-2909.120.3.323
McCrae, R. R. (2001). Trait psychology and culture: Exploring intercul-
tural comparisons. Journal of Personality, 69, 819 – 846. doi:10.1111/
1467-6494.696166
McCrae, R. R., & Terracciano, A. (2008). The Five-Factor Model and its
correlates in individuals and cultures. In F. J. R. van de Vijver, D. A. van
Hemert, & Y. H. Poortinga (Eds.), Multilevel analysis of individuals and
cultures (pp. 249 –283). New York, NY: Taylor & Francis/Erlbaum.
McCrae, R. R., Terracciano, A., & 79 Members of the Personality Profiles
of Cultures Project. (2005). Personality profiles of cultures: Aggregate
personality traits. Journal of Personality and Social Psychology, 89,
407– 425. doi:10.1037/0022-3514.89.3.407
Miniño, A. M., Murphy, S. L., Xu, J., & Kochanek, K. D. (2011). Deaths:
Final data for 2008. National Vital Statistics Reports, 59. Retrieved from
http://www.cdc.gov/nchs/data/nvsr/nvsr59/nvsr59_10
Obschonka, M., Schmitt-Rodermund, E., Silbereisen, R. K., Gosling, S. D.,
& Potter, J. (2013). The regional distribution and correlates of an
entrepreneurship-prone personality profile in the United States, Ger-
many, and the United Kingdom: A socioecological perspective. Journal
of Personality and Social Psychology, 105, 104 –122. doi:10.1037/
a0032275
Ord, J. K., & Getis, A. (1995). Local spatial autocorrelation statistics:
Distributional issues and an application. Geographical Analysis, 27,
286 –306. doi:10.1111/j.1538-4632.1995.tb00912.x
Pesta, B. J., Bertsch, S., McDaniel, M. A., Mahoney, C. B., & Poznanski,
P. J. (2012). Differential epidemiology: IQ, neuroticism, and chronic
disease by the 50 U.S. states. Intelligence, 40, 107–114. doi:10.1016/j
.intell.2012.01.011
Pesta, B. J., McDaniel, M. A., & Bertsch, S. (2010). Toward an index of
well-being for the fifty U.S. states. Intelligence, 38, 160 –168. doi:
10.1016/j.intell.2009.09.006
Pettigrew, T. F., & Tropp, L. R. (2006). A meta-analytic test of intergroup
contact theory. Journal of Personality and Social Psychology, 90, 751–
783. doi:10.1037/0022-3514.90.5.751
Plaut, V. C., Markus, H. R., & Lachman, M. E. (2002). Place matters:
Consensual features and regional variation in American well-being and
self. Journal of Personality and Social Psychology, 83, 160 –184. doi:
10.1037/0022-3514.83.1.160
Putnam, R. D. (2000). Bowling alone: The collapse and revival of Amer-
ican community. New York, NY: Simon & Schuster. doi:10.1145/
358916.361990
Rentfrow, P. J. (2010). Statewide differences in personality: Toward a
psychological geography of the United States. American Psychologist,
65, 548 –558. doi:10.1037/a0018194
Rentfrow, P. J., Gosling, S. D., & Potter, J. (2008). A theory of the
emergence, persistence, and expression of regional variation in basic
traits. Perspectives on Psychological Science, 3, 339 –369. doi:10.1111/
j.1745-6924.2008.00084.x
Rentfrow, P. J., Jost, J. T., Gosling, S. D., & Potter, J. (2009). Statewide
differences in personality predict voting patterns in 1996 –2004 U.S.
presidential elections. In J. T. Jost, A. C. Kay, & H. Thorisdottir (Eds.),
Social and psychological bases of ideology and system justification (pp.
314 –348). Oxford, United Kingdom: Oxford University Press. doi:
10.1093/acprof:oso/9780195320916.003.013
Rentfrow, P. J., Mellander, C., & Florida, R. (2009). Happy states of
America: A state-level analysis of psychological, economic, and social
well-being. Journal of Research in Personality, 43, 1073–1082. doi:
10.1016/j.jrp.2009.08.005
Richter, M. K. (1966). Invariance axioms and economic indexes. Econo-
metrica, 34, 739 –755. doi:10.2307/1910096
Roberts, B. W., Kuncel, N. R., Shiner, R., Caspi, A., & Goldberg, L. R.
(2007). The power of personality: The comparative predictive validity of
personality traits, SES, and cognitive ability for important life outcomes.
Perspectives on Psychological Science, 2, 313–345. doi:10.1111/j.1745-
6916.2007.00047.x
Rogers, K. H., & Wood, D. (2010). Accuracy of United States regional
personality stereotypes. Journal of Research in Personality, 44, 704 –
713. doi:10.1016/j.jrp.2010.09.006
Schmitt, D. P., Allik, J. A., McCrae, R. R., & Benet-Martínez, V. (2007).
The geographic distribution of Big Five personality traits: Patterns and
profiles of human self-description across 56 nations. Journal of Cross-
Cultural Psychology, 38, 173–212. doi:10.1177/0022022106297299
Srivastava, S., John, O. P., Gosling, S. D., & Potter, J. (2003). Develop-
ment of personality in early and middle adulthood: Set like plaster or
persistent change? Journal of Personality and Social Psychology, 84,
1041–1053. doi:10.1037/0022-3514.84.5.1041
Steel, P., & Ones, D. S. (2002). Personality and happiness: A national-level
analysis. Journal of Personality and Social Psychology, 83, 767–781.
doi:10.1037/0022-3514.83.3.767
Substance Abuse and Mental Health Services Administration. (2007).
Results from the 2006 National Survey on Drug Use and Health:
National findings. Retrieved from http://www.samhsa.gov/data/nsduh/
2k6nsduh/2k6results
Substance Abuse and Mental Health Services Administration. (2008).
Results from the 2007 National Survey on Drug Use and Health:
National findings. Retrieved from http://www.samhsa.gov/data/nsduh/
2k7nsduh/2k7results.htm
United States Bureau of Labor Statistics. (2008). Geographic profile of
employment and unemployment. Retrieved from http://www.bls.gov/gps/
United States Census Bureau. (2000). Population (Section 1). In Statistical
abstracts of the United States (pp. 1– 62). Washington, DC: U.S. De-
partment of Commerce.
United States Census Bureau. (2005–2007). American Community Survey.
Retrieved from http://www.census.gov/acs/www/Products/users_guide/
2005_2007/index.htm
United States Census Bureau. (2010). Statistical abstract of the United
States. Washington, DC: U.S. Government Printing Office.
Varnum, M. E. W., & Kitayama, S. (2011). What’s in a name? Popular
names are less common on frontiers. Psychological Science, 22, 176 –
183. doi:10.1177/0956797610395396
Vavreck, L., & Rivers, D. (2008). The 2006 Cooperative Congressional
Election Study. Journal of Elections: Public Opinion & Parties, 18,
355–366. doi:10.1080/17457280802305177
Voracek, M. (2009). Big Five personality factors and suicide rates in the
United States: A state-level analysis. Perceptual and Motor Skills, 109,
208 –212. doi:10.2466/pms.109.1.208-212
(Appendix follows)
1011PSYCHOLOGICAL REGIONS
Appendix
Sample Sizes and Aggregate Personality Scores for Each State
Note. Aggregate personality scores are the mean of the five samples, converted to T-scores (with a M � 50 and SD � 10).
E � Extraversion; A � Agreeableness; C � Conscientiousness; N � Neuroticism; O � Openness.
Received June 27, 2012
Revision received June 12, 2013
Accepted August 20, 2013 �
1012 RENTFROW ET AL.
What Will You Get?
We provide professional writing services to help you score straight A’s by submitting custom written assignments that mirror your guidelines.
Premium Quality
Get result-oriented writing and never worry about grades anymore. We follow the highest quality standards to make sure that you get perfect assignments.
Experienced Writers
Our writers have experience in dealing with papers of every educational level. You can surely rely on the expertise of our qualified professionals.
On-Time Delivery
Your deadline is our threshold for success and we take it very seriously. We make sure you receive your papers before your predefined time.
24/7 Customer Support
Someone from our customer support team is always here to respond to your questions. So, hit us up if you have got any ambiguity or concern.
Complete Confidentiality
Sit back and relax while we help you out with writing your papers. We have an ultimate policy for keeping your personal and order-related details a secret.
Authentic Sources
We assure you that your document will be thoroughly checked for plagiarism and grammatical errors as we use highly authentic and licit sources.
Moneyback Guarantee
Still reluctant about placing an order? Our 100% Moneyback Guarantee backs you up on rare occasions where you aren’t satisfied with the writing.
Order Tracking
You don’t have to wait for an update for hours; you can track the progress of your order any time you want. We share the status after each step.
From brainstorming your paper's outline to perfecting its grammar, we perform every step carefully to make your paper worthy of A grade.
Preferred Writer
Hire your preferred writer anytime. Simply specify if you want your preferred expert to write your paper and we’ll make that happen.
Grammar Check Report
Get an elaborate and authentic grammar check report with your work to have the grammar goodness sealed in your document.
One Page Summary
You can purchase this feature if you want our writers to sum up your paper in the form of a concise and well-articulated summary.
Plagiarism Report
You don’t have to worry about plagiarism anymore. Get a plagiarism report to certify the uniqueness of your work.
Free Features $66FREE
Most Qualified Writer
$10FREE
Plagiarism Scan Report
$10FREE
Unlimited Revisions
$08FREE
Paper Formatting
$05FREE
Cover Page
$05FREE
Referencing & Bibliography
$10FREE
Dedicated User Area
$08FREE
24/7 Order Tracking
$05FREE
Periodic Email Alerts
$05FREE
Our Services
Join us for the best experience while seeking writing assistance in your college life. A good grade is all you need to boost up your academic excellence and we are all about it.
On-time Delivery
24/7 Order Tracking
Access to Authentic Sources
Academic Writing
We create perfect papers according to the guidelines.
Professional Editing
We seamlessly edit out errors from your papers.
Thorough Proofreading
We thoroughly read your final draft to identify errors.
Thorough Proofreading
We thoroughly read your final draft to identify errors.
Delegate Your Challenging Writing Tasks to Experienced Professionals
Work with ultimate peace of mind because we ensure that your academic work is our responsibility and your grades are a top concern for us!
We Analyze Your Problem and Offer Customized Writing
We understand your guidelines first before delivering any writing service. You can discuss your writing needs and we will have them evaluated by our dedicated team.
Clear elicitation of your requirements.
Customized writing as per your needs.
We Mirror Your Guidelines to Deliver Quality Services
We write your papers in a standardized way. We complete your work in such a way that it turns out to be a perfect description of your guidelines.
Proactive analysis of your writing.
Active communication to understand requirements.
We Handle Your Writing Tasks to Ensure Excellent Grades
We promise you excellent grades and academic excellence that you always longed for. Our writers stay in touch with you via email.
Thorough research and analysis for every order.
Deliverance of reliable writing service to improve your grades.
