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Disproportionate Use of Lethal Force in Policing Is Associated With Regional Racial Biases of Residents


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Due to a lack of data, the demographic and psychological factors associated with lethal force by police officers have remained insufficiently explored. We develop the first predictive models of lethal force by integrating crowd-sourced and fact-checked lethal force databases with regional demographics and measures of geolocated implicit and explicit racial biases collected from 2,156,053 residents across the United States. Results indicate that only the implicit racial prejudices and stereotypes of White residents, beyond major demographic covariates, are associated with disproportionally more use of lethal force with Blacks relative to regional base rates of Blacks in the population. Thus, the current work provides the first macropsychological statistical models of lethal force, indicating that the context in which police officers work is significantly associated with disproportionate use of lethal force.
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Disproportionate Use of Lethal Force
in Policing Is Associated With Regional
Racial Biases of Residents
Eric Hehman
, Jessica K. Flake
, and Jimmy Calanchini
Due to a lack of data, the demographic and psychological factors associated with lethal force by police officers have remained
insufficiently explored. We develop the first predictive models of lethal force by integrating crowd-sourced and fact-checked
lethal force databases with regional demographics and measures of geolocated implicit and explicit racial biases collected from
2,156,053 residents across the United States. Results indicate that only the implicit racial prejudices and stereotypes of White
residents, beyond major demographic covariates, are associated with disproportionally more use of lethal force with Blacks
relative to regional base rates of Blacks in the population. Thus, the current work provides the first macropsychological statistical
models of lethal force, indicating that the context in which police officers work is significantly associated with disproportionate use
of lethal force.
intergroup dynamics, racial bias, stereotypes, prejudice, lethal force, police
Minorities killed by police officers in the United States is an
issue that regularly garners national attention. The extent to
which it is occurring and the role that racial prejudice might
play are regular questions in the discourse following these inci-
dents. However, because the U.S. government does not man-
date reporting of lethal force (Byers & Moskop, 2014), it has
been difficult to empirically investigate associated factors.
More recently, the Guardian news agency developed a data-
base of U.S. individuals killed by police. Integrating traditional
reporting with police reports, fact-checked witness state-
ments, monitoring of regional news, and other open-sourced
police fatality databases (Swaine, Laughland, & Lartey,
2015), it is currently the most comprehensive and reliable
database of individuals killed by police.
To examine what
factors might be associated with Black and White Americans
being disproportionately killed by police relative to their pres-
ence in the population, the current research integrated use of
lethal force data with demographics and a large database of
implicit and explicit biases.
Racial bias can take many forms. Prejudice refers to a
valenced evaluation (e.g., good, bad) of a group, and stereo-
types refer to mental associations between a group (e.g.,
Blacks) and attributes (e.g., threat). These distinct forms of bias
can be measured relatively directly or indirectly. For example,
prejudice can be measured directly through explicit questions
(e.g., “How warmly or coldly do you feel toward Black peo-
ple?”) or indirectly through so-called implicit tasks that infer
bias from the speed or accuracy with which a response is made,
rather than from the contents of the response itself (Fazio, Jack-
son, Dunton, & Williams, 1995; Greenwald, McGhee, &
Schwartz, 1998). Biases measured explicitly are assumed to
reflect relatively deliberate and conscious mental processes,
often predicting intentional judgments and behaviors. In con-
trast, implicit biases have traditionally been conceptualized
as reflecting less intentional or controlled processes (Dovidio,
Kawakami, & Gaertner, 2002; Gawronski, Peters, Brochu, &
Strack, 2008) that can influence judgments and behaviors out-
side of conscious awareness.
Rather than examine the racial biases of police officers
directly as in previous work (Correll et al., 2007; Sim, Correll,
& Sadler, 2013; Terrill & Reisig, 2003), we instead examined
the context in which officers operate. Specifically, we used
regional demographic factors and the racial biases of residents
to capture that context and tested the relationships between
racial demographics, biases, and lethal force. Context and
behavior are closely linked (Asch, 1946; Barden, Maddux,
Petty, & Brewer, 2004) because environmental factors (e.g.,
Department of Psychology, Ryerson University, Toronto, ON, Canada
Department of Psychology, York University, Toronto, ON, Canada
Department of Psychology, University of California, Davis, CA, USA
Corresponding Author:
Eric Hehman, Department of Psychology, Ryerson University, 350 Victoria
Street, Toronto, Ontario, Canada M5B 2K3.
Social Psychological and
Personality Science
ªThe Author(s) 2017
Reprints and permission:
DOI: 10.1177/1948550617711229
social norms, institutions) shape decisions made within that
environment. Thus, there are several reasons police officers
operating in racially biased contexts may be more likely to use
lethal force.
How might the biases of many people in a region translate
into disproportionate lethal force? Macropsychological factors
such as the prevailing attitudes and beliefs within a region
might shape the manner in which police encounters unfold.
Models of attitude spread hold that individuals can be influ-
enced by the attitudes of others in their communities and that
such biases can be “contagious” (Rentfrow, Gosling, & Potter,
2008; Weisbuch & Pauker, 2011). Attitudes and stereotypes
can spread through explicit conversations, as well as
through nonverbal vectors such as observing facial expres-
sions and body language (Weisbuch, Pauker, & Ambady,
2009). To the extent that police officers are exposed to the
biases of their fellow residents in their region, they may
adopt those same attitudes themselves. Accordingly, one
possibility is that prevailing regional biases might shape
police officers’ own attitudes, and their behaviors on the job
are a result of these attitudes.
For instance, lab-based research at the individual level has
revealed that attitudes and stereotypes can influence perceptual
decisions (e.g., whether a person is holding a wallet or a gun),
particularly when such decisions must be made rapidly (Kubota
& Ito, 2014; Payne, 2001). Common individual-level factors,
such as mental stressors or fatigue, exacerbate the influence
of attitudes and stereotypes on judgments and behaviors (Ma
et al., 2013; Payne, 2006). Thus, it is reasonable that the demo-
graphics and/or biases of a region might create a context that
influences police officers, as they make challenging, split-
second, life-and-death decisions in the line of duty.
Alternatively, the opposite causal direction is equally plau-
sible that disproportionate lethal force might contribute to
regional racial biases. Individuals being killed by police fre-
quently receive media attention. If minorities being killed by
police are given selective media attention, it may create or
strengthen links between racial groups and crime or threat in
the minds of residents. Therefore, there are multiple plausible
mechanisms by which we might expect a relationship between
regional biases and police behavior. Consequently, the analyti-
cal focus of the current research lies not on police officers’
individual demographics or personality factors but, instead,
on the broad contextual factors present in the environments
in which police officers live and work.
In summary, we examined associations between regional
bias and use of lethal force. Moreover, there is ample evi-
dence from across the social sciences that both explicit and
implicit biases and stereotypes can jointly influence judg-
ments and behaviors (Dovidio & Gaertner, 2000; Gawronski
& Creighton, 2013). Accordingly, we investigated both possi-
bilities in the current research: Analysis 1 examined the pos-
sible influence of racial prejudice (i.e., positive or negative
evaluations of racial groups), and Analysis 2 examined the
possible influence of racial stereotypes (i.e., threat-related
beliefs about racial groups).
Analysis 1: Prejudice
The most widely used method of assessing implicit biases is the
implicit association test (IAT; Greenwald et al., 1998), a
speeded dual-categorization task in which participants must
simultaneously respond to social targets (e.g., White, Black)
and attributes (e.g., good, bad) by timed computer-key press.
The speed and/or accuracy with which participants respond
to one set of target-attribute pairings (e.g., White–Good) than
another set of pairings (e.g., White-–Bad) is assumed to reflect
the strength with which the target is associated with one attri-
bute relative to the other. Project Implicit (implicit.harvar has been collecting various IATs and measures of expli-
cit bias over the Internet since 2002. By geolocating respon-
dents, we used this data set (Xu, Nosek, & Greenwald, 2014)
to compute point estimates of implicit and explicit biases by
region. We did so at the level of core-based statistical areas
(CBSAs), a geographic area defined by the U.S. Office of Man-
agement and Budget of at least 10,000 people and adjacent
areas that are socioeconomically tied to a metropolitan center
by commuting. Importantly, Project Implicit data are not sys-
tematic samples of CBSAs: Although the percentage of Black
and White Project Implicit respondents in each CBSA corre-
lates strongly with the racial demographics of each CBSA as
reported by the U.S. Census (r¼.931, p< .001), Project Impli-
cit data differ from the general population on other demo-
graphic factors and may not be representative.
To test whether Blacks were being killed by police officers
at a rate disproportionate to their CBSA populations, the per-
centage of Blacks living in each CBSA was subtracted from the
percentage of Blacks killed in each CBSA relative to the total
amount of individuals killed by police officers. Individuals
were not killed by police in every CBSA, and CBSAs could
only be included in analyses if at least one individual in the
region (Black or White) had been killed by police. Population
data were obtained from the 2010 census (U.S. Census Bureau,
2010) and lethal force data from the Guardian (Swaine et al.,
2015). A higher score on this variable reflected greater usage
of lethal force with Blacks than would be expected based on the
CBSA population. An identical score was calculated for White/
non-Hispanic individuals to test whether Whites were being
disproportionally killed by police.
Racial Prejudice IAT. To create CBSA-level implicit and explicit
prejudice scores of respondents to Project Implicit (Xu et al.,
2014), we used only those that were U.S.-based, had CBSA-
level geographic information included, and implicit and expli-
cit data. We focused on Black and White participants only, as
sufficient data were not available for reliable estimates from
other groups. These criteria left 1,860,818 (out of a total of
4,023,404) respondents collected between 2003 and 2013 from
which to calculate point estimates of CBSA-level implicit and
explicit biases. We created variables reflecting the biases of
Black and White respondents separately to assess the unique
contribution of each group to the overall context and outcomes.
2Social Psychological and Personality Science XX(X)
We created CBSA-level implicit prejudice scores by averaging
the IAT Dscores of Black and White respondents (separately)
in each CBSA. CBSA-level explicit prejudice scores come
from responses to two feeling thermometer items, separately
asking how warm or cold participants felt toward both Blacks
and Whites (0 ¼very cold,10¼very warm). Responses to the
Black feeling thermometer were subtracted from responses to
the White feeling thermometer for both Black and White
respondents and averaged for each CBSA. Consequently, pos-
itive scores on the implicit and explicit prejudice measures rep-
resent positive attitudes toward Whites relative to Blacks.
Demographics. We also included additional CBSA-level demo-
graphic variables in the models. Socioeconomic status for
Blacks and Whites in each CBSA was represented by 5-year
estimates of median household income calculated with data
reported in the 2011–2013 American Community Survey
(U.S. Census Bureau, 2010). Two education variables repre-
senting the percentage of Blacks and Whites of the CBSA pop-
ulation who received a high-school or equivalent degree or a
BA or equivalent degree were calculated using 3-year estimates
from data reported in the 2011–2013 American Community
Survey (U.S. Census Bureau, 2010). The residential segrega-
tion of each CBSA was represented by an isolation index cal-
culated with 2010 census data (Glaeser & Vigdor, 2012),
with higher scores representing greater residential segregation
of Blacks from all other racial groups in that CBSA. Population
density is expressed as the average number of people per square
mile, as assessed by 2010 census data (U.S. Census Bureau,
2010). Employment rate was calculated using 3-year estimates
from data reported in the 2011–2013 American Community
Survey (U.S. Census Bureau, 2010). Total lethal force (regard-
less of victim race) was included from the Guardian database
(Swaine et al., 2015). Violent crime rate represents the number
of crimes in this category per 100,000 inhabitants. Rates for
2010–2013 were obtained from the Federal Bureau of Investi-
gations and averaged (Federal Bureau of Investigation, 2015).
When incorporating this large number of covariates from
different databases, many CBSAs had missing data on one or
more covariates. To compensate, all analyses were repeated
using multiple imputation, the best currently available missing
data approach (Enders, 2010). Conclusions based on these
analyses were identical (Supplemental Table S1). In addition,
identical analyses were completed using disproportionate lethal
force calculated both as a risk ratio and as an odds ratio as out-
come measures. Conclusions based on these analyses were
identical (Supplemental Table S2).
From when the Guardian began aggregating the lethal force
database in January 1, 2015, to September 30, 2015, a total
of 875 (M
¼37.3 years, SD ¼13.3, 35 female), individuals
had been confirmed as killed by police officers in the United
States. Across all 196 CBSAs in which lethal force occurred,
Black people represented 22.76%of all deaths, but constituted
only 11.76%of those CBSA populations, indicating that Blacks
are killed by police at a rate roughly double their presence in
the population, t(195) ¼4.46, p< .001, 95%CI [6.14, 15.89]
(Figure 1A). In contrast, the percentage of White deaths
(77.24%of all lethal force) was consistent with the presence
of Whites in those populations (78.70%of CBSA populations),
and the disproportionate lethal force of Whites did not signifi-
cantly differ from zero, t(195) ¼.57, p¼.573, 95%CI
[0.07, 0.04]. This result indicates that Blacks, but not Whites,
are killed by police at rates disproportionate to their presence in
the U.S. population.
Because Blacks are being killed at a rate disproportionate to
their population and Whites are not, we frame the subsequent
models as predicting disproportionate use of lethal force with
Blacks (but note these variables are highly correlated). We
tested statistical models of racial biases to explain variance in
disproportionate lethal force. Because the number of respon-
dents in each CBSA varied significantly (range ¼1–23,753
respondents), we were concerned that a low number of respon-
dents in a CBSA would lead to unstable estimates of CBSAs’
mean bias. To balance this concern with maximizing the num-
ber of CBSAs included in the analysis, we included a CBSA
Figure 1. Disproportionate lethal force (A) and implicit racial prejudice of Whites (B) by core-based statistical area (CBSA). Tick marks on scale
represent zero points in which no disproportion is present. CBSAs are included in analyses if at least one individual had been killed by police.
Hehman et al. 3
only if at least 150 residents (on average, .0005%of a CBSA
population) completed an IAT, resulting in 135 CBSAs
included in the analysis. However, results for Analysis 1 are
identical when using no such threshold and including all
CBSAs. The number of respondents in each CBSA used in each
analysis is reported in Supplemental Table S3.
We regressed disproportionate lethal force on the implicit
and explicit prejudices of White and Black residents (though
the number of Black respondents in each CBSA was frequently
below our threshold of 150 set for White respondents) in each
CBSA in a single linear regression model. Covariates in this
model included Black and White income, education level, resi-
dential segregation, violent crime, unemployment, population
density, and total lethal force (Table 1).
Only the implicit prejudice of Whites (Figure 1B) was asso-
ciated with disproportionate lethal force of Blacks, b¼.354,
p¼.031, 95%CI of B[0.374, 7.885].
As the implicit prejudice
of Whites in a CBSA increased, so too did disproportionate use
of lethal force with Blacks (Figure 2). Overall, this model
explained 14%of the variance in disproportionate lethal force.
Post hoc estimate of the achieved power with White implicit
bias was .67.
There were many CBSAs in which no Black individuals had
been killed by police which contributed to a nonnormal distri-
bution of disproportionate lethal force with Blacks. Accord-
ingly, all analyses for this and the subsequent analysis were
additionally tested by examining 95%confidence intervals
derived from 5,000 bias-corrected bootstraps, a technique that
does not require normally distributed data (Efron & Tibshirani,
1993). All results using this technique were identical (i.e., in
the same direction and significant) to those reported through-
out. Additionally, all results using multiple imputation and cal-
culating outcomes as risk and odds ratios were identical
(Supplemental Tables S1 and S2). Finally, we note this model
is inflated with a large number of covariates and not parsimo-
nious. We have adopted this approach to develop initial
predictive models of lethal force but also used ridge regression
and forward and backward stepwise regression techniques to
converge on a parsimonious model (Cohen, Cohen, West, &
Aiken, 2013). Based on the results of these follow-up analyses,
we present parsimonious models in the supplemental materials
(Supplemental Table S3). Critical to our conclusions, the impli-
cit racial biases of Whites remain the primary predictor in the
most parsimonious model.
Analysis 2: Stereotypes
Racial bias can take many forms. In Analysis 1, we operationa-
lized racial bias in terms of prejudice, that is, as an association
between a group (e.g., White) and an evaluation (e.g., positive).
Another form of racial bias is stereotypes, that is, as an associ-
ation between a group (e.g., Black) and an attribute (e.g., threa-
tening) (Dovidio, Hewstone, Glick, & Esses, 2010). To test
whether specific stereotypes might better predict dispropor-
tionate lethal force than White implicit prejudice, we utilized
a different data set from Project Implicit examining racial
threat stereotypes. Individuals responded to pictures of Black
and White people paired with weapons and harmless objects.
Thus, responses on the Weapons Stereotypes IAT indicate the
strength with which weapons are stereotypically associated
with Blacks relative to Whites. Though this data set is smaller
than the Racial Prejudice IAT data set used in Analysis 1, we
used the same respondent inclusion criteria in Analysis 2,
which gave us 295,235 (out of a total of 631,276) participants
from which to calculate point estimates of CBSA-level associa-
tions. We used the same threshold used in Analysis 1 of
150 respondents per CBSA for inclusion, which left 81 CBSAs
for analysis.
The smaller size of this data set necessarily meant that fewer
CBSAs were included in this analysis. However, all the CBSAs
that met the inclusion threshold of 150 respondents for the
Racial Prejudice data set reported in Analysis 1 also met this
Figure 2. The correlation between the core-based statistical area
(CBSA)-level implicit racial prejudice and disproportionate use of
lethal force with Blacks. Circle size represents the number of
respondents in each CBSA.
Table 1. Full Model of Disproportionate Lethal Force From the Racial
Prejudice Implicit Association Test.
Effect BSEbpValue
White implicit bias 4.129 1.90 .354 .031
White explicit bias 0.519 0.29 .306 .079
Black median income 0.001 0.01 .074 .699
White median income 0.001 0.01 .223 .261
% HS degree Blacks 0.362 1.34 .270 .788
% HS degree Whites 0.681 1.01 .095 .503
% BA degree Blacks 2.613 2.24 .162 .246
% BA degree Whites 1.659 1.49 .153 .268
Segregation 0.040 0.36 .015 .912
Black implicit bias 1.130 0.84 .146 .182
Black explicit bias 0.117 0.14 .089 .392
Violent crime 0.001 0.01 .082 .489
Unemployment 0.013 0.02 .089 .476
Population density 0.001 0.01 .182 .123
Total lethal force rate 0.031 0.02 .181 .077
Note.HS¼high school. BA ¼bachelor of arts. R
4Social Psychological and Personality Science XX(X)
criterion for the Racial Stereotype data set. Consequently, we
were able to fit a model that included both prejudice and stereo-
type estimates for each CBSA, which allowed us to examine
which better explained disproportionate lethal force. Thus,
Analysis 2 simultaneously compared the relationships among
prejudice, stereotypes, and lethal force. We entered the average
Weapons Stereotypes IAT score of White residents in each
CBSA into the full model used in Analysis 1 including all
covariates (Table 2).
In this model, implicit threat stereotypes better predicted
disproportionate lethal force, b¼.390, p¼.001, 95%CI of
B[2.241, 8.752]
(Figure 3), than the implicit racial prejudice
of Whites, b¼.166, p¼.459, 95%CI of B[3.220, 7.050].
Moreover, this model explained a substantial 34%of the var-
iance in disproportionate lethal force in these CBSAs (as com-
pared to 14%in Analysis 1). Post hoc estimate of the achieved
power with Black–weapon association was .96.
We find that the implicit racial biases of White residents pre-
dict disproportionate regional use of lethal force with Blacks
by police. This association is robust, reliably emerging across
two conceptually distinct measures of racial bias, multiple
imputations, three different transformations of the outcome
measure, traditional and bootstrapped distributions, and above
and beyond 14 sociodemographic covariates. Though the
implicit prejudice of Whites is sufficient to significantly pre-
dict disproportionate lethal force (Analysis 1), the strongest
predictor of lethal force was the regional implicit stereotypical
association between Blacks and weapons (Analysis 2). These
results also suggest that disproportionate lethal force is not as
strongly related to sociodemographic characteristics of a
region as might be expected. Rather, in the present analyses,
the macropsychological characteristics of residents, operatio-
nalized at the CBSA level, are uniquely associated with mean-
ingful and important behavioral outcomes. Importantly,
CBSA-level effects may be quite different than individual-
level effects (Selvin, 1958). Hence, the research cannot
describe effects associated with racially biased individuals, and
the correct interpretation of these results is that racially biased
contexts are related to disproportionate lethal force.
That demographic covariates were consistently not associ-
ated with patterns of disproportionate lethal force in any anal-
ysis is as compelling as finding associations with bias.
Demographics are associated with a wide variety of important
behaviors and outcomes, and ostensibly might be expected to
be significant predictors in the analyses reported here. It is pos-
sible that the influence of demographic factors may be
obscured at the CBSA-level resolution of the present research.
CBSAs are large geographic units capturing metropolitan
areas, and the multiple communities within a CBSA may be
diverse, varying in important socioeconomic factors such as
wealth or ethnicity. Because these factors were averaged across
CBSAs, one possibility is that this process may have masked
the influence of these factors on lethal force. Because we can-
not draw inferences from null results, future research should
continue to consider these factors. But we can conclude that
in the CBSAs included here, racial prejudices and particularly
Black–weapon stereotypical associations are a stronger
predictor of lethal force than these demographic factors (see
Supplemental Materials for parsimonious models).
That implicit bias was the sole predictor raises some inter-
esting methodological and theoretical questions. Recent debate
has challenged the reliability and predictive validity of the IAT
(Blanton, Jaccard, Strauts, Mitchell, & Tetlock, 2015; Green-
wald, Poehlman, Uhlmann, & Banaji, 2009; Lai, Hoffman, &
Nosek, 2013; Oswald, Mitchell, Blanton, Jaccard, & Tetlock,
2015). Much of this debate has focused on the link between
individual-level IAT bias and behavior. In contrast, the unit
Figure 3. The correlation between the core-based statistical area
(CBSA)-level implicit weapon stereotypes and disproportionate use
of lethal force with Blacks. Circle size represents the number
of respondents in each core-based statistical area.
Table 2. Full Model of Disproportionate Lethal Force From the
Weapons Stereotype Implicit Association Test.
Effect BSEbpValue
Black–weapon association 5.497 1.63 .390 .001
White implicit bias 1.915 2.57 .166 .459
White explicit bias 0.100 0.45 .056 .824
Black implicit bias 1.372 0.123 .145 .267
Black explicit bias 0.105 0.23 .057 .646
Black median income 0.001 .01 .047 .844
White median income 0.001 .01 .163 .451
% HS degree Blacks 0.433 1.71 .042 .800
% HS degree Whites 2.698 1.47 .339 .072
% BA degree Blacks 2.434 2.77 .158 .383
% BA degree Whites 1.674 1.89 .137 .380
Segregation 0.570 0.389 .250 .148
Violent crime 0.001 0.01 .086 .547
Unemployment 0.031 0.03 .186 .196
Population density 0.001 0.01 .189 .196
Total lethal force rate 0.014 0.02 .100 .408
Note. HS ¼high school. BA ¼bachelor of arts. R
Hehman et al. 5
of analysis in the current research is geographic region, rather
than individuals, in which implicit and explicit bias scores are
the aggregate of many individuals. Whether similar psycho-
metric and validity criticisms apply to implicit bias aggregated
at a regional level remains an open question. Nevertheless, the
relationship between implicit bias and lethal force demon-
strated in the current work makes an important contribution
to this conversation.
As seen in Figures 2 and 3, multiple CBSAs do not have dis-
proportionate lethal force, in that no Blacks were killed by
police in these areas which, in turn, creates a nonnormal distri-
bution. Though statistical approaches were used to ensure accu-
rate standard errors for all of our tests, this distribution suggests
that two distinct processes may be driving these data. In other
words, CBSAs with zero disproportionate deaths may be
qualitatively different from those with disproportionate deaths.
Future research might incorporate zero-inflated Poisson
models to address this distinct question, facilitating an
understanding of differences between areas in which individu-
als are and are not killed by police.
The present research has several limitations due to the data and
its sources. First, the approach is correlational in nature, which
limits conclusions. Establishing the causes of disproportionate
use of lethal force with Blacks is important, but establishing
causality requires several steps. One step is demonstrating an
association between two variables, and another step is estab-
lishing clear temporal precedence. Reliable data on lethal force
do not exist prior to 2015 so we are limited in our ability to
establish temporal precedence. Consequently, we can only con-
clude that an association exists between racial biases and lethal
force, and future research can build upon this finding, provid-
ing more evidence of causal relationships.
Second, though we utilized the most comprehensive data-
base of U.S. lethal force currently available, the data rely partly
on crowd-sourcing. Lower population areas have a reduced
media presence, so deaths in these areas may be less likely to
be reported. Thus, a systematic bias toward high-population
areas may restrict the conclusions of the present research to
these areas. We note, however, that most of the U.S. population
resides in the areas covered, which means that our results may
be limited to areas where the majority of U.S. citizens reside
(Figure 1).
Further, these analyses examine data collected through the
Project Implicit website. Though our sample was representa-
tive of CBSA-level racial demographics, it is unlikely to be a
representative sample of residents on all other demographic
factors. That said, responses from this sample are correlated
with serious police outcomes. Moreover, previous research has
used this same data source (i.e., Project Implicit) to predict
other large-scale outcomes associated with the racial biases
in this sample (Leitner, Hehman, Ayduk, & Mendoza-
Denton, 2016a, 2016b). Thus, rather than considering the
representativeness of Project Implicit data, we believe it more
productive to consider why the biases reflected by their visitors
are related to lethal force above all other predictors. To be sure,
Project Implicit respondents differ from the general population
in at least two ways: They have access to the Internet and have
visited a website to learn more about bias. Having Internet
access may be a function of wealth or influence. Thus, one
explanation for the relationship between Project Implicit
responses and police behaviors is that police selectively act
in a manner consistent with the attitudes of the wealthy and
influential residents of the region. Conversely, Internet access
and/or motivation to learn about one’s biases may be character-
istics of people who pay attention to police behavior in their
area, which informs their racial biases. These and other links
might explain why Project Implicit respondents’ bias is related
to the behavior of police in their region. Nevertheless, future
research should examine whether these effects persist when
bias is measured with representative sampling methodology.
In the current research, we operationalize our baseline
against which to compare lethal force with Blacks as the pop-
ulation of Blacks in the United States. Other possible baselines
have been used by other research, including general crime
rates, violent crime rates, police encounters, arrest rates, con-
viction rates, or incarceration rates. However, as discussed
more fully elsewhere (Bayley & Mendelsohn, 1969; Smith,
1986; Terrill & Reisig, 2003), these rates all originate within
the criminal justice system and are therefore unreliable due
to biases that stem from factors such as the disproportionate
policing, behaviors, reporting, and enforcement of lower socio-
economic areas that typically have greater numbers of racial
minorities. For example, police may patrol an area with a
greater proportion of minorities more regularly, such that more
encounters and arrests in this area are likely than in areas with
fewer minorities, even controlling for crime rates. Compound-
ing the issue, the same infractions can result in an arrest in one
neighborhood but not another (Terrill & Reisig, 2003). There-
fore, we utilized general population statistics in the current
analyses to avoid the circularity inherent in using these other
potential baselines.
Another limitation is that our lethal force data is from 2015,
whereas our racial bias data were collected between 2003 and
2013. Other work has reported the stability of U.S. racial biases
over the past decade with this very data set (Schmidt & Nosek,
2010). Our supplementary analyses are consistent with this
conclusion: White implicit bias decreased very slightly by year,
B¼0.000355. Thus, these estimates of bias are extremely
stable and suggest that implicit bias scores collected in 2015
would not vary meaningfully from the data reported here in
their ability to predict disproportionate lethal force. Addition-
ally, we focused on Black/White relations only, because
(a) the most data were available for these groups, (b) Whites are
the largest group in the United States, and (c) Blacks are the
minority group most frequently killed by police. However,
whether these results hold for other minority populations is
an open question.
Finally, in the current work, we report relationships between
lethal force and implicit measures of both prejudice and
6Social Psychological and Personality Science XX(X)
stereotyping. A marginal relationship is additionally found
between explicitly reported prejudice, as measured by the dif-
ference between a feeling thermometer for Blacks and Whites,
and lethal force in Analysis 1 (though this relationship was not
found in Analysis 2). We used this measure of explicit bias
because it was available for the largest number of participants,
but there are limitations of validity and reliability of limited-
item measures (Flake, Pek, & Hehman, 2017; Nunnally,
1978). Thus, examining the relationships between explicit pre-
judice and lethal force with comprehensive, multi-item scales
would be valuable in future work.
Social scientists have long recognized that context is strongly
associated with behavior (Asch, 1946; Barden et al., 2004; Ter-
rill & Reisig, 2003), and the present research provides evidence
that prevailing racial attitudes and beliefs in a region are related
to life-or-death decisions that police officers make in the line of
duty. To our knowledge, the present work is the first to develop
models of disproportionate lethal force on such a scale, and the
first to implicate psychological processes (beyond socio-
demographic factors) as central to this phenomenon. Though
examined at the CBSA level, our results converge with
research at the individual level in finding that biased racial
associations may influence life-or-death decisions (Correll
et al., 2007; Correll, Crawford, & Sadler, 2015; Correll, Park,
Judd, & Wittenbrink, 2002; Sim et al., 2013). Again, however,
it is critical to avoid the ecological fallacy (Selvin, 1958) when
interpreting these results: The region-level effect may be quite
different than the individual-level effect. Given the correla-
tional nature of the analyses, the causal relationship between
CBSA-level biases and lethal force cannot be determined. One
interpretation of these results is that Whites’ biases create a
racially charged atmosphere that contributes to police killing
Blacks disproportionately. Alternatively, Blacks in some
regions may be more violent when interacting with police,
resulting in more justifiable lethal force, in turn influencing the
prejudice and stereotypes about Blacks held by people in the
region. Importantly, because of the correlational nature of the
analyses, we cannot rule out either interpretation. Moreover,
like all correlational work it is possible that a third, unobserved
variable better explains the relationship between lethal force
and regional biases. Thus, this research represents an important
first step in demonstrating an association between the regional
racial biases of Whites and the disproportionate use of lethal
force with Blacks. With increased data and improved reporting,
understanding the challenging contexts in which police
officers operate and decide to use lethal force will be possible
in future research.
Authors’ Note
All authors designed the study. E.H. and J.C. compiled the data. E.H.
and J.K.F. analyzed the data. All authors contributed to writing
the manuscript.
We thank Katherine Greenaway, Jordan Leitner, Michael Slepian, and
Jeff Sherman for providing feedback on earlier versions of this
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to
the research, authorship, and/or publication of this article.
The author(s) disclosed receipt of the following financial support for
the research, authorship, and/or publication of this article: This
research was partially supported by a SSHRC Institutional Grant and
SSHRC Insight Development Grant (430-2016-00094) to EH, and the
Alexander von Humbolt Post-Doctoral Fellowship to JC.
Supplemental Material
The supplemental material is available in the online version of the
1. The Washington Post and have independently
compiled similar databases. Because the Washington Post’s data-
base includes only deaths by police from firearms (instead of all
deaths caused by police), and is not fact checked
and validated by a reputable source, we opted to analyze the
Guardian’s database.
2. The zero-order correlation between the implicit bias of Whites and
lethal force with Blacks was also significant, b¼.187, p¼.031,
95%CI of B[0.186, 3.793].
3. Implicit association test data for Analysis 2 (n¼295,235) were
more sparse than Analysis 1 (n¼1,860,818). To ensure our results
were not a function of our threshold of 150 respondents per CBSA,
we tested our models using different sample size thresholds.
Results were conceptually identical with the reported results from
thresholds of 100 to our maximum tested threshold of 300 at inter-
vals of 10. When including CBSAs with fewer than 100 respon-
dents, White implicit associations between Blacks and weapons
were marginally related (p< .1) to disproportionate lethal force.
When including CBSAs with fewer than 70 respondents, and at all
lower thresholds, results were nonsignificant (p> .1).
4. The zero-order correlation between implicit threat stereotypes and
lethal force with Blacks was also significant, b¼.297, p¼.006,
95%CI of B[1.249, 7.134].
Asch, S. E. (1946). For impressions of personality. Journal of
Abnormal and Social Psychology1,41, 258–290.
Barden, J., Maddux, W. W., Petty, R. E., & Brewer, M. B. (2004).
Contextual moderation of racial bias: The impact of social roles
on controlled and automatically activated attitudes. Journal of
Personality and Social Psychology,87, 5–22. doi:10.1037/0022-
Bayley, D. H., & Mendelsohn, H. (1969). Minorities and the police:
Confrontation in America. New York, NY: Free Press.
Hehman et al. 7
Blanton, H., Jaccard, J., Strauts, E., Mitchell, G., & Tetlock, P. (2015).
Toward a meaningful metric of implicit prejudice. Journal of
Applied Psychology,100, 1468–1481. doi:10.1037/a0038379
Byers, C., & Moskop, W. (2014). Nobody counts police killings in the
U.S. St. Louis Post-Dispatch. Retrieved from http://www.stltoday.
Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2013). Applied mul-
tiple regression/correlational analysis for the behavioral sciences
(3rd ed.). New York, NY: Routledge.
Correll, J., Crawford, M. T., & Sadler, M. S. (2015). Stereotypic
vision: How stereotypes disambiguate visual stimuli. Journal of
Personality and Social Psychology,108, 219–233.
Correll, J., Park, B., Judd, C. M., & Wittenbrink, B. (2002). The police
officer’s dilemma: Using ethnicity to disambiguate potentially
threatening individuals. Journal of Personality and Social Psy-
chology,83, 1314–1329. doi:10.1037/0022-3514.83.6.1314
Correll, J., Park, B., Judd, C. M., Wittenbrink, B., Sadler, M. S., &
Keesee, T. (2007). Across the thin blue line: Police officers and
racial bias in the decision to shoot. Journal of Personality and
Social Psychology,92, 1006–1023. doi:10.1037/0022-3514.92.6.
Dovidio, J. F., & Gaertner, S. L. (2000). Aversive racism and selection
decisions: 1989 and 1999. Psychological Science,11, 315–319.
Dovidio, J. F., Hewstone, M., Glick, P., & Esses, V. M. (2010). Pre-
judice, stereotyping, and discrimination: Theoretical and empirical
overview. In The SAGE handbook of prejudice, stereotyping, and
discrimination (pp. 3–29). London, England: Sage.
Dovidio, J. F., Kawakami, K., & Gaertner, S. L. (2002). Implicit
and explicit prejudice and interracial interaction. Journal of
Personality and Social Psychology,82, 62–68. doi:10.1037//
Efron, B., & Tibshirani, R. J. (1993). An introduction to the bootstrap.
Enders, C. K. (2010). Applied missing data analysis. New York, NY:
Guilford Press.
Fazio, R. H., Jackson, J. R., Dunton, B. C., & Williams, C. J. (1995).
Variability in automatic activation as an unobtrusive measure of
racial attitudes: A bona fide pipeline? Journal of Personality and
Social Psychology,69, 1013–1027. Retrieved from http://www.
Federal Bureau of Investigation. (2015). Crime in the U.S. Retrieved
Flake, J. K., Pek, J., & Hehman, E. (2017). Construct validation in social
and personality research: Current practice and recommendations.
Social Psychological and Personality Science. Advance online pub-
lication. doi:10.1177/1948550617693063.
Gawronski, B., & Creighton, L. A. (2013). Dual process theories. In
D. E. Carlston (Ed.), The Oxford handbook of social cognition
(pp. 282–312). Oxford, England: Oxford University Press.
Gawronski, B., Peters, K. R., Brochu, P. M., & Strack, F. (2008).
Understanding the relations between different forms of racial
prejudice: A cognitive consistency perspective. Personality and
Social Psychology Bulletin,34, 648–665. doi:10.1177/014616
Glaeser, E., & Vigdor, J. (2012). The end of the segregated century:
Racial separation in America’s neighborhoods, 1890–2012.New
York, NY: Manhattan Institute for Policy Research.
Greenwald, A. G., McGhee, D. E., & Schwartz, J. L. (1998).
Measuring individual differences in implicit cognition:
The implicit association test. Journal of Personality and
Social Psychology,74, 1464–1480. doi:10.1037/0022-3514.
Greenwald, A. G., Poehlman, T. A., Uhlmann, E. L., & Banaji, M. R.
(2009). Understanding and using the implicit association test: III.
Meta-analysis of predictive validity. Journal of Personality and
Social Psychology,97, 17–41. doi:10.1037/a0015575
Kubota, J. T., & Ito, T. A. (2014). The role of race and expression in
weapons identification. Emotion,14, 1115–1124. doi:10.1037/
Lai, C. K., Hoffman, K. M., & Nosek, B. A. (2013). Reducing implicit
prejudice. Social and Personality Psychology Compass,7,
315–330. doi:10.1111/spc3.12023
Leitner, J. B., Hehman, E., Ayduk, O., & Mendoza-Denton, R.
(2016a). Blacks death rate due to circulatory diseases is positively
related to whites explicit racial bias: A nationwide investigation
using project implicit. Psychological Science,27, 1299–1311.
Leitner, J. B., Hehman, E., Ayduk, O., & Mendoza-Denton, R.
(2016b). Racial bias is associated with ingroup death rate for
Blacks and Whites: Insights from Project Implicit. Social Science
and Medicine,170, 220–227.
Ma, D. S., Correll, J., Wittenbrink, B., Bar-Anan, Y., Sriram, N., &
Nosek, B. A. (2013). When fatigue turns deadly: The association
between fatigue and racial bias in the decision to shoot. Basic and
Applied Social Psychology,35, 515–524. doi:10.1080/01973533.
Nunnally, J. (1978). Psychometric methods.NewYork,NY:
Oswald, F. L., Mitchell, G., Blanton, H., Jaccard, J., & Tetlock, P. E.
(2015). Using the IAT to predict ethnic and racial discrimination:
Small effect sizes of unknown societal significance. Journal of
Personality and Social Psychology,108, 562–571. doi:10.1037/
Payne, B. K. (2001). Prejudice and perception: The role of automatic
and controlled processes in misperceiving a weapon. Journal of
Personality and Social Psychology,81, 181–192. doi:10.1037/
Payne, B. K. (2006). Weapon bias: Split-second decisions and unin-
tended stereotyping. Current Directions in Psychological Science,
15, 287–291.
Rentfrow, P. J., Gosling, S. D., & Potter, J. (2008). A theory of
the emergence, persistence, and expression of geographic
variation in psychological characteristics. Perspectives on
Psychological Science,3, 339–369. doi:10.1111/j.1745-6916.
Schmidt, K., & Nosek, B. A. (2010). Implicit (and explicit)
racial attitudes barely changed during Barack Obama’s
presidential campaign and early presidency. Journal of
Experimental Social Psychology,46, 308–314. doi:10.1016/
8Social Psychological and Personality Science XX(X)
Selvin, H. C. (1958). Durkheim’s suicide and problems of empirical
research. American Journal of Sociology,63, 607. doi:10.1086/
Sim, J. J., Correll, J., & Sadler, M. S. (2013). Understanding police
and expert performance: When training attenuates (vs. exacer-
bates) stereotypic bias in the decision to shoot. Personality &
Social Psychology Bulletin,39, 291–304. doi:10.1177/
Smith, D. A. (1986). The neighborhood context of police behavior.
Crime and Justice,8, 313–341. doi:10.1086/449126
Swaine, J., Laughland, O., & Lartey, J. (2015). About the counted:
Why and how the Guardian is counting US police killings. The
Guardian. Retrieved from
Terrill, W., & Reisig, M. D. (2003). Neighborhood context and police
use of force. Journal of Research in Crime and Delinquency,40,
291–321. doi:10.1177/0022427803253800
U.S. Census Bureau. (2010). American Community Survey. Retrieved
Weisbuch, M., & Pauker, K. (2011). The nonverbal transmission of
intergroup bias: A model of bias contagion with implications for
social policy. Social Issues and Policy Review,5,257291.doi:
Weisbuch, M., Pauker, K., & Ambady, N. (2009). Subtle transmission
of race bias via televised nonverbal behavior. Science,326,
1711–1714. doi:10.1016/j.biotechadv.2011.08.021.Secreted
Xu, K., Nosek, B., & Greenwald, A. G. (2014). Data from the race
implicit association test on the project implicit demo website. Jour-
nal of Open Psychology Data,2. doi:10.5334/
Author Biographies
Eric Hehman is an assistant professor at Ryerson University. He
examines how perceptions across group boundaries are formed and
can manifest in outcomes of societal significance.
Jessica K. Flake received her PhD working with Betsy McCoach at
the University of Connecticut and is now a postdoctoral scholar work-
ing with Jolynn Pek and Dave Flora at York University. She is inter-
ested in applications and evaluations of latent variable models.
Jimmy Calanchini received his PhD working with Jeff Sherman at
UC Davis and is now an Alexander von Humboldt postdoctoral scho-
lar working at Albert-Ludwigs-Universita¨t Freiburg. He examines the
malleability, outcomes, and components of implicit attitudes.
Handling Editor: Kate Ratliff
Hehman et al. 9
... Response options ranged from 0 (coldest feelings) to 10 (warmest feelings). Consistent with previous researchers using the Project Implicit dataset to explore explicit bias (Leitner et al., 2016b;Hehman et al., 2018;Cunningham and Wigfall, 2020), we subtracted ratings of the marginalized group from those of the majority group (e.g., Feeling Thermometer rating for White people-Feeling Thermometer rating for Black people). Thus, higher scores reflected a strong preference White people, able-bodied people, and heterosexual, respectively. ...
Full-text available
Employees from minoritized and subjugated groups have poorer work experiences and fewer opportunities for advancement than do their peers. Biases among decision makers likely contributes to these patterns. The purposes of this study were to (a) examine the explicit biases and implicit biases among people in management occupations (e.g., chief executives, operations managers, advertising and promotions managers, financial managers, and distributions managers, among others) and (b) compare their biases with people in 22 other occupations. The authors analyzed responses from visitors to the Project Implicit website, including assessments of their racial, gender, disability, and sexual orientation biases from 2012 to 2021. Results indicate that managers expressed moderate levels of explicit and implicit bias across all dimensions. Managers differed from people in other occupations in roughly one-third of the comparisons. The biggest differences came in their implicit biases, with managers expressing more bias than people in other occupations. The study’s originality rests in the scope of the work (the authors analyzed data from over 5 million visitors representing 23 broad occupations); comparison of people in management occupations to those in other work settings; and empirically demonstrating the biases that managers have.
... For example, Schwartz and Jahn (2020) found that in metropolitan statistical areas (MSA) where the rate of fatal policecitizen encounters among White civilians was high, the Black-White and the Latinx-White disparities in police use of deadly force were smaller than in MSAs where the rate of fatal police-citizen encounters among White civilians was low. Additionally, scholars have also found a positive association between a state's racism index and the Black-White disparity in officer-involved shootings of unarmed civilians (Mesic et al., 2018;Siegel, 2020), as well as between implicit racial prejudices and stereotypes held by White residents and Black-White disparities in fatal police encounters (Hehman et al., 2018). Lastly, scholars have found that the level of racial segregation by neighborhood, city, and state has a positive association with both fatal police encounters among Black and Hispanic males and the Black-White disparity in fatal police shootings (Mesic et al., 2018;Siegel, 2020;Siegel et al., 2019). ...
The current study provides findings from a systematic review of the police use of deadly force literature over the most recently completed decade (2011–2020). After an exhaustive search of four scientific databases, 1,190 peer-reviewed articles related to the use of force were identified. Of these, 181 articles specifically examined deadly force, with 86 of them drawing on such force as the dependent variable. We found that the number of articles examining police use of deadly force increased dramatically over the course of the study period and encompassed a wide range of determinants of behavior. Citizen possession of a weapon continues to be the most consistent risk factor of police use of deadly force across decades of policing literature. Additionally, while many studies have attempted to examine the link between race and lethal force, a determination of such a relationship is difficult given both mixed findings and a lack of available national data.
... The bulk of previous literature examining racial threat theory within the realm of criminal justice has focused on policing outcomes. For example, research has examined the impact of racial threat on police force size and police expenditures (Jackson & Carroll, 1981;Liska, Lawrence, & Benson, 1981;Kent & Jacobs, 2005;Kent & Jacobs, 2004), stop and frisk (Levchak, 2017), arrests (Parker, Stults, & Rice, 2005;Ousey & Lee, 2008;Eitle & Monahan, 2009;Stucky, 2012;Kane, Gustafson, & Bruell, 2013), and use of force (Smith & Holmes, 2003;Hehman, Flake, & Calanchini, 2018). A number of studies have tested racial threat theory by examining the relationship between the relative size of the minority population in a jurisdiction and the number of police personnel. ...
According to Blalock’s racial threat theory, “threat” posed by minority populations to majority populations leads to racial disparities in formal social control, such as incarceration. Economic threat occurs when the Black population has the economic resources to compete with the White population for jobs, wages, and housing. The majority reacts by increasing formal social control against minorities. Blalock further predicted that the relationship between economic threat and social control would be curvilinear, with a decreasing effect as economic threat increased, and that it would be moderated by the size of the Black population, being strongest when the Black population was small. This study tests Blalock’s predictions using a sample of 2,092 United States counties. Results indicate that the relationship between economic threat and disparity in jail rates is curvilinear and is moderated by the percent of the population that is Black, but not in a manner predicted by Blalock. Economic threat is negatively related to incarceration disparities, particularly in counties with larger Black populations. This negative relationship becomes stronger as economic threat increases. Disparities are diminished where Blacks are demographically and economically more powerful.
... Scores on implicit measures are unstable across time within a given individual (Gawronski, Morrison, Phills, & Galdi, 2017), yet reliable across time within communities (Hehman, Calanchini, Flake, & Leitner, 2019;Payne et al., 2017). A regional history of slavery predicts anti-Black bias on the IAT (Payne, Vuletich, & Brown-Iannuzzi, 2019), and aggregated IAT scores in turn correlate with the use of lethal force by police against Black Americans within a given geography (Hehman, Flake, & Calanchini, 2018). Higher reliabilities and macro-level correlations with variables such as Black vs. White mortality rates, racial disparities in infant health, racially charged internet searches, county-level racial disparities in poverty rates, and national gender gaps in math and science (Hehman et al., 2019;Leitner, Hehman, Ayduk, & Mendoza-Denton, 2016;Nosek et al., 2009;Orchard & Price, 2017;Rae, Newheiser, & Olson, 2015) could result in whole or in part from the reduction of measurement error via aggregation (Connor & Evers, 2020). ...
In the United States, police are becoming increasingly militarized. Whereas the racialized nature of police militarization has been documented, the relationship between racial prejudice and police militarization is less understood. We assessed the link between racial prejudice against Black and Native Americans and police militarization at individual and regional levels. Study 1 ( N = 765) recruited a nationally representative sample of White Americans and found a positive association between racial prejudice and support for police militarization. Study 2 ( N = 3,129,343) sourced regional aggregates of prejudice among White Americans from Project Implicit and policing data from the Defense Logistics Agency and found that police departments in states higher in prejudice acquired greater amounts of militarized equipment. Together, these studies demonstrate that, in terms of attitudes and policies, racial prejudice predicts police militarization.
Dominant majority-group members living in areas with larger proportions of outgroup members tend to express more ingroup bias. However, prior research has rarely considered this in tandem with the bias-reducing effects of intergroup contact or tested whether outgroup proportions have similar effects for oppressed minority-group members. In two preregistered studies, we tested whether contact moderates the association between outgroup proportions and ingroup bias among White and Black Americans (total N > 75,000). As hypothesized, more Black residents in an area predicted greater explicit (but not implicit) ingroup bias among White respondents who reported low (but not high) contact with Black people. By contrast, more White residents in an area predicted lower explicit (but not implicit) ingroup bias among Black respondents regardless of intergroup contact with White people. We qualify previous findings by demonstrating that the association between outgroup proportions and ingroup bias depends on one’s group membership and level of intergroup contact.
Recent advances in large-scale data collection have created new opportunities for psychological scientists who study intergroup bias. By leveraging big data, researchers can aggregate individual measures of intergroup bias into regional estimates to predict outcomes of consequence. This small-but-growing area of study has already impacted the field with well-powered research identifying relationships between regional intergroup biases and societally-important, ecologically-valid outcomes. In this chapter, we summarize existing regional intergroup bias research and review relevant theoretical perspectives. Next, we present new and recent evidence that cannot be explained by existing theory, and offer a new perspective on regional intergroup bias that highlights aggregation as changing its’ qualitative nature relative to individual intergroup bias. We conclude with a discussion of some of the important challenges that regional intergroup bias research will need to address in moving forward, focusing on issues of prediction and causality; constructs, measures, and data sources; and levels of analysis.
Psychologists of many subfields are becoming increasingly interested in the geographical distribution of psychological phenomena. An integral part of this new stream of geo-psychological studies is to visualize spatial distributions of psychological phenomena in maps. However, most psychologists are not trained in visualizing spatial data. As a result, almost all existing geo-psychological studies rely on the most basic mapping technique: color-coding disaggregated data (i.e., grouping individuals into predefined spatial units and then mapping out average scores across these spatial units). Although this basic mapping technique is not wrong, it often leaves unleveraged potential to effectively visualize spatial patterns. The aim of this tutorial is to introduce psychologists to an alternative, easy-to-use mapping technique: distance-based weighting (i.e., calculating area estimates that represent distance-weighted averages of all measurement locations). We outline the basic idea of distance-based weighting and explain how to implement this technique so that it is effective for geo-psychological research. Using large-scale mental-health data from the United States ( N = 2,058,249), we empirically demonstrate how distance-based weighting may complement the commonly used basic mapping technique. We provide fully annotated R code and open access to all data used in our analyses.
The rate of police-involved killings in the U.S. greatly exceeds that of other industrialized nations and is highly racially disproportionate. Yet, we know relatively little about the antecedents of police violence, and even less about what explains the distribution of police killings across space. We ask whether there is a connection between contemporary police killings in the U.S. and the country's unique history of racial subjugation and violence. We focus particularly on lynching era violence in the South between 1877 and 1950 during which vigilantes killed thousands of Blacks and hundreds of Whites. We propose three main pathways through which lynchings shape law enforcement practices today: legacies of racialized criminal threat, brutalization, and legal estrangement. Analyzing Mapping Police Violence data that provide a more complete picture of lethal police force than currently available government databases, we find that lynching, regardless of victim race, moderately associates with present-day lethal police shootings of Blacks. We find some evidence that lynching also associates with lethal shootings of Whites, although this finding depends of model specification. On balance, our results suggest that lynching's legacy for law enforcement may operate through enduring cultural supports for severe punishment.
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This data archive includes Race Implicit Association Test (IAT) scores of 2,355,303 Internet volunteers who completed educational/demonstration versions of the Race IAT at from 2002 to 2012. Data in this archive can be downloaded for all years, either separately by year or in a single file. Codebooks, indicating the variable labels and value labels, and changes of variables over years, are available for both individual-year data sets and the entire data set. Participation in the (still on-going) Race IAT “study” at the Project Implicit (PI) demonstration site includes completion of the Race IAT along with demographic questions, self-report measures of racial attitude, and various additional measures received by a portion of the participants. These data allow analyses involving changes in responding over time and interrelations among IAT and self-report measures of race attitudes, as well as the association of each of these with demographics. This archive is available at Dataset The Data described in this paper is available from the Open Science Framework: [1]
Full-text available
Racial bias in the decision to shoot can be minimized if individuals have ample cognitive resources to regulate automatic reactions. However, when individuals are fatigued, cognitive control may be compromised, which can lead to greater racial bias in shoot/don't-shoot decisions. The current studies provide evidence for this hypothesis experimentally using undergraduate participants (Study 1) and in a correlational design testing police recruits (Study 2). These results shed light on the processes underlying the decision to shoot and, given the high prevalence of fatigue among police officers, may have important practical implications.
Full-text available
Greenwald, Banaji, and Nosek (2015) present a reanalysis of the meta-analysis by Oswald, Mitchell, Blanton, Jaccard, and Tetlock (2013) that examined the effect sizes of Implicit Association Tests (IATs) designed to predict racial and ethnic discrimination. We discuss points of agreement and disagreement with respect to methods used to synthesize the IAT studies, and we correct an error by Greenwald et al. that obscures a key contribution of our meta-analysis. In the end, all of the meta-analyses converge on the conclusion that, across diverse methods of coding and analyzing the data, IAT scores are not good predictors of ethnic or racial discrimination, and explain, at most, small fractions of the variance in discriminatory behavior in controlled laboratory settings. The thought experiments presented by Greenwald et al. go well beyond the lab to claim systematic IAT effects in noisy real-world settings, but these hypothetical exercises depend crucially on untested and, arguably, untenable assumptions. (PsycINFO Database Record (c) 2015 APA, all rights reserved).
Two experiments used a priming paradigm to investigate the influence of racial cues on the perceptual identification of weapons. In Experiment 1, participants identified guns faster when primed with Black faces compared with White faces. In Experiment 2, participants were required to respond quickly, causing the racial bias to shift from reaction time to accuracy. Participants misidentified tools as guns more often when primed with a Black face than with a White face. L. L. Jacoby's (1991) process dissociation procedure was applied to demonstrate that racial primes influenced automatic (A) processing, but not controlled (C) processing. The response deadline reduced the C estimate but not the A estimate. The motivation to control prejudice moderated the relationship between explicit prejudice and automatic bias. Implications are discussed on applied and theoretical levels.
The verity of results about a psychological construct hinges on the validity of its measurement, making construct validation a fundamental methodology to the scientific process. We reviewed a representative sample of articles published in the Journal of Personality and Social Psychology for construct validity evidence. We report that latent variable measurement, in which responses to items are used to represent a construct, is pervasive in social and personality research. However, the field does not appear to be engaged in best practices for ongoing construct validation. We found that validity evidence of existing and author-developed scales was lacking, with coefficient alpha often being the only psychometric evidence reported. We provide a discussion of why the construct validation framework is important for social and personality researchers and recommendations for improving practice. 3
Rationale: Research suggests that, among Whites, racial bias predicts negative ingroup health outcomes. However, little is known about whether racial bias predicts ingroup health outcomes among minority populations. Objective: The aim of the current research was to understand whether racial bias predicts negative ingroup health outcomes for Blacks. Method: We compiled racial bias responses from 250,665 Blacks and 1,391,632 Whites to generate county-level estimates of Blacks' and Whites' implicit and explicit biases towards each other. We then examined the degree to which these biases predicted ingroup death rate from circulatory-related diseases. Results: In counties where Blacks harbored more implicit bias towards Whites, Blacks died at a higher rate. Additionally, consistent with previous research, in counties where Whites harbored more explicit bias towards Blacks, Whites died at a higher rate. These links between racial bias and ingroup death rate were independent of county-level socio-demographic characteristics, and racial biases from the outgroup in the same county. Conclusion: Findings indicate that racial bias is related to negative ingroup health outcomes for both Blacks and Whites, though this relationship is driven by implicit bias for Blacks, and explicit bias for Whites.
Perceptions of racial bias have been linked to poorer circulatory health among Blacks compared with Whites. However, little is known about whether Whites’ actual racial bias contributes to this racial disparity in health. We compiled racial-bias data from 1,391,632 Whites and examined whether racial bias in a given county predicted Black-White disparities in circulatory-disease risk (access to health care, diagnosis of a circulatory disease; Study 1) and circulatory-disease-related death rate (Study 2) in the same county. Results revealed that in counties where Whites reported greater racial bias, Blacks (but not Whites) reported decreased access to health care (Study 1). Furthermore, in counties where Whites reported greater racial bias, both Blacks and Whites showed increased death rates due to circulatory diseases, but this relationship was stronger for Blacks than for Whites (Study 2). These results indicate that racial disparities in risk of circulatory disease and in circulatory-disease-related death rate are more pronounced in communities where Whites harbor more explicit racial bias.
Prejudice, stereotyping and discrimination: Theoretical and empirical overview This chapter has two main objectives: to review influential ideas and findings in the literature and to outline the organization and content of the volume. The first part of the chapter lays a conceptual and empirical foundation for other chapters in the volume. Specifically, the chapter defines and distinguishes the key concepts of prejudice, stereotypes, and discrimination, highlighting how bias can occur at individual, institutional, and cultural levels. We also review different theoretical perspectives on these phenomena, including individual differences, social cognition, functional relations between groups, and identity concerns. We offer a broad overview of the field, charting how this area has developed over previous decades and identify emerging trends and future directions. The second part of the chapter focuses specifically on the coverage of the area in the present volume. It explains the organization of the book and presents a brief ...
Three studies examined how participants use race to disambiguate visual stimuli. Participants performed a first-person-shooter task in which Black and White targets appeared holding either a gun or an innocuous object (e.g., a wallet). In Study 1, diffusion analysis (Ratcliff, 1978) showed that participants rapidly acquired information about a gun when it appeared in the hands of a Black target, and about an innocuous object in the hands of a White target. For counterstereotypic pairings (armed Whites, unarmed Blacks), participants acquired information more slowly. In Study 2, eye tracking showed that participants relied on more ambiguous information (measured by visual angle from fovea) when responding to stereotypic targets; for counterstereotypic targets, they achieved greater clarity before responding. In Study 3, participants were briefly exposed to targets (limiting access to visual information) but had unlimited time to respond. In spite of their slow, deliberative responses, they showed racial bias. This pattern is inconsistent with control failure and suggests that stereotypes influenced identification of the object. All 3 studies show that race affects visual processing by supplementing objective information. (PsycINFO Database Record (c) 2015 APA, all rights reserved).
The modal distribution of the Implicit Association Test (IAT) is commonly interpreted as showing high levels of implicit prejudice among Americans. These interpretations have fueled calls for changes in organizational and legal practices, but such applications are problematic because the IAT is scored on an arbitrary psychological metric. The present research was designed to make the IAT metric less arbitrary by determining the scores on IAT measures that are associated with observable racial or ethnic bias. By reexamining data from published studies, we found evidence that the IAT metric is “right biased,” such that individuals who are behaviorally neutral tend to have positive IAT scores. Current scoring conventions fail to take into account these dynamics and can lead to faulty inferences about the prevalence of implicit prejudice.