Finding Your Roots: Do DNA Ancestry Tests Increase Racial (In)Tolerance?

Abstract

While it is often assumed that Deoxyribonucleic acid (DNA) ancestry results illuminate one’s true racial or ethnic lineage, the consequence of this inference remains largely unknown. This leaves two conflictual hypotheses largely untested: Do DNA ancestry tests increase racial tolerance or, alternatively, racial intolerance? Two multiwave experiments aimed to test these hypotheses using either real or bogus DNA ancestry results in combination with random assignment and a tightly controlled repeated-measurements experimental design. Bayesian and inferential analyses on both general and student populations of majority-group members in the United States (i.e., White/European Americans) indicated no support for either hypothesis on measures including multiculturalism, essentialism, and outgroup bias, even when moderating factors such as the degree of unexpected ancestry and genetic knowledge were considered. Despite wide societal optimism as well as concern, receiving DNA ancestry results appears not to impact feelings and attitudes about other racial and ethnic groups. Implications for prospective test-takers and education are discussed.

Full text

Finding Your Roots: Do DNA Ancestry Tests Increase
Racial (In)Tolerance?
Sasha Y. Kimel
1
, Kinga M. Bierwiaczonek
2
, Milan Obaidi
3
, Anita Foeman
4
, Bessie Lawton
4
,
James Sidanius
5, 6
, and Jonas R. Kunst
2
1
Department of Psychology, California State University San Marcos
2
Department of Psychology, Oslo University
3
Department of Psychology, University of Copenhagen
4
Department of Communications, West Chester University
5
Department of Psychology, Harvard University
6
Department of African American Studies, Harvard University
While it is often assumed that Deoxyribonucleic acid (DNA) ancestry results illuminate one’s true racial or
ethnic lineage, the consequence of this inference remains largely unknown. This leaves two conflictual
hypotheses largely untested: Do DNA ancestry tests increase racial tolerance or, alternatively, racial
intolerance? Two multiwave experiments aimed to test these hypotheses using either real or bogus DNA
ancestry results in combination with random assignment and a tightly controlled repeated-measurements
experimental design. Bayesian and inferential analyses on both general and student populations of majority-
group members in the United States (i.e., White/European Americans) indicated no support for either
hypothesis on measures including multiculturalism, essentialism, and outgroup bias, even when moderating
factors such as the degree of unexpected ancestry and genetic knowledge were considered. Despite wide
societal optimism as well as concern, receiving DNA ancestry results appears not to impact feelings and
attitudes about other racial and ethnic groups. Implications for prospective test-takers and education are
discussed.
Public Significance Statement
Despite wide scientific consensus that the concept of “race”is not useful for explaining genetic variation,
many assume that their DNA ancestry results illuminate their true racial and ethnic lineages. This
tendency has led to significant public and scientific concern—and optimism—over the potentially far-
reaching implications of this inference. Yet, whether DNA ancestry tests actually increase racial
tolerance or, alternatively, intolerance has been largely untested. While we suggest that interventions to
increase genetic literacy continue and urge that DNA ancestry testing companies exercise caution when
presenting their results (e.g., emphasizing just how much DNA is shared between any two humans), our
results suggest that neither the considerable concern—nor intense enthusiasm—for these tests’potential
to alter thoughts and feelings toward other racial and ethnic groups is warranted.
Keywords: ancestry, DNA, essentialism, multiculturalism, intergroup
Supplemental materials: https://doi.org/10.1037/xap0000488.supp
Public interest in Deoxyribonucleic acid (DNA) ancestry has grown
exponentially (Regalado, 2019). Over 26 million at-home kits have
been sold worldwide (e.g., 23andMe,AncestryDNA;Regalado, 2019),
popular TV series reveal celebrities’results (e.g., Finding Your Roots)
and advertisements for these tests are ubiquitous. With less than $100
and a saliva sample, DNA ancestry companies provide an
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
Sasha Y. Kimel https://orcid.org/0000-0002-6476-657X
James Sidanius is deceased.
This research was supported by the Pershing Square Fund for Research on
the Foundations of Human Behavior.
The authors thank the project coordinator, Angelina Iannazzi, and the
numerous research assistants who were instrumental to this project. The
authors declare that they have no conflicts of interest to disclose.
Sasha Y. Kimel is an assistant professor of psychology at California
State University San Marcos focusing on culture and relationships
between groups in conflict. Kinga M. Bierwiaczonek is a postdoctoral
fellow at Oslo University researching factors that affect well-being and
health. Milan Obaidi is an professor of psychology at University of
Copenhagen who focuses on violent extremism. Anita Foeman is a
professor in West Chester University’s Communications department and
directors of its Deoxyribonucleic acid ancestry project. Bessie Lawton are
professor in West Chester University’s Communications department and
directors of its Deoxyribonucleic acid ancestry project. James Sidanius
was a Psychology and African–American Studies Professor at Harvard
University focused on prejudice and inequality. Jonas R. Kunst is
professor of psychology at Oslo University who researches on prejudice
and acculturation.
continued
Journal of Experimental Psychology: Applied
© 2023 American Psychological Association
ISSN: 1076-898X https://doi.org/10.1037/xap0000488
1
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individualized and seemingly precise percentage breakdown of dozens
of geographic populations worldwide. In turn, despite wide scientific
critique of both these tests (see Bolnick et al., 2007;S. S. J. Lee et al.,
2009) and a biologically based view of “race”(for a review, see
Templeton, 2013,alsoseeMarkus, 2008;Rosenberg et al., 2002),
many people are using them to make inferences about their own—and
others’—race and ethnicity
1
(see Nelson, 2008,2016). Sometimes,
they are even using their test results to alter their own identities, such as
by incorporating an additional racial/ethnic group (see Foeman et al.,
2015;Roth & Ivemark, 2018;Rubanovich et al., 2021)—a change that
can have major consequences for health, well-being, and relationships
(e.g., Haslam et al., 2008;Sani et al., 2008). Thus, concern (see
Anderson & Nickerson, 2005;Bolnick et al., 2007;Heine, 2017;
Hochschild & Sen, 2015;Lee et al., 2009)—and optimism (Jones,
2007;New York Times Editorial Board, 2005)—over the potentially
far-reaching implications of this inference is growing. Yet, because the
majority of existing work has used nonexperimental methods (e.g.,
Golbeck & Roth, 2012;Hochschild & Sen, 2015;Panofsky &
Donovan, 2019;Roth & Ivemark, 2018;Roth & Lyon, 2018;Scully et
al., 2016;Wagner & Weiss, 2012), there is considerable uncertainty
around the actual psychological impact of DNA ancestry tests.
DNA ancestry companies often market their potential to foster
connections across racial and ethnic lines (Jones, 2007;New York Times
Editorial Board, 2005). In perhaps the most striking example of this, a
viral YouTube video with over 19 million views shows a test-taker—
actually an actor—exclaiming: “There would be no such thing as
extremism in the world if people knew their heritage!”However, along
with being a smart advertising tactic, some existing empirical research
supports these companies’claims, especially when DNA ancestry tests
indicate newfound ancestral connections (see Kimeletal.,2016). Indeed,
considerable research on the dual and common ingroup identity models
suggests that tolerance can increase among, for instance, White/
European Americans who are reminded that they share a common
identity (e.g., “American”) with Black Americans (Dovidio & Gaertner,
2010;Gaertner et al., 2016). Moreover, a related experiment focusing on
DNA roots specifically found that Jews who were made aware of their
shared genetic connections with Arabs—via news articles—showed
more support for Israeli-Palestinian peacemaking (Kimel et al., 2016).
Importantly, however, this existing evidence is many steps removed
from the experience of receiving actual DNA results. More specifically,
DNA ancestry tests instead provide information about one’sown
individualized DNA. Furthermore, rather than emphasizing the rough
degree of overlap between groups by using words such as “similar,”
ancestry tests present DNA overlap in highly precise terms (e.g., 5.3%).
Moreover, DNA ancestry results reveal this information in terms of
dozens of worldwide populations at the same time (e.g., Middle Eastern,
Ashkenazi, Native American; Bryc et al., 2015).
The contrasting hypothesis—largely argued by members of the
scientific community—is that DNA ancestry tests fuel racial and ethnic
divisions (see Bolnick et al., 2007;Lee et al., 2009). Some empirical
evidence also supports this hypothesis, especially when DNA ancestry
tests fail to reveal newfound genetic connections (see Bolnick et al.,
2007;Condit et al., 2004;Dar-Nimrod & Heine, 2011;Gil-White, 2005,
Haslam et al., 2006;Heine, 2017;Keller, 2005;Kimel et al., 2016;Lee
et al., 2009;Napier et al., 2018;Phelan et al., 2014;Saini, 2019;
Williams & Eberhardt, 2008). Indeed, the experiment that randomly
assigned Jewish participants to news articles about genetic overlap
found increased support for war with Palestinians when these articles
emphasized genetic differences with Arabs (Kimeletal.,2016).
Importantly, however, again, this existing evidence is many steps
removed from the experience of receiving DNA ancestry results which
display personalized information about genetic differences in seemingly
concrete numeric terms (e.g., 0%). Yet, regardless of which specific
results one receives, presenting DNA ancestry results may foster the
false perception that race is actually biologically useful (for a review, see
Templeton, 2013,alsoseeRosenberg et al., 2002 and Markus, 2008).
Indeed, when race is described as originating from a stable underlying
“essence”such as genes, racial intolerance increases (see Bolnick et al.,
2007;Condit et al., 2004;Dar-Nimrod & Heine, 2011,Gil-White, 2005;
Haslam et al., 2006;Heine, 2017;Keller, 2005;Lee et al., 2009;Napier
et al., 2018;Saini, 2019;Williams & Eberhardt, 2008). Moreover, an
experiment focusing on DNA ancestry results specifically found that
reading vignettes about others’hypothetical results increases essential-
ism (Phelan et al., 2014).
As far as we are aware, only one previous study has used
individualized DNA ancestry results—actual or bogus—in an
experimental design. Importantly, although taking a DNA ancestry
test appeared to decrease essentialism among those with higher (vs.
lower) genetic knowledge, this study used several methods that
make it difficult to interpret their results (see Roth et al., 2020; also
reported in Roth et al., 2022). Because the control group did not
receive a test, it is unclear how much the act of taking a DNA test in
itself and then waiting for weeks may have influenced one’s view
about race and ethnicity above-and-beyond the impact of receiving
the DNA ancestry results specifically. Furthermore, the only
analyses that found an effect did not include the control group.
Moreover, because participants were simultaneously given access to
information about their genetic relatives (i.e., typically an optional
and independent feature), the impact of specifically receiving their
DNA ancestry result—independent of other personalized genetic
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
Sasha Y. Kimel played a lead role in conceptualization, data curation,
funding acquisition, investigation, methodology, project administration,
resources, writing–original draft, and writing–review and editing. Kinga M.
Bierwiaczonek played a lead role in formal analysis and visualization and a
supporting role in data curation and writing–original draft. Milan Obaidi
played a supporting role in conceptualization and methodology and an equal
role in writing–review and editing. Anita Foeman played an equal role in
conceptualization, funding acquisition, investigation, project administration,
and supervision. Bessie Lawton played a supporting role in supervision and
an equal role in funding acquisition, investigation, and resources. James
Sidanius played a supporting role in funding acquisition, methodology,
project administration, resources, and supervision. Jonas R. Kunst played a
supporting role in project administration and an equal role in conceptualiza-
tion, methodology, and writing–review and editing.
Correspondence concerning this article should be addressed to Sasha Y.
Kimel, Department of Psychology, California State University San
Marcos, 333 South Twin Oaks Valley Road, San Marcos, CA 92096,
United States. Email: skimel@csusm.edu
1
Although “race”is not considered biologically useful or interchangeable
with “ethnicity”(for a review, see Templeton, 2013, also see Markus, 2008;
Rosenberg et al., 2002), when referring to individuals who are considered to
share a culture, we use “race and ethnicity”or “race/ethnicity”throughout.
We believe that this combined term allows us to better capture both the
simultaneous power that influences how others sometimes define a group in
terms of “race”and how the group themselves tends to define themselves in
terms of ethnicity (see Markus, 2008).
2KIMEL ET AL.
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and social information—also appears unknown. Last, because
participants were unmonitored when they received their results, it is
likely that responses were influenced by a variety of external factors
(e.g., the opinions of others, internet research).
In order to isolate the influence of DNA ancestry results from
confounding factors, we conducted two repeated-measurements
experiments that randomly assigned majority-group members’to
receive their genetic ancestry percentage breakdown in a monitored
setting. More specifically, in Study 1, a general sample of (i.e.,
largely nonstudent) White/European Americans
2
—the largest U.S.
consumer group of DNA ancestry tests (Roth & Lyon, 2018;Tung
et al., 2011) and a group with especially high ancestral uncertainty
(Horowitz et al., 2019)—were randomly assigned to receive one of
three plausible, yet bogus, results that varied in their percent of
Native American genetic ancestry. This genetic ancestry was
selected over alternatives given the especially widespread—yet
typically false (Bryc et al., 2014)—belief among many White/
European Americans of distant indigenous ancestry (see Golbeck &
Roth, 2012;Reardon & TallBear, 2012). Importantly, because Study
1 was bogus, it was critical that both minimal (i.e., approx. 5%) and
moderate levels (i.e., approx. 18%) of the hidden DNA would be
believable to our participants. In Study 2, White/European American
students at a regional university (i.e., non-PhD granting and, thus, a
student population that tends to be relatively understudied compared
to their research-intensive peers) were given real DNA ancestry tests
and randomly assigned to when they received their results (e.g.,
directly before or after receiving the dependent measures). Because
these results were real, here, we were able to examine the impact of
receiving a variety of newfound genetic ancestries (e.g., sub-Saharan
African). Because effects may emerge as participants make sense of
their DNA information, this study also included a third measurement
timepoint to gauge long term effects. In both studies, we assessed the
degree to which participants’results diverge from expectations. We
also assessed genetic knowledge, given that the previous DNA
ancestry experiment suggested a potential influence of it on attitudes
(Roth et al., 2020). Moreover, to be able to test evidence for both the
null and alternative hypotheses, we conducted both Bayesian and
inferential analyses.
Study 1
Method
In both experiments, we report how sample size was determined plus
all excluded data, manipulations, and measures. Materials and replication
data are available on the Open Science Framework (OSF; https://osf.io/
dsjy9/?view_only=fb80e842aadd41fd9e2ef110dd180f3a; please con-
tact authors for code or to request the complete data sets). Both studies
underwent an extensive multisite ethics approval process (institutional
review board 16-1839 and institutional review board 20140225-R4).
Participants
Power analysis with the R package Superpower 0.1.0 (Lakens &
Caldwell, 2019) based on 1,000 Monte Carlo simulations indicated
that a sample of 170 participants would allow for detecting a small
between-within interaction effect (η2
p=.02) with a power of ≥.80 in a
mixed design with three between-subjects groups, two within-
subjects measurements, and interactions. To satisfy this sample size
criterion while accounting for attrition, we recruited a total of 260
White/European Americans (non-Latinx/non-Hispanic) from the
greater Boston area (U.S. state of Massachusetts) by posting flyers in
high-trafficked public areas (e.g., bus stops, libraries) and on online
community networks (e.g., Craigslist, Facebook). We also advertised
to a pool of community members (i.e., majority nonstudent)
interested in paid study opportunities at the Boston area university
that the first author was affiliated with. Only participants who met our
disguised eligibility criteria signed-up (see details on prescreening in
methods below) in exchange for earning up to $100 United States
dollar (i.e., all participants were paid at least $16 and entered into a
gift card drawing for the remaining). In total, 166 eligible participants
completed both waves of the study (61.8% female; M
age
=39.21,
SD
age
=16.18). The majority had a bachelor’s degree or lower
(59.5%), were not students (78.9%), and reported a household
income in the $30,000–$60,000 United States dollar range (51.9%).
Procedure
To reduce demand and selection effects, participants were told
we were collecting feedback on a new “at-home”product assessing
how “personal background, opinions, and judgment relate to stress”via
saliva samples and scientific questionnaires. To bolster this cover story,
an IDscience logo was professionally designed, all study materials
appeared brand-consistent—including the “salivary collection kit”
(e.g., sterilized collection tube, swab, biohazard specimen bag),
questionnaires, signage, and assistants’“uniforms”(see Appendix
Figure A1)—and the procedure was extensively piloted for suspicion.
To maintain privacy and eliminate distractions, participants’cell
phones were collected and we used anonymous IDs, private testing
rooms, locked internet browsers, and “white noise”machines.
At Time 1 (T1), participants provided their saliva sample and then
completed a computerized survey made up of fillers about the
IDscience product and “scientific questionnaires”consisting of the
measures described below. Approximately 2 weeks later (i.e., 12–16
days), at Time 2 (T2), participants returned to receive their alleged
“stress results”and, during the computerized survey asking for
additional product feedback, were informed that IDscience just
added a new feature assessing saliva for DNA ancestry. Per
institutional review board regulations, participants were then asked
to opt-in to see their DNA ancestry results. All participants opted-in
and were then randomly assigned to view one of three results that
were formatted to be consistent with 23andMe’s results presentation,
including in their use of a pie chart, precise percentages breakdowns
(including 0.02% “undeterminable”), accompanying color-coded
world map and textual descriptions (“Ethnicity Estimate: Thousands
of Years ago”). The presentation of the results was kept nearly
identical across the randomly assigned conditions. More specifi-
cally, the only change was whether the pie chart and percentages
listed “Native American”and, if so, to what degree. When Native
American was included in the results, the relevant section of the map
was also colored-in accordingly. All three results were presented via
an image embedded in their computerized questionnaire (see
Appendix Figure A2, for depictions of each result). In combination
with the disguised eligibility criteria assessed via prescreening; that
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2
We use the term “White/European Americans”throughout this article,
except when specifically referring to European genetic ancestry. This
combined term was selected given that “White”is generally used in the
United States to denote a person of European genetic ancestry but that,
without “European”being explicitly noted as well, this term could refer to
those of Middle Eastern or North African descent (Humes et al., 2011).
FINDING YOUR ROOTS 3
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is, (a) not adopted; (b) all grandparents White/European, non-Latinx;
(c) at least one great-grandparent born in the Americas; (d) not
having received DNA ancestry results information previously, each
percentage breakdown was deemed highly plausible for our
participants given previous research (see Golbeck & Roth, 2012;
Horowitz et al., 2019;Reardon & TallBear, 2012): (a) No
Native American: 0.02% Undeterminable; 99.98% European; (b)
Minimal Native American: 0.02% Undeterminable; 4.90% Native
American; 95.08% European; (c) Moderate Native American:0.02%
Undeterminable; 18.23% Native American; 81.75% European. Along
with various fillers about this new “stress”feature, participants
completed the dependent measures from T1 again at T2. Time 2 also
included an item requiring a specific numeric response to check
attention (161 participants or 97% identified this number correctly).
Participants were then asked to recall their results (155 participants or
93.4% correctly identified presence/absence of Native American DNA).
Rather than excluding participants, as described further in the results, we
ran sensitivity analyses to determine the degree to which our data were
impacted by failing these attention checks. Although participants were
led to believe that they would receive their alleged “stress results”next,
they were instead funnel debriefed to determine if they identified their
genetic ancestry results as real (158 participants or 95.2% did). They
also completed demographics to ensure they continued to meet the
eligibility criteria (94% continued to meet this criteria) and to assess
genetic knowledge. Finally, they were asked to confirm, after debriefing,
that they understood that their results were actually fake.
Moderator Measure. Along with various fillers, the following
moderator measure was completed at T1:
DNA Ancestry Expectations. Sliding scales assessed expectations
of DNA percentage in each major group (e.g., African, European, Native
American). In order to reduce summative errors, these were programmed
to ensure the total—across categories—addedupto100%.
Dependent Measures. Along with the fillers described above,
participants completed the following main measures at both T1 and T2
3
:
Multicultural Ideology Scale. Adapted from Berry and Kalin
(1995), 10-items assessed whether participants believed a society
should embrace the presence of many cultures (e.g., “Americans
should recognize that American society consists of groups with
different cultural backgrounds”) from 1 (strongly disagree)to7
(strongly agree;α=.86 at T1 and .90 at T2).
Coldness and Warmth Toward Racial/Ethnic Minorities. Adopted
from Verkuyten (2007), and using the procedure recommended by
Gaertner et al. (1997), positive (0 =no warm feelings;100=very
warm feelings) and negative feelings (0 =no cold feelings; 100 =very
cold feelings) were assessed separately. Seven different groups,
including six of the most common U.S. racial/ethnic groups that are
considered non-White/European American (e.g., Native American;
Black Americans; Asian Americans), were presented in randomized
order. Because the conditions varied in Native American DNA
specifically, here, we focused on Native Americans as the primary
minority outgroup (see Supplemental Material 1 Table S1–S7, for
results on all composite measures via OSF at https://osf.io/dsjy9/?vie
w_only=fb80e842aadd41fd9e2ef110dd180f3a).
Covariate. Embedded in the demographics at the end of T2,
participants completed the following measure:
Genetic Knowledge. Level of formal training/coursework in
genetics (or a closely related field) was assessed from 0 =No
training in that area to 6 =PhD in that area.
Analyses. Unlike inferential tests which only suggest “lack of
evidence for an effect”(e.g., as determined by a cutoff such as p<
.05), Bayesian analyses directly test the null hypothesis while
quantifying support both in favor and against it (van den Bergh et al.,
2020). This is done by calculating Bayes factors (BFs), which
indicate whether the data are more likely under H0 (BF
01
>1) or
under H1 (BF
01
<1; the value of 1 indicates that data are just as
likely under both hypotheses, the value of 2 indicates that they are
twice as likely under H0, etc.; Jarosz & Wiley, 2014;van den Bergh
et al., 2020). In order to make predictions, prior distributions are
assigned to model parameters based on the previous background
knowledge of the effects. Because previous knowledge was not
available in our case, we followed Rouder et al. (2012) default prior
specification, assuming equal probability of H0 and H1. To test for
main and moderated effects, we thus specified a series of Bayesian
repeated measures models. Then, we compared Bayesian models,
including each combination of predictors and their interaction terms,
and we analyzed separately the effects of each variable and of each
interaction using the exclusion BF. The exclusion BF indicates the
amount of evidence in the data in favor of excluding each of the
predictors. Specifically, based on model comparisons, it assesses
how much more (or less) likely the observed data are for models
without this specific predictor than for models including it (van den
Bergh et al., 2020), indicating thereby the strength of evidence for
the lack of effect.
As a form of sensitivity check, we ran all analyses reported in this
article with and without filters excluding participants based on
various characteristics that might bias results (e.g., incorrectly
answered attention check). Overall, we did not find substantial
differences that would alter our conclusions, with the exception of a
few results with some filters suggesting some evidence in favor of
the tested predictors (see Supplemental Material 1 Table S1;https://
osf.io/dsjy9/?view_only=fb80e842aadd41fd9e2ef110dd180f3a).
Results
Since we tested models with complex interactions resulting in a
very large number of Bayesian model comparisons, the results are
comprehensive. Full results of Bayesian analyses (Supplemental
Material 1 Tables S1–S5) and corresponding inferential statistics
(Supplemental Material 2 Tables S6 and S7) are presented in the
Supplemental Material via OSF (https://osf.io/dsjy9/?view_only=
fb80e842aadd41fd9e2ef110dd180f3a).
As shown in Appendix Table A1,wefirst conducted a Bayesian
repeated measures analysis (Jeffreys’s Amazing Statistics Program
Team, 2023;Sarafoglou, 2020) for models including only
measurement time, condition, and their interaction. We did so
separately for the three dependent variables: support for multicul-
turalism, warmth toward Native Americans, and coldness toward
Native Americans. In this analysis, exclusion BFs indicated weak (1
<BF
exl
<3) to substantial (3 <BF
exl
<10; Jarosz & Wiley, 2014)
evidence that measurement time, condition and the two-way
interaction between measurement time and condition were unrelated
to each of the dependent variables.
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3
Additional measures were included for separate article on identity and
well-being.
4KIMEL ET AL.
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We then repeated the Bayesian repeated measures analysis for
models including the main effects of measurement time, condition, and
the expected percentage of Native American DNA on the dependent
variables, as well as all two-way and three-way interactions between
these variables. Exclusion BFs(seeAppendix Table A1) showed that
our data were about 15 (for multiculturalism), 10 (for warm feelings),
and 8 (for cold feelings) times more likely under models that excluded
the three-way interaction between measurement time, condition and
expected Native DNA than under models with this interaction. In other
words, we found substantial to strong evidence that the three-way
interaction between measurement time, condition and the expected
percentage of Native American DNA does not predict our dependent
variables (Jarosz & Wiley, 2014). Moreover, the data were about 2
(for multiculturalism) and 4 (for warm feelings and cold feelings)
times more likely under models that excluded the interaction between
condition and the expected percentage than under models with this
interaction, indicating weak to substantial evidence in favor of H0.For
multiculturalism, H0 was not supported for the interaction between
measurement time and the expected percentage of Native DNA, and
for the interaction between measurement time and condition in this
analysis. Yet, we found weak to substantial evidence in favor of H0
for these interactions on warm and cold feelings. Finally, this analysis
provided weak to substantial evidence in favor of H0 for the main
effects of measurement time, condition, and expected percentage of
Native DNA for all three outcomes. Finally, we repeated the Bayesian
repeated measures analysis for models including the main effects of
measurement time, condition, and genetic knowledge on support for
multiculturalism, warm, and cold feelings, as well as all two-way and
three-way interactions between these variables. This analysis provided
weak to strong evidence in favor of H0 for all included predictors and
their interactions.
In sum, the overwhelming majority of Bayesian results suggested
no effects of measurement time, condition, the expected percentage
of Native American DNA, and their interactions on our outcomes.
Moreover, these resultsgenerally converged with inferential analyses
(see Supplemental Material 2 Tables S6 and S7 at https://osf.io/
dsjy9/?view_only=fb80e842aadd41fd9e2ef110dd180f3a).
Study 2
Study 2 aimed to conceptually replicate the null finding using real
DNA ancestry tests. Importantly, because these tests were not
bogus, we were able to examine the impact of receiving results
covering a variety of additional genetic ancestries (e.g., African)
rather than just focusing on Native American—an ancestry that
White/European Americans tend to consider highly desirable (see
Golbeck & Roth, 2012;Reardon & TallBear, 2012). Given that
DNA information is argued to fuel racial intolerance by increasing
beliefs in an underlying genetic “essence”(e.g., Williams &
Eberhardt, 2008), we also included a measure of essentialism.
Moreover, to determine whether the effects would remain consistent
with a slightly different DNA results presentation format (e.g.,
deemphasized pie chart, percentages categorized according to
worldwide region) and because we needed to be able to receive the
results on behalf of our participants (an option 23andMe did not
appear to offer at that time), we instead used AncestryDNA in this
next study. Furthermore, to examine effects while participants’
make sense of their results, a second postresults measurement time
was also included (i.e., T3).
Method
Participants
Power analysis with the R package Superpower 0.1.0 (Lakens &
Caldwell, 2019) based on 1,000 Monte Carlo simulations indicated
150 participants would allow for detecting a small between-within
interaction effect (η2
p=.02) with a power of ≥.80 for a mixed
experimental design with two between-subjects groups, three
within-subjects measurements and interactions. In an attempt to
meet these criteria while leaving room for attrition and exclusion
criteria, we recruited a total of 168 students from a public, regional
university (i.e., non-PhD granting and, thus, a student population
that tends to be relatively understudied compared to their research-
intensive peers) in the greater Philadelphia area (U.S. state of
Pennsylvania) who identified their primary race/ethnicity as White/
European American (75% female; M
age
=20.01, SD
age
=1.57).
Data collection occurred over three consecutive terms of an honors
communication course (between Fall 2017 and 2018). Once
enrolled, students received AncestryDNA kits (free of charge) as
part of an optional class activity that included three timepoints
(97.6% completed all timepoints). As the focus was on how DNA
ancestry tests change the attitudes of racial/ethnic majority-group
members, 21 bicultural/biracial participants were removed from the
analyses. Additionally, of these, two were removed because they
accidentally received their results in a different format.
Procedure
To allow for sufficient DNA processing time and so all threewaves
could be completed within a semester, saliva samples were provided
during the summer (approximately 1 month prior to the start of the
term). Then, at the start of the term, students were brought to a large
computer room to complete the T1 measures (see below).
4
They
completed these measures directly after being read a script aimed at
ensuring privacy (e.g., to spread out in the room) and confidentiality.
Approximately 2 weeks later, T2 was completed under the same
conditions. Given course requirements, at T2, all students were
randomly assigned—without their awareness—to view their
ancestry results either before or after completing the T2 dependent
measures (see below). The image of students’individualized
AncestryDNA percentage breakdown with an accompanying map
(For copyright reasons, this image has been omitted. Please contact
the authors for a copy)
5
was embedded in each computerized
questionnaire and accompanied by reminders not to take a
screenshot and assurance about receiving full access to their
AncestryDNA account at the end of the term. To check attention, at
the end of T2, participants recalled their results in each major
category (70.3% did not misremember any category). Although
ancestry information may take more time to become psychologically
integrated (Nelson, 2008,2016), the T3 measure occurred just
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This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
4
In the case of absences, students completed the study in their professor’s
office or at home as soon as possible. In total, only eight participants (5.5%)
did not complete the study with the rest of the class.
5
Results images were consistent, regardless of participants’specific
results (e.g., the screenshot of the results were taken at the same “zoom level”
and with all potential worldwide populations made visible in the percentage
breakdown) and any AncestryDNA design changes (i.e., when a change
occurred in 2018, a team of trained research assistants edited the images to
maintain consistency with the original design).
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2 weeks (and within a 4-day window) after T2 because all timepoints
needed to be completed within the span of one semester. Participants
complete T3 via a personalized link sent to their email.
Moderator Measure. Along with various fillers, the following
moderator measure was completed at the end of T1:
DNA Ancestry Expectations. As in Study 1, sliding scales
assessed expectations of DNA percentage in each major group (e.g.,
African, European, Native American). In order to reduce summative
errors, these were programmed to ensure the total—across
categories—added up to 100%. The vast majority of our participants’
DNA results (88.1%) ended up deviating from their expectations.
Dependent Measures. The following measures were assessed
at three timepoints. We again assessed Multicultural Ideology (αs=
.85 at T1, .87 at T2 and .91 at T3).
6
Given the different study design,
Coldness toward Minorities (αs=.95 at T1, .95 at T2 and .98 at T3)
and Warmth toward Minorities (αs=.96 at T1, .98 at T2 and .98 at
T3) were assessed by aggregating ratings toward six racial/ethnic
groups rather than focusing on one group specifically (i.e., Native
American) as in Study 1. Essentialism was measured with the eight-
item Biological Essentialism Scale by Bastian and Haslam (2006;
e.g., “The kind of person someone is can be largely attributed to their
genetic inheritance”) rated from 1 (strongly disagree)to7(strongly
agree;αs=.88 at T1, .89 at T2 and .89 at T3).
Genetic Knowledge Covariate. Rather than using a self-report
measure, genetic knowledgein Study 2 was measured with a quiz-like
question aimed at more directly assessing this knowledge (i.e., in T1:
“In your estimation, how much geneticmaterial isshared between any
two humans?”rated from 0%–100%), with lower numbers serving as
an indicator of less genetic knowledge, given that it is scientifically
established that humans share 99.9% of their genetic material
(National Human Genome Research Institute, 2020). Importantly,
given the complexity of this single item question, it should be noted
that it may not correlate strongly with exposure to DNA coursework
as was measured instead in Study 1.
Results
As in Study 1, we report the full Bayesian and inferential results in
the Supplemental Material via OSF (https://osf.io/dsjy9/?view_only=
fb80e842aadd41fd9e2ef110dd180f3a; two-timepoints: Supplemental
Material 3 Tables S8–S12 [Bayesian] and Supplemental Material 4
Tables S13 and S14 [Inferential]; three-timepoints: Supplemental
Material 5 Tables S15–S19 [Bayesian] and Supplemental Material 6
Tables S20 and S21 [Inferential]). Although Study 1 provided evidence
that genetic knowledge does not affect our outcomes, in Study 2
analyses, we conservatively controlled for this covariate.
First, we conducted Bayesian repeated measures analysis and model
comparisons for models including main effects and interactions of
measurement time and condition on biological essentialism, support
for multiculturalism, warm, and cold feelings toward racial/ethnic
minorities. We then analyzed the effects of each variable and of each
interaction separately using the exclusion BF. This analysis (see
Appendix Table A2) showed that our data were about 4 (for cold
toward minorities) to 5 (for essentialism, multiculturalism and warmth
toward minorities) times more likely under models that excluded the
interaction between measurement time and condition than under models
with this interaction. The data were about 1.5 (for multiculturalism) and
3 (for essentialism and warmth toward minorities) times more likely
under models that excluded the main effect of condition than under
models with these effects, with the exception of coldness toward
minorities, for which the result was inconclusive; and about 2 (for
multiculturalism), 4 (for essentialism), and 6 times (for cold toward
minorities) more likely for models that excluded the main effect of
measurement time, with the exceptionofwarmthtowardminorities,for
which the result was inconclusive. Although two results referring to main
effects were inconclusive, this analysis showed weak to substantial
evidence that measurement time and condition, examined separately and
in a two-way interaction, did not predict essentialism, support for
multiculturalism, warm feelings toward minorities and cold feelings
toward minorities. Moreover, we again found weak to substantial
evidence that genetic knowledge does not affect these outcomes.
To follow-up, we repeated the analysis for models including the
expected percentage of minority DNA and its two-way and three-way
interactions with measurement time and condition. The exclusion
BF showed that the data were about 3 (for essentialism and warmth
toward minorities), 4 (for multiculturalism), and 10 (for coldness
toward minorities) times more likely for models excluding the three-
way interaction between measurement time, condition, and the
expected percentage of minority DNA, indicating substantial to
strong evidence that this interaction was unrelated to our outcomes
(Jarosz & Wiley, 2014).
Then, we repeated the analysis for models including the actual
percentage of minority DNA and its two-way and three-way
interactions with measurement and condition. The exclusion BF (see
Appendix Table A2) showed that the data were about 2 (for
essentialism and multiculturalism) and 3 (for coldness toward
minorities) times more likely for models excluding the three-way
interaction between measurement time, condition, and the actual
percentage of minority DNA, indicating weak evidence that this
interaction was unrelated to these outcomes. However, for warm
feelings toward minorities, the result was inconclusive, tending
toward very weak support for the alternative hypothesis.
Finally, we repeated the analysis for models including the
expected and the actual percentage of minority DNA and their two-
way, three-way, and four-way interactions with measurement and
condition. The exclusion BF (see Appendix Table A2) showed that
for all outcomes, the data were about twice (i.e., 1.5–2.6 times) more
likely for models excluding the four-way interaction between
measurement, condition, actual percentage, and expected percent-
age, indicating weak evidence that this interaction was unrelated to
our outcomes.
In sum, similar to Study 1, the results overwhelmingly pointed to a
lack of consistent effects—of any of the tested variables—on our
outcomes. Moreover, these results generally converged with inferential
analyses (see https://osf.io/dsjy9/?view_only=fb80e842aadd41fd9e2e
f110dd180f3a; two-timepoints: Supplemental Material 4 Tables S13
and S14 and three-timepoints: Supplemental Material 6 Table S20
and S21).
Discussion
Contrary to the two leading hypotheses, Bayesian analyses on two
separate highly controlled repeated-measurements experiments
among White/European Americans suggest that DNA ancestry
results may not have a significant impact on majority-group member’s
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6
Additional measures were included for separate articles on identity and
well-being.
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attitudes and feelings toward other racial/ethnic groups. Importantly,
while inferential tests can only indicate that “we do not know,”
Bayesian analyses can provide additional evidence that there are
indeed no effects. Thus, we ran Bayesian statistics and additionally
computed BFs to assess the strength of evidence for the lack of effect.
This absence was maintained regardless of whether the results were
bogus or real, the format of the results, the population we assessed
(i.e., student and largely nonstudent), the extent to which their results
were unexpected, and their degree of genetic knowledge. Importantly
too, this lack of an effect was maintained regardless of the unexpected
genetic ancestries participants received (i.e., Native American, sub-
Saharan African, East Asian).
At first glance, the absence of an impact of DNA ancestry results
on any of our outcomes may be surprising. However, our findings
appear to overlap with previous related research (see S. S. Lee, 2013;
Nelson, 2016;Roth et al., 2020;Scully et al., 2016;Shim et al.,
2018). Perhaps most importantly, although the only previous
experiment that we are aware of that involved real tests used several
methods that make it difficult to disentangle the impact of receiving
the DNA ancestry results specifically, this study also found no main
effect on people’s level of essentialism, regardless of their specific
DNA results and their expectations of their results (Roth et al.,
2020). Instead, they found a decrease in essentialism following
DNA ancestry results only among those who are higher in their
genetic knowledge. Because we used Bayesian analyses, replicated
our effects across two different experimental paradigms, used a
variety of methods to isolate the influence of receiving one’s DNA
ancestry results from confounding factors (e.g., the act of taking a
test, information about their genetic relatives, the opinions of others)
and used two different measures of genetic knowledge, we provide
new evidence that the degree of knowledge about genetics is
unlikely to influence responses to DNA ancestry results, at least in
the present context and population. Our findings also appear
consistent with additional research using nonexperimental methods
(see Hochschild & Sen, 2015;Roth & Lyon, 2018;Scully et al.,
2016). More specifically, a survey of people’s retrospective
responses to their own DNA ancestry results suggests that only a
small percentage of people may fully embrace their new identities or
engage in meaningful new intergroup interactions (Roth & Lyon,
2018). Furthermore, studies using qualitative interviews (Scully et
al., 2016) and responses to vignettes (Hochschild & Sen, 2015) also
suggest that how people incorporate new ancestry information into
their lives may be much more subtle than previously assumed. Our
results are also consistent with research and theorizing suggesting
that genetic results may not be immediately accepted but, instead,
selectively considered alongside a variety of other information in
order to fit one’s identity goals (i.e., “affiliative self-fashioning”;
Nelson, 2008,2016).
Although our nonstudent participants are likely to be more
representative than traditional samples used in psychological
research, they are still not representative of White/European
Americans nor other majority groups (e.g., White Europeans).
Relatedly, aside from when referring this groups’genetic ancestry
specifically, we used the term “White/European Americans”
throughout given that “White”is generally used in the United
States to denote a person of European ancestry but that, without
“European”being explicitly noted as well, this term could refer to
those of Middle Eastern or North African descent (Humes et al.,
2011). While it is possible that this combined terminology could
be interpreted differently by different participants, it should be
noted that none of the main measures reported in this article (e.g.,
multicultural ideology) includes this term. Furthermore, while the
use of Native American DNA in Study 1 may have been critical to
this study’s believability (see Golbeck & Roth, 2012;Reardon &
TallBear, 2012), given that the threshold for being considered
non-White tends to be especially low when Black genetic material
is involved (i.e., “one drop rule,”hypodescent; see Ho et al.,
2011), it is important to keep in mind that receiving minimal
(approx. 5%) or moderate levels (approx 18%) of sub-Saharan
African DNA instead may have a larger impact on White/
European Americans. Alternatively, because people are more
likely to seek out desirable identities (Tajfel, 1981), it is also
possible that receiving sub-Saharan African DNA would have
been more readily discounted by White/European Americans (see
Panofsky & Donovan, 2019). While Study 2 provides some
insight that receiving any type of genetic ancestry information
(e.g., sub-Saharan African, Asian) is unlikely to shift attitudes
toward racial/ethnic minorities, future research that attempts to
explicitly alter White/European Americans’degrees of sub-
Saharan African DNA, rather than Native American, would help
clarify these conflicting hypotheses. Importantly too, it is unlikely
that our results would generalize to test-takers that are not
considered White/European American and especially those whose
genetic lineages have been dramatically impacted by rape,
extermination, and enslavement (e.g., Black Americans, Mexican
Americans; see Blanchard et al., 2019;Galeano et al., 1997;
Nelson, 2008,2016) and, thu s, research on non-White/European
American populations is also needed. Furthermore, given that the DNA
ancestry testing was done as part of an in-class activity that required
students to receive their results within the same general time frame, there
was no comparison group at Time 3 who had not received their results
already. Importantly too, because the in-class activity required that all
timepoints be completed within the span of a semester, the T3 measure
occurred just 2 weeks after receiving one’s results and, importantly, such
ancestry information may take moretimetobecomepsychologically
integrated (see Nelson, 2008,2016). Relatedly, it is important to note
that most DNA testing platforms offer a myriad of optional features
beyond the ancestry breakdown (e.g., health information, genetic
relatives). In the present study, our aim was to isolate the effect of
receiving the latter. However, while this increased the studies’internal
reliability, it may have weakened the generalizability of our results to
some consumers’experience. Finally, it is also important to note that the
genetics knowledge question in Study 2 was accidentally phrased in
terms of how interspecies overlap (rather than interhuman) tends to be
described and, instead, that using the phrasing “unique genetic variants”
mayhavebeenmoreclear.
It has been widely speculated that receiving your own precise
DNA ancestry profile will have a major impact on thoughts and
feelings about race/ethnicity and, in turn, that this may impact racial
tolerance. Moreover, because of the increasing ubiquity of these
tests, it is also argued that receiving their results can re-shape society
more broadly (see Hochschild & Sen, 2015). Indeed, given that
many individuals share their results with their friends and relatives,
including on social media (e.g., YouTube; Marcon et al., 2021), and
that TV series (e.g., Finding Your Roots) and advertisements related
to ancestry often emphasize the test-takers’results, any potential
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FINDING YOUR ROOTS 7
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impact of these tests could extend far beyond the individuals
receiving their results. For instance, there is a potential for these tests
to renew the outdated perception that “race”is a useful description of
genetic variation (Cornell & Hartmann, 2006) and to impact policy
decisions such as those around identity-based claims to citizenship
(Rose & Novas, 2005), tribal membership (TallBear, 2013) or,
even, affirmative action eligibility. In turn, in line with previous
suggestions (see Wagner et al., 2023), it is important to remember
that genetic ancestry is not definitive, that inferences continue to
evolve and that nongenetic evidence should simultaneously be
considered whenever possible (e.g., cultural connections). In
addition to continuing to build and disseminate interventions aimed
at genetic literacy (see Donovan et al., 2021), we urge DNA ancestry
testing companies and the media to indicate just how much DNA is
shared between any two humans (for a review, see Templeton, 2013,
also see Rosenberg et al., 2002), to emphasize the additional limitations
of these tests (see Bolnick et al., 2007;S. S. J. Lee et al., 2009;also
see Wagner et al., 2023), and to avoid using genetic ancestry
interchangeably with terms such as race and ethnicity (see National
Academies of Sciences, Engineering, and Medicine (NASEM) Report,
2023;alsoseeWagner et al., 2023). We also recommend that future
experiments test the consequences of presenting DNA ancestry
information in various new ways, how responses to this DNA ancestry
results may change over longer periods of time and to examine how
racial/ethnic minority groups may be differentially impacted by
receiving such information. Finally, cross-cultural replications are
needed to identify the universality versus cultural-specificity of our
results among majority-group members. However, at present, our
results suggest that neither the considerable concern—nor intense
enthusiasm—for these tests’potential to alter majority groups’
thoughts and feelings about other racial/ethnic groups is warranted.
Yet, as racial (in)tolerance is a broad construct, of which we only
operationalized certain facets (feelings, multiculturalism, essentialism),
future research may still find effects on other types of measures.
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Appendix
Main Tables and Figures
Figure A1
Example of Color Scheme, Font, and Graphics (Study 1)
Note. See the online article for the color version of this figure.
(Appendix continues)
10 KIMEL ET AL.
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Figure A2
Examples of DNA Ancestry Results (Study 1)
Note. DNA =Deoxyribonucleic acid. See the online article for the color version of
this figure.
(Appendix continues)
FINDING YOUR ROOTS 11
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Table A1
Bayesian Effect Analysis (Study 1)
Model DV Moderator Effects P(inc) P(incl|data) BF
excl
Two-way
interactions
Multiculturalism Measurement .400 .128 6.648
Condition .400 .279 2.493
Measurement ×Condition .200 .024 1.523
Warmth toward Native
Americans
Measurement .400 .137 6.249
Condition .400 .339 1.930
Measurement ×Condition .200 .006 7.607
Cold toward Native
Americans
Measurement .400 .128 6.736
Condition .400 .381 1.604
Measurement ×Condition .200 .007 6.441
Three-way
interactions
Multiculturalism Expected % of native
DNA
Measurement .263 .004 7.743
Condition .263 .131 2.469
Expected % of native DNA .263 .011 2.405
Measurement ×Condition .263 .479 .367
Measurement ×Expected % of native DNA .263 .950 .002
Condition ×Expected % of native DNA .263 .222 1.963
Measurement ×Condition ×Expected % of
native DNA
.053 .011 14.847
Genetic knowledge Measurement .263 .122 6.849
Condition .263 .283 2.231
Genetic knowledge .263 .408 1.254
Measurement ×Condition .263 .028 1.736
Measurement ×Genetic knowledge .263 .020 3.486
Condition ×Genetic knowledge .263 .064 2.336
Measurement ×Condition ×Genetic knowledge .053 .000 10.506
Warmth toward Native
Americans
Expected % of native
DNA
Measurement .263 .132 6.329
Condition .263 .267 2.647
Expected % of native DNA .263 .280 2.410
Measurement ×Condition .263 .007 6.613
Measurement ×Expected % of native DNA .263 .029 1.504
Condition ×Expected % of native DNA .263 .020 4.392
Measurement ×Condition ×Expected % of
native DNA
.053 .000 9.700
Genetic knowledge Measurement .263 .153 5.431
Condition .263 .315 1.984
Genetic knowledge .263 .234 3.017
Measurement ×Condition .263 .007 7.373
Measurement ×Genetic knowledge .263 .008 7.947
Condition ×Genetic knowledge .263 .055 1.359
Measurement ×Condition ×Genetic knowledge .053 .000 4.578
Cold toward Native
Americans
Expected % of native
DNA
Measurement .263 .181 4.351
Condition .263 .323 1.960
Expected % of native DNA .263 .272 2.539
Measurement ×Condition .263 .019 3.608
Measurement ×Expected % of native DNA .263 .013 4.538
Condition ×Expected % of native DNA .263 .026 3.622
Measurement ×Condition ×Expected % of
native DNA
.053 .000 8.178
Genetic knowledge Measurement .263 .118 7.343
Condition .263 .379 1.507
Genetic knowledge .263 .397 1.387
Measurement ×Condition .263 .008 7.148
Measurement ×Genetic knowledge .263 .010 5.321
Condition ×Genetic knowledge .263 .044 3.788
Measurement ×Condition ×Genetic knowledge .053 .000 8.656
Note.P(incl) refers to the prior inclusion probability, and P(incl|data) refers to the posterior inclusion probability. BF
excl
>1 indicates evidence in favor
of H0 for the specific predictor, BF
excl
=1 indicates inconclusive evidence, and BF
excl
<1 indicates evidence in favor of H1. In all analyses, the exclusion
Bayes factor was obtained by comparing models that contain the effect to equivalent models stripped of the effect. DV =dependent variable; DNA =
Deoxyribonucleic acid; BF =Bayes factor.
(Appendix continues)
12 KIMEL ET AL.
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Table A2
Bayesian Effects Analysis (Study 2)
DV Moderator Effects P(inc) P(incl|data) BF
excl
Biological
essentialism
Measurement .400 .213 3.646
Condition .400 .237 3.171
Genetic knowledge .500 .290 2.447
Measurement ×Condition .200 .010 4.810
Multiculturalism Measurement .400 .283 2.452
Condition .400 .394 1.477
Genetic knowledge .500 .313 2.200
Measurement ×Condition .200 .025 4.546
Warmth toward
minorities
Measurement .400 .445 1.193
Condition .400 .255 2.835
Genetic knowledge .500 .226 3.418
Measurement ×Condition .200 .023 5.050
Cold toward
minorities
Measurement .400 .133 6.417
Condition .400 .515 .909
Genetic knowledge .500 .211 3.747
Measurement ×Condition .200 .017 4.101
Biological
essentialism
Expected % of minority DNA Measurement .263 .212 3.566
Condition .263 .225 3.256
Genetic knowledge .500 .315 2.175
Expected % of minority DNA .263 .247 2.843
Measurement ×Condition .263 .013 4.752
Measurement ×Expected % of Minority DNA .263 .021 3.067
Condition ×Expected % of Minority DNA .263 .032 2.091
Measurement ×Condition ×Expected % of Minority DNA .053 .000 2.606
Actual % of minority DNA Measurement .263 .175 4.587
Condition .263 .221 3.332
Actual % of minority DNA .263 .282 2.387
Genetic knowledge .500 .334 1.996
Measurement ×Condition .263 .011 4.442
Measurement ×Actual % of Minority DNA .263 .014 4.392
Condition ×Actual % of Minority DNA .263 .032 2.152
Measurement ×Condition ×Actual % of Minority DNA .053 .000 1.628
Actual % ×Expected % of
Minority DNA
Measurement .108 .170 4.538
Condition .079 .195 3.268
Genetic knowledge .347 .340 1.750
Expected % of minority DNA .108 .239 2.581
Actual % of minority DNA .108 .273 2.160
Measurement ×Condition .311 .018 4.449
Measurement ×Expected % of Minority DNA .301 .023 3.494
Measurement ×Actual % of Minority DNA .301 .023 3.729
Condition ×Expected % of minority DNA .311 .071 1.473
Condition ×Actual % of Minority DNA .311 .061 1.941
Expected % of Minority DNA ×Actual % of Minority DNA .301 .070 1.847
Measurement ×Condition ×Expected % of Minority DNA .118 .000 3.078
Measurement ×Condition ×Actual % of Minority DNA .118 .001 1.401
Measurement ×Expected % of Minority DNA ×Actual % of
Minority DNA
.116 .001 2.519
Condition ×Expected % of Minority DNA ×Actual % of
Minority DNA
.118 .006 1.136
Measurement ×Condition ×Expected % of Minority
DNA ×Actual % of Minority DNA
.006 .000 2.611
Multiculturalism Expected % of minority DNA Measurement .263 .273 2.413
Condition .263 .345 1.484
Genetic knowledge .500 .340 1.938
Expected % of minority DNA .263 .579 .471
Measurement ×Condition .263 .033 4.395
Measurement ×Expected % of Minority DNA .263 .040 5.479
Condition ×Expected % of Minority DNA .263 .116 2.192
Measurement ×Condition ×Expected % of Minority DNA .053 .000 3.572
Actual % of minority DNA Measurement .263 .261 2.646
Condition .263 .308 1.983
Genetic knowledge .500 .358 1.793
Actual % of minority DNA .263 .305 2.031
Measurement ×Condition .263 .027 3.914
Measurement ×Actual % of Minority DNA .263 .022 4.769
(table continues)
(Appendix continues)
FINDING YOUR ROOTS 13
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Table A2 (continued)
DV Moderator Effects P(inc) P(incl|data) BF
excl
Condition ×Actual % of Minority DNA .263 .058 1.925
Measurement ×Condition ×Actual % of Minority DNA .053 .000 1.894
Actual % ×Expected % of
minority DNA
Measurement .114 .236 2.708
Condition .114 .263 1.877
Genetic knowledge .500 .386 1.591
Actual % of minority DNA .114 .217 2.097
Expected % of minority DNA .114 .386 .624
Measurement ×Condition .299 .041 3.820
Measurement ×Actual % of Minority DNA .299 .042 4.072
Measurement ×Expected % of Minority DNA .299 .060 3.762
Condition ×Actual % of Minority DNA .299 .106 1.772
Condition ×Expected % of Minority DNA .299 .136 1.924
Actual % of Minority DNA ×Expected % of Minority DNA .299 .243 .808
Measurement ×Condition ×Actual % of Minority DNA .114 .001 1.864
Measurement ×Condition ×Expected % of Minority DNA .114 .001 2.961
Measurement ×Actual % of Minority DNA ×Expected % of
Minority DNA
.114 .002 2.400
Condition ×Actual % of Minority DNA ×Expected % of
Minority DNA
.114 .014 1.458
Measurement ×Condition ×Actual % of Minority
DNA ×Expected % of Minority DNA
.006 .000 1.837
Warmth toward
Minorities
Expected % of minority DNA Measurement .263 .435 1.163
Condition .263 .238 2.832
Genetic knowledge .500 .249 3.015
Expected % of minority DNA .263 .282 2.219
Measurement ×Condition .263 .029 5.068
Measurement ×Expected % of Minority DNA .263 .033 4.938
Condition ×Expected % of Minority DNA .263 .065 1.293
Measurement ×Condition ×Expected % of Minority DNA .053 .000 3.360
Actual % of minority DNA Measurement .263 .359 .857
Condition .263 .219 3.040
Genetic knowledge .500 .302 2.312
Actual % of minority DNA .263 .428 .512
Measurement ×Condition .263 .043 4.518
Measurement ×Actual % of Minority DNA .263 .304 .857
Condition ×Actual % of Minority DNA .263 .075 2.475
Measurement ×Condition ×Actual % of Minority DNA .053 .006 .851
Actual % ×Expected % of
Minority DNA
Measurement .114 .279 .838
Condition .114 .161 3.053
Genetic knowledge .500 .331 2.022
Actual % of minority DNA .114 .248 .529
Expected % of minority DNA .114 .174 1.831
Measurement ×Condition .299 .067 4.831
Measurement ×Actual % of Minority DNA .299 .386 .709
Measurement ×Expected % of Minority DNA .299 .118 3.491
Condition ×Actual % of Minority DNA .299 .135 2.147
Condition ×Expected % of Minority DNA .299 .223 .692
Actual % of Minority DNA ×Expected % of Minority DNA .299 .315 .862
Measurement ×Condition ×Actual % of Minority DNA .114 .017 .807
Measurement ×Condition ×Expected % of Minority DNA .114 .004 3.172
Measurement ×Actual % of Minority DNA ×Expected % of
Minority DNA
.114 .015 2.908
Condition ×Actual % of Minority DNA ×Expected % of
Minority DNA
.114 .021 1.706
Measurement ×Condition ×Actual % of Minority
DNA ×Expected % of Minority DNA
.006 .000 1.485
Cold toward
Minorities
Expected % of minority DNA Measurement .263 .127 6.340
Condition .263 .137 .961
Genetic knowledge .500 .253 2.950
Expected % of minority DNA .263 .128 1.109
Measurement ×Condition .263 .042 3.133
Measurement ×Expected % of Minority DNA .263 .038 3.467
Condition ×Expected % of Minority DNA .263 .725 .093
Measurement ×Condition ×Expected % of Minority DNA .053 .001 10.250
(table continues)
(Appendix continues)
14 KIMEL ET AL.
Template Version: 27 December 2022 ▪8:51 pm IST XAP-2023-0018_format_final ▪26 July 2023 ▪2:46 pm IST

Received February 6, 2023
Revision received May 31, 2023
Accepted June 13, 2023 ▪
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Table A2 (continued)
DV Moderator Effects P(inc) P(incl|data) BF
excl
Actual % of minority DNA Measurement .263 .148 5.531
Condition .263 .469 .991
Genetic knowledge .500 .238 3.202
Actual % of minority DNA .263 .184 4.136
Measurement ×Condition .263 .022 3.763
Measurement ×Actual % of Minority DNA .263 .012 3.348
Condition ×Actual % of Minority DNA .263 .045 2.199
Measurement ×Condition ×Actual % of Minority DNA .053 .000 3.142
Actual % ×Expected % of
Minority DNA
Measurement .114 .137 5.563
Condition .114 .139 .982
Genetic knowledge .500 .268 2.735
Actual % of minority DNA .114 .193 3.093
Expected % of minority DNA .114 .121 1.314
Measurement ×Condition .299 .044 3.760
Measurement ×Actual % of Minority DNA .299 .029 2.779
Measurement ×Expected % of Minority DNA .299 .041 3.964
Condition ×Actual % of Minority DNA .299 .112 2.140
Condition ×Expected % of Minority DNA .299 .680 .119
Actual % of Minority DNA ×Expected % of Minority DNA .299 .101 2.448
Measurement ×Condition ×Actual % of Minority DNA .114 .001 2.958
Measurement ×Condition ×Expected % of Minority DNA .114 .003 2.634
Measurement ×Actual % of Minority DNA ×Expected % of
Minority DNA
.114 .001 2.436
Condition ×Actual % of Minority DNA ×Expected % of
Minority DNA
.114 .015 2.063
Measurement ×Condition ×Actual % of Minority DNA ×
Expected % of Minority DNA
.006 .000 2.033
Note.P(incl) refers to the prior inclusion probability, and P(incl|data) refers to the posterior inclusion probability. BF
excl
>1 indicates evidence in favor
of H0 for the specific predictor, BF
excl
=1 indicates inconclusive evidence, and BF
excl
<1 indicates evidence in favor of H1. In all analyses, the exclusion
Bayes factor was obtained by comparing models that contain the effect to equivalent models stripped of the effect. DV =dependent variable; DNA =
Deoxyribonucleic acid; BF =Bayes factor.
FINDING YOUR ROOTS 15
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