spectrum spring 2016 vol 6 (3)
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Volume 6 (3)
Spring 2016
SPECTRUMJournal of Student Research
at Saint Francis University
SPECTRUM 6 (3) 2
Table of Contents
“Women Didn’t Kill this Way”: 3
Sharp Objects and the Subversion of Femininity and Motherhood
Tara L. Fritz; Robin L. Cadwallader
Media Coverage of 2016 Presidential Primary Debates: 8
Information or Infotainment?
Amanda N. Schiavo; Patrick G. Farabaugh
Effects of Food Association on Color Preference 18
Sarah E. Polito; Alyson T. Pritts; Marnie L. Moist
2015 Office of Student Research Awards for Research Excellence 30
Call for papers 32
(Student authors’ names underlined.)
Faculty Editors: Balazs Hargittai Grant Julin
Professor of Chemistry Assistant Professor of Philosophy
[email protected] [email protected]
Student Editorial Board: Allison Bivens ’12 Kayla Brennan
Morgan Dutrow Hayden Elliott
Cathleen Fry Eric Horell ’13
Paul Johns ’07 Elise Lofgren ‘14
Sarah McDonald Jonathan Miller ’08
Steven Mosey ‘14 Morgan Onink
Miranda Reed Hannah Retherford
William Shee Margaret Thompson
Stephanie Wilson Staci Wolfe
Managing Designer: Grace McKernan
Cover: Photo by Grace McKernan
SPECTRUM 6 (3) 3
“Women Didn’t Kill this Way”:
Sharp Objects and the Subversion of Femininity and Motherhood
[Research conducted for ENGL 407 (Principles of Literary Research, Theory, and Practice)]
Tara L. Fritz Robin L. Cadwallader
Literature & Languages Department Literature & Languages Department
School of Arts & Letters School of Arts & Letters
[email protected] [email protected]
A mother is supposed to be supportive,
nurturing, and willing to give up anything for her
children—at least that is what we have been taught.
However, in the novel Sharp Objects, Gillian Flynn
subverts the idea of motherhood as we know it.
Though Adora, the mother figure of the story,
seems at face value to be the perfect mother, she
has, in fact, been twisted and corrupted by the
traditional values forced upon her by the town in
which she grew up, so much so that rather than
protect her children, she deliberately harms them
through Munchausen by Proxy syndrome. Flynn’s
construction of this ultimately villainous
character—as well as the other unlikeable female
characters within the novel—shows her dedication
to revealing what she refers to as the capacity for
female violence. This violence is reflected not only
in Adora but also in her daughters, both her real
ones (Camille, Marian, and Amma) and the girls
she mentors (Ann and Natalie). While Amma ends
up following in her mother’s footsteps, and Marian
dies due to Adora’s meddling with her health,
Camille is punished and punishes herself because
she does not embrace the same traditional values as
Adora and Amma. Furthermore, Ann and Natalie
are murdered because they do not conform to
typical “feminine” values. Thus, it is clear that
Flynn wished to show not only the potential for
violence in women but also that this violence is
created through oppression and traditional,
patriarchal values.
Sharp Objects details the story of Camille
Preaker, a crime reporter living in Chicago who
must return to her small home town of Wind Gap,
Missouri, to cover the mysterious deaths of two
young girls. Her homecoming forces her to
confront not only her loveless mother, Adora, but
also her volatile half-sister Amma and the ghost of
her dead sister, Marian, that still lingers. Camille
carries demons of her own: She is a heavy drinker,
is slow to make real connections with the other
characters, and carries around the secret of her self-
harm, which manifests itself as words cut into her
skin. As more and more of the mystery of the
murders of Ann Nash and Natalie Keene unravels,
Camille learns some of her own family’s dark
secrets. It is revealed that Adora suffers from
Munchausen by Proxy syndrome, which causes her
to inflict illness on her children in such a way that
she looks like the heroic mother when she saves
them. The murders of Ann and Natalie (as well as
the death of Camille’s sister Marian) are initially
pinned on Adora. However, when Camille takes
Amma back to Chicago with her and a similar
murder occurs, it is revealed that Amma was the
real murderer all along.
It is evident that Flynn’s novel is full of
strongly-written female characters. However, Flynn
has been accused of misogyny for the creation of
these characters, as none of them are really likeable
or heroic (Burkeman). Even Camille, as the
narrator of Sharp Objects, is unreliable and rather
unlikeable. Ann, Natalie, and Amma are described
as violent girls: Ann killed her neighbor’s pet,
Natalie was known for biting people, and Amma is
the murderer of three girls (Ann, Natalie, and Lily
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Burke, a friend of Camille’s at the end of the
novel). Finally, Adora is the ultimate villain, a
mother who deliberately harms her children
without remorse. Even the other women in the
novel are portrayed unsympathetically, from
Adora’s catty friends to Camille’s boring high
school acquaintances to Amma’s classmates.
Some readers and critics have condemned
Flynn’s creation of so many unlikeable and
sometimes villainous female characters, even
accusing her of “peddling ‘misogynist caricatures’”
and writing from a place of hatred for women
(Burkeman). However, Flynn defends her
characters by insisting that she is “‘frustrate[d] . . .
[by] this idea that women are innately good,
innately nurturing’” (qtd. in Burkeman). Through
Sharp Objects (as well as her other two novels,
Dark Places and Gone Girl, which feature similarly
villainous and hard-to-like female characters),
Flynn attempts to reveal the long-hidden secret of
female violence. “Libraries are filled with stories
on generations of brutal men, trapped in a cycle of
aggression,” she explains; “I wanted to write about
the violence of women” (Flynn, “I Was”). Rather
than harming the cause of feminism by creating
villainous female characters, Flynn is, in fact,
opening a dialogue on the multi-faceted nature of
women and the ways in which a patriarchal, sexist
society can negatively impact these women. It is
through this corruption of traditional values that a
character like Adora comes about.
“Illness Sits Inside Every Woman”:
Representations of Traditional Values
The town of Wind Gap, Missouri, is a breeding
ground for traditional values. It is both a small
town, as well as a town in the southern United
States, which is often characterized as leaning
toward the conservative side: For instance, once,
while in Wind Gap, Camille comes across graffiti
that simply reads, “Stop the Democrats” (Flynn,
Sharp Objects 170). Camille is one of the only
characters who has managed to escape her
hometown, at least physically, which, upon her
return, enables her to see its nastiness for what it is,
a place that “demands utmost femininity in its
fairer sex” and imposes the impossible standards of
traditional values on its residents (13). As a result,
its women are trapped in a vicious cycle of high
school cattiness, where the pretty girls prey on the
poor, ugly, and/or less fortunate girls. These catty
women spring from a culture that creates an
impossible standard of femininity and encourages
women to destroy each other in pursuing this ideal;
this culture is upheld by what Lisa Cosgrove has
identified as “coercive mechanisms of surveillance,
discipline, punishment, and compulsory
heterosexuality” used to keep gender norms intact
(93). Camille’s old high school friends are the
typical, submissive wives, concerned with nothing
more than keeping a clean house and having as
many babies as possible: “I’ve always dreamed of a
big houseful of kids, that’s all I’ve ever wanted . . .
[W]hat’s so wrong with being a mommy?” wails
one of the women as they all complain about their
hardships as mothers (132). But these women are
not looking for little girls of their own; indeed, Ann
Nash’s sisters are described as “extraneous,” while
her own birth as the third daughter in the Nash
family is described as a “righteous dismay” (16).
Furthermore, one of Camille’s friends insists that
she will keep having children until she has a boy.
In a culture where women are regarded in such a
negative way, it is evidently difficult for women to
respect not only other women but also themselves.
In creating a town so steeped in traditional, sexist
culture, Flynn is making a statement about the
negative effects these sorts of environments can
have and in what ways they destroy and corrupt the
women who are raised in them.
In such an environment, it is not hard to see
how Adora’s unique brand of “mothering” came
about. Though Adora presents herself as a perfect
mother, she, in fact, represents the institution of
motherhood as it becomes corrupted through
patriarchy. She is a mother of the worst form—a
mother who, rather than nurture her children, harms
them for her own self-gratification, in one case
resulting in the death of one daughter, Marian.
Silvia Tubert portrays motherhood as a symptom of
the patriarchy; thus, to an outsider growing up in a
patriarchal society, Adora seems to be a “good
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mother,” or, in Andrea O’Reilly’s words, a “white,
middle class, married, stay-at-home” woman (21).
Adora is white, married, and so wealthy that neither
she nor her husband needs to work; in this
institutional view of motherhood, Adora seems to
be the perfect mother. However, Adora suffers
from Munchausen by Proxy syndrome. In cases
like these, mothers induce illnesses in their children
so that, by rushing them to the hospital, the mother
seems to be the hero in the children’s recovery
(Rand and Feldman).
Thus, within the narrative, Adora functions as
the “Mother,” a representative of the traditional
society in which she grew up as well as a
representation of how this society can twist and
corrupt the Mother figure. Looking at just her
biological daughters—Camille, Marian, and
Amma—we see that her strain of mothering has
served to corrupt them as well. Marian died at her
hands; Camille was driven to deviancy, so much so
that she ruins her feminine beauty by carving words
into her skin; and Amma, who grows to be a
similar Mother/leader figure under Adora’s
tutelage, becomes a murderer in the name of
keeping traditional values intact. Adora also has
connections with the two murdered girls, Ann and
Natalie; she tutored them and acted as a mother to
them, and they not only rejected her feminine
influence by being violent tomboys but also paid
for this rejection with their lives due to Amma’s
jealousy.
Amma is manipulative, twisted, and violent,
and, as Camille points out, “[a] child weaned on
poison [who] considers harm a comfort” (251).
Amma grew up fighting for Adora’s attention while
being constantly ill, thanks to Adora’s need to
poison her children. Amma continually feels
threatened by the presence of Ann and Natalie;
even Camille’s arrival makes her jealous. Amma
(and Camille) grew up starving for Adora’s
attention; however, unlike Camille, who turned her
anger into her own destruction, Amma takes her
jealousy out on other people. It is a common theme
throughout the novel that a woman could never be
capable of the kind of violence it would take to
murder two young girls. However, Angela
Woollacott identifies violence as “foundational to
patriarchy” (16), and though Woollacott is
speaking specifically of male violence, the female
violence portrayed in the novel can be seen as
another way in which Flynn shows women
becoming corrupted through traditional patriarchal
values. Amma learns this violence through none
other than her mother, Adora. The murderer, even
before she is known, is described as “[a] woman
who wanted ultimate control . . . whose nurturing
instinct had gone awry . . . who resented strength in
females, who saw it as vulgar,” which concisely
describes both Adora’s and Amma’s mindsets in
relation to other women (232-33). It also is
significant that Adora is the first one arrested for
the murders of Natalie and Ann; even though
Amma is the real killer, she has learned her
mannerisms from Adora. Like Adora, Amma
represents the traditional values of Wind Gap and
women who are corrupted by them.
“Just Because They Were a Little Different”:
Representations of Deviance
Camille is the novel’s primary example of
deviance and how it is punished in a patriarchal
society. As a child, she was the one who rejected
her mother’s pills and concoctions, who rejected
Adora’s brand of love and left it to be inflicted on
her sister Marian. Rather than being directly
punished by the society around her, Camille begins
to destroy a symbol of her femininity: her beauty.
In reference to this, Camille observes, “Every time
people said I was pretty, I thought of everything
ugly swarming beneath my clothes” (156). Though
she leaves her face untouched, by carving words
into her body, she further rejects her mother, as
well as the culture in which she grew up. Many of
these words are related, in some way, to femininity:
bodice, lipstick, catfight, and girl are among the
over sixty references to Camille’s scars.
Furthermore, self-harm is not considered
culturally accepted and is often dismissed as
though it were not a real medical problem (Failler).
In some cases, it is described as “attention
seeking”; this signifies that Camille may have been
mirroring Amma’s desire for Adora’s undivided
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attention (Failler 14). However, she does not
receive this attention from Adora; in fact, her
mother often says how much she dislikes her. This
may be linked to Camille’s frequent rejection of
Adora’s medical treatments, the same that she
inflicted on Marian and Amma. “‘I wanted to love
you, Camille,’” Adora says to her, “‘[b]ut you were
so hard. Marian, she was so easy’” (Flynn, Sharp
Objects 238). Earlier, Camille observes that “Adora
hated little girls who didn’t capitulate to her
peculiar strain of mothering” (221). Thus, Camille
represents deviancy within the novel. Because she
is not able to be mothered by the Mother, she is
rejected by her and the community around her; in
turn, she rejects herself and her own femininity.
It is made clear throughout the narrative that
Ann Nash and Natalie Keene were murdered
because they were outspoken, sometimes violent
tomboys who disliked femininity. Ann was
“prettied up” before she died, and Natalie’s
fingernails were painted—both are signs of the
control that Amma wished to exhibit over them to
make them more feminine. When the girls
ultimately rejected this control, Amma had no
choice but to kill them. However, their rejection by
society did not begin with their deaths; Ann and
Natalie were teased and tortured by Amma and her
clique of pretty girls long before they were
murdered. They were seen as outsiders, deviants of
society’s expectations for little girls. Andrea Nicki,
writing about the cultural rejection of those with
mental illnesses, states that “a woman who displays
aggression and ambition, and is not feminine, risks
being labelled ‘mentally ill’” or, in this particular
case, deviant (81). Nicki further describes
“conventional female behavior” as including
“quietness, self-effacement, and cautiousness” (90).
Ann and Natalie both clearly break from these
traditional female roles. Ann is described as being
smart and outspoken; at times, she is even violent:
She is accused of killing a neighbor’s pet and stabs
Natalie with a needle during a sewing project.
Natalie is also known for being violent: Her family
is forced to move to Wind Gap after she injures
another girl with scissors. However, both girls are
extremely intelligent and outspoken, despite being
known as troublemakers. As Natalie’s brother
reflects in an interview conducted by Camille on
the murders, “‘It’s like they picked the two girls in
Wind Gap who had minds of their own and killed
them off’” (207). Thus, Ann and Natalie are killed
by Amma not only because she is jealous of the
attention they are receiving from Adora, but also
because the two girls represent a deviancy from the
cultural expectations for women.
Conclusion: “To Refuse Has so Many More
Consequences than Submitting”
Flynn’s Sharp Objects contains some of her
most destructive, unlikeable female characters to
date. Adora represents what could be the perfect
mother—white, wealthy, stay-at-home, and raised
in and devoted to maintaining a traditional
environment; however, this typically feminine and
caring character has been corrupted by the
impossible expectations society has set for her. Her
youngest daughter, Amma, grows up to mirror her,
carrying out punishments in the name of keeping
traditions intact. Though Marian dies under
Adora’s care, Camille’s rejection of Adora’s
mothering leads her to, in some ways, punish
herself for not conforming to tradition while also
retroactively allowing her to see the dangers of
following these traditions. Finally, Ann Nash and
Natalie Keene fall victim to the concept of
destructive motherhood: Because they did not
conform to the town of Wind Gap’s cultural values,
they were punished with death.
As Camille wryly observes, “‘Some women aren’t
made to be mothers. And some women aren’t made
to be daughters’” (112). In the context of the real
world, Adora should have never been a mother:
She is destructive, unloving, and utterly corrupt.
However, as a literary character, Adora serves as a
warning for what our traditional cultural values can
do. Without patriarchal values that define mothers
as nothing more than caring, nurturing, and
powerless above all, there would be no need to
display the violence of women as something hidden
just beneath a mother’s smile.
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Works Cited Burkeman, Oliver. “Gillian Flynn on Her Bestseller Gone
Girl and Accusations of Misogyny.” Guardian. The
Guardian, 1 May 2013. Web. 11 Nov. 2015.
Cosgrove, Lisa. “Feminism, Postmodernism, and
Psychological Research.” Hypatia 18.3 (2003): 85-112.
Project Muse. Web. 10 Nov. 2015.
Failler, Angela. “Narrative Skin Repair: Bearing Witness to
Representations of Self-Harm.” English Studies in Canada
34.1 (2008): 11-28. Project Muse. Web. 10 Nov. 2015.
Flynn, Gillian. “I was not a nice little girl . . .” Gillian Flynn.
Gillian Flynn, n.d. Web. 11 Nov. 2015.
---. Sharp Objects. New York: Broadway, 2006. Print.
Nicki, Andrea. “The Abused Mind: Feminist Theory,
Psychiatric Disability, and Trauma.” Hypatia 16.4 (2001):
80-104. Project Muse. Web. 9 Nov. 2015.
O’Reilly, Andrea. “Outlaw(ing) Motherhood: A Theory and
Politic of Maternal Empowerment for the Twenty-first
Century.” Hecate 36.1 (2010): 17-29. Literature Resource
Center. Web. 11 Nov. 2015.
Rand, Deirdre C., and Marc D. Feldman. “An Exploratory
Model for Munchausen by Proxy Abuse.” International
Journal of Psychiatry in Medicine 31.2 (2001): 113-26.
Proquest. Web. 15 Nov. 2015.
Tubert, Silvia. “The Deconstruction and Construction of
Maternal Desire: Yerma and Die Frau ohne Schatten.”
Mosaic 26.3 (1993): 69-88. Literature Resource Center.
Web. 10 Nov. 2015.
Woollacott, Angela. “A Feminist History of Violence:
History as a Weapon of Liberation?” Lilith: A Feminist
History Journal 16 (2007): 1-16. Literature Resource
Center. Web. 9 Nov. 2015.
Tara Fritz ('17) is an English major with minors in
French, Women's Studies, and Social
Responsibility. She is President of the Literary
Club, works as a tutor at the Writing Center, and is
actively involved in her sorority, Theta Phi Alpha.
She is also a member of Sigma Tau Delta, the
English honors society. After graduation, she
hopes to obtain an MFA in Creative Writing and
one day become a published author.
SPECTRUM 6 (3)
8
Media Coverage of 2016 Presidential Primary Debates:
Information or Infotainment?
Amanda N. Schiavo Patrick G. Farabaugh
Communications Department Communications Department
School of Arts & Letters School of Arts & Letters
[email protected] [email protected]
After viewing the first Democratic debate and the third Republican debate of the 2016 U.S. presidential
primary race, the author conducted a content analysis. The author analyzed the debate statements made by
each of the parties’ two leading (in the polls and at the time) candidates, as well as media coverage of their
debate statements, specifically coverage by The New York Times, the Washington Post and the USA Today
in the 24 hours following these debates. The author sought to determine whether or not the immediate
post-debate media coverage of these three major news outlets was reflective - in content and
proportionality - of the candidates’ debate statements. The findings reveal that post-debate media
coverage of the candidates’ debate statements is not proportionally reflective of the content of their
statements.
Introduction
A relatively new component of the U.S.
presidency, presidential debates provide American
voters with a chance to compare political party
candidates. Porter (2012) claims that “the history of
presidential debates might be brief, but it is packed
with memorable moments: zingers and flubs,
triumphs and flops, and tons of backroom dish” (p.
S.4).
While radio presidential debates date back to
1948, the first televised presidential debate took
place in 1960 between John F. Kennedy and
Richard Nixon. This important debate provides an
example of the power of television compared to
that of radio. Although most of the individuals
listening to the broadcast over the radio thought
Nixon won, Kennedy’s charismatic, attractive
appearance won over the television viewers. “The
instant collective wisdom in 1960 was that Nixon
was undone by television. Polls showed that more
than half the voters based their decision on the
debates” (Porter, 2012, p. S.4). The visual of
Kennedy standing confidently, next to a perspiring
Nixon, was enough for Kennedy to capture the
presidency. This might not have been possible
without the televised presidential debate.
Presidential debates have dramatically
transformed since that first televised debate in
1960. Online journalism is the latest “disruptive
technology” that has affected debates between
aspiring politicians. For many voters, most of what
they learn about the presidential candidates comes
from political debates or the media coverage
following these events. "As messages running an
hour or longer, debates offer a level of contact with
candidates clearly unmatched in spot ads and news
segments. The debates offer the most extensive and
serious view of the candidates available to the
electorate" (Jamieson in Benoit and Currie, 2001,
p. 28). Each debate viewer interprets a different
message from the candidates’ debate statements
than another viewer. With the media coverage
following the debates unable to capture significant
details for every viewer, individuals who base their
knowledge of the candidates on news coverage are
susceptible to risk of disproportionate coverage of
content.
Along with the millions of viewers watching
presidential debates, millions of other voters seek
information solely through the media coverage of
debates. “A study of the first 1976 presidential
SPECTRUM 6 (3)
9
debate revealed that, of the viewers who were
surveyed immediately after the debate (without
exposure to media commentary), about twice as
many thought that (Jimmy) Carter had done a better
job. Those who were surveyed after seeing post-
debate media commentary thought, again by
approximately a two-to-one ratio, that (Gerald)
Ford had done the better job” (Lang and Lang in
Benoit & Currie, 2001, p. 29). This study
highlights the significance of media coverage as an
influence on voters.
Identifying the significance of presidential
debates and media coverage of them, the author
conducts a content analysis of three major new
outlets’ reporting on candidates’ debate statements.
The results reveal that post-debate media coverage
of candidates’ debate statements is not
proportionally reflective of the content of their
statements. In terms of topic, the policy and the
proportion of character comments in media reports
on the debates were significantly higher than the
actual proportion of policy and character comments
in the debates.
Review of Literature
Presidential debates have been analyzed in
previous studies on the basis of “two key
dimensions: functions (positive and negative or
attack messages) and topic (issues or policy along
with image or character)” (Benoit and Currie, 2001,
p. 29). However, Benoit and Currie’s (2001)
content analysis of the 1996 and 2000 presidential
debates is used to provide a framework for this
content analysis. The framework established by
Benoit and Currie’s (2001) regarding methodology
and findings was followed in the current author’s
study and revealed two trends (theme and topic) in
the history of media coverage of presidential
debates.
Functions. Benoit and Harthcock (1999)
performed a content analysis on the functions
within the first televised presidential debate
between Nixon and Kennedy. The analysis
concluded that there were 49% positive (acclaim)
debate statements, 39% attack (negative) debate
statements, and 12% defense debate statements (p.
341). However, Benoit and Harthcock did not
examine whether media coverage of debate
statements is proportionately reflective of the
content of the debate statements made by
candidates.
A content analysis performed by Reber and
Benoit (2001) furthered the research of the
accuracy of media coverage pertaining to
presidential debates. The study revealed that media
coverage of the 2000 presidential primary debates
highlighted attack (negative) and defense
statements disproportionately to the actual
candidates’ debate statements. Reber and Benoit’s
(2001) research on 25 presidential primary debates
from 1948 to 2000 revealed that 58% of the debate
statements were acclaims (positive), 31% were
attacks (negative), and 12% were defenses. When
compared to media coverage (newspaper), the
authors revealed that attacks (negative) were
disproportionately reported by over-representing
the coverage, 45% to 31%. Defenses were also
over-represented, 16% to 12%. Acclaims were
under-represented, 40% to 58%. Reber and
Benoit’s study suggested that further research be
conducted on the media coverage of presidential
debates.
Benoit and Currie’s (2001) content analysis
furthered the research of Reber and Benoit. Benoit
and Currie’s (2001) content analysis of the 1996
presidential debates revealed that 59% of debate
statements were allocated to positive (acclaim)
statements, 33% to attack debate statements, and
7% of the debate statements were defenses. The
functions of the 2000 presidential debate content
analysis found that positive (acclaim) debate
statements totaled 74%, attack (negative)
statements equaled 24%, and defense statements
equaled 2% (p. 34). In the 1996 news coverage,
Benoit and Currie (2001) found that while only
33% of debate statements were categorized as
attacks, 54% of the news coverage focused on
attacks. Defense statements, which made up 7% of
the debate statements, were also over-represented
by 4% in the news coverage (11% total in the news
coverage) (p. 34).
By contrast, acclaims were under-represented,
which is similar to the Reber and Benoit (2001)
SPECTRUM 6 (3)
10
study, at 35% in the news coverage to 59% in
debate statements. This trend was also evident in
Benoit and Currie’s (2001) analysis of the 2000
presidential debates. Comparing the debate
statements to newspaper feature stories, rather than
news coverage, attack and debate statements were
over-represented, 38% and 13% in the newspaper
features, to 24% and 2% in the debates,
respectfully. Acclaims, which accounted for 74%
of debate statements, were under-represented
(49%) in the newspaper features. Both, content
analyses identify a consistency in media coverage
to over-represent attack and defense statements,
while under-representing acclaim statements.
Topics. For the Nixon-Kennedy presidential
debates, Benoit and Harthcock (1999) performed
an analysis on the topics (policy and character) of
the candidates’ debate statements (p. 341). This
analysis revealed that 78% of the candidates’
statements were policy remarks, whereas 22% of
the candidates’ statements were character remarks.
Another study, conducted by Benoit, Blaney, and
Pier (1998), analyzed the topics in the Clinton-Dole
presidential debates. The authors found that the
candidates’ remarks featured a higher concern on
policy (72%) compared to character remarks
(28%). While the debates featured a trend in higher
policy remarks compared to character remarks, the
accuracy of media coverage was not explored in
these analyses.
Reber and Benoit (2001) conducted a content
analysis on the proportion of policy and character
remarks reported in newspaper coverage on two of
the 2000 presidential primary debates. Their
analysis did not reveal a significant difference
between the coverage on policy or character
remarks.
The content analysis performed by Benoit and
Currie (2001) supported their hypothesis that media
coverage would report more on character remarks
than policy remarks from the 1996 presidential
debates. “Policy remarks accounted for 72% of the
comments in the debate, but only 55% of reportage,
whereas character constituted 28% of the remarks
in the debate, but 45% of the reports on the debate”
(p. 34). This same hypothesis was not supported for
the media coverage of the 2000 presidential
debates. According to Benoit and Currie (2001), no
significant difference in the reporting of the topics
was apparent (p. 34).
Although there was not indisputable evidence
that the proportion of character comments in media
coverage is significantly higher than the actual
proportion of policy and character comments in
debates, the author did not disregard the 1996
findings of Benoit and Currie (2001). The author
chose to explore the themes and topics as explored
in Benoit and Currie’s (2001) content analysis in an
attempt to reveal the evolution toward
“infotainment” within the political journalism
industry.
Purpose. Overall, presidential debates have been
shown to have a significant impact on the opinions
of American voters. However, until Benoit and
Currie’s (2001) content analysis, the media
coverage of presidential debates was under-
researched regarding accuracy in reporting the
debate substance (specifically, theme and topic of
the candidates’ statements).
Using Benoit and Currie’s (2001) methodology,
this study builds on the research of media coverage
of presidential debates. The findings provide
evidence of a trend in the media coverage focusing
on the theme of candidates’ debate statements and
provide evidence suggesting that character remarks
are more heavily covered than policy remarks.
Hypotheses
As noted above, the author utilized the
methodology used by Benoit and Currie (2001).
Maintaining consistency with the content analyses
of the 1996 and 2000 presidential debates, the
author looks for a pattern in the media coverage of
presidential debates throughout history. The author,
therefore, also explores Benoit and Currie’s (2001)
two hypotheses:
“H1. The proportion of attacks and defenses
will be higher in media reports than in debates;
whereas, the proportion of acclaims will be lower
in media reports than in debates” (p. 31);
“H2. The proportion of character comments in
media reports on the debates will be significantly
SPECTRUM 6 (3)
11
higher (and policy comments lower) than the actual
proportion of policy and character comments in the
debates” (p. 31).
Methods
The 2016 presidential primary race featured 17
debates – 11 between the Republican candidates
and six among the Democratic Party challengers.
After viewing the first Democratic debate and the
third Republican debate, the author performed a
content analysis on the statements made by each of
the leading (in the pools and at the time)
candidates.
First, the author classified statements made by
the parties’ two leading candidates. Their
statements were categorized as acclaims, attacks or
defenses (defined below). Benoit and Currie (2001)
explain that together, they “serve as an informal
form of cost-benefit analysis: acclaims stress a
candidate’s own benefits, attacks identify
opponents’ costs, and defenses attempt to refute
alleged cost” (p. 32).
“Acclaims are utterances that portray the
candidate favorably. Attacks are utterances
that portray the opposing candidate
unfavorably. Defenses are utterances that
explicitly respond to prior attack on the
candidate” (Benoit and Currie, 2001, p. 33).
An example of an acclaim in the first
Democratic debate by the party’s leading
candidate, Hillary Clinton, would be: “I have a long
history of getting things done, rooted in the same
values I’ve always had” (The New York Times,
2015, p. 4). Clinton’s statement portrays herself as
a successful politician, whose values have been
consistent throughout her career. Bernie Sanders,
the second-place Democratic candidate, attacked
Clinton when he said: “First of all, she is talking
about, as I understand it, a no-fly zone in Syria,
which I think is a very dangerous situation. Could
lead to real problems” (The New York Times, 2015,
p. 9). Sanders attacks Clinton on the basis of her
statement supporting a plan to respond to Russian
President Vladimir Putin’s military maneuvers in
Syria. Clinton provides an example of a defense in
her statement, “I have been very consistent. Over
the course of my entire life, I have always fought
for the same values and principles, but … I do
absorb new information” (The New York Times,
2015, p. 4). Clinton defends herself against an
attack on her consistency in her values and
principles by stating that her values and principles
have not changed, but that she was provided with
new information that called for a different reaction.
These three themes were then classified into
two categories: policy and character.
“Policy remarks concern governmental
actions and problems amendable to such
action. Character remarks address
properties, abilities, or attributes of the
candidates (or parties)” (Benoit and Currie,
2001, p. 33).
The examples provided below were taken from
the “Transcript: Republican Presidential Debate
(2015).” The Republican Party’s leading candidate,
Donald Trump, said: “We’re reducing taxes to
15%. We’re bringing corporate taxes down,
bringing money back in, corporate inversions.”
This is an example of a policy acclaim (p. 5).
Trump’s statement concerns governmental action
through his proposed tax plan.
Ben Carson, the second leading Republican
candidate at the time: “I do, however, believe in
Reagan’s 11th
commandment, and will not be
engaging in awful things about my compatriots
here” (p. 3). This is a character acclaim.
Not all of the candidates’ debate statements fell
within the coding classifications of acclaims,
attacks, and defenses. Sanders’ statement: “I think
everybody is in agreement that we are a great
entrepreneurial nation” (The New York Times,
2015, p. 2) is not coded because it simply states
Sanders’ opinion. He is not stating this to portray
himself favorably, unfavorably, or defend himself
in any way.
A total word count was taken from the first
Democratic debate (Las Vegas, Nevada; October
13, 2015) for Clinton and Sanders, as well as for
Trump and Carson for the third Republican debate
(Boulder, Colorado; October 28, 2015). Each
SPECTRUM 6 (3)
12
candidates’ total word count was divided by his or
her total number of words spoken within each
theme (acclaims, attacks, and defenses). This
number was then multiplied by 100 to determine a
percentage of acclaims, attacks, and defenses. For
example, Carson spoke a total of 1,478 words in
the third Republican debate. One hundred and fifty-
eight words of the total 1,478 words he spoke were
defense statements. The author divided 158 by
1,478 and multiplied by 100 to conclude that
Carson spent 11% of his talking time delivering
defense statements. This method was replicated to
achieve the percentage of total policy and character
remarks for each candidate and policy and
character remarks within each theme (acclaims,
attacks, and defenses) for each candidate.
After the candidates’ debate statements were
collected and classified, the author analyzed media
coverage from three major U.S. news outlets
following the debates – The New York Times, the
Washington Post and the USA Today.
First, the statements within the news outlets’
articles discussing the parties’ two leading
candidates’ comments were identified. The author
only coded articles released from the three
previously mentioned news outlets in the 24 hours
following each debate. The author’s purpose of
restricting her content analysis of media coverage
to only the 24 hours following the debate was to
analyze the debate statements the news outlets
chose to present to voters right away.
Second, the author classified the candidates’
debate statements in the media articles into the
three categories used for the candidates’ debate
statements (acclaims, attacks, and defenses).
Third, the three categories (acclaims, attacks,
and defenses) were categorized into the two topics
(policy and character) - just as candidates’ debate
statements were classified.
The researcher then classified the media
coverage of the debate statements, including what
percentage of themes or idea units occurred in the
debates were reported in the media coverage. A
total word count was taken for each of the articles
from The New York Times, the Washington Post
and the USA Today. The total word count for each
article released in the 24 hours following the debate
was combined to determine the total number of
words published by each of the three major news
outlets respectfully. The total number of words by
each news outlet was then parsed down to the total
number of candidates’ statements printed within
each theme (acclaims, attacks, and defenses). The
word counts were then used to determine a
percentage of media coverage on acclaims, attacks,
and defenses for each candidate. For example, The
New York Times released a total of 2,521 words in
their three articles released in the 24 hours
following the third Republican debate. Out of the
2,521 words, The New York Times spent 104 words
(of 2,521 total words printed) reporting on Carson’s
defense statements.
The author divided 104 by 2,521 and multiplied
by 100 to conclude that The New York Times spent
4.13% of their total words reporting on Carson’s
defense statements. This method was replicated to
achieve the percentage of media coverage on the
total policy and character remarks for each
candidate and policy and character remarks within
each theme (acclaims, attacks, and defenses) for
each candidate. The author also determined the
percentage of media coverage on total policy and
character remarks for each candidate and policy
and character remarks within each theme (acclaims,
attacks, and defenses) for each candidate within
each article published by each of the three news
outlets.
Similar to the candidates’ debate statements,
not all of the media’s statements within the news
articles fell within the coding classifications
(acclaims, attacks, and defenses). In The New York
Times, “Republican candidates take sharp tone in
third debate (2015)”: “Mr. Rubio, a first-term
senator, had the best night of his campaign,
showing political talent that many insiders had long
seen in him” (p. 1) the author did not code this
statement because Rubio was not one of the two
Republican Party’s two leading candidates during
the polls of the third Republican debate.
Similarly, statements such as the one below
were not coded by the author because it offers the
author’s opinion on Clinton’s “progressive” claim
(p. 1). The statement is not one that Clinton made
herself. “It was a practiced line – so practiced that
SPECTRUM 6 (3)
13
she used it, somewhat awkwardly, a second time an
hour later” (Bruni, 2015, p. 1).
For the first Democratic debate coverage, the
author analyzed two articles released by The New
York Times, three by the Washington Post, and one
by the USA Today. For the third Republican debate
coverage, the author analyzed three articles
released by The New York Times, three released by
the Washington Post, and six released by the USA
Today.
The author was the only coder for this content
analysis. She used printed versions of the debate
transcripts and news stories to act as guides for
consistency in coding alike statements.
Results
Table 1. Functions of the first Democratic debate of the 2016
U.S. Presidential primary race.
The first hypothesis, that the proportion of
attacks and defenses will be higher in media reports
than in debates, was partially supported (H1 –
partially supported). Referring to Table 1, the
attack debate statements made by the two leading
Democratic candidates in the debates totaled 4%;
whereas, 28% of the media coverage of the
candidates’ statements were attacks. This indicates
an over-representation by the media of attacks.
Conversely, both acclaims and defenses were
under-represented in the post-debate media
coverage. The media coverage of acclaim debate
statements was only 16%, compared to the actual
25% of acclaims spoken by the candidates.
Similarly, 8% of post-debate media coverage of the
Democratic candidates’ statements was dedicated
to defenses; the actual percentage of defense
statements made by the Democratic candidates was
18%.
Table 2. Topics of the first Democratic debate of the 2016
U.S. Presidential primary race.
The second hypothesis, that media coverage
would report character comments in the debates
significantly higher (and policy comments lower)
than the actual proportion of policy and character
comments in the debates, was not supported (H2 –
not supported). Policy was over-represented in
post-debate media coverage for the first
Democratic presidential debate by the three news
outlets (46% to 40%). The percentage of policy
remarks reported in post-debate media coverage
was 46%, compared to 7% of character remarks.
Table 2 shows that policy remarks were more
highly reported than character remarks within each
of the three news outlets: 20% to 2% for The New
York Times, 7% to 5% for the Washington Post,
and 19% to 0% for the USA Today. The amount of
post-debate media coverage from the first
Top
Candidates
Acclaims Attacks Defenses Total
Words
Hillary
Clinton
825
(15%)
89
(2%)
600
(11%)
5,397
Bernie
Sanders
905
(20%)
94
(2%)
305
(7%)
4,561
The New
York Times
136
(6%)
333
(15%)
22
(1%)
2,198
Washington
Post
128
(10%)
- 29
(2%)
1,304
USA Today
- 76
(13%)
29
(5%)
565
Top Candidates Policy Character Total
Words
Hillary Clinton
903
(17%)
611
(11%)
5,397
Bernie Sanders 1,061
(23%)
243
(5%)
4,561
The New York Times 447
(20%)
44
(2%)
2,198
Washington Post
87
(7%)
70
(5%)
1,304
USA Today 105
(19%)
- 565
SPECTRUM 6 (3)
14
Democratic debate was greater on governmental
actions than on the properties or attributes of the
candidates.
Table 3. Functions of the third Republican debate of the 2016
U.S. Presidential primary race.
The data collected identifies that media
coverage of the first Democratic debate of the 2016
presidential primary season over-represents attacks,
while under-representing acclaims and defenses.
The analysis also indicates that the topics of policy
and character of the media coverage of this 2016
presidential debate focused more on policy remarks
than character remarks.
Similarly, attack debate statements were over-
represented proportionally (7%) in the post-debate
media coverage compared to the actual candidates’
attack debate statements (5%). However, acclaims
and defenses were under-represented in the media
coverage of the third Republican debate. Referring
to Table 3, acclaims constituted 34% of the two
leading Republican candidates’ statements,
whereas only 4.4% of the candidates’ acclaim
debate statements were reported in post-debate
media coverage. Likewise, defenses constituted
13% of the candidates’ debate statements, whereas
only 4% of the candidates’ defense debate
statements were covered in the post-debate media
coverage. Therefore, the findings reveal that post-
debate media coverage of the acclaims, attacks, and
defenses made by the candidates in the debates is
not proportionally reflective in the post-debate
news coverage.
Table 4. Topics of the third Republican debate of the 2016
U.S. Presidential primary race.
The second hypothesis, that media coverage
would report character comments in the debates
significantly higher (and policy comments lower)
than the actual proportion of policy and character
comments in the debates, was not supported.
Neither character nor policy remarks were over-
represented in post-debate media coverage by the
three news outlets combined. However, total policy
remarks were higher than character remarks. The
total percentage of policy remarks reported in all
three outlets’ post-debate stories was 9% compared
to 6.4% character remarks (see Table 4). There was
no specific indication that policy remarks were
more highly reported than character remarks within
each of the three news outlets, as evident in the
topic (policy and character) of the media coverage
of the first Democratic debate. Thus, the topic of
post-debate media coverage of the third Republican
debate was focused more on governmental actions
and problems than the properties or attributes of the
candidates, just it was in the first Democratic
debate media coverage.
Top
Candidates
Acclaims Attacks Defenses Total
Words
Donald
Trump
540
(26%)
107
(5%)
34
(2%)
2,053
Ben
Carson
117
(8%)
- 158
(11%)
1,478
The New
York Times
26
(1%)
153
(6%)
104 (4%) 2,521
Washington
Post
133
(3%)
46
(1%)
- 4,481
USA Today
11
(.4%)
- - 3,132
Top Candidates Policy Character Total
Words
Donald Trump
548
(27%)
133
(6%)
2,053
Ben Carson 240
(16%)
35
(2%)
1,478
The New York Times 183
(7%)
100
(4%)
2,521
The Washington Post
68
(2%)
111
(2%)
4,481
The USA Today - 11
(.4%)
3,132
SPECTRUM 6 (3)
15
Discussion
The results partially supporting H1 reveal a
trend in the over-representation of attacks in media
coverage of presidential debates. Both Reber and
Benoit’s (2001) study and Benoit and Currie’s
(2001) study of the media coverage of functions
(acclaims, attacks, and defenses) in presidential
debates revealed an over-representation of attack
statements made by candidates in coverage than
actual attack statements made in the debates.
Therefore, results in this study partially support H1,
which reveals a trend in the media coverage of
presidential debates to over-represent attack
statements made by candidates.
There are potential explanations as to why
attack statements are over-represented in media
coverage. First, attacks are more entertaining
because of the “conflictual” aspect (Benoit and
Currie, 2001, p. 36). Voters are intrigued by the
excitement that attacks offer when two candidates
disagree in presidential debates. Mindich (2005)
states that “if their [journalists] information is
boring, they will lose readers and viewers” (p. 41).
To add interest to their stories, journalists over-
represent these attacks between candidates.
Second, the 2016 primary season is one that
included three nontraditional candidates. Sanders is
a self-described socialist. Trump, a former host of
the reality game show, “The Apprentice,” is
president of the Trump Organization and founder
of the Trump Entertainment Resorts (“Donald
Trump biography,” p. 1). Carson is a retired
neurosurgeon and author. Trump and Carson have
no previous political experience. Clinton is the only
“traditional politician” whose statements were
collected for this content analysis.
The nontraditional candidates, especially
Trump, could potentially create a higher reportage
of infotainment. “The news media act as an arm of
the state and help it maintain power by
manipulating the nature of news to teach the public
which events, people, and ideas will be rewarded or
punished” (Lazaroiu, 2008, p. 106). Through the
over-representation of attack statements made by
candidates in the presidential debates, voters are
able to decide whether or not the behavior (attack
statement) is one to be rewarded or punished. For
example, Trump’s attack on Gov. John Kasich:
“But then his poll numbers tanked. He has got –
that is why he is on the end. And he got nasty. And
he got nasty.” This could influence voters to vote
for or against Trump based on his attack on the
character of Kasich (“Transcript: Republican
Presidential Debate (2015)”, 2015, p. 8). Therefore,
the media coverage could be providing a method
for voters to select favorable or unfavorable
candidates through agreement or disagreement with
the actions, statements, and behaviors of the
candidates in the presidential debates.
Acclaim and defense statements were under-
represented by media coverage of the first
Democratic and third Republican debate. However,
both these themes of statements are important part
of the process of selecting a candidate. Benoit and
Currie (2001) claim that “we need to know the
candidates’ strengths and their goals and proposals
(campaign promises) to make informed decisions
about the relative merits of presidential candidates”
(p. 36). The information, less interesting than attack
statements, falls under the category of positive and
neutral statements. However, Lazaroiu (2008)
states “positive or routine occurrences are rarely
news because, if things are okay, there is no need to
highlight them” (p. 106). Therefore, the media
coverage could cover less of these themes because
of their significance compared to attack statements.
As for the media coverage of the topics (policy
and character) of candidates’ remarks, Benoit and
Currie’s (2001) study of the 1996 presidential
debates found that the media over-represented the
coverage of character remarks compared to policy
remarks. However, their findings from the 2000
presidential debate revealed that there was no
difference in the proportion of policy and character
remarks reported in the media coverage. Benoit and
Currie (2001) state that the “findings are
inconsistent with the finding of Study 1 on the
1996 debates, but consistent with Reber and
Benoit’s (in press) finding on 2000 primary
debates” (p. 36). Neither policy remarks nor
character remarks were revealed to be over-
represented by the media coverage of the first
Democratic and the third Republican debate of the
2016 presidential primary race.
SPECTRUM 6 (3)
16
However, policy remarks were found in post-
debate coverage to be reported at a higher
proportion than character remarks. The total policy
remarks reported for the first Democratic debate in
the three new outlets was 28%, compared to 7% of
total character remarks. The total percentage of
policy remarks reported in all three post-debate
media coverage for the third Republican debate
was 9%, compared to 6.4% of character remarks.
Potential reasons why policy remarks or character
remarks are more highlighted in media coverage of
specific debates could be the debate questions
asked by the different moderators in each debate
and for the different political parties, the answering
techniques of the candidates, or the latest issues the
media is concerned with during specific
presidential debates. Overall, there is not an
identified pattern of which topic is reported in
media coverage more often.
Conclusion
The importance of presidential debates should
not be underestimated. Presidential debates provide
the electorate an opportunity to form perceptions
on the political parties’ eligible candidates. And,
the media covering the presidential debates is
responsible for informing the American voters who
were unable to watch the debates themselves. The
articles provided by three major news outlets
examined in this study: The New York Times, the
Washington Post and the USA Today could have
led to inaccurate perceptions of the themes
discussed by the Democratic and Republican
candidates for president.
Through the findings of this content analysis
and the findings of previous analyses on the media
coverage of presidential debates, voters cannot
expect to receive proportionate coverage of the
themes of candidates’ debate statements. In terms
of topic, this study did not identify a higher
proportion of character or policy remarks in media
reports compared to the actual proportion of policy
and character remarks in the debates. Clemons and
Lang (2003) explain that the “reader expects
accurate descriptions of events and credible
commentaries and analyses” (p. 278). Journalists
have a responsibility to accurately inform their
readers. However, as this analysis reveals, when it
pertains to information on presidential debates,
voters should watch the debates to receive a more
accurate depiction of the themes of the candidates’
statements, and not trust news outlets’ reports on
the debates.
Notes Articles examined from the three news outlets:
The New York Times
– “Donald Trump Offers Stark Contrast With Democrats on
Guns.” Shear, Michael D. October 28, 2015.
– “John Kasich Rebukes Donald Trump and Ben Carson.”
Shear, Michael D. October 28, 2015.
– “Hillary Clinton’s Democratic Debate Magic.” Bruni,
Frank. October 13, 2015.
– “Hillary Clinton Turns Up Heat on Bernie Sanders in a
Sharp Debate.” Barbaro, Michael and Chozick Amy.
October 13, 2015.
– “Republican Candidates Take Sharp Tone in Third Debate.”
Healy, Patrick and Martin Jonathan. October 28, 2015.
Washington Post
– “GOP candidates tangle with one another – and CNBC – in
a chaotic debate.” Rucker, Philip and Johnson Jenna.
October 28, 2015.
– “Republican debate: Rubio, Cruz, Christie deliver strong
performances.” Fahrenthold, David A. and Phillip Abby.
October 28, 2015.
– “The candidate breaking through in the Democratic debate?
Bernie Sanders” Penzenstadler, Nick. October 13, 2015.
– “The Moment when Hillary Clinton Won the First
Democratic Debate.” Stromberg, Stephen. October 13,
2015.
– “Winners and Losers from the First Democratic Presidential
Debate.” Cillizza, Chris. October 13, 2015.
– “Winners and Losers from the Third Republican
Presidential Debate.” Cillizza, Chris. October 28, 2015.
USA Today
– “A look behind the scenes of tonight’s GOP debate.” Fox,
Brooke. October 28, 2015.
– “Chris Christie not amused by fantasy football question.”
Penzenstadler, Nick. October 28, 2015.
– “Cruz and others blast CNBC, media over debate.”
Penzenstadler, Nick. October 28, 2015.
– “Long-shot GOP candidates debate economy in Colorado.”
Penzenstadler, Nick. October 28, 2015.
– “For the Record: Happy debate night, let’s drink.” Estepa,
Jessica. October 28, 2015.
– “Sanders skips pleasantries, blasts Wall Street, campaign
finance system.” Jacobs, Jennifer. October 13, 2015.
– “The most quotable moments from the third Republican
debate.” Firozi, Paulina. October 28, 2015.
SPECTRUM 6 (3)
17
Works Cited Benoit, W. L., & Currie, H. 2001. “Inaccuracies in media
coverage of the 1996 and 2000 presidential debates.”
Argumentation and Advocacy, 38(1), 28-39.
Benoit, W. L., Blaney, J.R., & Pier, P.M. (1998). Campaign
’96: A functional analysis of acclaiming, attacking, and
defending. Westport, CT: Praeger.
Benoit, W. L., & Harthcock, A. (1999). Functions of the great
debates: Acclaims, attacks, and defenses in the 1960
presidential debates. Communication Monographs, 66(4),
341-357.
“Donald Trump biography.”(n.d.).
http://www.biography.com/people/donald-trump-9511238
Clemons, E.K. and Lang, K.R. (2003), "The Decoupling of
Value Creation from Revenue: A Strategic Analysis of the
Markets for Pure Information Goods," Information
Technology and Management 4(2-3): 278.
Lang, G. E., & Lang, K. (1980). The formation of public
opinion: Direct and mediated effects of the first debate. In
G. F. Bishop, R. G. Meadow, & M. Jackson-- Beeck,
(Eds.), The presidential debates: Media, electoral, and
policy perspectives (pp. 61- 80). New York: Praeger.
Lanoue, D. J., & Schrott, P. R (1991). The joint press
conference: The history, impact, and prospects of American
presidential debates. New York: Greenwood Press.
Lazaroiu, G. (2008). News Media, Infotainment, and the
Decline of Reporting. Economics, Management and
Financial Markets, 3(1), 104-109.
Mindich, D. (2005), Tuned Out: Why Americans Under 40
Don't Follow the News. New York: Oxford University
Press, 41.
Porter, W. 2012. “Parry and thrust: A look at the history of
U.S. presidential debates.” Denver Post, S.4.
Reber, B. H., & Benoit, W. L. (2001). Presidential debate
stories accentuate the negative. Newspaper Research
Journal, 22(3), 30-43.
The New York Times (2015). Full Transcript: Democratic
Presidential Debate.
http://www.nytimes.com/2015/10/14/us/politics/democratic
-debate-transcript.html
Transcript: Republican Presidential Debate. (2015).
http://www.nytimes.com/2015/10/29/us/politics/transcript-
republican-presidential-debate.html
Amanda Schiavo ('16) is a Communications major
with a minor in political science. She is involved
in the SFU community by being a member of the
SFU's Women's Volleyball Team, a member of the
Pre-Law Club, and the Editor-in-Chief of the
Troubadour. Amanda was selected as the 2016
SFU nominee to attend The Republican National
Convention, was selected to present her research
paper at The Examined Life: An Undergraduate
Conference in the Liberal Arts and National
Council on Undergraduate Research, and is a
member of the Student-Athlete Mentors. After
graduation, Amanda will continue her education by
obtaining her juris degree.
SPECTRUM 6 (3) 18
Effects of Food Association on Color Preference
[Research conducted for PSYC 202 (Research Methods and Statistics II)]
Sarah E. Polito Alyson T. Pritts
Occupational Therapy Department Occupational Therapy Department
School of Health Sciences School of Health Sciences
[email protected] [email protected]
Marnie L. Moist, Ph.D.
Psychology Department
School of Arts & Letters
Color preferences are impacted by various
object associations (Strauss, Schloss, & Palmer,
2013; Taylor & Franklin, 2012). Humans tend to
change their color preference based on objects they
like or dislike in relation to different colors.
Specifically, it was of interest if food association
also has a significant impact on color preference for
humans. This is important because it would suggest
more about the relationship between certain colors
and certain foods. Many studies have reported
significant findings in understanding color
preferences, specifically differences in gender color
preferences (Granger, 1955; Guilford, 1940;
Madden, Hewett, & Roth, 2000; Strauss et al.,
2013; Taylor & Franklin, 2012; Taylor, Schloss,
Palmer, & Franklin, 2013). Humans also tend to
like colors based on their emotional connection to
the colors (Palmer & Schloss, 2010). Previous
studies have only shown color preferences
changing specifically due to object-color
associations (Strauss et al., 2013; Taylor &
Franklin, 2012). This study did not compare gender
differences and took object association a step
further in comparison to previous studies by only
looking at food association related to color
preference.
Color preference is considered to be the
tendency for an individual or a group to prefer
some colors over others, including a favorite color.
Object association to color is when people
generally like colors to the degree that they like the
objects associated to the color (Taylor & Franklin,
2012). Color association can be considered what a
person might associate a color with, and this can
impact overall color preference. For example, when
he thinks of the color blue, he may associate it with
the ocean and because he likes the ocean he likes
the color blue. Food preference is similar to color
preference, in that different people prefer different
foods. Color emotion “is defined as feelings
evoked by either colors or color combinations”
(Palmer & Schloss, 2010, p. 2).
There is a great connection between color and
food. Garber, Hyatt, and Starr (2000) conducted a
study suggesting how significant color is when
consuming food. This study discussed how color
directly affected food choice and food liking rather
than the taste of the food. Another discussion of
color involves the ecological valence theory
(EVT). The EVT “proposes that color preferences
arise from affective responses to color associated
objects: people like/dislike colors to the degree that
they like/dislike the objects that are
characteristically associated with that color”
(Taylor et al., 2013, p. 916). Strauss et al., 2013
also supports the EVT as evidenced by findings.
Therefore, specifically, we believe that the more a
person likes a specific food, the more he/she will
also like the color associated with that food. The
cone-contrast theory “posits that color preferences
SPECTRUM 6 (3) 19
arise from hard wiring in early visual processing;
the color-emotion theory suggests [color
preference] arises from the emotional content of
colors” (Strauss et al., 2013, p. 935). Also, the
color emotion-theory “can be linked measuredly to
color preferences if colors are preferred to the
extent that viewing them produces positive
emotions in the observer” (Palmer & Schloss,
2010, p.2). EVT and color-emotion are linked in
certain ways but are also significantly different. For
the ecological valence theory, a colored object is
either liked or disliked which is followed by a
person matching the liking or disliking of the object
to their overall liking or disliking of the color itself.
Whereas, with the color-emotion theory, a person
produces an emotion based off of the color shown,
which then determines his liking or disliking of the
color. There is debate over which theory is more
influential over color preference, but both have a
strong influence.
Taylor and Franklin (2012) aimed to understand
sex differences in relation to color-object
associations and to determine if the relationship
between color preference and object association is
equal for males and females. Males were compared
to females while performing two tasks. The first
task measured color preference on a 5-pt rated scale
while, the second task, WAVE, measured object
preference by having participants list objects
associated with the different colors. Three different
sets of colors were used for comparison: saturated,
light, and dark. All males and females were
presented with all colors and tasks. The results
stated, “we found the association between the
WAVE and color preference to be significantly
weaker for females (45%) than for males (74%)”
(p. 194). Overall, the researchers found some
gender difference in color preference, and they also
discovered that there is a negative relationship
between the number of objects associated with a
color and overall preference of the color. This
study is important to the current study because it
shows there could be a gender difference when
comparing color preferences and could provide
another explanation to the results of our study.
Similarly, this study allows for us to better
understand that the more objects associated with a
color, the less the color is liked, beyond the
influence of object association content.
Granger (1955) wanted to better understand
why people like certain shades of colors more and
why certain shades are more appealing than others
to the human eye. Sixty different color sets, with
roughly seven different shades of each color, with
different balances of hue (offsets of normal colors),
value (light or dark), and chroma (purity or
intensity of color or strength) were presented to all
fifty participants, half females and half males. All
participants were asked to rank colors based on
liking, regardless of their association with objects
or people to the specific colors. Overall, this study
found that color preference is not impacted by
personal taste (relativistic meaning) but is impacted
by object stimulus properties (qualitative meaning).
This study also showed that cultural influence
could have a greater impact on color preferences
than objective, biological influences. Finally, this
study showed there was no difference between
genders for color preference. From this study,
object stimulus continued to have a great impact on
color preference despite this specific study
attempting to prevent its impact. Similarly, this
study proves the importance of different shades of
color and how different lighting can make a huge
difference in overall preference of a specific color.
Strauss et al. (2013) wanted to determine if
color preference can be changed when presented
with colored-objects that are either liked or
disliked. Forty-six participants were used in this
experiment. All 46 were initially asked to rate their
preference on thirty-seven colors. Then, the forty-
six participants were divided into two groups. The
first group saw ten positive green and ten negative
red objects, while the second group was presented
with ten positive red and ten negative green
objects. Both groups were also shown the same
twenty images of other-colored neutral objects.
After being presented with the objects, all forty-six
participants once again rated their color preference
to see if overall preference changed. Participants in
the red positive, green negative group showed
increased preference for red over green while the
SPECTRUM 6 (3) 20
green positive, red negative group showed a
decrease in preference of red over green. This study
shows the effect of posttest minus pretest, as well
as the importance of negative and positive objects
associated with certain colors. Overall, Strauss et
al. provided evidence to support the ecological
valence theory. This is known because in the study,
participants liked colors more after being presented
with positive/liked objects (not involving emotion).
Similarly, those participants presented with colored
objects that were disliked, they also disliked the
color when asked to state their preference of the
color. This study allows for future studies, like
ours, to be completed in determining if specific
themes of objects (like food) have an even greater
impact on changing color preference.
Taylor et al. (2013) determined if adult color
preferences begin early in life or if color preference
is continuously changing throughout life. This
study first experimented with infants to determine
overall color preferences of infants to then compare
to overall color preferences for adults. The adults
were given the same eight colors as the infants
(dark and light colors of red, yellow, green and
blue), along with four different hues for each color
to increase sensitivity. There were 123 participants
who were assigned to one of three tasks, either the
preferential-looking task, preferential-choice task,
or the preferential-rating task. Adults looked and
preferred different colors than the infants did,
which shows that color preference is not innate and
instead, changes throughout one’s lifetime.
However, both infants and adults prefer pink
equally, in general. Also, the results for the adults
showed that they look longer at colors they like
than at colors they dislike, which did not occur for
infants. This helps explain that color preference is
not a fixed opinion but instead a flexible one that
can change based on the situation. Overall, this
study is important because it proves that adults can
change their color preferences over time, making
the cone-contrast theory an unlikely explanation.
This can then be related to our study of saying food
can change color preference throughout the entire
life-span and also in a short period of time such as
pretest to posttest.
The EVT “proposes that color preferences arise
from affective responses to color associated
objects: people like/dislike colors to the degree that
they like/dislike the objects that are
characteristically associated with that color”
(Taylor et al., 2013, p. 916). When the participants
initially rated the liking of colors on a 5-pt scale
they were not thinking about the connection with
food. However, once they were instructed to think
of foods related to certain colors, their color liking
rating should change. Along with this, we also
wanted to better understand if color-emotion theory
has an impact on color preference. In order to do
this we asked participants to state what emotion
they were feeling when shown each color; this was
done prior to the food association task. In the
current study to extend the results found by Strauss
et al. (2011) we chose to focus on liking and
disliking of food, rather than an inanimate object
related to color.
Research explains object recognition related to
color but our study is unique because we are
focusing specifically on colored food recognition to
determine changes in color preference over time.
Some studies (Taylor & Franklin, 2012) do not
include object association but the current study
does, similar to the experiment done by Strauss et
al. (2013), and specifically we focus on only food
objects related to color. Along with food preference
impacting color preference, we also used previous
research to help us question participants on their
emotional response to colors (Palmer & Schloss,
2010). Palmer and Schloss (2010) used specific
words to ask emotional connections to different
shades of colors. For this study, we are allowing
participants to write their own emotional
connections to one shade of the four different
colors to try to tease apart the EVT and color-
emotion theories.
Color preferences are impacted by various
object associations, one of which includes food
association. Participants were divided into three
different groups. One group was instructed to list
one to three liked foods related to each color, one
to three disliked foods related to each color, or
instructed to simply list one to three foods related
SPECTRUM 6 (3) 21
to each color. Before being asked to list foods,
participants were shown one color at a time and
asked to write the first emotion they felt when
seeing the color. Then, participants were asked to
rate color liking of four colors on a 5-point scale,
rated 1 (highly disliked) to 5 (highly liked).
Following this, participants listed foods based on
specific instructions. Then, participants were asked
to rate liking of each food on a 5-pt scale, rated 1
(highly liked) to 5 (highly disliked). This was done
in order to understand just how much each
participant liked the food, as it could impact their
posttest ratings of each color. Following this,
participants were again asked to rate color liking of
the same four colors used in the pretest and also for
the food on a 5-point scale, rated 1 (highly disliked)
to 5 (highly liked).
We expected to find some difference in posttest
minus pretest for color-liking on a 5-pt scale
between the three food association instruction
groups. We expected this to happen because studies
have shown that color preference changes with
object association and/or color-emotion links
(Granger, 1955; Strauss et al., 2013). Strauss et al.
(2013) showed that liked/disliked object
association has a positive relationship with color
preference. From this, we believed we would find
the same results in our study even though we
specifically used liked/disliked food association.
Similarly, Taylor et al. (2013) proved that color
preference can change overtime, which also
supports our hypothesis in that we believed there
would be a change between posttest and pretest
because color preference is influenced by other
situations such as, in our experiment,
acknowledging certain liked/disliked foods.
Methods
Participants. A total of 31 participants were
used in this study. There were 8 (26%) males and
22 (71%) females and one person did not identify
as male or female. 31 (100%) of the participants
were white/Caucasian. All participants were
college students from a rural, Catholic Institution in
Central Pennsylvania. All undergraduate students
were invited to participate in this study via email
via the Blackboard site. 1,696 undergraduate
students were invited and 32 people participated in
the study resulting in a 2% response rate. The low
response rate could be due to the study being an
experiment, needing to be taken in person, unlike
an online survey which is more convenient. 41% of
the participants were from a convenient sample as
they were offered extra credit in their Psyc101
class. The mean age of participants was 19 but the
ages ranged from 18-22. There were 13 (42%)
freshman, 7 (23%) sophomores, 10 (32%) juniors,
1 (3%) senior. Volunteers were obtained by their
own free-will after receiving an email with details
about the experiment and when the study would be
taking place. We offered a chance in a raffle for a
$10.00 Sheetz gift card if participated in our study.
All participants were not colorblind and had
corrected vision or normal vision. We also
indirectly inferred if participants have an eating-
related disorder or are currently on a diet due to
responses in the demographic survey. In order to
avoid asking a sensitive dietary question, we asked
all participants to guess about how many calories
they consume per day. We then removed
participant’s data that stated they consume -2
standard deviations below the mean calories eaten
daily. For Females 19-30 the average calorie intake
is 2,000 calories and for males 19-30 it is 2,400
calories. For females 31-50 it is 1,800 calories and
for males it is 2,200 calories. Finally, for females
51+ it is 1,600 calories and for males it is 2,000
calories (How Many Can I Have?, 2011).
Materials. A self-created survey was used to
measure color preference. See the complete survey
in Appendix 1. The color squares used for the study
were to give a visual stimulus to help with rating of
colors to determine color preferences. We used four
Munsell color chips (see Appendix 2), C16 green,
F1 red, B12 yellow, and C7 orange (Berlin & Kay,
1969).We also used the Munsell color chip G gray
for a background between colors (Berlin & Kay,
1969). We used a distractor task similar to “Color
Flow” (Big Duck Games LLC, 2012) but instead of
connecting colors, participants connected shapes so
as not to be influenced by colors (see Appendix 3).
SPECTRUM 6 (3) 22
This was used to prevent participants from
becoming bored or tired during the study and to
help participants forget earlier pretest color ratings
to the highest extent possible. The email invitation
to all participants was sent out via Novell
GroupWise 12.0.2 (2013). We used DLP Optoma
Projection System and a white projector screen
with a height of 44.5 inches and width of 80 inches
in order to show all colors to participants at the
same time and in the same visual field. Participants
sat no closer than 88 inches from the screen. We
also used Windows PowerPoint (2014) to organize
and automate the colors when showing the
participants.
Due to there being little research and
consistency in standard colors from the Munsell
Color chart we conducted a pretest or pilot study to
determine which colors represented the most
widely accepted, true green, yellow, orange, and
red. There were thirty-nine pretest participants
obtained from various college students that did not
later participate in the study. Twenty-five (64%)
people liked C16 Green the best. Nine (23%)
people liked D16 Green the best. Five (13%)
people liked E16 Green the best. Zero (0%) people
liked F16 Green the best. Thirty-five (90%) people
liked B12 Yellow the best. Three (8%) people liked
B11 Yellow the best. One (3%) person liked B10
Yellow the best. Zero (0%) people liked C11
Yellow the best. Fifteen (38%) people liked C8
Orange the best. Twenty-four people (62%) liked
C7 Orange the best. Zero (0%) people liked D5
Orange the best. Zero (0%) people liked D8 Orange
the best. Twenty-five (64%) people liked F1 Red
the best. Seven (19%) people liked G1 Red the
best. Four (10%) people liked E1 Red the best.
Three (8%) people liked H2 Red the best. Majority
of participants, 64%, agreed that C16 Green was
the best example of pure green. Majority of
participants, 90%, agreed that B12 Yellow was the
best example of pure yellow. Majority of
participants, 62%, agreed that C7 Orange was the
best example of pure orange. Majority of
participants, 64%, agreed F1 Red was the best
example of pure red. See Appendix 4 for the
various rating scales used as well as a complete list
of results for pretest/pilot study. We chose the best
color based on the majority of participants that
chose it out of all the other color options. All color
squares were projected on the screen with a height
of 43 inches and a width of 75 inches.
Design and Procedure. We used three
different versions of the self-created survey. One of
the three versions was randomly assigned to
participants, each survey having different
instructions (see Appendix 1 for complete survey).
We studied change in color preference on a 5-pt
scale, rated 1 (highly disliked) to 5 (highly liked)
between posttest minus pretest for all participants.
In order to test this there were three different
instructions for listing of foods. Within our eight
different times of offering the experiment, two
groups were asked to list no more than three foods
liked related to each color, three groups were asked
to list no more than three foods disliked related to
each color, and three groups were asked to simply
list no more than three foods related to each color
as controls. All foods were listed in relation to a
visual color square of one specific color. We also
studied color emotion by asking all participants to
write down what emotion they were feeling when
they saw each color individually on the screen.
This was the first question asked on the survey,
before participants were asked about color liking or
the food association. From this, we were able to
look at whether the emotion they felt was positive
or negative and how it related to their liking of the
color in the posttest.
This was a survey experiment and was a
between-subjects, randomized experiment design in
order to allow there to be different instructions to
determine if that influences color preference. We
used random assignment to distribute participants
to one of three different instruction surveys, using
two testing sessions per condition. This way we
were able to read the instructions out loud as well
as them being available on the survey and
participants were able to ask questions if they were
uncertain while completing the study without
possibly giving away that there were different
instruction sets to the others participating.
SPECTRUM 6 (3) 23
Participants received an email inviting them to
participate in the study. The email stated where and
when the study would be held. When participants
arrived to one of the eight sessions, they were
handed a packet. This packet included the
demographic survey, informed consent, and one of
three experimental surveys. They first answered the
demographic survey questions. They then
proceeded to sign the informed consent. After this,
they began the experiment. They were first shown
one color at a time and were asked to write down
what emotion they were feeling as we were
showing the color. Participants were given the
following instructions, “When we show a color on
the screen, please immediately write down the
emotion you feel from seeing the color. Please
answer as quickly and automatically as you can.
You will have fifteen seconds to write down one
emotion for each color”. After all emotions were
generated, they were then asked to rate the liking of
four colors shown one by one on the projected
screen based off of their preference of liking
(1=highly disliked, 2= disliked, 3= neutral, 4=liked,
5=highly liked). Participants were given the
following instructions: “Please rate your personal
liking of the color shown on the screen. You will
be shown one color at a time for ten seconds so
carefully examine the color for the full ten seconds.
Then you will be asked to rate the color while the
gray screen is shown for five seconds after each
color. Please answer as quickly and automatically
as you can. (1=highly disliked, 2= disliked, 3=
neutral, 4=liked, 5=highly liked).”
We then showed them each color individually a
third time using the projector, with a gray
background in between each shown color to allow
time to rate one to three foods each participant
thought of. We asked them to list one to three, but
no more than three foods related to the color shown
on the screen (specific instructions based on each
condition). There was a time limit of one minute on
this and participants were encouraged to answer
quickly and instinctively per research that proves
this to be most effective (Taylor & Franklin, 2012).
For this part of the experiment the survey papers
were folded in half and participants were told not to
unfold until instructed to do so. The left side of the
paper was where the participants listed the foods.
Then, participants were asked to unfold the
paper and were asked to rate how much they like
each food on the right side of the paper (1=highly
liked, 2=liked, 3=neutral, 4= disliked, 5=highly
disliked) while the screen appeared gray. They
completed this task for all four colors. For example,
the color green was shown, which was when
participants were instructed to list foods on the left
side of the paper, then the gray screen appeared
which was when participants were instructed to
unfold the paper and rate liking of each food
related to the color green. Then, a new color was
shown with the same procedure previously
explained until all four colors were shown. The
rating of each food was used as a way to confirm
participants followed the directions of what type of
foods to list. Unfolding procedure was as not to
initially inform the participants we would also be
asking them to rate the foods and also in order for
the participants to not try to work ahead during the
study. Finally, participants were asked, again, to
rate liking of the four colors one at a time, shown
on the screen and rated on a 5pt scale. This
completed the study.
All participants, after listing and rating two of
the four colors, were instructed to complete the
“Shape Flow” distractor game. The instructions
given to the group asked to list no more than three
liked foods related to each color were given the
following instructions: “Please list 1-3 liked foods
you think of when you see the color on the screen.
Do your best to list three liked foods. If you run out
of time, do not worry and just prepare for the next
portion of the survey. You will have one minute to
list 1-3 foods. Please answer quickly and
automatically”. The group asked to list no more
than three disliked foods related to each color were
given the same instructions except the word like
was changed to dislike. The group asked to simply
list foods related to each color were given the same
instructions except the word like (or dislike) was
not included. After this, all three groups received
the instructions to: “Please unfold your response
sheet now and rate your liking to the right of each
SPECTRUM 6 (3) 24
food listed. You will have twenty-five seconds to
rate each food. Please answer quickly and
automatically. (1=highly liked, 2=liked, 3=neutral,
4= disliked, 5=highly disliked)”. All groups were
given the same posttest instructions as the pretest:
“Please rate your personal liking of the same colors
as shown earlier in the study. However, we do not
expect you to rate the colors the same as before. If
you do that is okay but please respond quickly and
automatically without trying too hard to recall your
earlier answer. You will, again, have ten seconds of
one color shown on the screen, followed by a five
second break where you will rate each color
previously shown with a gray color shown on the
screen”. Refer to Appendix 1 for all information
used on the survey.
The entire experiment was automated as we
timed each slide on the PowerPoint and wrote
down instructions to be read at each session in
order to keep all eight sessions the same. The
demographic survey took roughly three minutes,
the color emotion section took about one minute
(fifteen seconds for each color), the participants
were given ten seconds to rate each color in the
pretest and were given five seconds of gray screen
between each color; therefore, the total pretest took
roughly one minute. Color A was shown for ten
seconds, followed by five seconds of gray screen.
Color B was shown for ten seconds, followed by
five seconds of gray. And so on for colors C and D.
The order of the colors was randomized. Then,
participants were given one minute to list no more
than three foods for each color and then were
presented with a gray background for twenty-five
seconds where they were told to rate liking of each
food for each color. This took a total of five
minutes. Again, participants were shown on the
screen Color A for one minute which is when they
listed the foods, and then shown the gray screen for
twenty-five seconds which is when they rated their
liking of Color A foods listed. This continued for
Colors B, C, and D. The participants were, again,
given ten seconds to rate each color in the posttest
and were given five seconds of gray screen
between each color; therefore, the total posttest
took roughly one minute. The distractor task took
roughly three minutes and was done on the survey
paper. During this time, a gray background was
shown on the screen. The experiment took a total of
15-25 minutes to complete. This experiment took
place in a classroom setting with enough spacing
between each participant for comfort and privacy.
There were eight different times offered to
complete the survey. Two of the settings were
given the instructions to list no more than three
liked foods, two of the settings were given
instructions to list no more than three disliked
foods, and two of the settings were given the
instructions to simply list no more than three foods.
All eight settings were given a different order of
which color to list the foods for first, second, third,
and fourth. We randomized this in order to avoid
order effects. We also counterbalanced the pretest
and posttest order of colors so as to increase chance
of forgetting pretest ratings. The lighting of the
room was specifically determined to make sure the
colors were visible in the appropriate way for all
eight sessions. We had no lights on during the
experiment and we closed all blinds in order to
keep the colors as normal looking as possible.
Scoring. Due to wanting to determine if there
is change over time in color preference when asked
to list liked or disliked foods of various colors, we
calculated the difference in scores of the pretest and
posttest. Also, we wanted to analyze if emotion
contributes to color preference. We scored the
emotions by determining if it was positive or
negative. For the disliked food group, we
separately analyzed positive and negative emotions
to colors to see if any different results occurred.
Then, we looked at the posttest ratings and
analyzed the percentage decrease, if any, of the
color rating. Similarly, for the liked food group, we
separately analyzed positive and negative emotions.
This was done to tease apart the color-emotion
theory and ecological valence theory or to
determine they are both influential to color
preference. Then, we looked at the posttest ratings
and analyzed the percentage increase, if any, of the
color rating. For the third group (listing any foods),
we analyzed all four colors and their percentage
SPECTRUM 6 (3) 25
change in relation to the emotions listed and the
types of foods they listed.
Results
All effects significant at p .05 were reported.
All post hoc tests were calculated using the
Dunnetts T3 test because we had equal variances
but it was a near trend. We calculated the means by
adding up across the subjects and then dividing by
the total number of subjects. To find the mean
difference we took time 2 mean minus time 1
mean. We dropped one participant because they
were colorblind and we did not want anyone who
was colorblind to participate in our study.
A 1-way ANOVA was run on mean difference
color rating score from time one to time two to
compare the liked color instructions, the disliked
color instructions, and the normal (control) color
instructions. F(2,28) = 5.11, p=.013. The liked
color instructions elicited a more positive mean
difference color rating score, the disliked color
instructions elicited a more negative mean
difference color rating score, but compared to the
control there was little difference of mean
difference color rating scores. Comparing liked
instructions to disliked instructions there was a big
difference of color rating scores. ES = .27 which is
a medium effect size. We found equal variances but
a near trend for the Levine’s variance test. F(2,28)
= 2.91, p = .071.
Food Instructions influenced mean difference
color rating score from Time 2 to Time 1 (see
Table 1). There was a greater mean difference in
color rating score between the liked and disliked
instructions and a small mean difference color
rating score between the liked/disliked and control
instructions.
Food Instructions
Mean Control Liked Disliked
Difference Instructions Instructions
Mdiff .03 .41 -.23
SEdiff .058 .189 .137
Table 1. Mean Difference of Color Rating from Time 2 to
Time 1 in Food Instructions
Other Results
An ANCOVA was run on mean difference
color rating score from Time 2 to Time 1 to tease
apart EVT and color-emotion theory and to
compare the liked color instructions, the disliked
color instructions, and the normal (control) color
instructions, while analyzing the covariate for
color-emotion association rating at time 1. For the
Overall model: F(3,27) = 3.29, p =.036. The overall
model was significant. For the covariate (emotion):
F(1,27) = .014, p = .906. The covariate of color-
emotion association rating at time 1 was not
significant. For the Main Effect: F(2,27) = 4.52, p
= .020. The liked color instructions elicited a more
positive mean difference color rating score, the
disliked color instructions elicited a more negative
mean difference color rating score, but compared to
the control there was little mean difference of color
rating scores. Comparing liked instructions to
disliked instructions there was a big mean
difference of color rating scores. ES = .268 which
is a medium effect size.
Food Instructions influenced mean difference
color rating score from Time one to Time two, even
with color emotion association covaried out (see
Table 2).
Food Instructions
Mean Control Liked Disliked
Difference Instructions Instructions
Mdiff .022 .41 -.23
SEdiff .15 .15 .15
Table 2. Mean Difference of Color Rating from Time 2 to
Time 1 in Food Instructions [Note: Covariate of color-
emotion association at Tiime 1 was not significant.]
Discussion
We were able to support our main hypothesis
that the mean difference in color rating from Time
2 to Time 1 depends on food instructions (liked
instructions, disliked instructions, or normal
(control) instructions). We found significant
difference for the mean difference color rating
score between the liked instructions and disliked
instructions. However, we found little significance
for the mean difference color rating scores between
SPECTRUM 6 (3) 26
the liked/disliked instructions each when compared
to the control instructions. We also found that the
covariate color emotion rating from time 1 does not
significantly impact overall color rating. From
these results, we can reject the color-emotion
theory in that a person’s emotional response to
color does not impact their overall color liking.
However, from our results, we can support the
ecological valence theory (EVT) that color
preference is affected by associated objects and
color, specifically food as the object.
There are a few flaws with our study. The first
flaw is that there were many distractions in the
room due to noise from other people in the room
and from outside. This could have impacted the
participants’ responses because they were
distracted with something else. Another flaw is that
some participants appeared to not follow our
directions. Our study was extremely particular in
that we needed participants to respond quickly and
automatically in order for their answers to be true,
valid answers. Some participants did not follow the
directions exactly as we stated them which could
have negatively impact their results.
Garber et al. (2000) discovered how color
directly affects food preference beyond the taste of
the food. These results are inconsistent with our
results. We found that color preference can
temporarily change liking or disliking of food,
based on thoughts, whereas the past study found
that food preference depends on liking or disliking
of color related to each food. Taylor and Franklin
(2012) discovered that there is an inverse
relationship to number of objects associated with a
color and liking of the color. They also briefly
discovered that people seem to generally like
“colors to the degree that they like the objects
associated with those colors” (p. 196). However,
their results were extremely basic with minimal
proof of object association relating to color
preference. These results are consistent with our
results as we also found, in a more concrete way,
that object (food) association does relate to color
preference.
Granger (1955) looked at color preference but
tried to take away the option of associating color
with objects or people. However, he found that
even telling someone not to think about objects or
people the participants still related the colors to
objects or people. This study, therefore, proved that
it is impossible for people to not relate objects or
people (or food) to colors. This study is consistent
with our results because we also found that food
(object) association does impact color preference.
Strauss et al. (2013) found that participants
presented with red positive/green negative objects
increased their preference for red over green, while
the green positive/red negative objects decreased
the participants preference for red over green. Their
study directly supports the ecological valence
theory that: being presented with something
positive will increase liking of the color. These
results are directly consistent with our results
because we, too, found that when given instructions
to list disliked food, participants’ overall mean
difference color rating score decreased. Similarly,
when given instructions to list liked foods, their
overall mean difference color rating score
increased. Our study is also able to support the
ecological valence theory, similar to Strauss et al.
(2013).
Taylor et al. (2013) discovered color preference
has the potential to change from childhood to
adulthood. These results are consistent with our
results because we also found that color preference
could change from pretest to posttest. Both studies
show that color preference is not fixed but instead
flexible and able to change. Palmer and Schloss
(2010) discovered that positive emotions towards a
color increase overall color preference. Their
results supported the color-emotion theory. Our
results are inconsistent with their results in that we
found that emotion towards a color does not impact
overall color preference above and beyond food-
color association. From this, we do not support the
color-emotion theory.
Overall, our study is able to support the
ecological valence theory and reject the color-
emotion theory. As previously mentioned, our
study was able to tease apart both theories which is
able to help people better understand why our color
preferences change. Also, none of the studies
SPECTRUM 6 (3) 27
previously mentioned used food as the object
associated with color. We are able to prove that
food color impacts overall color preference. These
results could be used to help explain why we prefer
certain colors and why our color preferences
change based on different situations. The results
could also help people realize that what they eat
could impact what colors they prefer.
In the future, it might be beneficial to have a
posttest color emotion question, similar to the
pretest to see if that would make a difference in
overall color preference. Also, in the future it might
be beneficial to choose colors other than, red,
green, orange, and yellow to determine if the
theories hold true for all colors.
Appendix 1 Question 1
When we show a color on the screen, please immediately
write down the first emotion you feel from seeing the color.
Please answer as quickly and automatically as you can. You
will have fifteen seconds to write down one emotion for each
color.
Red:
Orange:
Green:
Yellow:
Question 2
Please rate your personal liking of the color shown on the
screen. You will be shown one color at a time for ten seconds
so carefully examine the color for the full ten seconds. Then
you will be asked to rate the color while the gray screen is
shown for five seconds after each color. Please answer as
quickly and automatically as you can. (1=highly disliked, 2=
disliked, 3= neutral, 4=liked, 5=highly liked).
Red: 1-------2-------3--------4-------5
Orange: 1-------2-------3--------4-------5
Green: 1-------2-------3--------4-------5
Yellow: 1-------2-------3--------4-------5
Question 3: Red
Question 4: Orange
Question 5: Green
Question 6: Yellow
IMPORTANT: Please keep
paper folded so only the left
half of the sheet is visible until
you are told to look at the right
half folded beneath.
Please list 1-3 foods you think of
when you see the color on the
screen. Do your best to list three
foods. If you run out of time, do
not worry and just prepare for the
next portion of the survey. You
will have one minute to list 1-3
foods. Please answer quickly and
automatically.
1.
2.
3.
Please rate your liking to the
right of each food listed. You
will have twenty-five seconds
to rate the foods. Please answer
quickly and automatically.
(1=highly liked, 2= liked,
3=neutral, 4=disliked, 5=highly
disliked).
1-------2-------3--------4-------5
1-------2-------3--------4-------5
1-------2-------3--------4-------5
Question 7
Please rate your personal liking the same colors as shown
earlier in the study. However, we do not expect you to rate
the colors the same as before. If you do, that is okay but
please respond quickly and automatically without trying too
hard to recall your earlier answer. You will, again, have ten
seconds of one color shown on the screen. Followed by a five
second break where you will rate the each color previously
shown with a gray color shown on the screen.
Red: 1-------2-------3--------4-------5
Orange: 1-------2-------3--------4-------5
Green: 1-------2-------3--------4-------5
Yellow: 1-------2-------3--------4-------5
* Order of colors was counterbalanced within the study.
** Two other sets of instructions were used: “Please list 1-3
liked foods you think of when you see the color on the
screen. Do your bed to list three foods. If you run out of time,
do not worry and just prepare for the next portion of the
survey. You will have one minute to list 1-3 foods. Please
answer quickly and automatically.” And “Please list 1-3
disliked foods you think of when you see the color on the
screen. Do your bed to list three foods. If you run out of time,
do not worry and just prepare for the next portion of the
survey. You will have one minute to list 1-3 foods. Please
answer quickly and automatically.
Highly
disliked
Highly
liked
Highly
disliked
Highly
liked
SPECTRUM 6 (3) 28
Appendix 2
Appendix 3 Instructions: Draw a line to connect matching shapes, creating
a flow. None of your lines are allowed to cross or overlap
other lines. Pair all shapes, and cover the entire board with
lines to solve the puzzle. You will have three minutes to work
on this. It is okay if you do not finish it.
Appendix 4 Please choose the color that best represents the true color
yellow.
A. B. C. D.
Please choose the color that best represents the true color red.
A. B. C. D.
Please choose the color that best represents the true color
orange.
A. B. C. D.
Please choose the color that best represents the true color
green.
A. B. C. D.
Yellow
Color A
(B10)
Color B
(B11)
Color C
(B12)
Color D
(C11)
1 (3%) 3(8%) 35 (90%) 0 (0%)
Red
Color A
(F1)
Color B
(H2)
Color C
(G1)
Color D
(E1)
25 (64%) 3 (8%) 7 (19%) 4 (10%)
Orange
Color A
(C7)
Color B
(C8)
Color C
(D5)
Color D
(D8)
24 (62%) 15 (38%) 0 (0%) 0 (0%)
Green
Color A
(C16)
Color B
(D16)
Color C
(E16)
Color D
(F16)
25 (64%) 9 (23%) 5 (13%) 0 (0%)
SPECTRUM 6 (3) 29
Works Cited Berlin, B. & Kay, P. (1969). Basic Color Terms. Berkeley,
CA: University of California Press.
Big Duck Games LLC (2012). Flow Free (2.8) [mobile
application software]. Retrieved on Dec. 4, 2014 from
GOOGLEPLAY.
Garber, L.L., Hyatt, E.M., & Starr, R.G. (2000). The Effects
of Food Color on Perceived Flavor. Journal of Marketing
Theory and Practice, 8(4), 59-72. Retrieved on Dec. 4,
2014 from PROQUEST database.
Granger, G.W. (1955). An Experimental Study of Colour
Preferences. The Journal of General Psychology, 52(1), 3-
20. Retrieved on Oct. 13, 2014 from ILLIAD database.
Guilford, J.P. (1940). There is System in Color Preference.
Journal of the Optical Society of America, 30(9), 455-459.
Retrieved on Oct. 13, 2014 from ILLIAD database.
How Many Can I Have? (2011). Retrieved Jan. 20, 2015 from http://www.phd5.idaho.gov/quickinfo/quickInfoHowManyCanIHave.php.
Madden, T. J., Hewett, K., & Roth, M. S. (2000). Managing
images in different cultures: A cross-national study of color
meanings and preferences. Journal of International
Marketing, 8(4), 90-107. Retrieved on Oct. 13, 2014 from
PROQUEST database.
Palmer, S., & Schloss, K. (2010). An Ecological Valence
Theory of Human Color Preference. PNAS, 107(19).
Retrieved on Jan. 20, 2014 from PROQUEST database.
Strauss, E. D., Schloss, K. B., & Palmer, S. E. (2013). Color
preferences change after experience with liked/disliked
colored objects. Psychonomic Bulletin & Review, 20(5),
935-43. Retrieved on Oct. 13, 2014 from PROQUEST
database.
Taylor, C., & Franklin, A. (2012). The relationship between
color-object associations and color preference: Further
investigation of ecological valence theory. Psychonomic
Bulletin & Review, 19(2), 190-7. Retrieved on Oct. 13,
2014 from PROQUEST database.
Taylor, C., Schloss, K., Palmer, S. E., & Franklin, A. (2013).
Color preferences in infants and adults are different.
Psychonomic Bulletin & Review, 20(5), 916-22. Retrieved
on Oct. 13, 2014 from PROQUEST database.
Sarah Polito (’16) is an Occupational Therapy
major with a minor in Psychology. She is a
member of Phi Eta Sigma National Honor Society
and Psi Chi International Honor Society in
Psychology. Sarah worked as a Peer Minister for
two years and Peer Minister Coordinator for one
year. She is also a member of the Student
Occupational Therapy Association. Sarah will
graduate with her Master of Occupational Therapy
Degree in 2017
Alyson Pritts (’16) is an Occupational Therapy
major with a minor in Psychology. She has been
actively involved in the Theta Phi Alpha sorority
where she held the position of New Member
Educator and held a spot on the SFU dance team
during her freshman to sophomore years. Alyson
also had fieldwork experiences at Camp Emerge in
Millville, PA and at the Crichton Center in
Johnstown, PA. After graduation in Alyson hopes
to complete her Master’s Degree in Virginia where
she will be completing her fieldwork experience.
SPECTRUM 6 (3) 30
2015 Office of Student Research Awards for Research Excellence
The goal of the Office of Student Research is to foster the culture of student research at Saint
Francis University. Student research has become an important part of the education of an
increasing number of Saint Francis University students. Each year the Office of Student
Research recognizes students who have shown exemplary effort conducting research at the
University. The following students were the recipients of the 2015 Office of Student Research
Awards for Research Excellence, given out at the Fifth Annual Saint Francis University
Research Day on November 19, 2015:
For research excellence in the School of Arts & Letters, Christie Olek, who conducted her
research in Dr. Lori Woods’ research group, detailing the experiences of the first graduates of the
"Woman's Medical College of Pennsylvania", the first college founded to offer the MD degree to
women. For the project, she traveled to Philadelphia where she conducted research at Drexel
University's "Legacy Center", a special collection, which houses the Woman's Medical College
Archive. She was later awarded a School of Arts & Letters Intrepid Research grant that funded a
second trip to Philadelphia, and the product of this research was a paper, "Opposing Obstacles
and Overcoming Opposition: The First Graduates of the Woman's Medical College of
Pennsylvania", which was published in the Fall 2015 issue of Spectrum. Christie presented her
research in poster form at last year's SFU Research Day.
For research excellence in the School of Business, Jeremy Merich, who is a senior accounting
and finance major. Jeremy has spent the last two years working on his honor's thesis exploring
an investment strategy to achieve abnormal returns. Dr. Maggie Garcia and Dr. Ed Timmons
advised his project. His strategy focuses upon buying stocks near the announcement of quarterly
earnings. Using regression analysis and data for 1500 companies for the month of October 2015
(entered by hand into Excel), Jeremy found evidence of substantial abnormal returns for periods
very close (1-day) and further away (30-days) for investors pursuing his strategy. Jeremy has
presented his preliminary research at the Issues in Political Economy conference and plans to
attend and present again in Spring 2016. He also plans to extend his research using additional
data. Jeremy was hired by JP Morgan Chase in Delaware after his internship with the firm in the
summer of 2015 and will begin working there immediately after graduation.
For research excellence in the School of Health Sciences, Brittany Swartzwelder, who
conducted her research in Dr. Curt Kindel’s research group. Her project is titled "The effects of
varying hip flexion and external rotational angles on the production of isometric hip external
rotation torque in healthy adults." She presented her work recently at the PA Physical Therapy
Association's Annual Conference at Seven Springs, PA. She was an integral part of the research
team and is very deserving of this award.
SPECTRUM 6 (3) 31
For research excellence in the School of Health Sciences, Jake DeMedal, who conducted his
research in Dr. Bill Stodart’s research group. His project is titled “The relationship between
surface electromyographical activity and force production of the infraspinatus muscle in shoulder
rehabilitation exercises.” This was a challenging undertaking involving the acquisition and
analysis of isometric strength and EMG recordings at 5 different positions for 3 different
exercises. The process was technically demanding and time consuming, but Jake and his group
displayed considerable amount of independence in carrying their research out. Jake’s study was
accepted for platform presentation at PPTA Annual Conference, where it was subsequently
awarded "Best Research 2015".
For research excellence in the School of Sciences, Cristina Marcillo, who started her research
career in an unusual way. Before even setting foot in the research lab, she translated an article
by SFU professor Dr. Bill Strosnider for publication in the Chilean journal Avences en Ciencias e
Ingeniería. Since then, Cristina has continued working with Dr. Strosnider focusing on co-
treating acid mine drainage and municipal wastewater, ultimately presenting the results of her
work at the 4th
International WaTER Conference at the University of Oklahoma this past
September. In addition, Cristina participated in the Research Experience for Undergraduates
program at Clarkson University in upstate NY this past summer.
For research excellence in the School of Sciences, William Shee, who is a junior chemistry and
mathematics major. He has been working on his research project in Dr. Balazs Hargittai’s
research group for the past two and a half years, designing the synthesis of novel proline
derivatives with basic side-chains. William has been a very active participant in the project,
suggesting modifications to the methods used and focusing the direction of the project to use
more contemporary and more environment-friendly procedures. William presented his research
as posters at the past two Annual SFU Research Days, and gave a seminar detailing the project at
the 2015 Research Day.
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