Kaitlyn Moon Media Exposure and Its Effects on Eating Disorder ...

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Nov 8, 2013 (3 years and 9 months ago)

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Kaitlyn Moon

Media Exposure and Its Effects on Eating Disorder Deaths












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Table of Contents

Introduction

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3

Literature Review

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3

Data/ Methods

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5

Limitations

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6

Results

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7

Conclusion

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9

Works Cited
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10

Appendix A: Sparhawk’s Hypotheses
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Appendix B: Data Chart
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Appendix C: Raw Data
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Introduction:


Body image is defined as involving, “our perception, imagination, emotions and physical
sensations of and about our bodies”

(Sparhawk, 2003)
.
This
self
-
image is neither static nor impacted
simply by one

single

factor. Family, culture,
genetics, and other social pressures can all influence one’s
self confidence

and body satisfaction
. One of the most prevalent influences today is the ever growing
media.

The average American sees approximately 3,000 ads per day (Killing us softly).
Slogans and titles
such as “Go Topless,” “What Guys Notice First,” and, “The Sexy Skin Diet,” are just
b
lips

on the
vast
social media
radar

(Cosmo, 2012)
.
These ads not only depict specific products every viewer should buy,
but they also co
nvey a wide variety of messages;

messages that give society an unobtainable depiction
of be
auty and perfection. Both men and women are
taught certain lessons. Thin is in. Be sexy, not
skanky. Snagging the perfect mate begins with your looks.
But when is the media’s entertainment
outweighed by its consequences.
The media’s embodiment of this driv
e for thinness could contribute to
both obesity and eating disorders in people around the world
, which sometimes
turn

fatal

(University of
Maryland Medical Center, 2011)
.
With both increasing rates of eating disorder deaths and me
dia
exposure, research is searching for a correlation.

Literature Review:


Magazines, billbo
ards, advertisements
,

and
other form
s

of media all depict th
e unobtainable
body ideal. M
edia sends the message to both sexes that they must look like the pe
rfectly

airbrushed
models in

magazines. Women receive the clear message that they are meant to be slim and “sexy,” or
else be doomed to
stay single and unimportant.
But women must be careful. If women cross too far
over the sexy line, then they are again imperfec
t. Cosmo’s traditional excerpt of “Sexy vs Skanky,”
exemplifies this concept perfectly.

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The

idea that today’s media could possibly be influencing negative body

satisfaction

i
s nothing
new.
Gr
aduate student, Julie Sparhawk
conducted a study at
the University of
Wisconsin

Stout
entitled,
“Body Image and the Media: The Media’s Influence on Body Image.” Sparhawk’s paper cites several
sources providing evidence that women exposed to appearance oriented images tended to be less
satisfied with their
personal bod
y image than women who were not

(Hienburgh and

Thompson as cited
in S
parhawk). This connection between the prevalent thin ideal and women’s body dissatisfaction can
lead to several types of eating disorders and sometimes death. Sparhawk’s main
purpose in her study
was to try and, “substantiate the media’s influence on the body image.” The study was conducted in
2003 using the Body Image States Scale. This scale consists of six questions relating positive or negative
feelings toward body size and

shape. The experimental group of women was given the questionnaire
after watching a short power point presentation that showed images of women in the media. After
completing the analysis, Sparhawk found that two of the original six hypotheses (appendix A)

were
rejected. The study concluded that there was a significant difference found between the groups when it
came to feelings about physical attractiveness and looks overall
(Sparhawk, 2003)
. The media is
obviously not the mono
lithic cause of low self
-

esteem or the emergence of eating disorders, but a
relationship does exist.


The United States is not the only country keeping track of
media influence

on eating disorders
and eating disorder deaths. These psychiatric disorders h
ave gone global, spreading to a wide
array of

countries such as India, South Korea, Singapore, and many others. But are these increases a product of
the media or something else? Srinivasan, Suresh, and Jayaram, authors of “Emergence of Eating
Disorders in
India,” seem to believe that a western influence could answer for this increase. Srinivasan
and coworkers found that while there are eating disorders in the populations of India, they are less
severe and less frequent than those often seen in Western cultu
re
(Srinivasan, Suresh, & Jayaram,
1998)
. This study found that several of those studied seemed to
have a “fear of fatness.” However,

this
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fear was not accompanied by any type of dieting or reactive behavior. Girls were found t
o be more
affected by this “Eating Disorder Syndrome,” though boys were also impacted.
During

their discussion
,
the authors speculated that these arising eating

“problems”
might possibly be the result of a western
style of thinking.
The Western body ideal
and unobtainable thinness has been spreading far beyond its
homeland.


S
outh Korea and Singapore are

another two countries that have seen a drastic increase in eating
disorder cases. The Singapore General Hospital estimates 140 new cases of eating disorde
rs per year.
The comm
on Western idea of ‘Thin is in,’

resonates with teenagers in Singapore. 80 % wish they could
change their looks and 60% have negative feelings towards their looks. Dieting starts early and can often
lead to eating problems
(Ng & Ng, 2007)
.
The youth of South Korea seem to be of the same mind. In her
article, “Eating Disorders on the Increase in Asia,” Sonni Efron said t
hat young women in South Korea
are, “victims of fashion”
(Efron)
. In

the past few years, eating disorders have spread to all ethnic and
class backgrounds in popular Asia
n

countries like Hong Kong and Singapore. The question remains. Is
Western fashion, music, and forms of media the cause for these increases around t
he globe?
In the
1990’s South Korea loosened their control over forms of media. When this happened, it opened the
door for more foreign media and morals to enter

(Efron)
.
Similar to Western cultures, body image and
looks
soon
took top priority in today’s young minds
.

Data
/
Methods:

The broad concept of my project is the possible correlation between media exposure and
negative body image. Since negative body image is difficult to quantify
, I decided to specifically look at
the
number of eating disorder deaths per c
ountry. I collected data from 26

countries. These countries
are all from the North Americas, Europe, and Australasia. This ensured that the samples were of similar
standing. I did use variation in the countries. In ord
er to quantify media exposure, I used the World Fact
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Book to find the number of in
ternet users per country.
I

then

found the total population of each
country. I put all of this data in an excel chart. I used the total population per country to calculate bo
th
the percentage of internet users and the percentage of eating disorder deaths per country. Using these
two columns and the Correlation function in excel, I found the correlation coefficient. I also created a
visual chart that shows
the general correlati
on (
Chart 1
).


I decided to collect a second set of data to represent media exposure. Using the World Fact
Book, I recorded the number of internet hosts per country. Similar to my previous test, I used the total
population per country to find the percenta
ge of internet hosts per country and then compared it to the
percentage of eating disorder deaths. I again used the Correlation function in excel to calculate the
correlation coe
fficient and graphed it (
Chart 2
).

Limitations:


Several limitations exist w
ithin the data. First, is the availability of the data. Eating disorder
deaths are hard to record. In the accounts of deaths per population, eating disorder deaths are often
categorized under different mortalities. Also, rates of eating disorder deaths are

not easily found for all
countries and numbers may be skewed. Second, as stated before, the media is hard to quantify. I only
used two forms of media in this project.
There are numerous other types of media that could have been
us
ed. Third
, in order for t
he correlation coefficient to be as accurate as possible, there needs to be a
large enough sample size. 30 is usually the minimum. Unfortunately I was unable to find specific eating
disorder death rates from 30 countries of similar economic standing. So my

sample size is a bit small

to
be incredibly reliable
. The last

limitation
is

in the comparisons. I wasn’t able to find specific data for each
sex. I would have liked to compare the difference between
the
prevalence of men’s eating disorder
deaths compared

to women’s. This could be a possible field for future research when m
ore specific data
is available.

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Results:


Test 1: The Correlation between Internet Users and Eating Disorder Deaths

(
Table 1
)

Correlation Coefficient Analysis



% of users

% of deaths

%

of users

1


% of deaths

0.464899585

1


Data from 26

countries was collected
and
put into an excel document. Using the correlation
function in the Data Analysis section of Excel, the correlation coefficient
of

internet users to eating
disorder deaths was found to be 0.4649 (0.464899585 unrounded). Correlation coefficients can range
from
-
1 to +1, with the ends being the strongest correlation
,

and the (+,
-
) representing the direction of
the correlation. The resu
lt of 0.4649 indicates that there is a positive correlation. In general
,

correlation
coefficients between 0.3 and 0.7 are described as moderate correlations
(Ratner)
. Therefore, results
conclude a moderate positive correlation
between number of internet users and number of eating
disorder deaths

.
Chart 1

provides a visual.


(
Chart 1
)

0
0.2
0.4
0.6
0.8
1
1.2
0
0.0000005
0.000001
0.0000015
0.000002
0.0000025
0.000003
0.0000035
% of Internet Users per Country

% of Eating Disorder Deaths per Country

Correlation of Internet Users and ED Deaths

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Test 2: The Correlation between number of Internet Hosts and number of Eating Disorder Deaths

(
Table 2
)

Correlation Coefficient Analysis




%
of Hosts

% of
deaths

% of Hosts

1


% of deaths

0.48756017

1


The second test conducted was very similar to the first. Data

was collected from the same 26

countries as in Test 1. Using the same Correlation function, Test 2 produced a correlation coefficient of
0.4876 (0.48756017 unrounded). This coefficient is very similar to that found in the first test. Test 2
showed a moderate positive correlation betwee
n the number of internet hosts per country compared to
the number of eating disorder deaths per country.
Chart 2

provides a visual of this correlation.


(
Chart 2
)



0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
0
0.0000005
0.000001
0.0000015
0.000002
0.0000025
0.000003
0.0000035
% of Internet Hosts per Country

% of Eating Disorder Deaths per Country

Correlation of Internet Hosts and ED Deaths

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Conclusion:


From both tests
,

it is clear that a type of correlation prevails.
While the correlations found in
Test 1 and 2 were not incredibly strong, they were not weak either. They sat right about in the middle.
This proves that while the media does not have a direct relationship with the number of eating disorder
deaths, it does
play a part. The similarity in strength between the two tests is very
interesting.
The
similarity of the media type may account for this.
This

shows that
numerous amounts
and variations
of
media could be a factor in the increase of eating disorder deaths.
As stated before, there are certain
limitations to the data. Once more data is available, this study could be expanded in several ways. More
types of media could be c
oded and used in reference to eating disorder
deaths.
Doing so would provide
a clearer ide
a of what kinds of media impact

society and how. The study would also benefit from looking
at a different set of countries. While this study primarily focused on countries of higher economic status,
other studies could collect data from undeveloped countri
es. This could depict how media i
s impacting
eating disorder

deaths globally.

Only more research can provide more answers.














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References

NationMaster.Com
. (2003). Retrieved November 26, 2012, from http://www.nationmaster.com

University of Maryland Medical Center
. (2011). Retrieved November 25, 2012, from Eating Disorders
-
Causes:
http://www.umm.edu/patiented/articles/what_causes_eating_disorders_000049_3.htm

(2012, October).
Cosmo
.

Central Intelligence Agency
. (n.d.). Retrieved

November 25, 2012, from World Fact Book:
https://www.cia.gov/library/publications/the
-
world
-
factbook/index.html

Efron, S. (n.d.).
Dimensions Online.

Retrieved November 27, 2012, from Eating Disorders on the Increase
in Asia: http://www.dimensionsmagazine.
com/news/asia.html

Ng, J., & J. Ng
. (2007, February 20).
channelnewsasia.com.

Retrieved November 25, 2012, from
Singapore news:
http://www.channelnewsasia.com/stories/singaporelocalnews/view/259641/1/.html

Ratner, B. (n.d.).
DM
-
1 STAT Articles
. Retrieved N
ovember 30, 2012, from Correlation Coefficient:
Definition : http://www.dmstat1.com/res/TheCorrelationCoefficientDefined.html

Sparhawk, J. M. (2003, August). Body Image and The Media: The Media's Influence on Body Image.
Wisconsin .

Srinivasan, T., T. Sure
sh
, &

V. Jayaram
. (1998).
International Journal of Social Psychiatry.

Retrieved
November 25, 2012, from Sage: http://isp.sagepub.com/content/44/3/189



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Appendix A:
Sparhawk’s Null Hypotheses

Ho1: There is no statistically significant difference within

the sample scores in the Body Image States
Scale between the two groups which consist of a control group and an experimental group.


Ho2: There is no difference between the control and experimental group’s feelings regarding physical
appearance.


Ho3: The
re is no difference between the control and experimental groups’ feelings regarding their body
shape and size.


Ho4: There is no difference between the control and experimental groups’ satisfaction with weight.


Ho5: There is no difference between the
control and experimental groups’ feelings about physical
attractiveness.


Ho6: There is no difference between the control and experimental groups feelings about looks.


















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Country
# of Internet Users
#of ED deaths
# Internet Hosts
Population
% of Users
% of Deaths
% of Hosts
1
US
245,000,000
218
505,000,000
313847465
0.78063399
6.946E-07
1.609061905
2
Canada
26,960,000
19
8,743,000
34300083
0.78600393
5.5393E-07
0.254897342
3
Cuba
1,606,000
1
3,244
11075244
0.14500809
9.0291E-08
0.000292906
4
Dominican Republic
2,701,000
1
404,500
10088598
0.26772798
9.9122E-08
0.040094768
5
Mexico
31,020,000
7
16,233,000
114975406
0.26979683
6.0883E-08
0.141186716
6
Costa Rica
1,485,000
1
147,258
4636348
0.3202952
2.1569E-07
0.031761637
7
Austria
6,143,000
5
3,512,000
8219743
0.74734697
6.0829E-07
0.427263967
8
Germany
65,125,000
40
20,043,000
81305856
0.80098782
4.9197E-07
0.246513609
9
Denmark
4,750,000
5
4,297,000
5543453
0.85686665
9.0196E-07
0.77514863
10
Iceland
301,600
1
369,969
313183
0.96301523
3.193E-06
1.181318909
11
Spain
28,119,000
3
4,228,000
47042984
0.59772994
6.3771E-08
0.089875251
12
Malta
240,600
1
14,754,000
409836
0.58706409
2.44E-06
35.99976576
13
Luxembourg
424,500
1
250,900
509074
0.83386698
1.9644E-06
0.492855656
14
Sweden
8,398,000
13
5,978,000
9103788
0.92247315
1.428E-06
0.656649737
15
Slovenia
1,298,000
2
415,581
1996617
0.65009964
1.0017E-06
0.208142573
16
Norway
4,431,000
3
3,588,000
4707270
0.94130993
6.3731E-07
0.762225239
17
Netherlands
14,872,000
8
13,699,000
16730632
0.88890844
4.7816E-07
0.818797521
18
Finland
4,393,000
2
4,763,000
5262930
0.83470614
3.8002E-07
0.905009187
19
UK
51,444,000
1
8,107,000
63047162
0.8159606
1.5861E-08
0.12858628
20
Romania
7,787,000
1
2,667,000
21848504
0.35640884
4.577E-08
0.122067854
21
Poland
22,452,000
5
13,265,000
38415284
0.58445487
1.3016E-07
0.34530527
22
Czech Republic
6,681,000
2
4,148,000
10177300
0.65646095
1.9652E-07
0.407573718
23
Croatia
2,234,000
1
729,420
4480043
0.49865593
2.2321E-07
0.162815402
24
Lithuania
1,964,000
1
1,205,000
3525761
0.55704286
2.8363E-07
0.341770188
25
Australia
15,810,000
8
17,081,000
22015576
0.71812793
3.6338E-07
0.775859782
26
New Zealand
3,400,000
2
3,026,000
4327944
0.78559242
4.6211E-07
0.699177254

Appendix B: Data Charts



















(NationMaster.Com, 2003)

(Central Intelligence Agency)





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A
ppendix C
: Raw Data


FIELD LISTING

:: INTERNET HOSTS

PRINT

Thi s entry l i sts the number of Int ernet hosts avai l abl e wi thi n a country. An I nternet host i s a comput er
connect ed di rectl y t o the
I nternet; normal l y an I nt ernet Servi ce Provi der's (ISP) comput er i s a host.
Internet users may use ei ther a hard
-
wi red t ermi nal, at an i nsti tuti on wi th a mai nframe comput er
connect ed di rectl y t o the I nternet, or may connect remotel y by way of a modem vi a t
el ephone l i ne,
cabl e, or sat el l i te t o the I nternet Servi ce Provi der's host comput er. The number of hosts i s one
i ndi cator of the ext ent of Internet connecti vi t y.

Country Comparison to the World

COUNTRY

INTERNET HOSTS

Australia

17.081 mi l l i on (2012)

Austria

3.512 mi l l i on (2012)

Canada

8.743 mi l l i on (2012)

Costa Rica

147,258 (2012)

Croatia

729,420 (2012)

Cuba

3,244 (2012)

Czech Republic

4.148 mi l l i on (2012)

Denmark

4.297 mi l l i on (2012)

Dominican Republic

404,500 (2012)

Finland

4.763 mi l l i on (2012)


Germany

20.043 mi l l i on (2012)

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Iceland

369,969 (2012)

Lithuania

1.205 mi l l i on (2012)

Luxembourg

250,900 (2012)

Malta

14,754 (2012)

Mexico

16.233 mi l l i on (2012)

Netherlands

13.699 mi l l i on (2012)

New Zealand

3.026 mi l l i on (2012)

Norway

3.588 mi l l i on (2012)

Poland

13.265 mi l l i on (2012)

Romania

2.667 mi l l i on (2012)

Slovenia

415,581 (2012)

Spain

4.228 mi l l i on (2012)

Sweden

5.978 mi l l i on (2010)

United Kingdom

8.107 mi l l i on (2012)

United States

505 mi l l i on (2012); note
-

t he US I nternet t otal host count i ncl udes the
fol l owi ng top l evel domai n host addresses: .us, .com,
.edu, .gov, .mi l,
.net, and .org








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Field Listing

:: Internet users

PRINT

This entry gives the number of users within a country
that access the Internet. Statistics vary
from country to country and may include users who access the Internet at least several times a
week to those who access it only once within a period of several months.

Country Comparison to the World

Country

Internet users

Australia

15.81 million (2009)

Austria

6.143 million (2009)

Canada

26.96 million (2009)

Costa Rica

1.485 million (2009)

Croatia

2.234 million (2009)

Cuba

1.606 million

note:

private citizens are prohibited from buying computers or
accessing the Internet without special authorization; foreigners
may access the Internet in large hotels but are subject to
firewalls; some Cubans buy illegal passwords on the black
market or take a
dvantage of public outlets to access limited email
and the government
-
controlled "intranet" (2009)

Czech Republic

6.681 million (2009)

Denmark

4.75 million (2009)

Dominican Republic

2.701 million (2009)


Finland

4.393 million (2009)

Germany

65.125 million (2009)

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Iceland

301,600 (2009)

Lithuania

1.964 million (2009)

Luxembourg

424,500 (2009)

Malta

240,600 (2009)

Mexico

31.02 million (2009)

Netherlands

14.872 million (2009)

New Zealand

3.4 million (2009)

Norway

4.431 million (2009)

Poland

22.452 million (2009)

Romania

7.787 million (2009)

Slovenia

1.298 million (2009)

Spain

28.119 million (2009)

Sweden

8.398 million (2009)

United Kingdom

51.444 million (2009)

United States

245 million (2009)








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# 1
Iceland
: 3.36999 deaths per 1 million peo

# 2
Malta
: 2.5092 deaths per 1 million peo

# 3
Luxembourg
: 2.13415 deaths per 1 million peo

# 4
Japan
: 1.45977 deaths per 1 million peo

# 5
Sweden
: 1.44412 deaths per 1 million peo

# 6
Slovenia
: 0.99453 deaths per 1 million peo

# 7
Denmark
: 0.920471 deaths per 1 million peo

# 8
United States
: 0.737149 de
aths per 1 million peo

# 9
Norway
: 0.653168 deaths per 1 million peo

# 10
Aus
tria
: 0.610874 deaths per 1 million peo

# 11
Canada
: 0.57918 deaths per 1 million peo

# 12
New Zealand
: 0.495663 deaths per 1 million peo

# 13
Netherlands
: 0.487597 deaths per 1 million peo

# 14
Germany
: 0.485254 deaths per 1 million peo

# 15
Australia
: 0.398208 deaths per 1 million peo

# 16
Finland
: 0.382922 deaths per 1 million peo

# 17
Israel
: 0.318624 deaths per 1 mi
llion peo

# 18
El Salvador
: 0.298285 deaths per 1 million peo

# 19
Urugu
ay
: 0.29274 deaths per 1 million peo

# 20
Korea, South
: 0.287823 deaths per 1 million peo

# 21
Lithuania
: 0.278009 deaths per 1 million peo

# 22
Costa Rica
: 0.249004 deaths per 1 million peo

# 23
Croatia
: 0.22242 deaths per 1 million peo

# 24
Czech Republic
: 0.195293 deaths per 1 million peo

# 25
Chile
: 0.187723 deaths per 1 million peo

# 26
Nicaragua
: 0.182983 deaths per 1 million peo

# 27
South Africa
: 0.157857 deaths per 1 million peo

# 28
Paraguay
: 0.15753 deaths per 1 million peo

# 29
Brazil
: 0.150446 deaths per 1 million peo

# 30
Poland
: 0.129675 deaths per 1 million peo

# 31
Dominican Republic
: 0.110497 deaths per 1 million peo

# 32
Argentina
: 0.101168 deaths per 1 million peo

# 33
Cuba
: 0.088129 deaths per 1 million

peo

# 34
Spain
: 0.074366 deaths per 1 million peo

# 35
Mexico
: 0.0659115 death
s per 1 million peo

# 36
Colombia
: 0.0465614 deaths per 1 million peo

# 37
Romania
: 0.0447828 deaths per 1 million peo

# 38
Venezuela
: 0.0394089 deaths per 1 million peo

# 39
Peru
: 0.0358089 deaths per 1 million peo

# 40
United Kingdom
: 0.0165451 deaths per 1 million peo