Using Statistics to Analyze Gender Preference for Unisex Gym and Time Spent on Cardio Machines

This report analyzes the gender preference for unisex gym and time spent on cardio machines using statistics. It explains the appropriate methods to summarize data based on the nature of data and provides simple conclusions based on the computer output of hypothesis tests. The report also discusses the structure of a good report that uses statistics.

Running head: BUS105 ASSIGNEMNT
BUS105 Assignment
Name of the Student:
Name of the University:
Author’s Note:

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Table of Contents
“Section 1..............................................................................................................................................3
“Section 2..............................................................................................................................................5
“Section 3..............................................................................................................................................7
“Section 4..............................................................................................................................................9
“Section 5............................................................................................................................................11
“Section 6............................................................................................................................................11
“Section 7............................................................................................................................................11
References...........................................................................................................................................12
Appendix.............................................................................................................................................12

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“Section 1
Reproduction of the data summaries including details of simple calculations
1a) Summary of the variable ‘do the customers want a unisex gym’ just considering the females”
Count of
gender Column Labels
Row Labels Female Grand Total
no 20 20
yes 33 33
Grand Total 53 53
As per the table, there are 53 females and 47 males from which 33 females want a unisex gym which
is more than 50% of the females. Hence, the unisex gym has more population of females than males.
“ 1b) Summary of the relationship between the variables ‘time on cardio machine’ and ‘time on
weight machine’”
0 10 20 30 40 50 60 70
0
10
20
30
40
50
f(x) = − 0.700584785647525 x + 34.3011108445893
R² = 0.772650147712829
Minutes spent on cardio and weight machine
Time spent on cardio
Time spent on weight
machine
The relationship between the time on cardio machine and time on weight machine can be drawn
using the scatter plot and depicting using a trendline. Both the variables have a negative correlation
depicting that as one spends more minutes on cardio would instil confidence the person; as a result,
the minutes of spending time on weight machine would be lesser. Hence, there is an inverse
proportion between the two; if minute spent on cardio increases then the minutes spent on weight
machine will decrease and vice versa.
“1c) Summary that lets you investigate the relationship between the variable ‘does the customer
want a unisex gym’ and ‘gender’”
Count of
gender Column Labels
Row Labels no yes Grand Total
Female 20 33 53

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Male 32 15 47
Grand Total 52 48 100
Count of
Should the
gym be
Unisex? Column Labels
Row Labels no yes Grand Total
Female 37.74% 62.26% 100.00%
Male 68.09% 31.91% 100.00%
Grand Total 52.00% 48.00% 100.00%
The sample size depicts that the number who does not wants the unisex gym is greater than the
ones who want. On the contrary, there is contrary data between the gender. 62.26% of the female
proportion wants the gym to be unisex but the 68.09% of the males do not want the gym to be
unisex. As a result, females prefer the gym to be unisex being more in number and also as per
likeliness amongst gender.
“1d)Summary that lets you investigate the relationship between the variable ‘time spent on the
cardio machine ’ and ‘gender’”
Row Labels
Count of
gender
Average of Minutes on
Cardio
StdDev of Minutes on
Cardio
Female 53 37.9245283 15.14144726
Male 47 15.85106383 11.57767477
Grand Total 100 27.55 17.47285051
The count of female in the sample is more than males and as per the average minutes spent on
cardio is more for females because they 38 minutes whereas males spent approximately 16 minutes.
Moreover, the standard deviation figure is more for females because the variability of time is from
the average time spent. However, females have a directional relationship whereas males have a
inverse relationship with the time spent on cardio machine.

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“Section 2
Calculation of confidence intervals and test statistics
2a) Calculation of confidence interval for the proportion of females that prefer a Unisex gym using
my allocated sample”
Sample proportion for females at 90% CI
Should the Gym be Unisex as per the sample?
Gender no yes Grand Total
Female 20 33 53
Male 32 15 47
Grand Total 52 48 100
Number of Females = 53
Proportion of sample ^p = 33/53 = 0.622
Std Error of ^p = √ 0.622( 1−0.6 22)
53 =¿ 0.06660
90% Confidence interval =(Lower Limit, Upper Limit) = {(0.622 – (1.645*0.06660)), (0.622 +
(1.645*0.06660))}
 90% Confidence interval =(Lower Limit, Upper Limit) = (0.5124, 0.7315)
“2b) Calculation of confidence interval for the proportion of males that prefer a Unisex gym using my
allocated sample”
Sample proportion for males at 90% CI
Should the Gym be Unisex as per the sample?
Gender no yes Grand Total
Female 20 33 53
Male 32 15 47
Grand Total 52 48 100
Number of Males = 47
Proportion of sample ^p = 15/47 = 0.319
Std Error of ^p = √ 0.319(1−0. 319)
47 =¿ 0.06798
90% Confidence interval =(Lower Limit, Upper Limit) = {(0.319 – (1.645*0.0679)), (0.319 +
(1.645*0.0679))}

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 90% Confidence interval =(Lower Limit, Upper Limit) = (0.20730, 0.43069)
“2c) Calculation of Test stat for checking if the females that want a unisex gym is above 50% (Find
the sample proportion’s z score assuming p=0.5) using my allocated sample”
Test Statistics for p = 0.5
𝑧= 𝑝ො−𝑝
ට
𝑝(1−𝑝)
𝑛
Should the Gym be Unisex as per the sample?
Gender no yes Grand Total
Female 20 33 53
Male 32 15 47
Grand Total 52 48 100
Number of Females = 53
Proportion of sample ^p = 33/53 = 0.622
z= 0.6 22−0.5
√ 0.5 (1−0.5)
53
= 0.1 22
0.06 868 =1.7763
“2d) Calculation of Test stat for checking if the males that want a unisex gym is
above 50% (find the z score of proportion assuming p=0.5) using my allocated
sample”
Test Statistics for p = 0.5
𝑧= 𝑝ො−𝑝
ට
𝑝(1−𝑝)
𝑛
Should the Gym be Unisex as per the sample?
Gender no yes Grand Total
Female 20 33 53
Male 32 15 47
Grand Total 52 48 100
Number of Females = 53
Proportion of sample ^p = 33/53 = 0.622

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z= 0.6 22−0.5
√ 0.5 (1−0.5)
53
= 0.1 22
0.06 868 =1.7763

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“Section 3
Evidence I can decide the appropriate method to summarize data based on the nature of data (the
variable types)
a) My explanation why the different main findings in the sample report use different methods to
summarize data.”
The sample report uses variables as the nature and description of the data. The study in sample
report is to analyse “Whether there should be a unisex gym or not?”
Firstly, the survey questions were made pertaining to gender, likeliness/ preference for the unisex
gym and time spent on cardio and weight machines. Four variables are defined.
Variable Type of Data
Gender Categorical
Preference for unisex gym Categorical
Time spent on cardio machine Nominal (Quantitative)
Time spent on weight machine Nominal (Quantitative)
The summary of datasets is taken using pivot charts/ tables as the data can be summarized for
categorical variable and further percentage of the total to check for likeliness. For example if the
data set has two variable as “product version (Version A or Version B)” and “would the person buy it
(Yes or No)”. To consider the sample, pivot table is used and based on which a stacked bar graph is
made. The below table is added for understanding.
This helps in analysing the response in categorical variable with further proportion hypothesis
testing. Secondly, for two quantitative variable can be drawn using a scatter plot for two variables
like “distance travelled by the car” and its “selling price” which can enable to outline the trend
analysis using the equation.
The sample report produced the data in summarised and enable to understand so that neither
makes it difficult nor haphazard to examine the data set even if the sample size is big.
b) “My summary of an article that discusses gyms and an appropriate numerical summary using
my allocated sample from the section 3 dataset”

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Body shaming and gender differences are a common phenomenon where gender look decides the
body image such that a negative body image is given an ideal male/ female body image. Elíasdóttir
(2016), a graduate in psychology further ascertains that body image, body surveillance and esteem
is more highlighted in females than in males because that is socialization and further inspections
leading sexual objectifications. However, it is important to build a proper body image and improve
the wellbeing.
The same can be depicted by the sample taken for analysis where “variable 1 - gender” and
“variable 17 - are you ashamed of your body”, the two categorical data sets that are considered. The
results of the allocated sample are given as:
Count of
Gender Column Labels
Row Labels no yes
Grand
Total
female 11 35 46
male 41 13 54
Grand Total 52 48 100
Count of
Gender Column Labels
Row Labels no yes
Grand
Total
female 23.91%
76.09
% 100.00%
male 75.93%
24.07
% 100.00%
Grand Total 52.00%
48.00
% 100.00%
female male
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Gender and Body Shame
yes
no
The results depict that the females are more body shamed by the males. From 46 females, 35
females are ashamed of the body because of the socialization pressure and the comparison with the

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ideal image body look. This constitutes to 76.09% whereas males 75.93% males are not ashamed of
their body and continue to have the body esteem as one has to.
“Section 4
Discussion of the webpages used to do all the calculation of a hypothesis tests
4a use a webpage to do a hypothesis test of the difference between two proportions using my
allocated sample
Using
http://www.socscistatistics.com/tests/ztest/Default2.aspx”
“4b) a use a webpage to do a hypothesis test of the difference between two means using my
allocated sample
Using
https://www.graphpad.com/quickcalcs/ttest1/?Format=SD”

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The results are:

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“Section 5
Appropriate simple conclusions based on the computer output of hypothesis tests in section 4”
The results from the preference of unisex gym based on gender gives significant results depicting
that p is less than 0.05 level so making it a strong evidence (rejecting null hypothesis) that the
proportion is above 50% who would prefer unisex gym , such that most of females approve of the
same.
Secondly from the results of minutes on cardio machines in case of gender shows that the results is
less than 0.001 level, making it statistically significant with strong evidence (rejecting null
hypothesis) that the females spent more minutes on cardio machines such that the difference
between male and female is -22.073500 which is noteworthy for the results.
“Section 6
Demonstrates I can explain the structure of a good report that uses statistics”
The structure of the report needs to followed with basic criteria of introduction, background of the
study, data description, analysis and results with tables and charts, followed by recommendations
and conclusion. The references and appendix can make it complete. The overview of the report can
be given in an executive summary/ abstract. Also, the report needs to be presentable and organized
for best results.
The sample report is structured while adding executive summary even. However, there is one flaw in
the sample report such as headings like “recommendations and limitations” have not been
discussed. This can be further elaborated on ideas like “what other variables can be analysed” for
the study to be more put into practical use, followed by limitations like a set pattern is followed and
there is no scope of making it broad for a general public overview.
“Section 7
Demonstrates I can explain the hypothesis tests used in report and hypothesis testing in general”
The sample report discusses the hypothesis tests using preference to unisex gym with two different
tests, one with females and the other one with males defining the 50% proportion to be statistically
significant response.
For the hypothesis, Z score and p value is calculated, in this case p value is important to examine on
the study being valid. Here, p = 0.05 is taken stating that if the study is repeated for 100 times, then
95 times. The response on results would be the same. If the p value results in less than 0.05 then the
study is statistically significant.
The first hypothesis test on females was easy to understand as only one category of gender is taken
on preferability of unisex gym. The tests seem to have significant results on females wanting a unisex
gym with more than 50% of proportion. The computer results can be ascertain the manual
calculations done. The same can be done for the second hypothesis where other category of gender
that is males is taken. The results, in this case, have not come out to be significant because the
results are not valid at 0.05 level. The results not being significant on males wanting to have gym
does not support 50% of the proportion.

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For hypothesis 3 and 4, the results are not easily understandable on the hypothesis being performed
either on proportions or on means respectively but can be easy when done using webpages like
“GraphPad” and “socscistatistics”. In this case, the computer can perform the hypothesis statistics.
The results have come significant as p value is less than 0.05 level and statistically valid for
proportions of male and female likeliness on the unisex gym and the minutes on the cardio machines
for hypothesis 3 and 4 respectively. However, when it comes to analysing Type 1 and Type 2 errors,
understanding of hypothesis being performed with p value can be helpful in further calculation of α
and β.
References
Elíasdóttir, E.L.F., 2016. Is body shaming predicting poor physical health and is there a gender
difference? (Doctoral dissertation). Available at: https://skemman.is/bitstream/1946/25652/1/BSc
%20thesis%20-%20Eva%20Lind%20Fells%20.pdf
Appendix
Is body shaming predicting poor physical health and is there a gender difference?
What ever it is that we are doing, we certainly do it with our bodies. From the moment we wake, we
are consciously or sub-consciously relying on our body. We may look in the mirror and notice
changes in our body, and for some of us, even the most minor changes may impact upon how we
feel about ourselves. The image that we have of our bodies’ affects our experience of our bodies in
everyday life (Nettleton & Watson, 2002).
Body Shame and Body Image
Body shame is a concept that is used for the individuals’ self-conscious, negative emotional response
against one’s self. It appears in the individuals’ misstep to meet the ideal body standards, and the
acknowledgement of this failure (Fredrickson & Roberts, 1997). For women in the western countries,
the ideal body contains standards for outward appearance, for an example thinness and youth.
Because meeting these standards is important, women may internalize or self-objectify these
standards. Many women may feel that they do not meet these standards, resulting in a negative
self-directed emotions and one of these emotions is body shame (Fredrickson & Roberts, 1997). The
Objectification theory is one of the theories that have formed much research in the field of body
image (Fredrickson & Roberts, 1997). It explains that woman’s life experiences, as well as gender
role socialization of sexual objectification, induces them to look at themselves as objects, and
increasing inspections of their bodies (Moradi & Huang, 2008). The process increases the exposure
of anxiety and body shame. Given that self objectification and body image require the individual to
focus their attention to self presentation of their bodies and to enrol in actions that involve personal
as well as societal standards, it comes as no surprise that researchers are investigating the prelude
and emanation of self conscious emotions regarding the body (Castonguay, Brunet, Ferguson, &
BODY SHAME, PHYSICAL ILLNESS AND SELF-REPORTED PHYSICAL HEALTH 5 Sabiston, 2012; Noll &
Fredrickson, 1998).
The Objectification theory posits that selfobjectification affects girls, but not boy’s subjective well-
being. Although it has been determined that women self objectify themselves more than men,
research on this topic has been limited to women (Grabe, Hyde, & Lindberg, 2007). However, recent

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cultural developments suggest that the objectification of men regarding their bodies, has been
emerging (Grogan, 2010). Shame related to our bodies is an emotional state that can be quite
painful. This may originate from a social rejection from others, as well as the fear of inducting disgust
(Roberts & Goldenberg, 2007). Shame is often related to a diversity of maladaptive behavioral,
somatic, psychological and subjective experiences (Dickerson, Gruenewald, & Kemeny, 2004; Gilbert,
2007) and therefore, it is an essential emotional state in both research and practice, for
interventions regarding body image.
However, current research in the area of body image and body shame seems to focus on its effect on
psychological health, lacking research on its effects on physical health. By classifying the influences
of positive body image, we might increase the ability to produce successful interventions that are
appropriate for both genders. In doing so, we might be able to build up positive body image and
improve well being and health (Grogan, 2010).
Physical Health, Perceived Health and Self-reported Health
Previous studies have linked shame to poor psychological health, for an example depression (Grabe,
Hyde, & Lindberg, 2007), as well as eating disorders (Tiggemann & Slater, 2001). Furthermore,
shame is also related to poor physical health, indicative of deregulations of the immune system
(Kamen & Seligman, 1987). A few numbers of studies have been conducted on the consequences of
body shame on physical health. However, BODY SHAME, PHYSICAL ILLNESS AND SELF-REPORTED
PHYSICAL HEALTH 6 researchers are starting to think that body shame or the tendency to judge our
body harshly, is not just affecting our mental states, but also actually causing physical illnesses.
A study made by Jean M. Lamont (2015), tested whether body shaming was predicting poor physical
health. That is, by promoting attitudes that are negative against bodily processes and therefore
decreasing health assessments and having an impact on physical health. The results indicated that
body shaming predicted poor self-rated health. Body shame also predicted expansion in infections
and symptoms (Lamont, 2015). However, body shame might develop in perception of poor health
and future research might consider employing methods to assess health outcomes that do not build
on self-reports of health. These findings raise some important questions that need to be answered,
for example how much health toll body shame is taking. That is something that we do not know yet.
However, the results suggest that body shame could harm our physical health and we could use that
as a motivation to love our bodies (Lamont, 2015). It should be noted that previous studies regarding
health research indicate that women continuously report poorer physical health than men (Gijsbers
van Wijk, Huisman, & Kolk, 1999). This difference usually consists of higher self-reports of both
illness behavior and physical symptoms by women.
Further research on this difference is needed, but it has been suggested that mood states might
mediate these gender differences in self-reports of health (Gijsbers van Wijk, Huisman, & Kolk,
1999). Perceived health is an essential predictor of health, and a great interest is in self-assessed
health during adolescence. A previous study made by Meland, Haugland, & Breidablik (2007), tested
the relationship between body image and perceived negative health in adolescence, with the focus
on gender and age differences (Meland, Haugland, & Breidablik, 2007). It has been suggested, that
adolescents who have a negative body image are more prone to perceive their health as poor
(Alsaker, 1992). The results showed that perceived BODY SHAME, PHYSICAL ILLNESS AND SELF-
REPORTED PHYSICAL HEALTH 7 negative health increased with age, and that girls were more prone
to report negative health, compared to boys (Meland et al., 2007).
Gender differences

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Men who lack masculinity are often considered feminine (Grogan & Richards, 2002). Previous studies
imply that 50% to 71% of male undergraduates are not satisfied with their bodies and 90% of them
would like to be more muscular (Frederick et al., 2007). Furthermore, men in all age groups have the
desire for masculinity (Fisher, Dunn, & Thompson, 2002) and they report negative body image when
exposed to images of the ideal male body (Marian M. Morry, 2001), as well as the ideal female body
(Lavine, Wagner, & Sweeney, 1999). In the light of such images, both men and women might be in
greater exposure of developing persistent body shame (Frederick et al., 2007). A previous study
explained gender differences in body esteem using the Objectified Body Consciousness or OBC scale.
The results indicated that the relation among body shame, body esteem and body surveillance were
stronger for women, than for men. Women scored higher both on body surveillance and in body
shame, than did men (Poulin, Hand, Boudreau, & Santor, 2005). Further research on gender
differences regarding body image and body shame is needed, to address what could be causing
these differences.
The current study
The aim of this study was to examine whether body shame was predicting physical illnesses, as well
as negative self-reported physical health. Furthermore, the aim was to examine gender differences
regarding body shame, physical illnesses and self-reported physical health. Based on the above
literature, the following hypotheses were tested: (1) compared to men, women are more likely to
score higher on the body shame scale, self-report their BODY SHAME, PHYSICAL ILLNESS AND SELF-
REPORTED PHYSICAL HEALTH 8 physical health as poor and report physical illnesses, (2) participants
who score high on the body shame scale, are more likely to report physical illnesses, compared to
participants who score low on the body shame scale, and (3) participants who score high on the
body shame scale, are more likely to self-report their physical health as poor, compared to
participants that score low on the body shame scale.
Method
Participants
The participants were 92 undergraduate psychology and sports science students in Reykjavik
University. Participants were 30 males and 62 females, aged from 20 to 38. The mean age was 24,70
(SD = 4,03). Participants were chosen with a convenience sample, wherein two departments within
Reykjavik University were selected to participate. All students in these departments were invited to
participate in the study. Participants were asked to read a detailed information sheet about the
study, in which they were made aware of that their answers to the questionnaire were equivalent to
their approval of participation in the study. Participation was voluntary and no compensation was
given.
Instruments and measures
Participants completed a questionnaire, which included questions regarding gender, age, self-
reported physical health, body shame, and physical illness.
Self-reported physical health.
One question regarded participant’s physical health, where they were asked to self-report their
physical health. Answer options to the question were 1 = “Poor”, 2 = “Reasonable”, 3 = “Good” and
4= “Very good”.
Body shame.

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Body shame was measured using the The Objectified Body Consciousness Scale (OBCS), which is a
self-report measure of body consciousness. The scale has a total of 24 statements and three
subscales, including body surveillanve, body shame and appearance BODY SHAME, PHYSICAL ILLNESS
AND SELF-REPORTED PHYSICAL HEALTH 9 control beliefs. One of the subscales, including four
statements in the questionnaire, was used to measure participants’ body shame. Participants were
asked to read each statement and mark the option that described their attiture or beahvior the best.
They were asked (1) if they thought that something was wrong with them when they did not have
control over their wheight, (2) if they felt ashamed when they had not bothered to look their best,
(3) if they felt like a terrible person when they did not look as good as they could, and (4) if they
would feel ashamed if others really knew their weight. Answer options to these statements were 1 =
“Strongly Disagree”, 2 = “Disagree”, 3 = “Somewhat Disagree”, 4 = “Neither Agree nor Disagree”, 5 =
Somewhat Agree”, 6 = “Agree” and 7 = “Strongly agree”. These four items were then computed into
one variable. The scale ranged from 4-28, whereas a lower score indicated a lower body shame, and
a higher score indicated a higher body shame. A factor analysis was performed and indicated that
the questions loaded on one component and the loadings ranged from 0.443 to 0.802. Cronbach’s
Alpha for the body shame scale was rather poor α= 0.57. It should be noted that the questions from
The Objectified Body Consciousness Scale (OBCS) had never been translatet into Icelandic before,
and therefore this was the first time that questions from that scale were used in Icelandic to my
awareness.
Physical illness.
Nine questions regarded participants’ physical illness. They were asked how often they had been
aware of headache, dizziness, back pain, nausea or upset stomach, numbness or tingling somewhere
in the body, pain in the stomach, joint pain, shivering and pain in hands or feet, in the last 30 days.
Answer options to these questions were 1 = “Almost never”, 2 = “Rarely”, 3 = “Sometimes” and 4 =
“Often”. These nine items were then computed into one variable, wherein a higher score indicated
more frequency of physical illnesses. A factor analysis was performed and indicated that the
questions loaded on two components. In the first component the loadings ranged from 0.024 to
0.820 and in the BODY SHAME, PHYSICAL ILLNESS AND SELF-REPORTED PHYSICAL HEALTH 10 second
component the loadings ranged from 0.013 to 0.903. Cronbach’s Alpha was good α= 0.82.
Procedure
The study was sent to The Data Protection Authority, and The National Bioethics Committee
provided the permission for the study (VSN-16-030). Data collection took place 25 to 28 April. The
questionnaire was distributed to participants in an electronic format and it was hosted on a special
website. Several teachers in the psychology and sport science departments had approved to
distribute the questionnaire to their students. The teachers sent the students a URL with information
about the study and the questionnaire itself. Participation was voluntary. It was expected that it
would take participants about 15-20 minutes to complete the questionnaire. When participants
entered the given URL, they were asked to read a detailed information sheet about the purpose of
the study and its implementation, in which they were made aware of that their answers to the
questionnaire were equivalent to their approval of participation in the study. The information sheet
also made participants aware of that they could opt out at any given time and that they could
contact the person responsible for the study, if they felt any discomfort. After participants had
completed the questionnaire they were thanked for their participation.
Data analysis

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An Independent-Sample T Test was performed to examine whether women were on average more
likely to score higher on the body shame scale, self-report their health as poor and report physical
illness, compared to men. A two-way ANOVA was performed to examine the effect of gender and
body shame on physical illness, as well as the effect of gender and body shame on self-reported
physical health. The main effects and the interaction effect were examined. The body shame scale
was BODY SHAME, PHYSICAL ILLNESS AND SELF-REPORTED PHYSICAL HEALTH 11 divided into two
groups with a median split, where 1 = low score on the body shame scale, and 2 = high score on the
body shame scale.
Results
Table 1 shows descriptive statistics for the measures used in the study. The table indicates means
and standard deviations for self-report on physical health, physical illness and body shame, for men
and women separately. Looking at the table it can be seen that there is not a big difference in the
means between the genders. There was a statistically significant difference in the means between
genders in self-reported physical health, (t)88 = 2.39, p = 0.02, wherein women were more likely to
self-report their physical health as poor. There was not a statistically significant difference in the
means between genders in physical illness, (t)84 = -0.17, p = 0.87, or body shame, (t)66 = -0.98, p =
0.33.
Table 1. Descriptive Statistics for the measures self-report on physical health, physical illness, and
body shame, by gender Variables Men Women Range n Mean SD n Mean SD Self-reported physical
health 1-4 30 3.43 0.68 60 3.03 0.78 Physical illness 9-36 29 15.55 4.86 57 15.75 5.62 Body shame 4-
28 22 10.18 3.98 46 11.33 4.71
Table 2 shows the effect of gender and body shame on physical illness. A two-way ANOVA was
conducted to examine the main effects and interaction effect. There was not a statistically significant
main effect of gender on physical illness, F(1, 82) = 0.004, p = 0.95 BODY SHAME, PHYSICAL ILLNESS
AND SELF-REPORTED PHYSICAL HEALTH 12 and body shame on physical illness, F(1, 82) = 0.030, p =
0.86. There was not a statistically significant interaction between the effects of gender and body
shame on physical illness, F(1, 82) = 0.262, p = 0.61. Table 2. The effect of gender and body shame on
physical illness
Table 3 shows the effect of gender and body shame on self-reported physical health. A two-way
ANOVA was conducted to examine the main effects and interaction effect. There was a statistically
significant main effect of gender on self-reported physical health, F(1, 86) = 4.400, p = 0.039. There
was not a statistically significant main effect of body shame on self-reported physical health, F(1, 86)
= 0.475, p = 0.49. There was not a statistically significant interaction between the effects of gender
and body shame on self-reported physical health, F(1, 86) = 0.591, p = 0.44. Table 3. The effect of
gender and body shame on self-reported physical health Body shame Men Women Total n Mean SD
n Mean SD n Mean SD Low 11 15.82 4.99 24 15.25 5.34 35 15.43 5.17 High 18 15.39 4.91 33 16.12
5.87 51 15.86 5.51 Total 29 15.55 4.86 57 15.75 5.62 86 15.69 5.35 Body shame Men Women Total n
Mean SD n Mean SD n Mean SD Low 11 3.27 0.65 24 3.04 0.69 35 3.11 0.68 High 19 3.53 0.70 36
3.03 0.84 55 3.20 0.83 Total 30 3.43 0.68 60 3.03 0.78 90 3.17 0.77 BODY SHAME, PHYSICAL ILLNESS
AND SELF-REPORTED PHYSICAL HEALTH 13
Discussion In this study the influences of body shame on physical illness and self-reported physical
health were examined, as well as examining differences between genders. Interest was in examining
how a higher level of body shame might be predicting physical illnesses, and a more negative self-
reported health.

BUS105 ASSIGNEMNT
The first hypothesis was that women were more likely to score higher on the body shame scale, self-
report their physical health as poor and report physical illnesses, compared to men. The results of
the study indicated that women were more likely to self-report their health as poor, compared to
men. However, women were not more likely to score higher on the body shame scale and report
physical illnesses, compared to men. These results are in line with previous studies regarding self-
reported physical health, for example, Meland, Haugland, & Breidablik (2007) found that girls were
more prone to self-report their physical health as negative, compared to boys. However, these
results are not in line with previous studies regarding body shame and physical illnesses. For
example, Poulin, Hand, Boudreau, & Santor, (2005) found that women scored higher on the body
shame scale, than did men and Gijsbers van Wijk, Huisman, & Kolk (1999) reported that women
continuously reported poorer physical health with higher reports of both illness behaviour as well as
physical symptoms, than men.
The second hypothesis was that participants who scored high on the body shame scale were more
likely to report physical illnesses, compared to participants who scored low on the body shame scale.
The results of the study indicated that participants who scored high on the body shame scale were
not more likely to report physical illnesses than participants who scored low on the body shame
scale. These results are not in line with previous studies, for example, Lamont (2015) indicated that
body shame predicted expansion in infection as well as symptoms. Furthermore, Kamen & Seligman
(1987) reported that shame is related to poor BODY SHAME, PHYSICAL ILLNESS AND SELF-REPORTED
PHYSICAL HEALTH 14 physical health, indicative of deregulations of the immune system. To my
knowledge, previous studies have not examined the effect of both gender and body shame on
physical illness, and therefore it is not possible to compare the results of this study with previous
research conducted on both men and women. This points out the importance of examining this
relationship between genders.
The third hypothesis was that participants who scored high on the body shame scale, were more
likely to self-report their physical health as poor, compared to participants that scored low on the
body shame scale. The results of the study indicated that participants who scored high on the body
shame scale were not more likely to self-report their physical health as poor, compared to
participants who scored low on the body shame scale. These results are not in line with previous
studies, for example, the results from Jean M. Lamont’s (2015) study indicated that body shaming
predicted poor self-reported physical health. However, body shame might develop in perception of
poor health and future research should bear that in mind (Lamont, 2015).
This study has some limitations that need to be addressed. First of all, the study was cross sectional
and had relatively few participants, and therefore causal inferences could not be concluded. It would
have been preferable to have a random sample with more participants. Also, the questionnaire was
rather long and one-third of participants chose not to answer the questions that were supposed to
measure participants’ body shame, which undoubtedly had some impact on the results of the study.
It had also been interesting to examine this subject in a broader age range, because the results show
that most participants were not experiencing a major illness at the time the study was conducted. It
would be interesting to examine body shame in the elderly and whether body shame is related to
physical illness in that age group. Another limitation of this study is that the internal validity for the
body shame scale was BODY SHAME, PHYSICAL ILLNESS AND SELF-REPORTED PHYSICAL HEALTH 15
rather poor; suggesting that the instrument needs further testing and future studies should bear
that in mind.

BUS105 ASSIGNEMNT
Although this study has some limitations, it also has its strength. First of all, the results highlight the
importance of studying body shame among both men and women. The study demonstrates that
women are not more likely to experience body shame than men, and that is something that future
research should focus on, that is to examine body shame among both men and women. Another
strength of this study is that this was the first time, to my knowledge, that research on exactly this
topic has been conducted in Iceland. That is, to examine if body shame is predicting physical illnesses
as well as negative self-reported physical health. Additionally, this was also the first time that the
body shame scale was used in Icelandic to my awareness.
In conclusion, further research is required on this topic to better understand the relationship
between body shame and physical illness. The results of this study indicate that men are just as likely
to score high on the body shame scale as women. Studies regarding body shame have mainly been
performed on women through the years; however, research on men seems to be increasing in this
area. This might be because it has been stated that women objectify their bodies more than men
(Grabe, Hyde, & Lindberg, 2007), but recent cultural developments suggest that the objectification
of men regarding their bodies, has been emerging. Thus, The Objectification Theory may also be
relevant for men, but not only for women (Grogan, 2010). This topic is certainly interesting and the
results of this study highlight the importance that more research should be conducted on body
shame and the effect on physical health, to better understand the impact of our attitudes to our own
bodies. We only have one body and if merely changing our thoughts about our bodies can help us to
cope better with life, then it is certainly something that future research should focus on examining.
BODY SHAME, PHYSICAL ILLNESS AND SELF-REPORTED PHYSICAL HEALTH 16
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