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Social Network Usage in
Argentina
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Figure 1: Usage of Online Social Network
Between Males and Females
Collaboration and Decision Technologies
MT 8312
Professor Murtaza Haider
Peter (Yi Nan) Zhang
500597806

2. Introduction
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The rise of internet and mobile technology
ushered in a proliferation of social network
sites. These sites represent a new platform that
allows marketeers to target specific audiences
with unprecedented ease. However, the
adoption rate of social network sites varies
across countries. For instance, the U.S. has 46%
social network usage rate compared with
Germany (31%), after controlling for availability
of internet access (Pew Research Centre, 2010).
This means that other socio-economic/
demographic factors must contribute to the
adoption of social network. In this report, the
effects of contributing factors (e.g. age,
education) on social network usage in
Argentina are evaluated, using survey data
retrieved from Pew Research Centre. In
addition, key factors are summarized for our
investors based on statistical findings.
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Demographical Influences
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Gender has negligible impact on the usage of
social network (3 percentage points difference
between males and females) (figure 1). Chi-
square test, which is a statistical tool to find out
whether there exist true systematic differences
between variables, supports our conclusion
above (p=.82). Furthermore, no gender gaps
exist in the ownership of cellphones and access
to internet.
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On the other hand, age is a significant
contributor to social network usage. In our
analysis, people aged from 18 to 25 are coded
as“young adult”,; 26 to 35“adult”; 36 to 50
“middle aged”; 51 to 100“seniors”. As age group
increases by one category, social network usage
decreases by approximately 10 percentage
points (figure 2). Chi-square test (p=.72)
revealed that the two genders distribute fairly
amongst age groups, meaning that the
observed decrements in the usage of social
network is not attributed to gender.
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0%
25%
50%
75%
100%
young adult adult middle aged seniors
Age Group
UsageofOnlineSocialNetwork
no
yes
0%
25%
50%
75%
100%
male female
Gender
UsageofOnlineSocialNetwork
no
yes
Figure 2: Usage of Online Social Network
Amongst Different Age Groups

3. In an analysis using logit model, all contributing
factors (see appendix B) are taken into
consideration in order to identify statistically
significant indicators. In other words, all else
being equal, only few variables truly contribute
to the change of usage of social network.
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Using“young adults”as a reference point, we
find that middle aged group is 80% less likely to
use social network, and seniors are 85% less
likely to use social network, ceteris paribus
(appendix B). In figure 2, we saw that there is
about 10 percentage points difference between
every age groups, but here we only provided
two statistically significant findings. This is
because of a high probability that the
statistically insignificant findings happen due to
chance.
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Socio-economic Influences
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The level of education has reverse function as
age groups, for when levels of education rise,
the usage of social network increases (figure 3).
However the levels of education have a
declining rate of impact (i.e. the“university”
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group has 7 percentage points more social
network usage rate compared to the“tertiary
school”group, while the difference between
“primary school”and“secondary school”is 14
percentage points). Gender and age groups do
not affect distribution of level of education (Chi-
square values: p=.72, p=.36 respectively).
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All else being equal, people with university
education are 526% more likely than people
with primary school education to use social
network.
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For convenience sake, income levels are
amalgamated into high ($5,000 to $ 20,000+),
medium ($1,000 to $5,000) and low ($200 to
$1,000). The good news is that social network
usage is 58% for both the medium and low
income group (figure 4). Social network usage
rate is highest in the high income group (76%).
All else being equal, individuals in the the low
income group is 80% less likely to use social
network.
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0%
25%
50%
75%
100%
high medium low
Level of Income
UsageofOnlineSocialNetwork
no
yes
0%
25%
50%
75%
100%
primary school secondary school tertiary school university
Level of Education
UsageofOnlineSocialNetwork
no
yes
Figure 3: Usage of Online Social Network
Amongst Different Levels of Education
Figure 4: Usage of Online Social Network
Amongst Different Income Groups

4. Income has direct relationship with levels of
education. Table 1 shows the composition of
income groups by education levels. For
example, amongst high earning group, 5%
respondents have primary school education,
29% have secondary school, etc.
There is a disproportion in table 1, as one would
expect, for individuals who completed
university to be in the high income group, and
people with primary school education to be
over-represented in the low income group. Chi-
square test also validate this disproportion as
statistically significant (p=2.2e-16).
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Religious Attitudes
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Religious attitudes are denoted by three
variables: importance of religion to the
respondent’s life, number of prayers offered by
respondents, and frequency of religious
services. All else being equal, only the
frequency of religious services is a predictor of
usage of social network. Using“never
participating in religious services”as a base line,
the usage of social network increases as
frequency of religious services increases (table
2).
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In the frequency distribution of“usage of social
network”(figure 5), we can observe a general
negative trend which contradict with the
findings in the logit model. This is likely due to
correlations between the three variables
masking the true influence of frequency of
religious services.
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Paradoxically,“number of prayers”and
“importance of religion to the respondent’s life”
have diminishing effect on the usage of social
network. The ceteris paribus finding indicates
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high medium low
primary
school
5% 30% 59%
secondary
school
29% 48% 30%
tertiary
school
13% 13% 10%
university 53% 9% 2%
Total 100% 100% 100%
Table 1: Education Levels within Income Groups
Frequency of
participating in
religious services
Likelihood of
using social
network
Never 100%
Seldom 408%
Few times a year 400%
Once or twice a
month
1331%
Once a week 1027%
0%
25%
50%
75%
100%
never seldom a few times a year once or twice a month once a week more than once a week
Frequency of Religious Services
UsageofOnlineSocialNetwork
no
yes
Table 2: Frequency of participation in
religious services as predictor of
likelihood of using social network
Figure 5: Usage of Online Social Network Amongst
Different Frequency of Religious Services Groups
Income Groups
LevelsofEducation

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that individuals who report religion as
“somewhat important”and“very important”
have 87% and 93% less chance of using social
network. In addition, the frequency distribution
support this observation (figure 6 & figure 7).
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With the available data, the only explanation for
the paradox above is that respondents who
attend religious services are“casual goers”, and
religious attendance is considered more as a
social norm rather than a personal initiative.
Therefore the data in table 2 can be translated
into: the more social events a person attend, the
more likely it is for him/her to use social network.
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Technology Influences
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73% of the respondents have cellphones, while
only 45% have internet access. 43% of total
respondents have both cellphone and internet;
32% of total respondents have cellphones but
have no internet (table3). Cellphone ownership
is more prevalent in Argentina than internet
access.
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0%
25%
50%
75%
100%
never once a week or less a few times a week once a day several times a day
Number of Prayers
UsageofOnlineSocialNetwork
no
yes
0%
25%
50%
75%
100%
not at all important not too important somewhat important very important
Importance of Religion
UsageofOnlineSocialNetwork
no
yes
Figure 6: Number of Prayers & the Usage of Online Social
Network
Figure 7: Importance of Religion & the
Usage of Online Social Network
No Yes Total
No 23.2% 31.5% 54.8%
Yes 2.8% 42.5% 45.2%
Total 26% 74% 100%
Cellphone
Internet
Table 3: Internet and Cellphone ownership

6. Even without being statistical significant
indicators, internet access and cellphone
ownerships are still precursors for social
network usage. Owners of cellphone reported
24 percentage points more social network
usage rate compared to individuals without
cellphones (figure 8). 13 percentage points
difference exists between respondents with and
without internet access (figure 9).
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Table 4 summarizes the characteristics of social
network users, almost 100% of users have both
cellphones and internet.
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Summaries
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Investors who wish to expand their social media
consumer base in Argentina should be mindful
of five critical factors:
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a) 78% of young adults age 18 to 25 use online
social network; all else being equal, individuals
between the age of 36 to 100 are approximately
80% less likely to use social network compared
to their younger counterparts.
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b) All else being equal, people with university
education are 526% more likely than people
with primary school education to use social
network.
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c) Social network usage rate is highest in the
high income group (76%); all else being equal,
individuals in the the low income group is 80%
less likely to use social network.
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d) All else being equal, individuals who
participate in religious (social) services most
frequently are 1000% more likely to use social
0%
25%
50%
75%
100%
no yes
Ownership of Cellphones
UsageofOnlineSocialNetwork
no
yes
0%
25%
50%
75%
100%
no yes
Internet AccessUsageofOnlineSocialNetwork
no
yes
No Yes
No 0 9
Yes 1 223
Cellphone
Internet
Table 4: Internet and Cellphone ownership
among social network users
Figure 8: Ownership of Cellphones & the
Usage of Online Social Network
Figure 9: Access to internet & the Usage of
Online Social Network

7. network compared with individuals who never
attend religious services.
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e) Individuals who report religion as a very
important aspect of their life use social network
93% less than individuals who don’t consider
religion important.
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f) Almost 100% social network users have
access both to internet and cellphones.
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8. Reference
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Pew Research Center. (December 15, 2010). Computer and cell phone usage up around the world:
Global publics embrace social networking. Global Attitudes Project. Retrieved from: http://
www.pewglobal.org/2010/12/15/global-publics-embrace-social- networking/
!Pew Research Center. (2010). Global Social Media Usage [Data file].
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Appendix A
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Coding for the Pew data set.
!#Country division into subgroups
Argentina <-
subset(pew,subset=country=="argentina",select=c(country,q63,q65,q66,q69b,q80,q120,q121,q124,q127,q128,q1
37,q129arg,q138arg,q131arg,q133arg, weight))
!!#Renaming questions into texts
names(Argentina)[c(2,3,4,5,6,7,8,9,10,11,12,13,14,15,16)] <-
c("internet","cell","social_net","women_work","women_rights","gender","age","n_prayers","importance_reli
gion","freq_reli_services","n_household","education","political","income","ethnicity")
!#Cleaning ambiguous answers, ordering items
Argentina$country <- recode(Argentina$country, '"argentina"="argentina"; else=NA')
Argentina$internet <- recode(Argentina$internet, '"yes"="yes"; "no"="no";
else=NA',as.factor.result=TRUE)
Argentina$cell <- recode(Argentina$cell, '"yes"="yes"; "no"="no"; else=NA',as.factor.result=TRUE)
Argentina$social_net <- recode(Argentina$social_net, '"yes"="yes"; "no"="no";
else=NA',as.factor.result=TRUE)
Argentina$women_work <- recode(Argentina$women_work, '"completely agree"="completely agree"; "mostly
agree"="mostly agree"; "mostly disagree"="mostly disagree"; "completely disagree"="completely disagree";
else=NA',as.factor.result=TRUE)
Argentina$women_rights <- recode(Argentina$women_rights, '"should"="should"; "should not"="should not";
else=NA',as.factor.result=TRUE)
Argentina$n_prayers <- recode(Argentina$n_prayers, '"several times a day"="several times a day"; "once a
day"="once a day";"a few times a week"="a few times a week";"once a week or less"="once a week or
less";"never"="never";"(Other)"="(Other)"; else=NA',as.factor.result=TRUE)
Argentina$importance_religion <- recode(Argentina$importance_religion, '"very important"="very
important"; "somewhat important"="somewhat important";"not too important"="not too important";"not at
all important"="not at all important"; else=NA',as.factor.result=TRUE)
Argentina$n_household <- recode (Argentina$n_household, '"99"=NA', as.factor.result=TRUE)
Argentina$freq_reli_services <- factor(Argentina$freq_reli_services, levels=c('never','seldom','a few
times a year','once or twice a month','once a week','more than once a week'))
Argentina$ethnicity <- recode (Argentina$ethnicity, '"argentina"="argentina"; "other"="other"; else=NA')
Argentina$age <- recode(Argentina$age, '18:25= "young adult"; 26:35="adult"; 36:50="middle aged";
51:120="seniors"; else=NA', as.factor.result=TRUE)
Argentina$age <- factor(Argentina$age, levels=c('young adult','adult','middle aged','seniors'))
Argentina$n_prayers <- factor(Argentina$n_prayers, levels=c('never','once a week or less','a few times a
week','once a day','several times a day'))
Argentina$women_work <-factor(Argentina$women_work, levels=c('completely disagree','mostly
disagree','mostly agree','completely agree'))
Argentina$income <-recode(Argentina$income, 'c("more than $ 20.000","from $ 15.001 to $ 20.000","from $
10.001 to $ 15.000","from $ 8.001 to $ 10.000","from $ 5.001 to $ 8.000") = "high";c("from $ 3.001
to $ 5.000","from $ 2.001 to $ 3.000","from $ 1.601 to $ 2.000","from $ 1.301 to $ 1.600","from $
1.001 to $ 1.300")= "medium";c("from $ 851 to $ 1.000","from $ 701 to $ 850","from $ 551
to $ 700","from $ 451 to $ 550","from $ 351 to $ 450","from $ 201 to $ 350")="low";
else=NA')
Argentina$income <- factor(Argentina$income, levels=c('high','medium','low'))
Argentina$education <-recode(Argentina$education,'c("no formal education","incomplete primary
school","complete primary school ")="primary school";c("incomplete secondary school","complete secondary
school ")="secondary school";c("complete tertiary school ", "incomplete tertiary school")="tertiary
school";c("incomplete university","complete university ")="university"; else=NA')
!#Analytical part
!attach(Argentina)

9. round(prop.table(xtabs(weight~social_net+gender),2)*100)
round(prop.table(xtabs(weight~social_net+age),2)*100)
round(prop.table(xtabs(weight~social_net+n_household),2)*100)
round(prop.table(xtabs(weight~social_net+income),2)*100)
round(prop.table(xtabs(weight~social_net+education),2)*100)
round(prop.table(xtabs(weight~social_net+n_prayers),2)*100)
round(prop.table(xtabs(weight~social_net+importance_religion),2)*100)
round(prop.table(xtabs(weight~social_net+freq_reli_services),2)*100)
round(prop.table(xtabs(weight~social_net+political),2)*100)
round(prop.table(xtabs(weight~social_net+ethnicity),2)*100)
!!!#Negation on women’s rights is 2/500, therefore not considered usable
round(prop.table(xtabs(weight~social_net+women_rights,2)*100)
!!#Social_network Logit Model
!GLM.1 <- glm(social_net ~ age + cell + education + ethnicity + freq_reli_services + gender +
importance_religion + income + internet + n_household + n_prayers + political + social_net +
women_rights + women_work, family=binomial(logit), weights=weight, data=Argentina)
summary(GLM.1)
!round(cbind((exp(coef(GLM.2))-1)*100),3)
!
Appendix B
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14 variables are used in the logit model, and few variables did not yield statistical findings, and they
are not meaningful or conducive towards the discussion of the report. They are not mentioned in the
report (i.e. ethnicity, number of people in the household, political affiliation, opinions on women’s
rights). Estimated coefficients are converted to their exponential values, subtracted from 1 and
multiplied with 100, in order to find out the true effects of estimated coefficients.
! Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.504e+01 1.385e+03 -0.011 0.99134
age[T.adult] -6.109e-01 4.538e-01 -1.346 0.17818
age[T.middle aged] -1.602e+00 4.940e-01 -3.243 0.00118 **
age[T.seniors] -1.933e+00 6.546e-01 -2.953 0.00314 **
cell[T.yes] 3.196e-01 7.693e-01 0.416 0.67777
education[T.secondary school] 1.289e+00 6.823e-01 1.889 0.05889 .
education[T.tertiary school] 1.229e+00 7.231e-01 1.699 0.08925 .
education[T.university] 1.835e+00 7.824e-01 2.345 0.01902 *
ethnicity[T.other] -5.204e-02 7.785e-01 -0.067 0.94670
freq_reli_services[T.seldom] 1.626e+00 6.348e-01 2.562 0.01042 *
freq_reli_services[T.a few times a year] 1.609e+00 6.482e-01 2.482 0.01307 *
freq_reli_services[T.once or twice a month] 2.661e+00 8.805e-01 3.022 0.00251 **
freq_reli_services[T.once a week] 2.423e+00 7.789e-01 3.110 0.00187 **
freq_reli_services[T.more than once a week] 9.940e-01 1.288e+00 0.771 0.44042
gender[T.female] 3.588e-01 3.668e-01 0.978 0.32806
importance_religion[T.not too important] -5.447e-01 6.670e-01 -0.817 0.41409
importance_religion[T.somewhat important] -2.067e+00 8.263e-01 -2.502 0.01235 *
importance_religion[T.very important] -2.653e+00 9.298e-01 -2.853 0.00433 **
income[T.medium] -7.436e-01 5.152e-01 -1.443 0.14889
income[T.low] -1.592e+00 7.971e-01 -1.997 0.04583 *
internet[T.yes] 1.565e+01 1.385e+03 0.011 0.99098
n_household[T.10] -1.612e+01 1.329e+03 -0.012 0.99032
n_household[T.11] -1.616e+01 1.430e+03 -0.011 0.99098
n_household[T.2] -2.161e-01 8.469e-01 -0.255 0.79858
n_household[T.3] -6.439e-02 8.412e-01 -0.077 0.93899
n_household[T.4] 8.089e-01 8.208e-01 0.986 0.32436
n_household[T.5] -8.986e-02 8.879e-01 -0.101 0.91939
n_household[T.6] -5.087e-01 9.261e-01 -0.549 0.58280
n_household[T.7] 7.007e-01 1.389e+00 0.504 0.61401
n_household[T.8] 7.399e-01 1.878e+00 0.394 0.69367
n_household[T.9] -1.855e+01 1.456e+03 -0.013 0.98983
n_prayers[T.once a week or less] 4.100e-01 6.355e-01 0.645 0.51876
n_prayers[T.a few times a week] -1.744e-01 6.145e-01 -0.284 0.77652
n_prayers[T.once a day] -3.482e-01 6.497e-01 -0.536 0.59200
n_prayers[T.several times a day] 1.052e+00 9.906e-01 1.063 0.28800

10. political[T.justicialismo pro kirchnerista] 6.674e-03 6.766e-01 0.010 0.99213
political[T.justicialismo oposition of the kirchner's] -8.844e-02 7.517e-01 -0.118 0.90635
political[T.coalicixf3n cxedvica (carrio coalition)] 3.887e-03 1.094e+00 0.004 0.99717
political[T.pro (macri political party)] -9.830e-02 9.890e-01 -0.099 0.92083
political[T.other ] -5.138e-01 8.300e-01 -0.619 0.53589
political[T.none/no party] -2.174e-01 5.891e-01 -0.369 0.71211
women_rights[T.should not] -1.632e+00 1.686e+00 -0.968 0.33313
women_work[T.mostly disagree] 3.412e-01 1.584e+00 0.215 0.82945
women_work[T.mostly agree] -5.411e-01 9.882e-01 -0.548 0.58396
women_work[T.completely agree] 2.696e-02 9.344e-01 0.029 0.97698
!!! [,1]
(Intercept) -100.000
age[T.adult] -45.716
age[T.middle aged] -79.851
age[T.seniors] -85.531
cell[T.yes] 37.664
education[T.secondary school] 262.868
education[T.tertiary school] 241.710
education[T.university] 526.368
ethnicity[T.other] -5.071
freq_reli_services[T.seldom] 408.357
freq_reli_services[T.a few times a year] 399.633
freq_reli_services[T.once or twice a month] 1331.351
freq_reli_services[T.once a week] 1027.553
freq_reli_services[T.more than once a week] 170.211
gender[T.female] 43.155
importance_religion[T.not too important] -42.000
importance_religion[T.somewhat important] -87.347
importance_religion[T.very important] -92.953
income[T.medium] -52.461
income[T.low] -79.644
internet[T.yes] 627988432.298
n_household[T.2] -19.435
n_household[T.3] -6.236
n_household[T.4] 124.545
n_household[T.5] -8.594
n_household[T.6] -39.874
n_household[T.7] 101.517
n_household[T.8] 109.567
n_household[T.9] -100.000
n_household[T.10] -100.000
n_household[T.11] -100.000
n_prayers[T.once a week or less] 50.687
n_prayers[T.a few times a week] -16.007
n_prayers[T.once a day] -29.403
n_prayers[T.several times a day] 186.478
political[T.justicialismo pro kirchnerista] 0.670
political[T.justicialismo oposition of the kirchner's] -8.464
political[T.coalicixf3n cxedvica (carrio coalition)] 0.389
political[T.pro (macri political party)] -9.362
political[T.other ] -40.179
political[T.none/no party] -19.538
women_rights[T.should not] -80.445
women_work[T.mostly disagree] 40.665
women_work[T.mostly agree] -41.791
women_work[T.completely agree] 2.732
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