Bus226FinalProject-2

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Executive Summary
The topic that is going to be the focus on this presentation is factors that affect the
development of children. In this research report we will be assessing the child development in 60
countries, in four geographical regions. These regions include the Caribbean, European, South
America and Asia.
Amidst these regions there are various factors that we have compiled that would affect
the development of a child, although in this report we will be focusing our attention on four main
variables: proximity to urban areas, divorce rate, corruption, and government type. All these
variables are compared to the child development index (CDI). Various methods of analysis are
used in this report to conclude our findings and results. The methods used are the correlation
analysis, an independent sample T-test, Chi-Squared Test, Analysis of Variance (ANOVA) test,
and the multiple regression analysis. The information used for all these tests is directly drawn
from the World Factbook, which is referenced below in our reference section.
From the information drawn from the tests we were able to draw conclusions for the
significance and effect of each variable in regards to child development. Through the analysis we
came to the conclusion that corruption, urban population, and poverty have a direct connection
with the development of children in those selected regions, whereas variables like government
and divorce rate do not significantly affect the development.
The purpose of this report is to provide the reader with a understanding as to what factors
and important in the development of a child in their region, and analyze how this can be
accomplished.

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Introduction
Industry Overview
The Caribbean is made up largely of immigrants from the European colonization and plantation
systems, with many of its peoples having roots in the slavery that took place there, with the total
number of people in the region estimated at 37.5 million by the year 2000 (The World Factbook,
2014). South America the region is made up of 12 individual countries which can be traced to the
pre-Columbian era. A major problem that this region has however is the education that children
receive. Lack of equipment, poor teaching, and overcrowding cause problems for teens to pursue
education beyond primary schools. Furthermore, both the Caribbean and South America is made
up of many islands and Territories which have their roots in European expansion and exploration
and as a result many of the islands still have ties to the original countries which laid claim to
them during the 1500’s. These differences are also present between eastern and western Europe
allowing for a very contrasted image of how a child develops, and since Asia is so large many
countries have very different environments that children grow up in.
Research Model and Hypothesis
Research Model
The purpose of this study is to understand the factors that affect child development most.
Using the data that we have collected throughout the process, we have determined the factors
that affect child development at a significant level. This information provided in this report thus

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can be used by countries who wish to increase the child development, by giving them a accurate
path that they can follow.
Hypothesis
With the regions being so diverse, there are many factors which can influence a child’s
development. Factors that we hypothesized would affect child development are proximity to
urban areas (urban and Rural), divorce rate (divorce and CDI), corruption (corruption and
poverty), government (government type and CDI), etc. As the null hypothesis all of the variables
presented are assumed to have an impact on the development of children.
Terminology of variables
The specific variables that we chose to measure were:
● Proximity to urban areas
Proximity to urban areas may also have some effect on the development of a child as
well. Rural areas have a significantly poorer health status than urban areas. This could
be due to many factors such smoking, or less nutritional diets. This lack of proper
health in rural areas could lead to many children not receiving the proper nutrition
that they should receive.
● Divorce rate
Divorce rate is another variable that we are interested in analyzing through this study.
Overall, higher divorce rates in a certain country would lead to difficulty for children
to receive both the support of their father and mother, leading to potential problems in
the development of the child, both physically and psychologically.

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● Corruption and Poverty
Environmental factors such as corruption and poverty are also variables that affect the
development of children. Unsanitary living conditions can lead to harming an
individual's health, and can ultimately impede the development and growth of a child.
For the smaller environmental factors, like the environment for the community and
school, if the children surrounded by the people who are selling, and taking drugs, or
face unemployed people, who has higher criminal rate, the children’s development
will also be affected. Also on the big picture, like the refugee problems in Europe
now, an unstable domestic politics will also have impact on the children’s
development. The poverty rate for a country can also affect the development of a
child. Poverty is a huge concern in the developing countries because it poses a
chronic stress on children and in return negatively affects their education. Ultimately,
this will lower the developing rate for children.
● Government
While collecting the regions that we wanted to analyze for our research, we found
that there were five main government types: 5, Republic, Monarchy, Communist
State, and Federation. This five government types are all fairly different from a
government that elects members into the parliament to a monarchy in which the
leader reigns until the death or abdication of the leader. Through this research testing
we will conclude if government has any effect on the development of a child in the
different regions.

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The reasons we compared each of these variables to CDI was an attempt to understand the direct
impact each variable has on CDI and based on these findings we will determine a more accurate
path countries can follow to increase their CDI rating.
Factors that we are not taking into account during this study were social factors such as
maternal depression, exposure to violence, and inadequate cognitive stimulation. Other factors
include physical activity of the child, and level of involvement of the grandparents in the child's
life. Each of these factors could have a significant impact on the development of the child I
should be further explored in a related analysis.
Research Methodology
The tests we chose to perform on this data set were:
● Correlation analysis
● T-test
● Chi-squared test
● Analysis of Variance (ANOVA) test
● Multiple Regression Analysis
(see Appendix for a full list of tests conducted to each test)
We chose to carry out correlation analysis to determine whether the data had any
correlation, and if so in which direction each variable was related to CDI and based on whether
they were determined to be correlated or uncorrelated we were able to determine which variables
to ignore during your tests and which to focus our attention on.
After the correlation analysis was complete we transitioned to a t-test to determine what
the mean and confidence intervals were for the population of all countries in the world for each

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of the variables chosen. By then performing a Chi-Square test we are able to see how related the
different variables are to CDI and which statistically affect a country’s CDI number most.
We then sought to show that each of the determined variables was different from each other
statistically through one way ANOVA testing and if there wasn’t a strong enough variance
between two or more groups we then performed a Post Hoc test to see which of the variables was
creating the problem. We decided this was appropriate because we would need to further
eliminate variables which would hinder our analysis of CDI. Finally we performed the multiple
regression tests to help us predict future CDI using all of the determined variables that were
found to affect a country’s CDI. This allows perspective countries to make strong educated
decision about CDI based on the variables that will affect it most. The result of our analysis can
then be used by researchers to measure the effect of a change in a single variable has on CDI.
Analysis of Outcome
It was found that urban population, corruption, and poverty are all directly correlated to a
country’s CDI. Government type and divorce rate however are not directly correlated to CDI.
Through the T-test we were able to gain some preliminary figures by which we could carry out
the subsequent test. These figures were the overall means and the standard deviation. With these
numbers we were able to determine a 95% confidence interval for each variable. After carrying
out Chi-square tests for each variable comparing them to CDI we saw definite, strong
relationships between each of the variables and CDI. This was true for each variable except
government, indicating further that a country’s government has little effect on its CDI. What was

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interesting was that each variable consistently had over 20% expected counts less than five. This
indicates that there is a possible issue with the data.
We successfully established that high corruption was found in countries with high
poverty above the expected value and the expected level of low corruption was higher in
countries with medium poverty. The test determined that countries with a low divorce rate and an
excellent CDI were considerably lower than expected in the number of countries with a high
divorce rate was above expected to have an excellent CDI. After conducting our ANOVA test we
found that there was an issue between a low urban population and a medium urban population.
We saw a decrease in the mean CDI as populations centralized on urban areas and this is
mirrored in the level of population in rural areas. CDI improved drastically as the populations of
the countries had a higher divorce rate. We also saw that the level of corruption rose as a
country’s CDI score worsened. Utilizing the multiple regression models that we generated
countries can now accurately predict how changing a variable will change CDI score using this
formula:
CDI = 20.712 + -0.259(percentage of population in urban areas) +0.197(percentage
below the poverty line)
(The reason we omitted divorce rate, corruption rate and government type was because they were
not significant indicators of a country’s CDI.)

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These results that we analyzed were determined through the statistics that were
uncovered during our T-test in Appendix C. From the graph above we see that CDI increases
with the higher mean level of Rural population and as corruption is high, we also see that the
greater the percentage of the population below the poverty line the larger the CDI. On the
contrary, the higher the population located in urban areas the lower the CDI. Also, looking at the
divorce rate you can interpret that the higher the mean average of the divorce rate is in a region,
the lower the CDI is for that place. Another thing to notes that regions that have a high rural area
population also have a very high CDI. This could be the result of poor education in the area, as
well as other health concerns. From the analysis of the T-tests you can safely analyze that the
higher the proximity of urban locations and divorce rate, the better then child development of the
child is.

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The graphs illustrated above show the relationship that each variable has with CDI. From each of
the graphs above, it can be observed that the CDI decreases as populations condense in urban
local, leading to the conclusion that populations living in urban areas have a better CDI.
Furthermore, it is also shown that as the poverty and corruption levels of a particular place
increase the CDI decreases alongside that. The only plot that is unexpected is that of divorce rate.
This unexpected plot is due to the fact that as a country’s CDI is reduced the level of freedom

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increases and individuals do not feel forced to stay in a poor marriage. For these reasons each of
these variables are directly correlated to CDI.
We can understand these graphs as showing us that a country’s CDI is related to the
percent of the population below the poverty line, in rural areas and the divorce rate of the
country. In the above graphs the number of nations with poor CDI is largely focused to those that
have high poverty, high urban populations, low rural populations and virtually no divorce rate.

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This reinforces our correlation analysis and gives further credibility to our hypothesis that high
poverty, urban populations, and divorce rate are directly related to the child development in
regions around the world.
Conclusion
In conclusion the purpose of this report was to determine what factors affect the development of
a child. The T-test, Multiple Regression, Chi Square, Correlation and Anova tests were used to
analyze the five variables that we had chosen. We successfully determined that CDI is affected
by the percentage of the population that have high corruption and poverty, in rural and urban
areas, and divorce rate. It does not however have a significant impact through the government
type. With all this we can conclude that our null hypothesis was incorrect, all the variables are
not significance to the child development in the regions.
Recommendation
By determining these variables that affect CDI the most, it can aid in giving developing nations
an accurate path to follow when improving a child’s development. This is encouraging for
countries that are having political issues but still want to help their people. The conclusion for
this analysis is that in order to increase the quality of a child’s development it is necessary that a
region with a high poverty line and corruption percentage look into finding solutions to those
issues so that the CDI for those regions can become better. In order to do so we would
recommend using this formula as guide countries can now determine the best ways to improve
their CDI score:
CDI = 20.712 + -0.259(percentage of population in urban areas) +0.197(percentage below the
poverty line

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Limitations:
The limitations that we had in this report include the fact that we only analyzed five of the thirty
variables that were provided for us. This limited the outputs that we were received through our
tests that we conducted. Furthermore, since such a small sample was taken, the CDI is a lot
simpler in terms of the formula that we have come up with.

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Reference
"The World Factbook." Central Intelligence Agency. CIA, July 2014. Web. 24 Sept. 2015.
<https://www.cia.gov/library/publications/the-world-factbook/rankorder/2119rank.html>.
"Quick guide: The slave trade." BBC News. BBC, 15 Mar. 2007. Web. 24 Sept. 2015.
<http://news.bbc.co.uk/1/hi/world/africa/6445941.stm>.
"The WorldAtlas List of Geography Facts." world atlas. World Atlas, 2015. Web. 24 Sept. 2015.
<http://www.worldatlas.com/geoquiz/thelist.htm>.

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Appendices
Appendix A- List of Regions Analyzed
Carribbean Europe South America Asia
1. Cuba
2. Dominican Republic
3. Haiti
4. Jamaica
5. Trinidad and Tobago
1. Austria
2. Belgium
3. Bulgaria
4. Croatia
5. Czech Republic
6. Denmark
7. Finland
8. France
9. Germany
10. Ireland
11. Italy
12. Luxembourg
13. Netherlands
14. Romania
15. Spain
16. Sweden
17. United Kingdom
18. Albania
19. Belarus
20. Iceland
21. Macedonia
22. Moldova
23. Norway
24. Russia
25. Switzerland
26. Turkey
1. Argentina
2. Bolivia
3. Brazil
4. Chile
5. Colombia
6. Ecuador
7. Guyana
8. Paraguay
9. Peru
10. Suriname
11. Uruguay
12. Venezuela
1. Armenia
2. Japan
3. India
4. Malaysia
5. Nepal
6. Myanmar
(Burma)
7. Vietnam
8. Indonesia
9. Bangladesh
10. pakistan
11. thailand
12. mongolia
13. maldives
14. Cambodia
15. Sri Lanka
16. Tajikistan
17. Timor Leste

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Appendix B- Correlation Testing
Correlation
Correlations
Percentage
of
population
in rural
areas
Corruption
Perceptions
Index (CPI)
Urban
population
(% of total
population)
Divorce
to
marriage
ratio (%)
Population
below
poverty line
(%)
Percentage of
population in
rural areas (%
of total
population)
Pearson
Correlation
1 -.619
**
-1.000
**
-.448
**
.109
Sig. (2-
tailed)
.000 .000 .003 .426
N 60 59 60 41 55
Corruption
Perceptions
Index (CPI)
Pearson
Correlation
-.619
**
1 .619
**
.457
**
-.494
**
Sig. (2-
tailed)
.000 .000 .003 .000
N 59 59 59 41 54
Urban
population (%
of total
population)
Pearson
Correlation
-1.000
**
.619
**
1 .448
**
-.109
Sig. (2-
tailed)
.000 .000 .003 .426
N 60 59 60 41 55
Divorce to
marriage ratio
(%)
Pearson
Correlation
-.448
**
.457
**
.448
**
1 -.268
Sig. (2-
tailed)
.003 .003 .003 .114
N 41 41 41 41 36
Population Pearson .109 -.494
**
-.109 -.268 1

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below poverty
line (%)
Correlation
Sig. (2-
tailed)
.426 .000 .426 .114
N 55 54 55 36 55
Child
Development
Index (CDI)
Pearson
Correlation
.723
**
-.651
**
-.723
**
-.586
**
.376
**
Sig. (2-
tailed)
.000 .000 .000 .000 .006
N 58 57 58 40 53
Appendix C- T test Results
T-tests
One-Sample Statistics
N Mean Std. Deviation Std. Error Mean
Child Development Index
(CDI)
58 8.622759 8.6762846 1.1392520
Urban population (% of total
population)
60 63.950000 22.7599299 2.9382943
Percentage of population in
rural areas (% of total
population)
60 36.050000 22.7599299 2.9382943
Divorce to marriage ratio (%) 41 35.463415 17.7483768 2.7718308
Corruption Perceptions Index
(CPI)
59 48.186441 21.9195037 2.8536763
Population below poverty line
(%)
55 22.685455 13.0704702 1.7624218
Government_2 60 1.8000 1.20451 .15550

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One-Sample Test
Test Value = 0
t df
Sig. (2-
tailed)
Mean
Difference
95% Confidence Interval of the
Difference
Lower Upper
Child Development
Index (CDI)
7.569 57 .000 8.6227586 6.341446 10.904071
Urban population (% of
total population)
21.764 59 .000 63.9500000 58.070487 69.829513
Percentage of
population in rural areas
(% of total population)
12.269 59 .000 36.0500000 30.170487 41.929513
Divorce to marriage
ratio (%)
12.794 40 .000 35.4634146 29.861336 41.065494
Corruption Perceptions
Index (CPI)
16.886 58 .000 48.1864407 42.474187 53.898694
Population below
poverty line (%)
12.872 54 .000 22.6854545 19.152011 26.218898
Government_2 11.575 59 .000 1.80000 1.4888 2.1112
Appendix D- Chi Square Tests
Chi-square
Child Development Index (CDI) * Urban population (% of total population) Crosstabulation
Urban population (% of total population)
Total
Low Urban
Population
Medium
Urban
Population
High Urban
Population
Child
Development
Index (CDI)
Excellent Count 0 4 14 18
Expected
Count
1.6 6.2 10.2 18.0
Good Count 2 13 4 19
Expected
Count
1.6 6.6 10.8 19.0
Poor Count 3 3 15 21
Expected
Count
1.8 7.2 11.9 21.0

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Total Count 5 20 33 58
Expected
Count
5.0 20.0 33.0 58.0
Chi-Square Tests
Value df
Asymptotic
Significance (2-
sided)
Pearson Chi-
Square
18.478a
4 .001
Likelihood Ratio 20.467 4 .000
Linear-by-Linear
Association
.713 1 .399
N of Valid Cases 58
a. 3 cells (33.3%) have expected count less than 5. The
minimum expected count is 1.55.
Symmetric Measures
Value
Approximate
Significance
Nominal by
Nominal
Phi .564 .001
Cramer's V .399 .001
N of Valid Cases 58
Child Development Index (CDI) * Population below poverty line (%) Crosstabulation
Population below poverty line (%)
Total
Low
Poverty
Medium
Poverty
High
Poverty
Child Development
Index (CDI)
Expected
Count
4.2 4.2 5.5 14.0
Good Count 4 7 7 18
Expected
Count
5.4 5.4 7.1 18.0

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Expected
Count
6.3 6.3 8.3 21.0
Expected
Count
16.0 16.0 21.0 53.0
Chi-Square Tests
Value df
Asymptotic
Significance (2-
sided)
Pearson Chi-
Square
12.429a
4 .014
Linear-by-Linear
Association
9.830 1 .002
N of Valid Cases 53
Symmetric Measures
Value
Approximate
Significance
Nominal by
Nominal
Phi .484 .014
N of Valid Cases 53
Child Development Index (CDI) * Corruption Perceptions Index (CPI) Crosstabulation
Corruption Perceptions Index (CPI)
Total
Low
Corruption
Medium
Corruption
High
Corruption
Child
Development
Index (CDI)
Expected
Count
3.8 9.2 5.1 18.0
Good Count 7 10 1 18

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Expected
Count
3.8 9.2 5.1 18.0
Expected
Count
4.4 10.7 5.9 21.0
Expected
Count
12.0 29.0 16.0 57.0
Chi-Square Tests
Value df
Asymptotic
Significance (2-
sided)
Pearson Chi-
Square
11.383a
4 .023
Linear-by-Linear
Association
2.006 1 .157
N of Valid Cases 57
Symmetric Measures
Value
Approximate
Significance
Nominal by
Nominal
Phi .447 .023
N of Valid Cases 57

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Child Development Index (CDI) * Percentage of population in rural areas (% of total population) Crosstabulation
Percentage of population in rural areas (% of total
population)
Total
Low
Population in
Rural Area
Medium
Population in
Rural Area
High
Population in
Rural Area
Child
Development
Index (CDI)
Expected
Count
7.7 5.8 3.5 17.0
Good Count 4 9 6 19
Expected
Count
8.6 6.5 3.9 19.0
Poor Count 9 3 5 17
Expected
Count
7.7 5.8 3.5 17.0
Expected
Count
24.0 18.0 11.0 53.0
Chi-Square Tests
Value df
Asymptotic
Significance (2-
sided)
Pearson Chi-
Square
11.661a
4 .020
Linear-by-Linear
Association
2.356 1 .125
N of Valid Cases 53
Symmetric Measures
Value
Approximate
Significance
Nominal by
Nominal
Phi .469 .020

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N of Valid Cases 53
Child Development Index (CDI) * Government_2 Crosstabulation
Government_2
Tot
al
Republi
c
Monarch
y
Communis
t State Democracy Federation
Child
Development
Index (CDI)
Excellen
t
Count 9 4 0 4 1 18
Expected
Count
10.9 3.1 .6 3.1 .3 18.0
Good Count 10 3 1 5 0 19
Expected
Count
11.5 3.3 .7 3.3 .3 19.0
Poor Count 16 3 1 1 0 21
Expected
Count
12.7 3.6 .7 3.6 .4 21.0
Total Count 35 10 2 10 1 58
Expected
Count
35.0 10.0 2.0 10.0 1.0 58.0
Chi-Square Tests
Value df
Asymptotic
Significance (2-
sided)
Pearson Chi-
Square
7.962a
8 .437
Linear-by-Linear
Association
3.652 1 .056

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N of Valid Cases 58
minimum expected count is .31.
Symmetric Measures
Value
Approximate
Significance
Nominal by
Nominal
Phi .371 .437
N of Valid Cases 58
Child Development Index (CDI) * Divorce to marriage ratio (%) Crosstabulation
Divorce to marriage ratio (%)
TotalLow medium high
Child Development
Index (CDI)
Expected
Count
5.0 9.5 3.6 18.0
Good Count 7 5 2 14
Expected
Count
3.8 7.4 2.8 14.0
Poor Count 3 5 0 8
Expected
Count
2.2 4.2 1.6 8.0
Expected
Count
11.0 21.0 8.0 40.0
Chi-Square Tests

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Value df
Asymptotic
Significance (2-
sided)
Pearson Chi-
Square
10.607a
4 .031
Linear-by-Linear
Association
6.768 1 .009
N of Valid Cases 40
Symmetric Measures
Value
Approximate
Significance
Nominal by
Nominal
Phi .515 .031
N of Valid Cases 40
Appendix E- Anova Testing, Tukey Tests and Post Hoc
Anova
Urban and CDI
Descriptives
Child Development Index (CDI)
N Mean
Std.
Deviation
Std.
Error
95% Confidence Interval
for Mean
Minim
um
Maxim
um
Lower
Bound
Upper
Bound
Low Urban
Population
6 16.3116
67
6.4025039 2.61381
13
9.592651 23.030682 10.770
0
25.620
0
Medium Urban
Population
19 14.5473
68
10.436990
2
2.39440
98
9.516900 19.577837 2.3100 33.590
0

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High Urban
Population
33 3.81363
6
3.2609410 .567657
0
2.657357 4.969916 .4100 12.140
0
Total 58 8.62275
9
8.6762846 1.13925
20
6.341446 10.904071 .4100 33.590
0
Test of Homogeneity of Variances
Levene Statistic df1 df2 Sig.
21.858 2 55 .000
ANOVA
Sum of Squares df Mean Square F Sig.
Between Groups 1784.848 2 892.424 19.586 .000
Within Groups 2505.994 55 45.564
Total 4290.841 57
Multiple Comparisons
Dependent Variable: Child Development Index (CDI)
Tukey HSD
(I) Urban population
(% of total
population)
(J) Urban population
(% of total
population)
Mean
Difference
(I-J)
Std.
Error Sig.
Lower
Bound
Upper
Bound
Low Urban
Population
Medium Urban
Population
1.7642982 3.161012
5
.843 -5.849794 9.378391
High Urban
Population
12.4980303*
2.995769
1
.000 5.281968 19.714093
Medium Urban
Population
Low Urban
Population
-1.7642982 3.161012
5
.843 -9.378391 5.849794
High Urban
Population
10.7337321*
1.943911
7
.000 6.051333 15.416132

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High Urban
Population
Low Urban
Population
-
12.4980303*
2.995769
1
.000 -19.714093 -5.281968
Medium Urban
Population
-
10.7337321*
1.943911
7
.000 -15.416132 -6.051333
*. The mean difference is significant at the 0.05 level.
Tukey HSDa,b
Urban population (% of total
population) N
Subset for alpha = 0.05
1 2
High Urban Population 33 3.813636
Medium Urban Population 19 14.547368
Low Urban Population 6 16.311667
Sig. 1.000 .798
Means for groups in homogeneous subsets are displayed.
a. Uses Harmonic Mean Sample Size = 12.019.
b. The group sizes are unequal. The harmonic mean of the group sizes is used. Type I
error levels are not guaranteed.
CDI and Poverty
Descriptives
N Mean
Std.
Deviation
Std.
Error
95% Confidence
Interval for Mean
Minimum Maximum
Lower
Bound
Upper
Bound
Low
Poverty
14 7.00 7.527 2.012 2.65 11.34 1 30

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Medium
Poverty
17 8.78 7.513 1.822 4.91 12.64 1 26
High
Poverty
22 10.82 10.445 2.227 6.19 15.46 0 34
Total 53 9.16 8.836 1.214 6.72 11.59 0 34
2.170 2 50 .125
ANOVA
Between Groups 128.886 2 64.443 .820 .446
Within Groups 3930.670 50 78.613
Total 4059.555 52
Post Hoc Tests
Tukey HSD
(I) Population
below poverty
line (%)
(J) Population
below poverty
line (%)
Mean
Difference (I-
J)
Std.
Error Sig.
Lower
Bound
Upper
Bound
Low Poverty Medium Poverty -1.777 3.200 .844 -9.51 5.95

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High Poverty -3.826 3.031 .423 -11.15 3.50
Medium Poverty Low Poverty 1.777 3.200 .844 -5.95 9.51
High Poverty -2.049 2.863 .755 -8.96 4.87
High Poverty Low Poverty 3.826 3.031 .423 -3.50 11.15
Medium Poverty 2.049 2.863 .755 -4.87 8.96
Homogeneous Subsets
Tukey HSD
a,b
Population below
poverty line (%) N
Subset for
alpha = 0.05
1
Low Poverty 14 7.00
Medium Poverty 17 8.78
High Poverty 22 10.82
Sig. .424
Means for groups in homogeneous subsets are
displayed.
b. The group sizes are unequal. The harmonic
mean of the group sizes is used. Type I error
levels are not guaranteed.

29 | P a g e
Means Plots
Rural CDI
Descriptives
N Mean
Std.
Deviation
Std.
Error
for Mean
Minim
um
Maxim
um
Lower
Bound
Upper
Bound
Low Population in
Rural Area
24 3.87958
3
3.133599
8
.639643
4
2.556380 5.202786 .5700 12.140
0
Medium
Population in Rural
Area
17 8.53470
6
6.768749
1
1.64166
28
5.054536 12.014876 .8600 29.890
0
High Population in
Rural Area
12 20.9291
67
8.262132
1
2.38507
21
15.679658 26.178675 10.770
0
33.590
0
Total 53 9.23301
9
8.822507
0
1.21186
45
6.801235 11.664803 .5700 33.590
0
7.869 2 50 .001

30 | P a g e
ANOVA
Between Groups 2337.711 2 1168.855 34.181 .000
Within Groups 1709.794 50 34.196
Total 4047.505 52
Post Hoc Tests
Tukey HSD
(I) Percentage of
population in rural
areas (% of total
population)
(J) Percentage of
population in rural
areas (% of total
population)
Mean
Difference
(I-J)
Std.
Error Sig.
Lower
Bound
Upper
Bound
Low Population in
Rural Area
Medium Population
in Rural Area
-4.6551225*
1.853739
5
.040 -9.132683 -.177562
High Population in
Rural Area
-
17.0495833*
2.067482
7
.000 -22.043424 -12.055743
Medium Population
in Rural Area
Low Population in
Rural Area
4.6551225*
1.853739
5
.040 .177562 9.132683
High Population in
Rural Area
-
12.3944608*
2.204808
0
.000 -17.720000 -7.068922
High Population in
Rural Area
Low Population in
Rural Area
17.0495833*
2.067482
7
.000 12.055743 22.043424
Medium Population
in Rural Area
12.3944608*
2.204808
0
.000 7.068922 17.720000
Homogeneous Subsets
Tukey HSDa,b
Percentage of population in rural N Subset for alpha = 0.05

31 | P a g e
areas (% of total population) 1 2
Low Population in Rural Area 24 3.879583
Medium Population in Rural Area 17 8.534706
High Population in Rural Area 12 20.929167
Sig. .069 1.000
b. The group sizes are unequal. The harmonic mean of the group sizes is used. Type I error
levels are not guaranteed.
Means Plots
Divorce with CDI
Descriptives
N Mean
Std.
Deviation
Std.
Error
95% Confidence Interval for
Mean
Minimu
m
Maximu
m
Lower
Bound Upper Bound
Low 11 10.0672
73
10.686969
7
3.22224
26
2.887669 17.246877 .5700 33.5900
mediu
m
22 7.96272
7
9.4716611 2.01936
49
3.763228 12.162227 .4100 31.3200
high 6 4.96500
0
4.2377529 1.73005
54
.517751 9.412249 .9100 9.6100
Total 39 8.09512
8
9.2021456 1.47352
26
5.112138 11.078119 .4100 33.5900

32 | P a g e
1.119 2 36 .338
ANOVA
Between Groups 101.955 2 50.977 .589 .560
Within Groups 3115.866 36 86.552
Total 3217.820 38
Post Hoc Tests
Tukey HSD
(I) Divorce to
marriage ratio (%)
(J) Divorce to
marriage ratio (%)
Mean
Difference
(I-J)
Std.
Error Sig.
Lower
Bound
Upper
Bound
Low medium 2.1045455 3.435479
8
.814 -6.292788 10.501879
high 5.1022727 4.721615
3
.532 -6.438758 16.643303
medium Low -2.1045455 3.435479
8
.814 -10.501879 6.292788
high 2.9977273 4.284796
1
.765 -7.475587 13.471042
high Low -5.1022727 4.721615
3
.532 -16.643303 6.438758
medium -2.9977273 4.284796
1
.765 -13.471042 7.475587
Homogeneous Subsets
Tukey HSDa,b
Divorce to marriage ratio (%) N Subset for alpha =

33 | P a g e
0.05
1
high 6 4.965000
medium 22 7.962727
Low 11 10.067273
Sig. .449
b. The group sizes are unequal. The harmonic mean of the group sizes is used.
Type I error levels are not guaranteed.
Means Plots
Corruption and CDI
Descriptives
N Mean
Std.
Deviation
Std.
Error
95% Confidence Interval for
Mean
Minimu
m
Maximu
m
Lower
Bound
Upper
Bound
High 12 19.75333
3
10.4697663 3.022361
2
13.101161 26.405505 5.0500 33.5900
Mediu
m
27 8.089259 4.9873300 .9598121 6.116337 10.062181 .8600 26.6200
Low 18 1.642778 1.2145433 .2862706 1.038800 2.246756 .4100 4.8900
Total 57 8.509123 8.7097549 1.153635
2
6.198114 10.820132 .4100 33.5900
26.318 2 54 .000

34 | P a g e
ANOVA
Between Groups 2370.587 2 1185.294 34.090 .000
Within Groups 1877.563 54 34.770
Total 4248.150 56
Post Hoc Tests
Tukey HSD
(I) Corruption
Perceptions Index
(CPI)
(J) Corruption
Perceptions Index
(CPI)
Mean
Difference
(I-J)
Std.
Error Sig.
Lower
Bound
Upper
Bound
High Medium 11.6640741
*
2.045785
9
.000 6.733763 16.594385
Low 18.1105556
*
2.197526
6
.000 12.814552 23.406559
Medium High -
11.6640741
*
2.045785
9
.000 -16.594385 -6.733763
Low 6.4464815*
1.794272
9
.002 2.122313 10.770650
Low High -
18.1105556
*
2.197526
6
.000 -23.406559 -12.814552
Medium -6.4464815*
1.794272
9
.002 -10.770650 -2.122313
Homogeneous Subsets

35 | P a g e
Tukey HSDa,b
Corruption Perceptions Index (CPI) N
Subset for alpha = 0.05
1 2 3
Low 18 1.642778
Medium 27 8.089259
High 12 19.753333
Sig. 1.000 1.000 1.000
b. The group sizes are unequal. The harmonic mean of the group sizes is used. Type I error levels are not guaranteed.
Appendix F- Multiple Regression
Means Plots
Multiple Regression
Model Summary
Model R R Square
Adjusted R
Square
Std. Error of the
Estimate
1 .721a
.520 .512 5.9583655
2 .776b
.603 .589 5.4696618
a. Predictors: (Constant), Urban population (% of total population)
b. Predictors: (Constant), Urban population (% of total population),
Population below poverty line (%)
ANOVA
Model Sum of Squares df Mean Square F Sig.
1 Regression 2231.718 1 2231.718 62.862 .000b

36 | P a g e
Residual 2059.123 58 35.502
Total 4290.841 59
2 Regression 2585.561 2 1292.780 43.212 .000c
Residual 1705.280 57 29.917
Total 4290.841 59
a. Dependent Variable: Child Development Index (CDI)
b. Predictors: (Constant), Urban population (% of total population)
c. Predictors: (Constant), Urban population (% of total population), Population below poverty line
(%)
Coefficients
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig.
Collinearity Statistics
B Std. Error Beta Tolerance VIF
1 (Constant) 25.904 2.311 11.207 .000
Urban
population (%
of total
population)
-.270 .034 -.721 -7.929 .000 1.000 1.000
2 (Constant) 20.712 2.604 7.954 .000
Urban
population (%
of total
population)
-.259 .031 -.691 -8.230 .000 .989 1.011
Population
below poverty
line (%)
.197 .057 .289 3.439 .001 .989 1.011
Excluded Variables
Model Beta In t Sig.
Partial
Correlation
Collinearity Statistics
Tolerance VIF
Minimum
Tolerance

37 | P a g e
1 Population
below poverty
line (%)
.289b
3.439 .001 .415 .989 1.011 .989
Corruption
Perceptions
Index (CPI)
-.328b
-3.039 .004 -.373 .622 1.607 .622
Percentage of
population in
rural areas (%
of total
population)
.b
. . . .000 . .000
2 Corruption
Perceptions
Index (CPI)
-.199c
-1.673 .100 -.218 .478 2.091 .478
Percentage of
population in
rural areas (%
of total
population)
.c
. . . .000 . .000
b. Predictors in the Model: (Constant), Urban population (% of total population)
c. Predictors in the Model: (Constant), Urban population (% of total population), Population below poverty line (%)
Collinearity Diagnostics
Model Dimension Eigenvalue
Condition
Index
Variance Proportions
(Constant)
Urban
population (%
of total
population)
Population
below poverty
line (%)
1 1 1.943 1.000 .03 .03
2 .057 5.838 .97 .97
2 1 2.755 1.000 .01 .01 .03
2 .199 3.724 .01 .18 .72
3 .046 7.737 .98 .81 .25

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