Factors That Influence a Person’s Happiness

This paper investigates various factors that determine person happiness and tries to compare the level of happiness in the relationship between those who are engaged and those who are single. A survey was conducted to determine various factors that are associated with a persons’ happiness.

Factors That Influence a Person’s Happiness
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Factors That Influence a Person’s Happiness
Introduction
It’s the desire of every person to be happy and also make those around them to be happy too.
However, happiness sometimes needs to be found. Also, a person needs to know the various
factors that make them happy. In search of happiness, different people experience different
challenges. Some people consider having material possessions as their source of happiness while
others consider having loved one and their soul mates as their source of happiness. In contrast,
the American psychology association released their issue 12 journal which indicated that from a
study that they had conducted the major source of happiness should be from within the person
and not determined by factors not within the person like a material possession. It also indicated
that those people who consider material possession as their source of income suffer a lot
especially depression when they lose their possessions (Extremera & Fernández-Berrocal,
2014). Although, the study was conducted among the people aged 35 years old and above this
may contradict what makes the young people happy.
Another major factor that determines person happiness is age. Young people tend to be happy
than older people, this is a result of fewer responsibilities the young people have which makes
them have a wide range of choices of what can make them happy (Blanchflower & Oswald,
2011). This paper investigates various factors that determine person happiness and tries to
compare the level of happiness in the relationship between those who are engaged and those who
are single.
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Abstract
A survey was conducted to determine various factors that are associated with a persons’
happiness. A survey was carried out in order to determine what may be influencing happiness in
relationship both positively and negatively. The survey included more about the relationship
happiness which was measured in a 5-scale Likert scale. There were seven variables of interest
and 400 observations. Simple random sampling was used for obtaining the sample from the
survey and ensure every member of the population had an equal chance of being represented.
Data analysis was performed and results were used to make conclusions. It was found out that
there was a strong correlation between financial comfort status and the relationship happiness
status of the partners which implied a change in one variable lead to a change in the other
variable. The results from the analysis also indicated that most partners were very happy about
the relationship happiness. However, there was a weak correlation between the partners’ age and
partners’ income. This implied that a change in the age of a person had only a slight change in
income (Iani, Lauriola, Layous, & Sirigatti, 2014). In conclusion, in future more observation
should be included and also the range between the partner's incomes should be reduced.
Keywords:
Happiness, partners, t-test, age, comfort
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Methodology
In order to investigate the various factors that are associated with people happiness especially
those in a relationship, a survey was conducted. The survey was conducted by the use of
questionnaires and simple random sampling was used to select the sample that participated in the
survey. Simple random sampling ensured that there was no bias during the survey and the results
obtained may be used for drawing the conclusion that is free of suspicion (Capaldi, Dopko, &
Zelenski, 2014). The table below represents the variables that were involved in the survey and
that will be used in the analysis.
Variable label Variable description
INC1 Income
INC2 Partner’s income
FCORMFORT Financial comfort
AGE1 Age
AGE2 Partner’s age
MSTAT Marital status
HAPPY Relationship happiness
GENDER2 Partner’s gender
There were three categorical variables while the rest were continuous variables. The variable
labels will be used to allow conveniences during the analysis of data. There were 400
observations in the dataset.
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Data analysis and results.
To understand the distribution, the shape of the variables a descriptive statistics were performed
for the continuous variables in the dataset. Below is the output of the descriptive statistics.
Statistic Partner’s income Income Age Partners age
N 400 400 400 400
Range 200000 285000 42 61
Minimum 0 0 21 18
Maximum 200000 285000 63 79
Mean 38602.11 47112.92 33.12 34.3
Std. deviation 32358.925 40477.089 9.453 10.141
Skewness 1.53 2.137 0.732 0.866
The above table indicates that there were no missing observations in the data. For the partner’s
income, it had an average of 38602.11 this implied that most partners received an annual income
of 38602.11. It also had a standard deviation of 32358.925 which indicated that most income was
32358.925. The range of the partner’s income was 200000 which indicated that there was great
variability between those who received maximum annual income and those who received
minimum annual income (Vázquez, Panadero, & Rivas, 2015). The coefficient of skewness
indicated that the partner's income was positively skewed which implied that most people
received less annual partner’s income while fewer people received less partner’s annual income.
Comparing the partners’ income with the income of the other individual it depicted there was a
huge difference between. For instance, the coefficient of skewness was 2.137 for the income of
all individuals while that of partners was 1.53. This implied that one was more positively skewed
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than the other. Additionally, the other statistics also clearly indicated a noticeable difference
between the two variables.
Considering the descriptive statistics for the partner’s age and age of all other individuals. The
partner’s age had an average of 34.3 which implied that the majority of the partners were 34
years old. It had a standard deviation of 10.141 which indicated that most age was 10 times about
the mean which is about 68% of the observations (Knapp, 2017). The range of the partner’s age
was 61 years which was an indication of great variability between the partners who was the
oldest and those who were the youngest. The coefficient of skewness was 0.866 which indicated
that there was a slight positive skewness. This meant that there were many partners with young
age and few partners with old age.
For the categorical variables, a contingency table was generated to determine what number of
people had happy relationships. Below is the contingency for categorical variables.
Relationship happiness * Financial Comfort Crosstabulation
Count
Financial Comfort Total
Comfortable Struggling
Relationship happiness
Very unhappy 7 9 16
Unhappy 5 11 16
Mixed 32 59 91
Happy 79 69 148
Very happy 93 36 129
Total 216 184 400
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The above table indicated that the highest number of people indicated that they had very happy
relationship happiness and had comfortable financial comfort. On the contrary, the smallest
number of people indicated that they had to struggle financial comfort and they were very
unhappy with relationship happiness. This indicated that the level of financial comfort
determined the level of the relationship happiness. In general, most people were comfortable
with financial comfort while the majority of the people were happy with the relationship
happiness.
A chi-squared test was carried out to determine whether there was an association between
relationship happiness and financial comfort based on the following hypothesis.
H0: there is no association between relationship happiness and financial comfort
Versus
H1: there is an association between relationship happiness and financial comfort.
The test was carried out at 5% level of significance. Below is the output of the test.
Chi-Square Tests
Value Df Asymp. Sig. (2-
sided)
Pearson Chi-Square 34.031a 4 .000
Likelihood Ratio 34.880 4 .000
Linear-by-Linear Association 26.309 1 .000
N of Valid Cases 400
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a. 0 cells (0.0%) have expected count less than 5. The minimum
expected count is 7.36.
The Pearson chi-square had a p-value of 0.000 which was less than 0.05 level of significance
thus the null hypothesis was rejected. This lead to the conclusion that there is an association
between relationship happiness and financial comfort. This meant that relationship happiness
was dependent on financial comfort and vice versa (Carr et al, 2014).
The following pie chart represents the proportion of partners and their corresponding relationship
happiness.
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From the pie chart, above the majority of the partners were happy about their relationships while
only a few proportions of partners who were unhappy with their relationship happiness.
Additionally, a great proportion was also happy with their relationship happiness.
A correlation test was carried out to determine how the continuous variables were associated
with each other. The output of the test is in the table below.
Correlations
Age Partner's age Partner's income Income
Age
Pearson Correlation 1 .822** .167** .331**
Sig. (2-tailed) .000 .001 .000
N 400 400 400 400
Partner's age
Pearson Correlation .822** 1 .253** .273**
Sig. (2-tailed) .000 .000 .000
N 400 400 400 400
Partner's income
Pearson Correlation .167** .253** 1 .045
Sig. (2-tailed) .001 .000 .366
N 400 400 400 400
Income
Pearson Correlation .331** .273** .045 1
Sig. (2-tailed) .000 .000 .366
N 400 400 400 400
**. Correlation is significant at the 0.01 level (2-tailed).
The correlation coefficient that is close to one in absolute implies that there is a strong
relationship between the two variables. On the other hand correlation coefficients that are close
to zero in absolute indicates a weak relationship between the variables. From the table above, all
the variables had a weak positive correlation except the age of all individuals and the age of the
partners (Cordero et al, 2017). A strong positive correlation implies that an increase in one
variable leads to an increase in the other variable.
In order to make conclusions about how the variables were related inferential statistics were
performed. The first test involved investigating how the average annual income differed with the
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financial comfort of the partners. An independent t-test was performed based on the following
hypothesis,
H0: average annual income is the same between the financial comforts and financial struggles of
the partners
Versus
H1: average annual income is different between the financial comfort and financial struggles of
the partners
The test was performed at a 5% level of significance and the table below represents the output of
the test.
The test had a p-value of 0.006 which was less than 0.05 level of significance this lead to the
rejection of the null hypothesis. The conclusion of the test was that the average annual income
was different between those who had comfort financial status and those who had to struggle
financial status.
An independent t-test was carried out to investigate whether the average age differed with
marital status based on the following hypothesis.
H0: the average age is the same between married and single people
Versus
H1: the average age is different between married and single people
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The test was carried out at 5% level of significance and below is the output of the test.
The test had a p-value of 0.00 which was less than 0.05 level of significance thus the null
hypothesis was rejected. The lead to the conclusion that there was difference in average age
between the married and single people.
To investigate whether the average partners’ age differed among the five levels of relationship
happiness, one-analysis of variance (ANOVA) test was performed. The hypothesis below was
formulated for the test.
H0: the average partner’s age is the same in all relationship happiness levels
Versus
H1: the average partner’s age differs in the relationship happiness levels
Below is the output of the test;
ANOVA
Partner's age
Sum of Squares Df Mean Square F Sig.
Between Groups 878.749 4 219.687 2.161 .073
Within Groups 40154.848 395 101.658
Total 41033.597 399
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The test had a p-value of 0.073 which is greater than 0.05 level of significance thus the null
hypothesis was accepted. This lead to the conclusion that the average partner's age is the same
regardless of the happiness level of the partner.
Discussion and recommendation
The analysis that was conducted indicated that there was great variability between the annual
incomes of the partners. This implied that having a partner was a determinant of whether or what
income a person could receive. It was also found out that the happiness of partners was
influenced by their financial comfort. Holding other variables constant, and controlling the age
of the partner, partners who were had financial comfort reported to have a very happy
relationship status. The correlation test also revealed that the age of the partner influenced how
happy a person could be (Dynan & Ravina, 2012). Happiness among partners increased with the
age of the person, and those age about 34 years reported to be very happy in relationship
happiness. This implies that for people to be happier especially partners, they have to
continuously work about their financial comfort first. This means that those who have struggling
financial comforts should improve them sold can have very happy relationship happiness.
In the future, the researcher should consider including other factors such as the level of
education, family background, and career since they can also influence the happiness of a person.
Highly educated people may feel like it’s very prestigious to have such academic qualification
which may make them live a more happy life. In conclusion, the researcher in the future should
consider increasing the number of observation and also make use of the Likert scale in
quantifying the categorical variables so that more reliable and accurate information can be
obtained (Frijters & Beatton, 2012).
References
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