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A study on Happiness based on
Gender and Occupation
By
Team 4
Abinesh Moorthy S
Ashwini B V
Briyanga T G
Jayavignesh J T
Nithya V
Sriram Chandar P S
Yamini Soundarya B
What is all about Happiness
• For every minute you are ANGRY you
lose sixty seconds of your happiness.
• True Happiness is to enjoy the present,
without anxious dependence upon the
future, not to amuse ourselves with
either hopes or fears but to reset
satisfied with what we have, which is
sufficient, for he that is so wants
nothing.
• The Greatest Blessings of Mankind are
within us and within our reach. A wise
man is content with his lot, whatever it
may be, without wishing for what
Happiness
• Happiness definitely rules each individual.
Happiness is so important to us, both as
individuals and as a world.
• As human beings, although we possess
cognitive abilities and are highly "thought"
oriented, the quality of our lives is ultimately
entirely determined by our emotions.
• Happiness is no mystery. Most people are
quite clear about what happiness is, and can
easily describe how happy or not they are.
• Most people also consider happiness as
their most important goal in life.
Objectives
• The main objectives are as follows:
– To understand how people are happy and what influences them
– To compare the difference of happiness degree among different
genders and different occupation
– To identify what are all the factors that can affect the happiness
of an individual
Research Methodology
• Research Design
– A research design is the arrangement of conditions for collection and analysis of data in a
manner that aims to combine relevance to the research purpose with economy in
procedure.
– The methodology used in project is Descriptive Research.
– The main characteristic of this method is that the researcher has no control over the
variables; one can only report what has happened or what is happening.
• Data Sources:
– Our objective is to find out a real life problem so we opted for primary data.
– Primary data are those which are collected afresh and for the first time, and thus happen
to be the original in character.
– Primary data can be collected through experiment or through survey.
Research Methodology
– Survey method was adopted because it helps to procuring data and detailed
information from the respondents.
• Questionnaire:
– A questionnaire consists of a number of questions related to the survey.
– A questionnaire is a research instrument consisting of a series of questions and other
prompts for the purpose of gathering information from respondents.
– Questionnaires are cheap, do not require as much effort from the questioner as verbal
or telephone surveys, and often have standardized answers that make it simple to
compile data.
– A 5 scaled - response questionnaire was set up to measure the intensity of a
respondent’s answer.
Google Form
Research Methodology
• Sampling:
– The respondents selected should be a representative of the total population as
possible in order to produce a miniature cross-selection.
– The selected respondents constitute what is technically called as ‘sample’ and the
selection technique is called sampling technique.
– Most predominantly used techniques to select samples are probability sampling
and non probability sampling, and we used probability sampling.
– Under probability sampling, we used stratified sampling technique to select the
sample.
– In stratified sampling, the population is divided into several sub-populations of
homogenous groups.
– Sample size refers to the number of respondents selected from the universe to
constitute the sample.
– Our sample size was 320.
Factor Analysis
– Factor analysis tool is used for analysis and interpretation.
– Factor analysis is used in many fields such as behavioral and social sciences, medicine,
economics, and geography as a result of the technological advancements of computers.
– Large datasets that consist of several variables can be reduced by observing ‘groups’ of variables
called factors.
– It is easier to focus on some key factors rather than having to consider too many variables that
may be trivial, and so factor analysis is useful for placing variables into meaningful categories.
– The basic statistic used in factor analysis is the correlation coefficient which determines the
relationship between two variables.
– The correlation matrix is often used because it is easier to interpret compared to the covariance
tables, although there is not a strict requirement for which matrix to use.
Formulate the Problem
Construct Correlation matrix
Determine model of FA
Determine number of factors
Rotate the factors
Interpret the factors
Calculate factor scores Select the surrogate variable
Determine model fit
Factor Analysis
Components of Factor Analysis
• Factor Extraction:
– Factor analysis is based on the common factor model which is a theoretical model.
– This model postulates that observed measures are affected by underlying common
factors and unique factors, and the correlation patterns need to be determined.
– Principal Components analysis is used to extract maximum variance from the data
set with each component thus reducing a large number of variables into smaller
number of components Principal Components produces components whereas
Principal Axis Factor produces factors.
– Principal Components analysis is the first step to reduce the data, then follow-up
with a ‘true’ factor analysis technique.
Components of Factor Analysis
• Rotation:
• Factors are rotated for better interpretation since unrotated factors are ambiguous.
• The goal of rotation is to attain an optimal simple structure which attempts to have
each variable load on as few factors as possible, but maximizes the number of high
loadings on each variable.
• Two types of rotation:
– Orthogonal rotation
– Oblique rotation
Components of Factor Analysis
• Interpretation of Factor Loadings:
– Factors can be identified by the largest loadings, but it is also important to examine the zero
and low loadings in order to confirm the identification of the factors.
– The signs of the loadings show the direction of the correlation and do not affect the
interpretation of the magnitude of the factor loading or the number of factors to retain.
– Extracting too many factors may present undesirable error variance but extracting too few
factors might leave out valuable common variance.
– So it is important to select which criterion is most suitable to your study when deciding on
the number of factors to extract.
– The eigen values and scree plot are used to determine how many factors to retain.
ANOVA
• ANOVA is an abbreviation for the full name of the method: Analysis of Variance Invented by
R.A. Fisher in the 1920’s.
• The one-way Analysis of Variance can be used for the case of a quantitative outcome with a
categorical explanatory variable that has two or more levels of treatment.
Assumptions:
• There are three main assumptions, listed here:
– The dependent variable is normally distributed in each group that is being compared in
the one-way ANOVA.
– There is homogeneity of variances. This means that the population variances in each
group are equal.
– Independence of observations. This is mostly a study design issue and, as such, you will
need to determine whether you believe it is possible that your observations are not
independent based on your study design (e.g., group work/families/etc).
ANOVA Table
Analysis and Interpretation
• Factor analysis allows us to look at groups of variables that tend
to be correlated to each other and identify underlying
dimensions that explain these correlations.
• A group of variables is formed under one factor that explains
the correlations among the variables.
• Statistics associated with factor analysis:
– Kaiser-Meyer-Olkin Measure of Sampling Adequacy
– Bartlett's Test of Sphericity
KMO Table
KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .880
Bartlett's Test of Sphericity
Approx. Chi-Square 2829.134
df 210
Sig. .000
Method of Factor Analysis
Scree Plot
Variables Component
1 2 3 4 5
I believe that what can go wrong, will go wrong 0.783
I feel lonely 0.78
When I find myself overwhelmed with stress , I completely shut
down
0.762
No value can be learned from failure 0.759
There is no point in maintaining close relationships, nothing lasts
for ever
0.737
Being myself guarantees that people will dislike me 0.712
Even if I am sure of my decision , I still ask others before making
an important or risky decision
0.702
It is better not to raise my hopes so that I don't get disappointed 0.645
I can find good in most disagreeable people 0.466
No matter what life throws at me , I believe that I will deal with it 0.741
When I have a difficult problem , I try to look into different angles in
order to come up with a solution
0.703
I know how to calm myself down and relax when my life gets too
hectic
0.62
I refuse to give up , no matter how tough things get 0.598
If I ever need help , I believe that my friends / relatives will be there
for me
0.529
Given the choice ,I think that the majority of people would choose
to do good rather than evil
0.716
I take steps to be happy in most serious situation to overcome it 0.674
When times get tough , I turn to family / friends for support 0.76
Most people can’t be trusted -0.709
I actively keep in touch with family and friends 0.492
I keep my problems to myself 0.783
I can find positive even in the most difficult situations -0.462 0.54
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 7 iterations.
Factors Variables Name of the factor
Factor 1 No value can be learned from failure Acceptance and being
yourself
Not able to manage Stress and it makes me shutdown completely
Being myself guarantees that people will dislike me
There is no point in maintaining close relationships, nothing lasts for ever
I believe that what can go wrong, will go wrong
It is better not to raise my hopes so that I don't get disappointed
Even if I am sure of my decision , I still ask others before making an important or risky
decision
I feel lonely
Factor 2 I can find positive even in the most difficult situations Resilience
I know how to calm myself down and relax when my life gets too hectic
No matter what life throws at me , I believe that I will deal with it
I refuse to give up , no matter how tough things get
Factor 3 If I ever need help , I believe that my friends / relatives will be there for me Relationship
When times get tough , I turn to family / friends for support
I actively keep in touch with family and friends
I keep my problems to myself
Most people can’t be trusted
Factor 4 When I have a difficult problem , I try to look into different angles in order to come up with a
solution
Trying out
I take steps to be happy in most serious situation to overcome it
Factor 5 Most people can’t be trusted
TrustGiven the choice ,I think that the majority of people would choose to do good rather than
evil
ANOVA table for analyzing Factors based
on genderFactors Sum of
Squares
df Mean Square F P value
Acceptance and Being
Yourself
Between
Groups 2.796 1 2.796
2.812 0.095
Within Groups 316.204 318 0.994
Total 319 319
Resilience
Between
Groups 2.608 1 2.608
2.621 0.106
Within Groups 316.392 318 0.995
Total 319 319
Relationship
Between
Groups 1.037 1 1.037
1.037 0.309
Within Groups 317.963 318 1
Total 319 319
Trying out
Between
Groups 0.073 1 0.073
0.073 0.787
Within Groups 318.927 318 1.003
Total 319 319
Trust
Between
Groups 1.452 1 1.452
1.454 0.229
Within Groups 317.548 318 0.999
Total 319 319
ANOVA table for analyzing Factors based
on occupation
Factors Sum of Squares df Mean Square F P value
Acceptance and Being
Yourself
Between Groups 92.663 4 23.166
32.241 .000Within Groups 226.337 315 .719
Total 319.000 319
Resilience
Between Groups 45.503 4 11.376
13.102 .000Within Groups 273.497 315 .868
Total 319.000 319
Relationship
Between Groups 53.843 4 13.461
15.991 .000Within Groups 265.157 315 .842
Total 319.000 319
Trying out
Between Groups 11.685 4 2.921
2.994 .019Within Groups 307.315 315 .976
Total 319.000 319
Trust
Between Groups 69.148 4 17.287
21.795 .000Within Groups 249.852 315 .793
Total 319.000 319
Frequency of Respondents
Factors influence based on Gender and
Occupations
Conclusion
• Many have argued that happiness is the ultimate purpose of human life and that an intrinsic
motivation to pursue it exists.
• The positive psychology movement strives to add to the developing database of knowledge of the
positive aspects of human experience.
• So this study helped us to understand about the various factors of happiness and how it differs
from one occupation to other.
• They are,
– Acceptance and Being yourself
– Resilience
– Relationship
– Trying out
– Trust
Happiness ppt (2) (1)

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Happiness ppt (2) (1)

  • 1. A study on Happiness based on Gender and Occupation By Team 4 Abinesh Moorthy S Ashwini B V Briyanga T G Jayavignesh J T Nithya V Sriram Chandar P S Yamini Soundarya B
  • 2. What is all about Happiness • For every minute you are ANGRY you lose sixty seconds of your happiness. • True Happiness is to enjoy the present, without anxious dependence upon the future, not to amuse ourselves with either hopes or fears but to reset satisfied with what we have, which is sufficient, for he that is so wants nothing. • The Greatest Blessings of Mankind are within us and within our reach. A wise man is content with his lot, whatever it may be, without wishing for what
  • 3. Happiness • Happiness definitely rules each individual. Happiness is so important to us, both as individuals and as a world. • As human beings, although we possess cognitive abilities and are highly "thought" oriented, the quality of our lives is ultimately entirely determined by our emotions. • Happiness is no mystery. Most people are quite clear about what happiness is, and can easily describe how happy or not they are. • Most people also consider happiness as their most important goal in life.
  • 4. Objectives • The main objectives are as follows: – To understand how people are happy and what influences them – To compare the difference of happiness degree among different genders and different occupation – To identify what are all the factors that can affect the happiness of an individual
  • 5. Research Methodology • Research Design – A research design is the arrangement of conditions for collection and analysis of data in a manner that aims to combine relevance to the research purpose with economy in procedure. – The methodology used in project is Descriptive Research. – The main characteristic of this method is that the researcher has no control over the variables; one can only report what has happened or what is happening. • Data Sources: – Our objective is to find out a real life problem so we opted for primary data. – Primary data are those which are collected afresh and for the first time, and thus happen to be the original in character. – Primary data can be collected through experiment or through survey.
  • 6. Research Methodology – Survey method was adopted because it helps to procuring data and detailed information from the respondents. • Questionnaire: – A questionnaire consists of a number of questions related to the survey. – A questionnaire is a research instrument consisting of a series of questions and other prompts for the purpose of gathering information from respondents. – Questionnaires are cheap, do not require as much effort from the questioner as verbal or telephone surveys, and often have standardized answers that make it simple to compile data. – A 5 scaled - response questionnaire was set up to measure the intensity of a respondent’s answer.
  • 8. Research Methodology • Sampling: – The respondents selected should be a representative of the total population as possible in order to produce a miniature cross-selection. – The selected respondents constitute what is technically called as ‘sample’ and the selection technique is called sampling technique. – Most predominantly used techniques to select samples are probability sampling and non probability sampling, and we used probability sampling. – Under probability sampling, we used stratified sampling technique to select the sample. – In stratified sampling, the population is divided into several sub-populations of homogenous groups. – Sample size refers to the number of respondents selected from the universe to constitute the sample. – Our sample size was 320.
  • 9. Factor Analysis – Factor analysis tool is used for analysis and interpretation. – Factor analysis is used in many fields such as behavioral and social sciences, medicine, economics, and geography as a result of the technological advancements of computers. – Large datasets that consist of several variables can be reduced by observing ‘groups’ of variables called factors. – It is easier to focus on some key factors rather than having to consider too many variables that may be trivial, and so factor analysis is useful for placing variables into meaningful categories. – The basic statistic used in factor analysis is the correlation coefficient which determines the relationship between two variables. – The correlation matrix is often used because it is easier to interpret compared to the covariance tables, although there is not a strict requirement for which matrix to use.
  • 10. Formulate the Problem Construct Correlation matrix Determine model of FA Determine number of factors Rotate the factors Interpret the factors Calculate factor scores Select the surrogate variable Determine model fit Factor Analysis
  • 11. Components of Factor Analysis • Factor Extraction: – Factor analysis is based on the common factor model which is a theoretical model. – This model postulates that observed measures are affected by underlying common factors and unique factors, and the correlation patterns need to be determined. – Principal Components analysis is used to extract maximum variance from the data set with each component thus reducing a large number of variables into smaller number of components Principal Components produces components whereas Principal Axis Factor produces factors. – Principal Components analysis is the first step to reduce the data, then follow-up with a ‘true’ factor analysis technique.
  • 12. Components of Factor Analysis • Rotation: • Factors are rotated for better interpretation since unrotated factors are ambiguous. • The goal of rotation is to attain an optimal simple structure which attempts to have each variable load on as few factors as possible, but maximizes the number of high loadings on each variable. • Two types of rotation: – Orthogonal rotation – Oblique rotation
  • 13. Components of Factor Analysis • Interpretation of Factor Loadings: – Factors can be identified by the largest loadings, but it is also important to examine the zero and low loadings in order to confirm the identification of the factors. – The signs of the loadings show the direction of the correlation and do not affect the interpretation of the magnitude of the factor loading or the number of factors to retain. – Extracting too many factors may present undesirable error variance but extracting too few factors might leave out valuable common variance. – So it is important to select which criterion is most suitable to your study when deciding on the number of factors to extract. – The eigen values and scree plot are used to determine how many factors to retain.
  • 14. ANOVA • ANOVA is an abbreviation for the full name of the method: Analysis of Variance Invented by R.A. Fisher in the 1920’s. • The one-way Analysis of Variance can be used for the case of a quantitative outcome with a categorical explanatory variable that has two or more levels of treatment. Assumptions: • There are three main assumptions, listed here: – The dependent variable is normally distributed in each group that is being compared in the one-way ANOVA. – There is homogeneity of variances. This means that the population variances in each group are equal. – Independence of observations. This is mostly a study design issue and, as such, you will need to determine whether you believe it is possible that your observations are not independent based on your study design (e.g., group work/families/etc).
  • 16. Analysis and Interpretation • Factor analysis allows us to look at groups of variables that tend to be correlated to each other and identify underlying dimensions that explain these correlations. • A group of variables is formed under one factor that explains the correlations among the variables. • Statistics associated with factor analysis: – Kaiser-Meyer-Olkin Measure of Sampling Adequacy – Bartlett's Test of Sphericity
  • 17. KMO Table KMO and Bartlett's Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .880 Bartlett's Test of Sphericity Approx. Chi-Square 2829.134 df 210 Sig. .000
  • 18. Method of Factor Analysis
  • 20. Variables Component 1 2 3 4 5 I believe that what can go wrong, will go wrong 0.783 I feel lonely 0.78 When I find myself overwhelmed with stress , I completely shut down 0.762 No value can be learned from failure 0.759 There is no point in maintaining close relationships, nothing lasts for ever 0.737 Being myself guarantees that people will dislike me 0.712 Even if I am sure of my decision , I still ask others before making an important or risky decision 0.702 It is better not to raise my hopes so that I don't get disappointed 0.645 I can find good in most disagreeable people 0.466 No matter what life throws at me , I believe that I will deal with it 0.741 When I have a difficult problem , I try to look into different angles in order to come up with a solution 0.703 I know how to calm myself down and relax when my life gets too hectic 0.62 I refuse to give up , no matter how tough things get 0.598 If I ever need help , I believe that my friends / relatives will be there for me 0.529 Given the choice ,I think that the majority of people would choose to do good rather than evil 0.716 I take steps to be happy in most serious situation to overcome it 0.674 When times get tough , I turn to family / friends for support 0.76 Most people can’t be trusted -0.709 I actively keep in touch with family and friends 0.492 I keep my problems to myself 0.783 I can find positive even in the most difficult situations -0.462 0.54 Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. a. Rotation converged in 7 iterations.
  • 21. Factors Variables Name of the factor Factor 1 No value can be learned from failure Acceptance and being yourself Not able to manage Stress and it makes me shutdown completely Being myself guarantees that people will dislike me There is no point in maintaining close relationships, nothing lasts for ever I believe that what can go wrong, will go wrong It is better not to raise my hopes so that I don't get disappointed Even if I am sure of my decision , I still ask others before making an important or risky decision I feel lonely Factor 2 I can find positive even in the most difficult situations Resilience I know how to calm myself down and relax when my life gets too hectic No matter what life throws at me , I believe that I will deal with it I refuse to give up , no matter how tough things get Factor 3 If I ever need help , I believe that my friends / relatives will be there for me Relationship When times get tough , I turn to family / friends for support I actively keep in touch with family and friends I keep my problems to myself Most people can’t be trusted Factor 4 When I have a difficult problem , I try to look into different angles in order to come up with a solution Trying out I take steps to be happy in most serious situation to overcome it Factor 5 Most people can’t be trusted TrustGiven the choice ,I think that the majority of people would choose to do good rather than evil
  • 22. ANOVA table for analyzing Factors based on genderFactors Sum of Squares df Mean Square F P value Acceptance and Being Yourself Between Groups 2.796 1 2.796 2.812 0.095 Within Groups 316.204 318 0.994 Total 319 319 Resilience Between Groups 2.608 1 2.608 2.621 0.106 Within Groups 316.392 318 0.995 Total 319 319 Relationship Between Groups 1.037 1 1.037 1.037 0.309 Within Groups 317.963 318 1 Total 319 319 Trying out Between Groups 0.073 1 0.073 0.073 0.787 Within Groups 318.927 318 1.003 Total 319 319 Trust Between Groups 1.452 1 1.452 1.454 0.229 Within Groups 317.548 318 0.999 Total 319 319
  • 23. ANOVA table for analyzing Factors based on occupation Factors Sum of Squares df Mean Square F P value Acceptance and Being Yourself Between Groups 92.663 4 23.166 32.241 .000Within Groups 226.337 315 .719 Total 319.000 319 Resilience Between Groups 45.503 4 11.376 13.102 .000Within Groups 273.497 315 .868 Total 319.000 319 Relationship Between Groups 53.843 4 13.461 15.991 .000Within Groups 265.157 315 .842 Total 319.000 319 Trying out Between Groups 11.685 4 2.921 2.994 .019Within Groups 307.315 315 .976 Total 319.000 319 Trust Between Groups 69.148 4 17.287 21.795 .000Within Groups 249.852 315 .793 Total 319.000 319
  • 25. Factors influence based on Gender and Occupations
  • 26. Conclusion • Many have argued that happiness is the ultimate purpose of human life and that an intrinsic motivation to pursue it exists. • The positive psychology movement strives to add to the developing database of knowledge of the positive aspects of human experience. • So this study helped us to understand about the various factors of happiness and how it differs from one occupation to other. • They are, – Acceptance and Being yourself – Resilience – Relationship – Trying out – Trust

Editor's Notes

  1. Orthogonal rotation is when the factors are rotated 90° from each other, and it is assumed that the factors are uncorrelated. Two common orthogonal techniques are Quartimax and Varimax rotation. Varimax minimizes the number of variables that have high loadings on each factor and works to make small loadings even smaller. Oblique rotation is when the factors are not rotated 90° from each other, and the factors are considered to be correlated. Oblique rotation is more complex than orthogonal rotation, since it can involve one of two coordinate systems: a system of primary axes or a system of reference axes. The common oblique rotation techniques are Direct Oblimin and Promax.