Independent variables: depression, satisfaction with life, hampered daily activities

Discuss about the Investigating Relationship and Perceived Stress.

The variable list in the given problem consisted of twenty five variables. There were seven dependent and eighteen independent variables(Fredrickson, 2013). For the current work “happy” (how happy you are) has been chosen as the dependent variable. From the list of independent variables initially six variables were chosen. They were, age of the respondent (agea), legal marital status (maritalb), important to do what is told and follow rules (ipfrule), felt depressed, how often past week (fltdpr), hampered in daily activities by illness (hlthhmp) and how satisfied with life as a whole (stflife). Among these six variables three variables were finally chosen as set of independent variables(Quoidbach, et. al., 2010). They were “fltdpr”, “hlthhmp” and “stflife”. The current work related the effect of chosen independent variables on happiness. The choice was based on the probable superior effects of satisfaction in life, every day struggle with physical and mental illness and feeling of depression on happiness in life management.

The emphasis of overall satisfaction in life of a human being is essential. Self-contentment and control of emotional intelligence of human brain are considered to be the major causes of happiness. The pursuit of happiness has pushed mankind towards a singularity in life, which is infinite in nature and diverging in sense. The satisfaction in life opens the door of happiness which drives away negative philosophical speculations. Family values, regional culture shape a human mind(Jose, Lim & Bryant, 2012). The choice of satisfaction in life (stflife) as the first independent variable is thus justified. The degree of satisfaction and depression are inversely correlated and the eagerness for satisfaction brings gloominess with it. Negative effect of depression creates variety of psychological and physical disorders, hence adversely effecting happiness in human life. So the choice of depression in past week (fltdpr) becomes an obvious second choice. The third independent variable could have been ‘marital status’ or ‘importance of following orders’. But association of happiness with health was considered to be stronger. Positive effect of greater physical health with high energetic lifestyle affects happiness in an affirmative way. Empirical results of previous studies were also considered for the choice of the independent variables (Cohn & Fredrickson, 2009). The three independent variables were essential personality traits which affects the happiness universally.

The present analysis has attempted to relate the independent variables with the dependent variable, happiness. Based on the ordinal character of the variables appropriate statistical measures have been chosen for reporting the descriptive values management.The missing values of all the variables were carefully excluded and descriptive values were validated.

Dependent variable: happiness

Dependent variable measuring happiness was an ordinal variable and hence frequency distribution has been reported. SPSS package software was used for this purpose. There were four missing values and they only consisted of 0.2 percent of the entire data of happiness. Hence number of missing values was not considered as significant. The average happiness score was 7.2 which indicated that the accumulation of happiness score was near the median value of 8.0. The minimum and maximum value of the dependent variable was 0 and 10.

Table 1: Frequency distribution of Happiness

How happy are you
N	Valid	2386
Missing	4
Mean	7.20
Median	8.00
Range	10

The scores of 2390 subjects were considered and category wise valid frequency percentage has been provided in the table2.

Table 2: Percentage validation of happiness data

How happy are you
	Frequency	Percent	Valid Percent	Cumulative Percent
Valid	Extremely unhappy	15	.6	.6	.6
1	12	.5	.5	1.1
2	31	1.3	1.3	2.4
3	65	2.7	2.7	5.2
4	87	3.6	3.6	8.8
5	229	9.6	9.6	18.4
6	240	10.0	10.1	28.5
7	453	19.0	19.0	47.4
8	706	29.5	29.6	77.0
9	328	13.7	13.7	90.8
Extremely happy	220	9.2	9.2	100.0
Total	2386	99.8	100.0
Missing	System	4	.2
Total	2390	100.0

The frequencies for the happy variable has been plotted in a bar diagram for all the categories of happiness. The bar diagram in figure 1 represented a left skewed distribution and an almost normal nature of the data was evident.

Satisfaction with life (stflife) was the first independent variable. There were 9 missing values which was only 0.4 percent of the entire data of the stflife. Total valid observations were 2381 which defined the variable.

Table 3: Frequency distribution of satisfaction in life

Statistics
How satisfied with life as a whole
N	Valid	2381
Missing	9
Mean	6.94
Median	7.00
Range	10

The variable stflife was also ordinal in nature, but mean of 6.94 signified the accumulation of the values around the median 7.00. The variable was measured in a scale of 0-11, where zero was the measure of extremely dissatisfied and 11 measured extremely satisfied.

Table 4: Percentage validation of satisfaction in life data

How satisfied with life as a whole
	Frequency	Percent	Valid Percent	Cumulative Percent
Valid	Extremely dissatisfied	34	1.4	1.4	1.4
1	14	.6	.6	2.0
2	45	1.9	1.9	3.9
3	79	3.3	3.3	7.2
4	89	3.7	3.7	11.0
5	293	12.3	12.3	23.3
6	245	10.3	10.3	33.6
7	470	19.7	19.7	53.3
8	616	25.8	25.9	79.2
9	279	11.7	11.7	90.9
Extremely satisfied	217	9.1	9.1	100.0
Total	2381	99.6	100.0
Missing	System	9	.4
Total	2390	100.0

The frequencies for the independent variable stflife has been plotted in a bar diagram for all the categories of satisfaction. The bar diagram in figure 2 represented a left skewed distribution and an almost normal nature of the data was evident.

The second independent variable was fltdpr which symbolized frequency of depression felt in past week. Missing number of observations were 21 which was 0.9 percent of the entire data. The average depression was evaluated and it was observed that accumulation of the depression scores was near the median value of 1.00. It has to be noted that the variable was ordinal in nature and hence mean was only an indication of accumulation.

Table 5:Frequency distribution of depression in past week

Statistics
Felt depressed, how often past week
N	Valid	2369
Missing	21
Mean	1.33
Median	1.00
Range	3

The percentage validation of data for each of the four categories has been given in table 6. The 21 missing values consisted of insignificant percentage of the entire depression data.

Statistical measures used to describe the data: frequency distribution, percentage validation

Table 6:Percentage validation of depression in past week

Felt depressed, how often past week
	Frequency	Percent	Valid Percent	Cumulative Percent
Valid	None or almost none of the time	1730	72.4	73.0	73.0
Some of the time	527	22.1	22.2	95.3
Most of the time	79	3.3	3.3	98.6
All or almost all of the time	33	1.4	1.4	100.0
Total	2369	99.1	100.0
Missing	System	21	.9
Total	2390	100.0

The diagrammatic representation revealed that most of the subjects were not depressed in past weeks. This was evident from figure 3 and a negative correlation of the variable with happiness was expected(Jose, Lim & Bryant, 2012). A negative exponential nature was observed for depression and inverse relation with dependent variable was anticipated.

The third independent variable was ‘hlthhmp’ which symbolized frequency of hampered daily activities due to illness. Total 5 observations were missing which was 0.2 percent of the entire data. The average hampered daily activities due to illness were evaluated and it was observed that accumulation of the depression scores was near the median value of 3.00(Sanchez and Vazquez, 2014). It has to be noted that the variable was nominal in nature and hence mean was only a mere indication of accumulation of data management.

Table 7: Frequency distribution of hampered daily activities due to illness

Statistics
Hampered in daily activities by illness/disability/infirmity/mental problem
N	Valid	2385
Missing	5
Mean	2.77
Median	3.00
Range	2

The percentage validation of data for each of the three categories has been given in table 8. The 5 missing values consisted of insignificant percentage of the entire depression data.

Table 8: Percentage validation of hampered daily activities due to illness

Hampered in daily activities by illness/disability/infirmity/mental problem
	Frequency	Percent	Valid Percent	Cumulative Percent
Valid	Yes a lot	93	3.9	3.9	3.9
Yes to some extent	366	15.3	15.3	19.2
No	1926	80.6	80.8	100.0
Total	2385	99.8	100.0
Missing	System	5	.2
Total	2390	100.0

The bar diagram discovered that most of the subjects daily activities were not hampered due to illness in past weeks. This was evident from figure 3 and positive correlation of the variable with happiness was expected. An exponential nature was observed for depression and constructive relation with dependent variable was anticipated.

The interdependency of the variables was tested using Pearson’s correlation. Association of the dependent variable “happy” with the three chosen independent variables was inspected. Three set of hypotheses were constructed based on the relation of three independent variables with the dependent variable. The relation between happiness and overall satisfaction in life was a positive association whereas depression in past week was negatively associated with happiness. But the relation of hampered daily activities due to illness with happiness was not apparent in nature,though the variables were assumed to be positively associated.  A one tailed (right tailed) test was performed for the same.

The first hypothesis was for the relation between happiness and overall satisfaction in life. The null hypothesis was based on the assumption that the dependent and independent variable was uncorrelated and stated as follows:

H0: there was no significant relation between the scores of happiness and overall satisfaction of the subjects.

Results indicate an inverse correlation between depression and happiness, and a positive correlation between satisfaction with life and happiness

The alternate hypothesis was,

 HA:  there was significant relation between the scores of happiness and overall satisfaction of the subjects, and the relation was positive in nature.

The statistical test performed to reject the null hypothesis was one tailed (right tailed) in nature.

The second hypothesis was for the relation between happiness and depression in past week. The null hypothesis based on the assumption of non-correlation of the variables was as follows:

H0: there was no significant relation between the scores of happiness and depression in past week of the subjects.

The alternate hypothesis was,

 HA:  there was significant relation between the scores of happiness and depression in past week of the subjects, and the association was considered to be negative in nature.

The statistical test performed to reject the null hypothesis was one tailed (left tailed) in nature.

The third hypothesis was for the relation between happiness and hampered daily activities due to illness. The null hypothesis was as follows:

H0: there was no significant relation between the scores of happiness and hampered daily activities due to illness of the subjects.

 HA: there was significant relation between the scores of happiness and hampered daily activities due to illness of the subjects, and due to the nature of association the statistical test performed to reject the null hypothesis was one tailed in nature.

The three hypotheses were tested using Pearson’s correlation, and further results were further justified using bivariate regression analysis.

The interrelation of the dependent variable ‘happy’ with the independent variable ‘stflife’ has been revealed in the bar chart of figure 5. The optimal number of happy subjects was observed to be comparatively soaring for higher level of satisfaction level.

The representative scale of happiness was almost normally distributed for lower scale of life satisfaction and left skewed for highest level of satisfaction level.

Bar chart representing the distribution of happiness for hampered daily life due to illness revealed the positive relation between the variables under consideration. The number of subjects for happiness was concentrated with soaring frequencies for life without illness or disability.

The diagrammatic representation discovered the negative association of depression and happiness in life. The elevated columns for the number of subjects of happiness due to depression less life in past few weeks was in accordance with the earlier research results.

Further analysis was prepared by comparison of mean scores. It was also noted that variables were ordinal or nominal in nature and hence comparison of means was considered as non-rigorous but still representative in nature(Seligman& Csikszentmihalyi, 2014). The comparison between happiness and three independent variables yielded that the values of F statistic in analysis of variance were significant with zero p values. Indication of rejection was evident for all the three null hypotheses. The detailed calculations with ‘eta’ values representing the correspondence between the dependent and independent variables have been given in tables 12 -17 of appendix.

Most subjects did not report hampered daily activities due to illness

Pearson’s correlation was used as the chief measuring instrument. Correlation between the dependent variable ‘happy’ and three independent variables ‘stflife’, ‘fltdpr’ and ‘hlthhmp’ was calculated for 0.05 level of significance(Schiffrin.& Nelson, 2010). The tests were one tailed in nature due to the direction of association of the variables.

As expected, the correlation between happiness and overall satisfaction in life was significantly positive with coefficient of 0.645 and p value of 0.000. The coefficient for correlation was 0.174 between happiness and illness or disability hampered daily activities. The coefficient was less than 0.3 and hence considered low in nature. The p value of 0.000 indicated that the low association was significant. Following the line of anticipation and earlier research works, depression was significantly (p value of 0.000) and negatively correlated with happiness (table 18 in Appendix). The coefficient of correlation was less than -0.3 and considered significantly negative.

The correlation between the independent variables was also noted. Overall satisfaction in life was positively related to hampered daily life due to illness or disability whereas the association with depression in life was negative in nature. The results were on the expected line but the low correlation of hampered daily life (hlthhmp) with other variables was noticeable information.

Table 9: Pearson’s Correlation coefficient between the dependent and independent variables

Pearson’s Correlation	How satisfied with life as a whole	Hampered in daily activities by illness/disability/infirmity/mental problem	Felt depressed, how often past week
How happy are you	Pearson Correlation	.645**	.174**	-.384**

Past week depression was significantly correlated with other two independent variables where the coefficients of correlation were-0.319 and -0.266 with satisfaction in life (stflife) and hampered daily activities (hlthhmp). The negative correlation values disclosed the adverse effect of depression on human brain.

The confirmation about the intensity of the association was obtained from bivariate regression along with analysis of variance(Shin &Johnson, 1978). The ANOVA established the correlation results since the value of F statistic was well within the rejection region with p value of 0.000.

Table 10: ANOVA for regression model

Model	Sum of Squares	df	Mean Square	F	Sig.
1	Regression	3972.071	3	1324.024	654.929	.000b
Residual	4746.784	2348	2.022
Total	8718.855	2351

The regression model provided the final conclusion and hampered of daily activities was considered to be insignificant with p value 0.188 which was greater than 0.05. The intercept was 4.076 with p value of 0.000 which signified the fact that the intercept was significant in the model. The slope of overall satisfaction was 0.536 with p value of 0.00 and positive relation of the dependent variable was again observed. The independent variable ‘fltdpr’ had a slope of -0.613 with significance (p value) of 0.00 and the negative association was well defined. The slope for the variable ‘hlthhmp’ was very low positive and insignificant. The regression line was evaluated as: happy=4.076+0.536* stflife-0.613*fltdpr+0.08*hlthhmp.

Empirical results of previous studies were also considered for the choice of the independent variables

Table 11: Regression Coefficients for bivariate regression

Model	Unstandardized Coefficients	Standardized Coefficients	t	Sig.	95.0% Confidence Interval for B
B	Std. Error	Beta	Lower Bound	Upper Bound
1	(Constant)	4.076	.224		18.19	.00	3.637	4.515
How satisfied with life as a whole	.536	.015	.582	36.05	.00	.506	.565
Felt depressed, how often past week	-.613	.052	-.194	-11.78	.00	-.715	-.511
Hampered in daily activities by illness/disability/infirmity/mental problem	.080	.060	.021	1.316	.188	-.039	.198

The choice of the third independent variable was a matter of concern from the beginning of the work, where low positive correlation increased the chances of indifferent association with the dependent variable. The regression model clearly established the fact that hampered daily life from illness or disability was not significantly associated with happiness and unlocked the scope of a new choice of the third independent variable, in spite of the fact that the coefficient of correlation between happiness and other probable choices were not significantly greater than the correlation value of ‘hlthhmp’ with ‘happy’ (table 19 in Appendix).

Conclusion

The dependent variable happiness was positively related with overall satisfaction in life and absence of illness in daily life. The association with stress or depression was negative as expected. The results were in support of earlier research works by Gerdtham and Johannesson (2001)(Gerdtham& Johannesson,2001). In 2008 Veenhoven revealed in his work that exact reasons for happiness are difficult to be pin pointed(Veenhoven, 2008). In current study the choice of the third independent variables was also not very comprehensible. The positive effect of less illness in recent past on happiness was also earlier established by Mroczek and Kolarz (1998)(Mroczek & Kolarz, 1998). In 2003,Argyle discussed aboutthe factors affecting happiness and marriage was one of the major causes, but in this study legal marriage was almost uncorrelated with happiness(Argyle, 2003). Overall stress free life and depression less history were the major reasons for happiness according to the study.

References

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