# Exercise Life Satisfaction among Older People who provide Care?

Exercise 1: What is the correlation
between smoking on week days and smoking on weekends among older people?

To be able answer this question, the researcher has to
filter out the dataset to include respondents who are 60years and above. The
total number of respondents irrespective of the age level were 10,601 and after
it was filtered to include those who were 60years and above, the number stood
at 7664. The number of missing observation for HeSkb is 7,109 and that of the HeSkc also 7,109.

We Will Write a Custom Essay Specifically
For You For Only \$13.90/page!

order now

Table 1.1: Correlations between HeSkb and HeSkc

Number of cigarettes smoke per weekday

Number of cigarettes smoke per weekend day

Number of cigarettes smoke per weekday

Pearson Correlation

1

0.934

Significance value

0.000

Total Number

555

555

Number of cigarettes smoke per weekend day

Pearson Correlation

0.934

1

Significance value

0.000

Total Number

555

555

Source:
Researcher’s Own Calculation, 2018

From the correlation analysis table as indicated in
the Table 1 above, the association between the variables was approximately 93%
which indicates high level of the strength of the association and this
association is being confirmed by the small p-value of 0.000 at 5% significance
level, which indicates high level of significance between the two variables.
This means that number of cigarettes smoke per weekdays is highly correlated
with number of cigarettes smoke per weekend.

1 (a) Figure 1.1:
Plot of Heskc against Heskb

Source:
Researcher’s Own Calculation, 2018

1
(b) Figure 1 above shows the scatter
plot of the HeSkb against HeSkc. From the figure, it can be observed that there
is an indication that there is a strong and positive relationship existing
between the two variables understudy.

1 (c)

Source: Researcher’s Own Calculation, 2018

Exercise 2: What are the Effects of
Care Provision, Age and Nature of Reciprocity of Life Satisfaction among Older
People who provide Care?

Table 2.1

Statistics

Sex

age

Hours
spent looking after other people last week

Respondent
is satisfied with what they have gained so far from caring for others

Respondents feel they have been
adequately appreciated for caring for others

In
most way, his/her life is close to his/her ideal

The
conditions of his/her life are excellent

Is
satisfied with his/her life

So
far, he/she has gotten the important things wants in life

If
could live his/her life again, would change almost nothing

Valid

10601

10601

1935

2725

2722

8737

8713

8838

8807

8816

Missing

0

0

8666

7876

7879

1864

1888

1763

1794

1785

Source: Researcher’s Own
Calculation, 2018

Source:
Researcher’s Own Calculation, 2018

According to (William Pavot & Ed Diener, 2008),
they indicated that SWL values range from 5-35. They stated that SWl value of 20
indicates a neutral point when using the SWL scale. The study indicated that
values between 5-9 means that the respondents are extremely dissatisfied in
their way of life. Whiles those with scores between 31-35 represent those who
are extremely satisfied with their way of life. Values between 21-25 years were
considered slightly satisfied and 15-19 indicating slightly dissatisfied in
life.

Table 2.2: Sum All

Frequency

Percentage (%)

Percentage (%)

Neutral

306

2.9

3.4

Extremely
dissatisfied

280

2.6

3.1

Extremely
satisfied

1418

13.4

15.9

Slightly
satisfied

1749

16.5

19.6

Slightly
dissatisfied

927

9.2

10.9

Satisfied

580

5.5

6.5

Extremely
satisfied

3607

34.0

40.5

Total

8912

84.1

100.0

System

1689

15.9

Total

10601

100.0

Source:
Researcher’s Own Calculation, 2018

RECODE sum_all (20=1) (5 thru 9=2) (31 thru 35=3) (21 thru 25=4)
(15 thru 19=5) INTO sumall.
EXECUTE.
RECODE sum_all (20=1) (5 thru 9=2) (31 thru 35=3) (21 thru 25=4)
(15 thru 19=5) (10 thru 14=6) (26 thru 30=7) INTO sumall.
EXECUTE.
FREQUENCIES VARIABLES=sumall
/ORDER=ANALYSIS

Source:
Researcher’s Calculations, 2018

d.   Create two new dummy variables
measuring the reciprocal relationships in care giving by recoding ErCarA and
ErCarB: Recode 1 and 2 to 1, 3 and 4 to 2 so that 1 indicates “strongly
agree/agree” and 2 indicates “disagree/strongly disagree”.

After Recoding

Table 2.3 (Ner)

Frequency

Percentage
(%)

Valid
Percentage (%)

Refusal

4

0.0

0.0

Item
not appropriate

7838

73.9

74.2

Strongly
agree/agree

2528

24.6

23.9

Disagree/strongly
disagree

118

1.1

1.8

Total

10567

99.7

100.0

System

34

0.3

Total

10601

100.0

Source:
Researcher’s Calculations, 2018

Table 2.4 (Nerb)

Frequency

Percentage
(%)

Valid
Percentage (%)

Refusal

5

0.0

0.0

Item
not appropriate

7838

73.9

74.2

Strongly
agree/agree

2528

23.8

24.7

Disagree/strongly
disagree

194

1.8

1.1

Total

10567

99.7

100.0

System

36

0.3

Total

10601

100.0

Source:
Researcher’s Calculations, 2018

2.2 (a)

i) The appropriate regression method to
fit the model 1 is the Simple Linear regression. This method fit the data well
because it uses one dependent and one independent for the analysis.

(ii).The regression method that fit
the second model 2 is the Multiple Regression technique. The model is appropriate
because it uses one dependent and more than two independent variables.

(b)

Table 2.5: Coefficients for the Two Models (Simple Linear
and Multiple Linear Regression)

Model

Unstandardized
Coefficient

Standard
error

Standard
coefficients

t-ratio

Significance
value

B

B

Simple
linear regression

Constant

24.946

0.074

336.024

0.000

Hours
spent looking after other people last week

-0.015

0.002

-0.068

-6.459

0.000

Multiple
linear regression

Constant

32.533

1.418

22.948

0.000

Hours
spent looking after other people last week

-0.017

0.002

-0.136

-6.896

0.000

Dum1

-2.919

0.691

-0.086

-4.227

0.000

Dum2

-3.768

0.534

-0.144

-7.057

0.000

Sex

-0.054

0.275

-0.004

-0.197

0.844

Age

0.000

0.015

-0.001

-0.030

0.976

Source:
Researcher’s Calculations, 2018

Coefficient of Determination Table for the Two Models
(Simple and Multiple Linear Regression)

Regression Model

R

R-Square

Simple
Linear Regression Model

0.068

0.005

Multiple
Linear Regression Model

0.235

0.055

Source: Researcher’s Calculations, 2018

(c)

2.3 (a)

Table 2.6: Coefficients
for the Two Model (Simple and Multiple Linear Regression)

Model

Unstandardized
Coefficient

Standard
error

Standard
coefficients

t-ratio

Significance
value

B

B

Multiple
linear regression

Constant

32.533

1.418

22.948

0.000

Hours
spent looking after other people last week

-0.017

0.002

-0.136

-6.896

0.000

Dum1

-2.919

0.691

-0.086

-4.227

0.000

Dum2

-3.768

0.534

-0.144

-7.057

0.000

Sex

-0.054

0.275

-0.004

-0.197

0.844

Age

0.000

0.015

-0.001

-0.030

0.976

Source:
Researcher’s Calculation, 2018

Table 2.7: Coefficient of
Determination for the Multiple Linear Regression Model

Regression Model

R

R-Square

Multiple
Linear Regression Model

0.235

0.055

Source:
Researcher’s Calculations, 2018

The result
in the Table 2.7 provides the coefficient statistics for the variables under
consideration. From the result as indicated in the table, hours spent looking
after other people last week (ErCAC) is statistically significance having
impact on the Satisfaction with life.

Also, the dummy variables created by the researcher were
all statistically significance at 0.05. The dum1 and dum2 have small
significant p-values of 0.000, which are less than 0.05 alpha level.

Furthermore, sex of respondents was not significant at
0.05. Its means that sex does not have impact on the SWL.

Finally, age of respondents is not significant at
0.05. It means that the ages of the respondents have no impact on the
satisfaction level in the lives of the respondents.

2.3 (b)

Life satisfaction is what every
individual is expecting to have. According to a study done by (Deary, Corley, Gow,
et al, 2009), they were of the view that ageing is usually associated with
declining economic resources, decreasing cognitive ability, deteriorating
physical health and weakening social support especially among older people in
society. This means that in most case, the satisfaction level among the older
people decline. The study conducted by the researchers titled “what Matters for
Life Satisfaction among the Oldest-Old?”
indicated that when it comes to life satisfaction, more women rated
themselves good or very good to enjoy life satisfactory as compared to the men.
The result obtained by the women is giving as (?=-0.308, 95% CI = -0.438 to -0.177,