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.

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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,
p<0.001). Also, when it comes to the provision of care (Li et al, 2008) indicated that the provision of care for an individual has significant impact on one's life. They were of the view that when there are provision of family care and in addition, there is modern facilities, good infrastructure and high level of pension allowance for the aged all in the form of providing care, then it is likely that such individual would enjoyed life to the fullest as compared to those who do not enjoy any of such facilities mentioned above.     2.4 The predicted regression equation for models 1 and 2 are given below;   Model 1     Model 2 From equation 1, the estimated impact of the independent variable (ErCAC) on the dependent variable (SWL) is inversely related with effect of 0.015. This value shows the contribution the independent variable has on the dependent using the unstandardized regression coefficients. However, in the case of model 2 or equation 2, which has five (5) independent variables with sex and age of the respondents being the least contributors to SWL of the respondents. From the result in the equations, it shows that ErCAC is having negative (0.017) impact on SWL. The Dummy1variable has negative (2.919) effect on SWL and dummy2 also has an inverse relationship with SWL, with a value of 3.768. Model 1 has only one independent variable to the dependent variable (SWL), whereas model 2 has five (5) independent variables to the dependent variable, (SWL).       Section 2:  Exercise 3:   3.1(a) The social participation is a key indicator of successful ageing and which is associated with many variables such as the mortality, morbidity and the quality of life. Enhancing social participation is a central component of the World Health Organization's response to concerns about population ageing.  Croezen, Avendano, Burdorf, and Van Lenthe, (2015) examined whether changes in different forms of social participation were attributed or associated with changes in depressive symptoms. The study also examined the effect of social participation factors such as; voluntary or charity work, educational or training courses, sports, social clubs, or other kinds of club activities, participation in religious organizations, and participation in political or community organizations on the respondent's level of depression. The research question formulated for this assignment is giving as; What are the impact of social participation factors on level of depression among older people? Hypothesis Null: Social participation factors do not cause depression among older people.   3. 1(b) From the website of the (https://www.elsa-project.ac.uk/), the Psychosocial measures at each wave of the ELSA study were as follows; Informal care giving, Volunteering, Provision of unpaid help, Civic, social and cultural participation, Accessing local amenities and services, TV watching and Social networks. According to the study by (Marmot, Banks, Blundell, Lessof, & Nazroo, 2004), the variables used in the study were measured based on the following method.  The respondents were selected from the Survey for England (HSE), using face to face interview and this was followed by a self-completion questionnaire). Respondents who were eligible for the study were those born on or before 29 February 1952, had been living in a responding HSE household and as at the time of the study still living in the private residential address in England. The study included partners that were under the age of 50 and partners that have just moved into the household since the HSE, were involved in the study.       3.1(c) Table 3.1 Transformation of PScedA from Negative Values to Missing Values The result below shows that the variable level of the PScedA that had been transformed, from negative values to missing values   Options Frequency Percentage (%) Valid Yes 1293 12.2   No 8620 81.3   Total 9913 93.5 Missing Refusal 27 0.3   Don't Know 34 0.3   Item Not Applicable 627 5.9   Total 688 6.5 Total   10601 100.0 Source: Researcher's Calculations, 2018   3.2(a) Multinomial Logistic Regression is one of the techniques used to classify subjects based on values of a set of predictor variables. It is used in situations where the dependent variable is not restricted to two categories and because the dependent variable has three categories, yes, no, and missing. It was necessary to use the multinomial logistic instead of binary logistic       3.2 (c) Table 3.2: Likelihood Ratio Tests     Effects Model Fitting Criteria -2Log Likelihood of Reduced Model   Chi-Square Likelihood Ratio Tests df   Significance Value Intercept 1208.798 0.000 0 - Sex 1209.307 0.509 1 0.475 Age 1282.831 74.033 53 0.030 ErCAC 1298.875 90.077 66 0.026 ErResCK 1209.316 0.519 1 0.471 Erfvolmo 1210.114 1.316 1 0.251 Erfvolle 1209.303 0.505 1 0.477 Erfvoller 1209.367 0.569 1 0.450 Erfvolvi 1209.941 1.143 1 0.285 Erfvolbe 1209.652 0.854 1 0.355 Erfvoled 1210.152 1.354 1 0.245 Erfvolin 1211.079 2.281 1 0.131 Erfvolse 1210.427 1.629 1 0.202 Erfvoltr 1210.271 1.473 1 0.225 Erfvolre 1210.225 1.427 1 0.232 Erfvolca 1210.099 1.301 1 0.254 Erfvolpr 1211.859 1.062 1 0.080 Erfvol96 1213.945 5.147 1 0.023   Pearson Chi-Square value = 1552.532 df = 1560 Significance Value =0.549   Source: Researcher's Own Calculation, 2018     3.3(a) The result in the model 3.2 above could be tested for adequacy using the person chi values indicated at the bottom of the table, from the result the , since the significance value is greater than 10% significance level, it means that the data is consistent with the model assumptions. In determining the variables that are significant to the model, variables with significant value of less than 0.05 would be considered important or significantly contributing to the model. From the table, the researcher used 17 ID variables as against 1 DV. From the result age, ErCAC and ErFVo196, were significant at 5% to the dependent variable. Each of the variable had sig-value less than 0.05, indicating high level of contribution to the DV. The rest of the variables as shown in the table in 3.2 were not significant at 5% significance level.     3.3 (b) Depression according to the world health organization and other renowned researcher is seen as one of the most common chronic mental health conditions which mostly occurred among older adults. Chi I, Yip PS et, 2005. From the study it was realized symptoms associated with depression are mostly experienced in later life have serious implications for the health and functioning of older persons as emotional distress is consistently associated with higher levels of cognitive. They made it known that it causes functional impairment and increases the risk of physical illnesses such as heart disease and stroke. Depressive symptoms also place older adults at the increased risk for suicide as indicated by (Gottfries CG, 2001). Social participation is seen as the situation whereby individuals engage in certain activities that provide interaction with others according to (Levasseur, Richard, Gauvin, Raymond, 2010; James et al., 011). Social participation among the old age is very important because it is one of the methods that has been identified to help in the reducing of depression among the older people (Lee et al., 2008). They were on the view that life changes such as the retirement, death, illness among friends and family, health conditions and socio- economic status can have impact on the social participation (Ashida and Heaney, 2008). Depression is seen as one of the factors that led so many to death in their early ages of their lives. It has been established that when the people engage in social participation activities, it reduces their level of depression and reduces their death rate among them. Lee et al., 2008 stated that when there is increase in the level of social participation on health, its impact is increase in age. Studies have shown that depression in life can be reduced when people in ages in high level of social participation, and another one who invest in it has the chance to reduce their depression level which in the long run reduces the death rate among the people and prolong their life span (Thomas, 2011). The following researchers have outlined the following benefit of social participation on the health of the individual. 1 Enhanced Quality of Life (Leavasseur, Desrosiers, & Noreau, 2004) 2 Longer survival (Glass, Mendes de Leon, Marttoli, & Berkman, 1999) 3 Lowe Morbidity (Berkman, Glass, Brissette, & Seeman, 2000) 4 Better Self-Rated Health (Lee et al., 2008) 5 Decreased risk of disability and functional and mobility decline (Avlund et al., 2003; Buckman, etal.,2009; Mendes de Leon, Glass & Berkman, 2003; James, Boyle, Buckman & Bennett, 2011;Thomas, 2011) 6 Decreased likelihood of depression (Glass, Mendes de leon, Bassuk, & et al., 2006; Golden, Conroy, Lawlor, 2009; Isaac, 2009) 7 Decreased likelihood of generalized anxiety disorders (Golden, Conroy, Lawlor, 2009) 8 Decreased risk of cognitive decline (Golden, Conroy, Lawlor, 2009; James et al., Thomas, 2011) 9 Decreased risk of dementia (Fratigliono, Desrosiers, & Paillard-Borg, & Winblad, 2004)         3.4 Syntax GET   FILE='C:Program FilesIBMSPSSStatistics23SamplesEnglishcereal.sav'. DATASET NAME DataSet3 WINDOW=FRONT. DATASET ACTIVATE DataSet1. NOMREG PScedA (BASE=LAST ORDER=ASCENDING) BY sex age ErCAA ErCAC ErResCk erfvolmo erfvolle erfvolor  erfvolvi erfvolbe erfvoled erfvolin erfvolse erfvoltr erfvolre erfvolca erfvolpr ErFVol96  /CRITERIA CIN(95) DELTA(0) MXITER(100) MXSTEP(5) CHKSEP(20) LCONVERGE(0) PCONVERGE(0.000001) SINGULAR(0.00000001) /MODEL=sex age ErCAA ErCAC ErResCk erfvolmo erfvolle erfvolor erfvolvi erfvolbe erfvoled erfvolin erfvolse erfvoltr erfvolre erfvolca erfvolpr ErFVol96  /STEPWISE=PIN(.05) POUT(0.1) MINEFFECT(0) RULE(SINGLE) ENTRYMETHOD(LR) REMOVALMETHOD(LR)  /INTERCEPT=INCLUDE  /PRINT=CELLPROB CLASSTABLE FIT PARAMETER SUMMARY LRT CPS STEP MFI.