Exercise Life Satisfaction among Older People who provide Care?

Exercise 1: What is the correlationbetween smoking on week days and smoking on weekends among older people?To be able answer this question, the researcher has tofilter out the dataset to include respondents who are 60years and above. Thetotal number of respondents irrespective of the age level were 10,601 and afterit was filtered to include those who were 60years and above, the number stoodat 7664. The number of missing observation for HeSkb is 7,109 and that of the HeSkc also 7,109. 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 inthe Table 1 above, the association between the variables was approximately 93%which indicates high level of the strength of the association and thisassociation is being confirmed by the small p-value of 0.000 at 5% significancelevel, which indicates high level of significance between the two variables.This means that number of cigarettes smoke per weekdays is highly correlatedwith number of cigarettes smoke per weekend.    1 (a) Figure 1.1:Plot of Heskc against Heskb Source:Researcher’s Own Calculation, 20181(b) Figure 1 above shows the scatterplot of the HeSkb against HeSkc. From the figure, it can be observed that thereis an indication that there is a strong and positive relationship existingbetween the two variables understudy.

 1 (c)      Source: Researcher’s Own Calculation, 2018Exercise 2: What are the Effects ofCare Provision, Age and Nature of Reciprocity of Life Satisfaction among OlderPeople 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 OwnCalculation, 2018  Source:Researcher’s Own Calculation, 2018According to (William Pavot & Ed Diener, 2008),they indicated that SWL values range from 5-35. They stated that SWl value of 20indicates a neutral point when using the SWL scale. The study indicated thatvalues between 5-9 means that the respondents are extremely dissatisfied intheir way of life. Whiles those with scores between 31-35 represent those whoare extremely satisfied with their way of life. Values between 21-25 years wereconsidered slightly satisfied and 15-19 indicating slightly dissatisfied inlife.

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 variablesmeasuring the reciprocal relationships in care giving by recoding ErCarA andErCarB: Recode 1 and 2 to 1, 3 and 4 to 2 so that 1 indicates “stronglyagree/agree” and 2 indicates “disagree/strongly disagree”.       After RecodingTable 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 tofit the model 1 is the Simple Linear regression. This method fit the data wellbecause it uses one dependent and one independent for the analysis.(ii).The regression method that fitthe second model 2 is the Multiple Regression technique.

The model is appropriatebecause it uses one dependent and more than two independent variables.            (b)Table 2.5: Coefficients for the Two Models (Simple Linearand 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: Coefficientsfor 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 ofDetermination for the Multiple Linear Regression Model Regression Model R R-Square Multiple Linear Regression Model 0.235 0.055 Source:Researcher’s Calculations, 2018The resultin the Table 2.7 provides the coefficient statistics for the variables underconsideration.

From the result as indicated in the table, hours spent lookingafter other people last week (ErCAC) is statistically significance havingimpact on the Satisfaction with life.  Also, the dummy variables created by the researcher wereall statistically significance at 0.05. The dum1 and dum2 have smallsignificant p-values of 0.

000, which are less than 0.05 alpha level.Furthermore, sex of respondents was not significant at0.

05. Its means that sex does not have impact on the SWL.Finally, age of respondents is not significant at0.05. It means that the ages of the respondents have no impact on thesatisfaction level in the lives of the respondents. 2.3 (b)Life satisfaction is what everyindividual 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 withdeclining economic resources, decreasing cognitive ability, deterioratingphysical health and weakening social support especially among older people insociety. This means that in most case, the satisfaction level among the olderpeople decline. The study conducted by the researchers titled “what Matters forLife Satisfaction among the Oldest-Old?” indicated that when it comes to life satisfaction, more women ratedthemselves 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 impacton one's life. They were of the view that when there are provision of familycare and in addition, there is modern facilities, good infrastructure and highlevel of pension allowance for the aged all in the form of providing care, thenit is likely that such individual would enjoyed life to the fullest as comparedto those who do not enjoy any of such facilities mentioned above.

  2.4 The predicted regression equationfor models 1 and 2 are given below; Model 1   Model 2 Fromequation 1, the estimated impact of the independent variable (ErCAC) on the dependentvariable (SWL) is inversely related with effect of 0.015. This value shows the contributionthe independent variable has on the dependent using the unstandardizedregression coefficients. However, in the case of model 2 or equation 2, whichhas five (5) independent variables with sex and age of the respondents beingthe least contributors to SWL of the respondents. From the result in theequations, it shows that ErCAC is having negative (0.

017) impact on SWL. TheDummy1variable has negative (2.919) effect on SWL and dummy2 also has aninverse relationship with SWL, with a value of 3.768. Model 1 has only oneindependent 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 keyindicator of successful ageing and which is associated with many variables suchas the mortality, morbidity and the quality of life. Enhancing socialparticipation is a central component of the World Health Organization’sresponse to concerns about population ageing.  Croezen,Avendano, Burdorf, and Van Lenthe, (2015) examined whether changes indifferent forms of social participation were attributed or associated withchanges in depressive symptoms. The study also examined the effect of socialparticipation factors such as; voluntary or charity work, educational or trainingcourses, sports, social clubs, or other kinds of club activities, participationin religious organizations, and participation in political or communityorganizations on the respondent’s level of depression.The research questionformulated for this assignment is giving as; What are the impactof social participation factors on level of depression among older people?Hypothesis Null: Social participation factors donot cause depression among older people. 3. 1(b)Fromthe website of the (https://www.

elsa-project.ac.uk/),the Psychosocial measures at each wave of the ELSA study were as follows;Informalcare giving, Volunteering, Provision of unpaid help, Civic, social and culturalparticipation, Accessing local amenities and services, TV watching and Socialnetworks. According to the study by (Marmot,Banks, Blundell, Lessof, & Nazroo, 2004), the variables used in the studywere measured based on the following method. The respondents were selected from the Survey for England (HSE), usingface to face interview and this was followed by a self-completionquestionnaire). Respondents who were eligible forthe study were those born on or before 29 February 1952, had been living in aresponding HSE household and as at the time of the study still living in theprivate residential address in England.

The study included partners that wereunder the age of 50 and partners that have just moved into the household sincethe HSE, were involved in the study.   3.1(c)Table 3.1Transformation of PScedA from Negative Values to Missing ValuesThe result below shows that the variable level of the PScedA that hadbeen 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 classifysubjects based on values of a set of predictor variables.

It is used insituations where the dependent variable is not restricted to two categories andbecause the dependent variable has three categories, yes, no, and missing. Itwas 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 OwnCalculation, 2018  3.3(a) The result in the model 3.2 above could be tested for adequacyusing the person chi values indicated at the bottom of the table, from theresult the , since the significance value is greater than 10% significancelevel, 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 consideredimportant or significantly contributing to the model. From the table, the researcher used17 ID variables as against 1 DV. From the result age, ErCAC and ErFVo196, weresignificant at 5% to the dependent variable. Each of the variable had sig-valueless than 0.05, indicating high level of contribution to the DV. The rest ofthe variables as shown in the table in 3.2 were not significant at 5% significancelevel.

  3.3(b)Depression according to the worldhealth organization and other renowned researcher is seen as one of the most commonchronic mental health conditions which mostly occurred among older adults. ChiI, Yip PS et, 2005. From the study it was realized symptoms associatedwith depression are mostly experienced in later life have serious implicationsfor the health and functioning of older persons as emotional distress isconsistently associated with higher levels of cognitive. They made it knownthat it causes functional impairment and increases the risk of physicalillnesses such as heart disease and stroke. Depressive symptoms also placeolder adults at the increased risk for suicide as indicated by (GottfriesCG, 2001).

Social participation is seen as thesituation whereby individuals engage in certain activities that provideinteraction with others according to (Levasseur, Richard, Gauvin, Raymond,2010; James et al., 011). Social participation among the old age is veryimportant because it is one of the methods that has been identified to help inthe reducing of depression among the older people (Lee et al., 2008). They wereon the view that life changes such as the retirement, death, illness amongfriends and family, health conditions and socio- economic status can haveimpact on the social participation (Ashida and Heaney, 2008). Depression isseen as one of the factors that led so many to death in their early ages oftheir lives. It has been established that when the people engage in socialparticipation activities, it reduces their level of depression and reducestheir death rate among them.

Lee et al., 2008 stated that when there isincrease in the level of social participation on health, its impact is increasein age. Studies have shown that depression in life can be reduced when peoplein ages in high level of social participation, and another one who invest in ithas the chance to reduce their depression level which in the long run reducesthe death rate among the people and prolong their life span (Thomas, 2011). Thefollowing researchers have outlined the following benefit of socialparticipation 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)     3.4Syntax 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.