Background and Literature Review

This report aims to compare the health needs of the elderly people with younger generations.  The majority of the researches have focused on the health needs of the elderly people where studies have evaluated the health needs of people at elderly age in different countries.  A wide range of studies has focused on integrated care for the elders (Beard et al., 2016).  The world health organization has prescribed measures for healthy ageing of people and considerable amount of work has been done in this field to examine the measures needed to provide effective health care facilities (Aboderin & Beard, 2015). However, these facilities have fallen short in fulfilling the needs of the elderly people. Moreover, a major aspect of the debate is the fact that whether health care benefits for the elderly and young same or not.

Prince et al., (2015) states that elderly people require more care when compared to the younger generation as they have better immunity and regeneration capacity.  However, positive aging is a topic that highlighted that people are less prone to diseases if they age positively.  

As per the evaluation of different peer reviewed journals and articles, there is clear gap in examining the needs of the elderly and younger people (Steptoe, Deaton & Stone, 2015). This research will facilitate in understanding whether health needs remain same throughout their age or there is difference. This will throw a light on whether more focus should be given on providing adequate health facilitates to the younger generations or more emphasis should be given on providing the best possible health care during their old age. This is the reason that the research is highly significant and will provide a different dimension when compared to the past researches.

Sampling and experimental design with rationale

Sampling is the method of minimization the sample population by select elements from the total population. There are two types of samplings methods, one is probabilistic sampling and non-probabilistic sampling. Probabilistic sampling can be furthered divided into cluster sampling, simple random sampling, stratified sampling and systematic sampling (Lotterhos & Whitlock, 2015). Non probabilistic sampling can be divided into convenience sampling, quota sampling, snow ball sampling and purposeful sampling.

Sampling consists of three phases, first is identification of the target samples, and second is the identification of the sampling frame and the third part is identification of the sample size (Palinkas et al., 2015). In this study, the target samples are the population in United States of America, the sampling frame has narrowed down the population sample to the elderly and young population in the country and sample size is 200. The study will use stratified sampling to randomly select elements from the population for ease of calculation and cost reduction.  This will eliminate the problems faced due to generalization.

Sampling and Experimental Design with Rationale

This means that the study will use probabilistic sampling which will provide equal opportunity to all the elements in the sample population of being selected and research bias can be eliminated (Csikszentmihalyi & Larson, 2014).  The chosen sample size will provide a clear view of the needs of the elderly and the younger generation and how it should be dealt with. The demographics of different countries are different, in some countries the average age of the population is high which denotes that majority of the population are above a certain age.  The chosen data will facilitate in examining the developed hypothesis in the research.

Data analysis techniques 

The data has been collected from secondary sources consisting of demographic data of the overall population and mortality rates. The data will also consist of the factors such as annual spending on the health issues for both the younger generation and the elderly generation which will provide a basic understanding of the current situations (Silverman, 2018).  The secondary data has been collected from government websites and primary samples of blood will be collected. The samples of blood will be analysed to verify the immunity level and level of care needed for each generation. The data collected from the primary and the secondary samples will be compared to reach significant conclusion.

The data collected will be analysed using inferential statistical techniques like ANOVA, regression analysis and hypothesis testing (Aziz, Lindgaard & Whitfield, 2015). These statistical techniques will be used to develop research models, examine association between variables and test the hypothesis.  The F-value generated should be less than 0.05 to be statistically significant in the research. The F–test is appropriate as the study aims to measure the population variance between two variables. The study will use statistical sampling and ratio measurement will be used to generate ratios related to the hurricane. Statistical tools like MS Excel will be used to conduct the F test in order to check whether the means are equal or not where the data analysis function will be used. The hypothesis that will be tested in the research is as follows:

H₀: Null Hypothesis stated health needs of young and elderly people are same

H₁: Alternative Hypothesis stated health needs of young and elderly people are not same

The hypothesis aims to examine the needs of the young and elderly people so that effective strategies can be developed to provide the general population with better health care facilities. In this study, two tailed test will be used as the alternative hypothesis considers the variance to be non-equal and test will be conducted in both directions. In this scenario, if the F statistics is less than the alpha level, then the null hypothesis is significant and it can be rejected.

Expected results 

In this study, it is expected that the p- value would be 0.05, which means that the null hypothesis can be rejected in the research. This means that the health needs of the young and elderly people will be different.  It is expected that the null hypothesis will be rejected but due to lack of reliability of the data it may fail to reject it.  This may also be due to the lack of evidence to reject the null hypothesis.

Suggestions for future research

The study will be conducted specific to United States of America and future studies in different sampling frame may provide different insights. Moreover, the study uses a single research design and mixed research design can be used in the future to conduct both quantitative and the qualitative analysis of the data. Future research has the scope of using other control variables to examine the relation between the given variables.

References 

Aboderin, I. A., & Beard, J. R. (2015). Older people’s health in sub-Saharan Africa. The Lancet, 385(9968), e9-e11.

Aziz, M. S. A., Lindgaard, G., & Whitfield, T. A. (2015). Evaluating a visual tool for systematic data collection and analysis for design students. ARPN Journal of Engineering and Applied Sciences, 10(23), 17853-62.

Beard, J. R., Officer, A., de Carvalho, I. A., Sadana, R., Pot, A. M., Michel, J. P., … & Thiyagarajan, J. A. (2016). The World report on ageing and health: a policy framework for healthy ageing. The Lancet, 387(10033), 2145-2154.

Csikszentmihalyi, M., & Larson, R. (2014). Validity and reliability of the experience-sampling method. In Flow and the foundations of positive psychology (pp. 35-54). Springer, Dordrecht.

Lotterhos, K. E., & Whitlock, M. C. (2015). The relative power of genome scans to detect local adaptation depends on sampling design and statistical method. Molecular ecology, 24(5), 1031-1046.

Palinkas, L.A., Horwitz, S.M., Green, C.A., Wisdom, J.P., Duan, N. and Hoagwood, K., 2015. Purposeful sampling for qualitative data collection and analysis in mixed method implementation research. Administration and Policy in Mental Health and Mental Health Services Research, 42(5), pp.533-544.

Prince, M. J., Wu, F., Guo, Y., Robledo, L. M. G., O’Donnell, M., Sullivan, R., & Yusuf, S. (2015). The burden of disease in older people and implications for health policy and practice. The Lancet, 385(9967), 549-562.

Silverman, B. W. (2018). Density estimation for statistics and data analysis. Routledge.

Steptoe, A., Deaton, A., & Stone, A. A. (2015). Subjective wellbeing, health, and ageing. The Lancet, 385(9968), 640-648.