Getting Your Research Project Started – Assignment
Internet has become an important part of our life. Since its inception in the early 90’s, the importance that it has played in society has grown. Nowadays Internet is considered to be an essential tool for Knowledge creation (i.e. enables medical research) and sharing and acts as an intermediary between life expectancy and economic growth (Abidi, Cheah & Curran,2005). However, it is not clear whether knowledge sharing via the Internet has had an impact on the health and wellbeing of society (Gerrusu & French, 2005). Hence I would like to work with the Gapminder data.
Research Question 1:
Is high Life Expectancy associated with high Internet Usage rates?
I am particularly interested in the association between Internet Usage Rate and Life Expectancy. My aim while exploring a part of this dataset would be to investigate and shed some light on the relationship between the Internet and life expectancy by examining if the knowledge that is being shared via the Internet could have an impact on the health and wellbeing of society. Most of the people from younger generation have the access to Internet and knowledge about its usage. However, some senior citizens remain somewhat wary of technology and in particular computers and the internet. That said, a growing proportion of the elderly go online, either as younger generations who have used the internet move into the older age classes, or as people develop internet skills in their old age. Indeed, the internet opens up a wealth of new opportunities and services that may be of particular interest to the elderly. I want to check if this would have a positive effect on their quality of life.
Codebook for Question 1:
My Codebook for addressing this Question would contain the data from the following variables:
1. List of Countries
2. Life expectancy of all the countries from the dataset
3. Internet usage rate in all the countries.
Out of Curiosity, I plotted the available data (Internetuserate vs Lifeexpectancy) from the Gapminder dataset:
Based on the above graph there seems to be good correlation between the two variables.
Research Question 2:
Should countries with higher Life expectancy have more cases of Cancer?
I would like to find if high Disease rates (i.e. Cancers) in a particular country correlates to the overall life expectancy in that country. I would want to particularly focus on Breast cancer rates. By far the biggest risk factor for most cancers is simply getting older or having a high life expectancy. More than three-quarters of all people diagnosed with cancer in the UK are over the age of 60. (http://www.cancerresearchuk.org/about-us/cancer-news/press-release/2015-02-04-1-in-2-people-in-the-uk-will-get-cancer).
Cancer is a disease of our genes – the bits of DNA code that hold the instructions for all of the microscopic machinery inside our cells. Over time, mistakes accumulate in this code. And it’s these mistakes that can kick start a cell’s journey towards becoming cancerous. The longer we live; the more time we have for errors to build up. And so, as time passes, our risk of developing cancer goes up, as we accumulate more of these faults in our genes.
Again out of curiosity, I tried to correlate the two variables (Breast cancer rate vs Life expectancy) and plotted them. In the graph below, you can see that life expectancy has a positive effect on high Breast cancer rates. This could imply there are now more people than ever living to an age where they have a higher risk of developing cancer.
Codebook for Question 2:
My Codebook for addressing this Question would contain the data from the following variables:
1. List of Countries
2. Life expectancy of all the countries from the dataset
3. Breast cancer rates in these countries.
Summary:
According to me, Gapminder Data seems to be one of the very interesting data to work with. One can correlate or associate many factors which seems to be dependent on one another. I would like to summarize the answers to above raised questions with the help of this flow diagram.
I believe that three variables (Internet Usage, Life Expectancy and Breast cancer rates) are associated to each other either directly or indirectly and the fourth variable Economic growth or per capita income could play an important part in shaping up the observations of these variables.
References:
Abidi, S. S. R., Cheah, Y. N., & Curran, J. 2005. “A knowledge creation info-structure to acquire and crystallize the tacit knowledge of health-care experts,” Information Technology in Biomedicine, IEEE Transactions on (9:2), pp 193-204.
Gerressu, M. & French, R. S. 2005. “Using the Internet to promote sexual health awareness among young people,” Journal of Family Planning and Reproductive Health Care, (31:4), pp 267-269.