The World Bank's Kenya Malaria Indicator Survey 2015, Report (The World Bank, 2018) provide all the relevant data that is related to Malaria Epidemiology in Kenya. First, the dataset contains information that can be used to determine the prevalence of malaria in different age groups as well as the socio-demographic characteristics associated with the disease. For instance, the number of children aged 6-59 months infected with malaria was categorized by age in months as follows: 6-8, 9-11, 12-17, 18-23, 24-35, 36-47, and 48-59 months respectively. Using this data, I will be able to determine the prevalence of malaria per age group.
Second, The World Bank's Kenya Malaria Indicator Survey 2015 is appropriate for my final project because it provides data related to the prevalence of malaria across different variables such as sex, residence, mother's education, and socio-economic status. Third, the information is relevant for my final project because it shows the prevalence of malaria by species. Even though Plasmodium falciparum is the species that is commonly associated with disease, other species such as Plasmodium malariae, Plasmodium ovale, and Plasmodium vivax are also found in the country. Consequently, determination of the number of malaria cases across different vector species is vital. Lastly, The World Bank's Kenya Malaria Indicator Survey 2015 provides data on the transmission patterns of the disease in Kenya across different regions. This is useful in further exploring the factors linked to malaria infections in different geographical areas.
It is also worth noting that Tanzania's Malaria Indicator Survey 2015-2016 (The World Bank Group, 2018) provided relevant data regarding the epidemiology of malaria in Tanzania. One of the most critical data is Malaria prevalence among children aged children age 6-59 months. More importantly, the number of malaria infections across different socio-demographic variables such as sex (male or female), residence (urban or rural), region, mother's educational status, and socio-economic status. By examining how the prevalence of malaria is distributed across these variables, the researcher is capable of determining the best predictor of malaria incidence. Lastly, the data is also crucial to my final study because it addresses various strategies for preventing the disease household use of mosquito nets, indoor residual spray programs, and access the insecticide-treated net.
The Validity and Integrity of the Data
The validity and integrity of secondary data can be examined in various ways. First, the validity of the data is determined by looking at the type of sampling used. The sampling design used to pick a sample for Tanzania's Malaria Indicator Survey 2015-2016 included a combination of cluster and systematic sampling techniques. In Kenya's Malaria Indicator Survey 2015, a two-stage stratified cluster sampling and systematic sampling designs were employed. These sampling methods have high generalizability because they are probability sampling techniques (Curry & Nunez-Smith, 2014; Coe, Waring, Hedges, & Arthur, 2017). That is, findings of the survey can be extended to other populations other than the sample that was studied (Bausell, 2015; Fielding, Lee, & Blank, 2016; Neumayer & Plumper, 2017).
The external validity of both Tanzania's and Kenya's surveys was also strengthened by the large sample chosen for the study. According to Langbein (2014), large random samples are more likely to be high in external validity because they are likely to be representative of the larger population. It can also be concluded that the questionnaires used to collect participants' data were both reliable and valid. This is because they were developed by experts in malaria data collection such as ICF International and Roll Back Malaria Monitoring and Evaluation Reference Group. However, the questionnaires' reliability and validity scores were not reported.
Strategies for Overcoming the Limitations
One of the limitations that were identified in both Tanzania's and Kenya's Malaria Indicator surveys is a failure to report questionnaires' reliability and validity data. Reliability is the extent to which the findings obtained by measurement is capable of being replicated (Farrell, 2016; Russell, Lintern, Gauntlett, & Davies, 2016). On the other hand, validity refers to the extent to which an instrument measures what it purports to measure (Bolarinwa, 2015). Overcoming the limitation related to the reliability of the questionnaire will be achieved through pilot studies. That is, before the main study, the survey instrument few randomly selected participants and data obtained used to calculate Cronbach's alpha reliability score using statistical packages such as SPSS and Minitab (Sunder, 2011). On the other hand, the content and construct validity of questionnaires can be enhanced by incorporating experts in their development.
The Process Incurred In Selection of a DataSet and Determination of Integrity
The process that was incurred in the selection of data set was comprised of three steps. First, the research topic, as well as the research question, was defined. The research topic pertains to the epidemiology of malaria in Kenya and Tanzania. On the other hand, the research question related to the topic is: What is the epidemiology of malaria in Kenya and Tanzania? Second, selection of a relevant dataset was made by examining the credible website, mainly The World Bank Group dataset. Third, I took the time to know the dataset through intensive reading of the surveys. On the other hand, the integrity of data was evaluated by examining the reliability and validity of the data.
Bausell, R. B. (2015). The Design and conduct of meaningful experiments involving human participants: 25 scientific principles. Oxford University Press.Bolarinwa. (2015). Principles and methods of validity and reliability testing of questionnaires used in social and health science researches. Retrieved from http://www.npmj.org/article.asp?issn=1117-1936;year=2015;volume=22;issue=4;spage=195;epage=201;aulast=Bolarinwa
Coe, R., Waring, M., Hedges, L. V., & Arthur, J. (2017). Research methods and methodologies in education. Sage Publishers.
Curry, L., & Nunez-Smith, M. (2014). Mixed methods in health sciences research: a practical primer. SAGE Publications.
Farrell, P. (2016). Writing built environment dissertations and projects: practical guidance and examples. John Wiley & Sons.
Fielding, N. G., Lee, R. M., & Blank, G. (2016). The SAGE handbook of online research methods. SAGE Publications.Langbein, L. (2016). Public program evaluation: a statistical guide. Routledge Publishers.
Neumayer, E., & Plumper, T. (2017). Robustness tests for quantitative research. Cambridge University Press.Russell, J., Lintern, F., Gauntlett, L., & Davies, J. (2016). Cambridge international AS and A level psychology coursebook. Cambridge University Press.
Sunder, V. K. (2011). Outsourcing and customer satisfaction. Xlibris Corporation.
The World Bank Group (2018). Kenya - malaria indicator survey 2015. Retrieved from http://microdata.worldbank.org/index.php/catalog/2570
The World Bank Group (2018). Tanzania demographic and health survey and malaria indicator survey 2015-2016. Retrieved from http://microdata.worldbank.org/index.php/catalog/2739
Cite this page
Free Essay Example on How Data Set Relates to My Final Project. (2022, Aug 05). Retrieved from https://speedypaper.com/essays/how-data-set-relates-to-my-final-project
If you are the original author of this essay and no longer wish to have it published on the SpeedyPaper website, please click below to request its removal:
- Representative Tree - Free Essay in Political Science
- Essay Sample on Walmart: Innovative Capabilities
- Free Essay with Analysis of Canada's Healthcare System
- Free Paper with Questions and Answers on Food and Climate Change
- Education and Politics Essay Sample
- Essay Sample: Your Body Language May Shape Who You Are: A Reflection
- Free Essay: Comparison Between Term Insurance and Permanent Insurance