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Data set
Quantitative Section
Introduction to Data set
Data sets represent collected data from a single database or data matrix. Every column present in data sets represents a particular variable while every row represents a certain member of the data set in question. All values present in data sets are recorded as variables. Each value is referred to as datum. Data sets contain more than one form of data. The term data set was coined to mean a method of data collection from tables which are related and correspond to certain events or experiments (Bryman, 2012). Data sets have been utilised to identify and analyse both qualitative and quantitative data.
Data Management Analysis Technique
Quantitative data analysis bases on different statistical methods. The commonly used methods are measures of central tendency and measures of dispersion. Measures of central tendency include the mean, mode and median. Mean represents an average value while median refers to the centre value (Bryman, 2006). Mode refers to the value with the highest frequency.
The mean is the commonly used method that is used to analyse results since it monitors and determines the exact value that can be used to measure different variables. It can be used to compare different values. Measures of dispersion include variance and standard deviation and inter-quartile ranges (Mugenda, 2009). Measures of dispersion and central tendency are used together to analyse data. Standard deviation and variance are two forms used together with the mean. Variance and standard deviation show how much values differ from the mean.
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Presentation of Data
In this case an example of the disease affecting different parts of the world will be utilised. The data will be based on several continents. All the information that has been provided has been divided into data sets. The sets include the Africa region, western pacific region, South East Asia, Europe, Eastern Mediterranean, and the American region. The data provided included the different diseases that affected different areas in different years. The quantitative method that was used to analyse the data was the mean. It encompassed calculating all the different death values per disease. The following results were obtained
| Mean value | Region |
| 134.39 | Western pacific region |
| 132.23 | South east Asia |
| 194.65 | Europe |
| 117.50 | Eastern mediterrean |
| 117.92 | Region of Americas |
| 208.13 | Africa |
Table 1. Mean values showing the deaths of different diseases per 100,000 people per population
Description and Interpretation of Data
The above data compares different rates of morbidities across different continents. The average value when all values are incorporated equal 150.22. When this value is compared to mean values, the deviation and variances becomes easily established. The results reveal that Africa has the highest rates of mortality followed closely by Europe (Malterud, 2011). Eastern Mediterranean has the lowest value. All results reveal that the values lie within the accepted deviation standards.
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References to Research Methods
Mean values have been used to compare results from different samples. The mean as is used as a comparison method since it eliminates all outliers present in a set of data. Median and mode are two methods not utilised since they include outliers in their analysis. Additionally, the median shows the number that is in the middle while the mode shows the number appearing in most cases (Creswell & Clark, 2007). Such values cannot be utilised to compare different samples since they show bias in their analysis. Inclusion of standard deviation and variance in the mean provide a better method of analysis.
Relevance of Data to Policy
The above information can be utilised to implement new laws and heath standards that can be utilised to decrease morbidities rates. The above information provides a comparison of different health standards across the world. It can be used by non-governmental organisations health standards across the world (Neuman, 2005). The above information can be used to investigate the underlying the causes of high levels witnessed in some parts of the world.
Policy makers will be keen to address the underlying causes of the diseases. Policy makers may also opt to utilise information provided by the results to address the high levels witnessed in some areas. More specifically, it can utilise some of the policies implemented in areas that have shown low incidences. Policy makers are able to understand the number of people that affected.
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Qualitative Section
Introduction to Qualitative Data Sets
The qualitative section involves analysis of data sets that are not expressed numerically. These data sets will also be expressed in rows and columns. Data sets included in qualitative data sets will not be limited to descriptive and inferential statistics (Creswell, 2013). In this case values that are utilised to analyse the data are in nominal form or ordinal scales. This means that the data to be utilised will not base on number but in certain wordings which either shows the level or degree of sickness in this case or the number of people suffering from a given disease.
Data Management Analysis Techniques.
Two methods have commonly been used to analyse qualitative data. These methods are coding and theming processes (Mays & Pope, 2006). Coding refers to the arrangement of data by combing different aspects such as themes, categories and ideas. Coding as a method has been utilised to bring out certain themes that are dominate in qualitative data (Patton, 2005).
The theming process involves the identification of particular theme that is largely shown in qualitative analysis. It identifies the major themes that are easily brought out in different aspects. It normally focuses on outlining all major themes present in any form of qualitative study.
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Presentation of Data
The information provided below seeks to address the question on the most prevalent disease that affected individuals in the year 2000. The data was selected using the theming process where the disease status of the world was going to be compared. The disease states that are available are the 2000 and 2012. Therefore, one had to be chosen and investigated.
Thus in this case the disease that was investigated was the one shown in 2000. All relevant information containing the different disease that occurred in the year 2000 as well as their levels were analysed. Their means were then identified and formulated into a pie chart to show the levels.
Description and Interpretation of Data
From the above results it can be deduced that all causes of disease were the major contribution to the diseases rates witnessed . it was closely follwoed with cardiovascualr disease, mailgnant diseases, neoplam conditions, HIV/AIDS, malaria and tuberculosis. A pie chart is used to bring out this form of information because it represent one of the best methods that brings out qualititive data. A comparison can thus be carried to identify the disease thataffacted more people in the year 2000. From this information different piechrts can be made depending on the individual gender of individuals.
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References to Research Methods
Pie charts have been used in numerous occassions to bring out a qualititive method of analysis. Unlike other methods, a pie chart can be readily used to identify the disease that affected more people. it can also be further utilised to ideintify other small areas withi basing on other vraibales such as sex and region. From the above pie chart small pieces of infromation regarding the other variables can be easily collcted and utilised.
Qulitative data involves the idenfication of which disease was shon to affect more populations across the world. from the above values, one ca easily identify the differen values belonging to different data sets. For example , one can make a conclusion that cardiovascular diseases make up the huge percentage of diseases affecting people across the world as compoared to any other form of disease. This statement can be made in regard of the disease in the year 2000.
Relevance of Data to Policy
The above information becomes quite important to all policy makers. The identificatoon of the disease that casing more diseases across the world becomes a major concern for all policy makers. Once the disease has been identified, all necesaary measures required to decrease the disease are taken into consideration (Patton, 2005).Some of this measures include health promotion exercises, public awareness on the causes and prevention of some of this diseases.
Policy makers can also come up with laws aimed at decreasing the incidences of all the above diseases. Polyc makers can also manage their budgets so that more cash is allocated n fighting such diseases in the society (Smith, 2013). The piechart provides policy makers with an opportunity to identify disease that require immediate attention. At the same time it tells the polcy makers of the disease status of their society.
The information that has been provided an be used to as a standard measurement against the disease status of different individuals in the society. this is because the values represent the pricture of the world. It can also be used to compare results with the coming years for example the year 2012. Lastly, the pie chart can be dissected to adress all concerns of all parties in the society. It can be dissected basing on regions and gender.
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References
Bryman, A. (2012). Quantitative and qualitative research: further reflections on their integration. Mixing methods: Qualitative and quantitative research, 57-78.
Bryman, A. (2006). Integrating quantitative and qualitative research: how is it done?. Qualitative research, 6(1), 97-113.
Creswell, J. W. (2013). Research design: Qualitative, quantitative, and mixed methods approaches. Sage publications.
Mugenda, O. M. (2009). Research methods: Quantitative and qualitative approaches. African Centre for Technology Studies.
Neuman, W. L. (2005). Social research methods: Quantitative and qualitative approaches (Vol. 13, pp. 26-28). Boston, MA: Allyn and bacon.
Patton, M. Q. (2005). Qualitative research. John Wiley & Sons, Ltd.
Smith, J. K. (2013). Quantitative versus qualitative research: An attempt to clarify the issue. Educational researcher, 12(3), 6-13.
Mays, N., & Pope, C. (Eds.). (2006). Qualitative research in health care (pp. 10-19).London: BMJ.
Creswell, J. W., & Clark, V. L. P. (2007). Designing and conducting mixed methods research.
Malterud, K. (2011). Qualitative research: standards, challenges, and guidelines. The lancet, 358(9280), 483-488.
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