Data Analysis and Dissemination Essay

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Self-reflection

For a successful completion of any program, data analysis and results dissemination is a crucial part of the processes. Data analysis is the processes of project reporting that involves inspection, cleansing, transformation, and modeling of the data collected with the aim of establishing information that is useful in suggesting the possible conclusions and in providing insights to support the decisions made (Ott & Longnecker, 2015). Dissemination on the other hand is the processes of getting out the findings of the project. This can be done through various platforms among them presentations and written report. Results dissemination is a critical process in program implementation for it creates a link between the researchers and the field.

Data analysis for me is a rather challenging process and often, I get entangled in the various processes. The method selected for data analysis will depend on the nature and the type of the research. There are basically two dominant types of research, quantitative – which involves quantities i.e. data that is in numerical value, and qualitative – which involves qualities e.g. the color of flowers. These two types of research methods warrant different data analysis methods. I am well comfortable with qualitative data analysis methods which included grounded theory analysis, narrative analysis, and content analysis among others.

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These methods of analyzing qualitative data are pretty much bordered on the theme of understanding the data presented thus, easy and straightforward.

However, the analysis of quantitative data is not very easy for me. There are numerous methods of analyzing data collected through quantitative research. Some of the most common methods are F-test, ANOVA test, Chi-square, and T-test (Ott & Longnecker, 2015). Even though these methods are typically easy and straight forward, for me, its normally difficult and confusing to know which method is the right one to use in analyzing a piece of given data, especially when the type of analysis is not indicated. Currently, these methods of analyzing quantitative data can be carried out through software with the most common being SPSS. However, I am frequently not sure what the results mean, and this is further made worse by the confidence level.

Areas for improvement

Professionally, it is obvious that I need more exposure with the analysis of quantitative data. I need to firmly grasp the various methods of analyzing quantitative data and when to use which method. I also….....

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