Plan for data analysis has been prepared as per the type of questions that has been proposed in the previously completed sections of the literature review. The questions are will have both qualitative and quantitative data collection. Qualitative data will be the first checkpoint for the results. It will provide the basis for assumption about the expected results. On the other hand use of statistical tools for quantitative analysis will be suitable for the study. The methodology is based on survey questionnaires where respondents will provided answers based on the scale of 5, yes or no and other forms. using tabular form of data analysis, it will be easier to evaluate the user response to the research questions.
I have searched for various data analysis tools available online and found that the tools were intensive and for big data. My research is not expected to collect too much data from the users, but it will be appropriate data that I will be collecting to conduct my study. Hence, basic tools powered with statistics can be helpful for my analysis.
Making a data complicated may result in user lack of interest in the study. It happens that the study with too many mathematical calculations lose their message and users might not get the real picture from the analysis. Hence, my target is to keep the study basic that can produce acceptable results based on my research questions.
Practical challenges in data analysis is presenting data and running an analysis. It is confirmed that I will employ statistical tools to verify the hypothesis and the research questions. Hence, the first challenge is to put data in the tabular form so that appropriate formulas or analysis can be applied using
Another challenge is validating data that will be collected from the respondents. Soon after collecting data, hypothesis results are assumed for validity. It is a strong possibility that the data analysis can produce totally opposite results of what is expected. Hence, it is important to justify the results produced by data analysis. For example, there are numerous literature reviews that have provided an insight on my research topic. All the researchers have employed various techniques for data collection and analysis. I will be considerate to cross check my results with the previously done literature review so that I have some correlation with the previous studies. This is the biggest practical challenge as I am not expecting results to be completely opposite of what other researchers have produced in the past. In case results are totally opposite, there should be a valid justification.
Ethical issue with data analysis can occur from my side. As I am conducting this research, I have some expectations regarding the data results. I want my results to be aligned with some previous studies and it makes me biased about my study. Hence, I need to stay focus without bothering myself about the expected results of the study. There might be chances that results are antithesis of what has been done in the past, but I should be able to provide a logical justification for them (Focusgrouptips.com, 2015).
As part of my research and data analysis, I have to manage data and produce authentic and reliable results. It is manageable from the data analysis and tools perspective, but the biggest challenge is not to be biased about the study I am conducting. As a researcher I have my own perception about the given subject, hence I might be inclined to prepare questions that suit my need and justify my perception. To avoid the problem with the researcher bias, I will get my questions checked from someone who is not aware about my research. I will take a feedback regarding all the different perspectives to ensure that I haven’t missed out anything that can be valuable to my research findings. Last but not the least, it is highly probable that results generated from the same data are different for two researchers, hence I have to believe in the study I am conducting (Bailey and Jackson).
To perform the data analysis, I will require to understand some of the statistical tools that can help me in validating my hypothesis and questions. I haven’t worked on the statistical tools before and only have a theoretical background of different type of tests available.
Apart from the basic understanding of the statistics tools, I also need to learn about data reporting. It is imperative to have a effective data presentation so that any user can understand it. The above mentioned skills are must for anyone looking forward to analyze research data. I also need to master my skills related to MS Excel. There are various statistical tools available, but excel in itself is a comprehensive tool for performing statistical analysis of any data. Hence, I will spend time in understanding the various plugins and functions in Excel (People.umass.edu, 2015).
To acquire the skills, I will be required to follow some tutorials online related to statistics and reporting .For example, t-test is one of the most common tests that is used by the researchers to validate the data. Hence, there is ample information available about using t-tests using Excel. Moreover, there are many youtube videos about using MS Excel as a statistical tool and performing analysis on data. Hence, my target will be to understand the functionality and versatility of Excel.
I thought about using graphical representation of data using histogram and pie charts. Moreover, I have also considered the tabular representation of the data which is the easiest two dimensional format. I have also considered creating pivot tables in MS Excel for data representation, but I later decided to stick only with the tabular representation of data that is easy to comprehend for any user. Moreover, there is no comparison of the results with previous results, hence I have dropped the idea of using histograms and other graphical representation.
Data should be presented in a way that user can understand it easily. Hence, to avoid any misinterpretation, all the data fields should be appropriately labeled. Moreover, if there is any special message associated with the data, it should be put in the description of tables for better understanding. Furthermore, expectations of the users should be set from the beginning about gathered data. It becomes easier for the user to relate to the data collected in the end. Analysis of data becomes easier to digest.
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