Data analysis in research

Research analysis is one of the main steps of the research process, it is by far the most important steps of the research. How to analyze the data is an important question that every researcher asks. The researcher collects the data using one of the qualitative or quantitative methods of data collection. Data analysis highly depends on whether the data is a qualitative data or a quantitative data.

What is data analysis?

Data analysis is the process of scanning, examining and interpreting data available in tabulated form. The purpose of data analysis is to understand the nature of the data and reach a conclusion. Data analysis actually provides answers to the research questions or research problems that you have formulated. Without data analysis you cannot draw any conclusion. Data organization alone cannot help you in drawing conclusions but data analysis helps you in this regard. After analyzing data you get an organized and well examined form of data that can help you know whether your hypothesis got accepted or rejected.

How to analyze data?

There is not a single hard and fast rule for data analysis but you need to look at your data and decide on the method of data analysis. There are some basic tips you need to follow to analyze data in research papers and dissertations.

• Data organization

Organize your data before scanning, examining or interpreting it. Data organization is necessary because you cannot analyze haphazard data. You can arrange and organize data in tables or groups. This is easier to do if your data is quantitative on the other hand qualitative data is difficult to tabulate. You can first arrange your data in groups or categories and under each category you can tabulate the data. For qualitative data you have to follow different methods of data organization. Well organized data lends itself easily to analysis.

• Graphical representation

Now look at the tabulated data and make graphs to show the data in more clear form. Plotting graphs is necessary because it helps you in looking at the extreme points as well as the average points. You can use any one of the methods of graphs. You can use a statistical software to  make graphs. Otherwise if you are good in statistics you can make the graphs  yourself. Graphs will make the data more presentable and easy to comprehend.

• Data explanation

In the next step explain the data that is present in both tabulated and graphical forms. This explanation will help you draw main conclusions. Explore the graphs and tables and find out how you can write down the interpretation of your research study. You can correlate the variables and you can also explain the results. Try to make the interpretation specific and to the point. Extremely lengthy explanations are unnecessary in most cases, on the other hand a specific interpretation of the data is easy to understand.

• Statistical methods

In the last stage the hypothesis is rejected or accepted in the light of your interpretations. You have to confirm that your hypothesis proved right or it proved wrong. You can use any one of the statistical methods for confirmation of the hypothesis. Generally you can use ANOVA , t-test, z-test or chi square to test the hypothesis. There are also software that can help you in this regard. You can also get help of a statistician to apply statistical methods to your research. Statistical application is important because it makes your research valid and generalizable.

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