Data is information; data is power. It gives you the strength to make an informed decision about your business and academic research. Data can support or oppose your research claim; in either case, it tells a fruitful story about research. However, the data itself is nothing. It is the analysis tool, trick, and technique that allows you to see a pattern in data and get meaningful insights out of it. This article will focus on the analysis of data in research and give seven simple steps to conduct it in your academic research.

Analysis of Data in Research- a brief introduction:

As a matter of course, data analysis is the process of systematically using statistical or other logical techniques to explain, describe, illustrate, condense, evaluate, and recap data. It must proceed after data collection steps as it sort or organize the raw data available in a scattered form to get some meaningful insights from it. Moreover, the data is collected as well as analyzed to answer the specific research question, test the hypothesis, and approve or disprove specific theories.  

  Data analysis can be performed in a variety of ways. For conducting research of a qualitative nature, the non-statistical data analysis methods are used, while in quantitative research, statistical analysis methods can make your way. Examples of non-statistical data analysis include content analysis, thematic analysis, phenomenological research, and discourse analysis. However, examples of non-statistical data analysis include descriptive statistical methods and inferential methods. 

The aims, objectives, research questions, and research problem of your research plans decide on the most suitable method of analysis of data in research. Remember, the selection of the right method of analysis must be made at the synopsis or proposal writing phase. In the methodology section of a research proposal, you must specify the method of data analysis you will use in the dissertation. Making the right decision about a suitable data analysis method at such an early phase of research is indeed difficult, but to get through this difficult phase, you can seek research proposal help from field experts and professional writers. 

7-step guide to effective analysis of data in research:

Basic steps to effective analysis of data in research depend on which method of analysis you used in research, but the following are some steps that will generally help you know about it:

  • Define clear research goals:

The first step in data analysis is to clearly focus on the research problem or goals. Clear goals will help you define the right resources to collect data. 

  • Integrate tools for data analysis:

Again, your research goals and objectives will help you know about the right tool to analyse data in research. NVivo, AMOS, NVivo, STATE, R, Python, Excel, SPSS, and Minitab are some tools that you use to conduct analysis of varying natures.  

  • Collect data by choosing an appropriate sampling technique:

At this phase, you must collect data by using appropriate sampling techniques that may be probability or non-probability. After defining research goals, you can directly jump into sampling to collect the best quality data for analysis. 

  • Trim, and clean the data:

Data trimming is a technique to remove or exclude outliers or extreme values from the set. This step helps your data look good and better analyse the trends.  

  • Analyse data by either using qualitative or quantitative analytical tools:

At this step, you must run the analyses either by using qualitative or qualitative analysis methods and tools. For example, if your collected data in the form of text or statements, you must make themes and code for those themes to analyse the data. Instead, if the collected data is in the form of figures or values, you must use a tool like SPSS or any other of your choice. Select tests offered by selected tools to analyse the data.  

  • Reach out specific conclusion based on the trends observed in collected data:

The techniques like coding and tests like T-tests or F-tests help you find trends and evaluate a situation in the light of the data collected. This is the step to link these repeated trends observed in data with the aims and objectives of a study. 

  • Interpret the data by taking its graphical, tabular, or textual form under consideration: 

Almost all analytical software analyse data and gives results in the form of screen plots and graphs. Thus, the last step in the analysis of data in research is to use such graphs and other visual aids to describe what you found after analysing data gathered from all potential participants.

Final thoughts:

For those who think data analysis is a difficult process, I have summarised the whole process in seven steps. As per the details of this article, you just need to focus on the main goals to select, define, and conduct analysis. However, if you are feeling difficulties in performing a particular type of analysis, you must enter its keywords to get a specific guideline. 

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