How To Analyze Data
If you’ve made it this far, it’s because you’ve already collected all your research data, but you don’t know how to analyze it. Did I get it right? In this article, we will look at how to analyze data for a research paper.
What is Data Analysis?
Data analysis is the stage of academic work where you turn numerical data or loose qualitative data into a conclusion to solve your research problem. In other words, it is the moment when the researcher transforms the data into information for the research and responds to the question that started the research. So, at this stage, the objective is to organize all the data that were collected so that, based on that, it is possible to reach the research objectives. Whether to confirm or refute the hypotheses.
It is important to remember that data analysis must be developed mainly in alignment with the research methodology and theoretical foundation.
The Importance of Data Analysis
Every work – be it a term paper, a dissertation, or even a thesis – must be committed to adding in some way to academic knowledge. The formation of academic knowledge requires, necessarily, the understanding that science must meet a systematic procedure, which ranges from the choice of safe research sources. So, in addition to addressing a relevant topic, the work must strictly follow the academic method. Only then will the research have good quality and scientific rigour. Hence the importance of performing quality data analysis. After all, there is no point in carrying out a serious and judicious data collection and reaching conclusions with mistakes due to the lack of rigour in the analysis.
Methodology for data analysis
The methodology for analyzing the data will depend on the type of academic research. Even because, quantitative researchers analyze numbers, while qualitative ones analyze texts.
1. Quantitative Survey Data Analysis
For quantitative research, statistical analysis can be used, such as frequency distributions, correlations, and graphical representations, dispersion measures, measures of central tendency, in short.
2. Data analysis for qualitative research
In the case of qualitative research, content analysis can be carried out, which will serve as a basis for a qualitative analysis of interviews, observations, open questions in questionnaires, etc. This analysis seeks to understand the meaning of the collected data and also aims to facilitate the understanding of the contents through some classification presented in a systematic way, which can be the division into categories or by counting words and terms contained in the answers.
Types of data analysis
1. Predictive analytics
Predictive analytics is the most popular type of analytics. In general, conclusions have been reached that it helps to predict future scenarios, based on the analysis of database patterns. From this analysis, it is possible to predict events and make more accurate decisions. The most common methods in this analysis are statistical and historical, in addition to data mining and artificial intelligence.
This type of analysis is indicated, for example, to project future public and market behaviour. Or to assess fluctuations in the economy and population consumption trends.
2. Prescriptive analysis
The idea of prescriptive analysis is to verify the consequences of actions. So, it is possible to know what should happen when choosing a specific attitude.
It is a type of analysis that defines the path to reach an objective. In other words, the paths to go through and reach a goal are analyzed.
3. Descriptive analysis
Descriptive analysis aims to describe and understand events in real-time. As the name implies, it is an analysis that is limited to describing the event or object. So, it doesn’t issue value judgment.
Likewise, it limits itself to understanding the impact of data in the present, without relating to the past and the future.
4. Diagnostic analysis
In diagnostic analysis, the aim is to understand the causes of an event. So, questions like:
The ideal is to analyze the impact and scope of action. From this, it is possible to devise strategies to improve the results.
How to analyze your research data?
Well, having examined the definition of data analysis, its uses, and the various types that exist, it is time to examine how to analyze data. It is important to point out that data analysis depends on some important processes.
Review your research methodology
First, you should review your research methodology. This means revisiting the research problem and the general and specific objectives. After all, you must keep in mind that the conclusion of the data analysis must answer the problem and reach the objectives. It’s important to visualize the goal to chart the path, isn’t it?
Write the introductory text for the data analysis
Next, you must write the introductory text for the data analysis. At this stage, you should simply retrieve the information you used to guide your research. So, you should include how many people you interviewed and the ways you got in touch with them, if you did an interview, for example. It should include how you did the sample calculation and everything you considered relevant to define your sampling.
Present the data you have collected
After that, present the data you found systematically. Create criteria to present them. The systematization of data can use graphs and spreadsheets; whichever is better to demonstrate the data you’ve collected. If you have carried out an interview, present the questions followed by the respective answers.
Present the relationships and conclusions you reached with the data
Finally, you must present the relationships you made between the data you found and the conclusions you reached. This is the time you should apply some kind of analysis and show where your research has gone.
You can feature a highlight, which is what you found most importantly from your research. Most of the time, the highlight is directly related to the research objectives. It is important that you present information clearly and specific to your survey.
You should point out which conclusions you have proved, based on scientific rigour, with your research. This is indispensable for written work, but it should also be the highlight of your presentation. Your conclusion may also comprise topics for future research or suggestions for new work. This happens when you’ve concluded that research should proceed to analyze some additional information.
Indeed, we are in the age of Big Data, which is a tool that helps to obtain information to optimize decision-making. However, simple data collection is insufficient for this practice, as everything depends on good analytical skills. After all, to transform data into useful information, it is necessary to know how to interpret it.
Now that you know how to do data analysis, can you get to work? You must not forget that, regardless of the type of academic work, you must follow the formatting standard of the institution you are writing to.