First of all, we have to define data; data is a raw form of unprocessed information. **Data analysis**, on the other hand, is the process of finding and developing patterns and relationships among different variables. So that it could make sense and help us in the decision making.

The question arises in our mind that how to perform data analysis, then there are different techniques and methodologies. Before choosing any type of data analysis, first, you should check the type of data because different types of data require different methods for your data analysis. Today we’ll discuss the methods of data analysis, here’s how.

Table of Contents

**What are Data Analysis Methods?**

Methods of data analysis comprise of certain tools that you can use to analyze data. When we talk about data, then there are two main categories of data; qualitative and quantitative data analysis. It is essential that you should know the difference between these types of data. Once you know the difference, only then you would be able to select the right method.

*Qualitative Data Analysis Method*

Qualitative data does not comprise of numbers and figures. However, it comprises of views and experiences of people that you can’t quantify and convert it into the number. Therefore, qualitative data is unique and you can’t rely on it for a scientific conclusion.

*Quantitative Data Analysis Methods*

Quantitative data comprises of numbers and figures, by using such statistical numbers and figures to develop a pattern and draw a conclusion from it. Therefore, data analysts prefer using the quantitative method for analysis because it minimizes the margin of error. There could be only one possible interpretation from it.

There are different methods for each type of data analysis, and we will discuss them one by one.

**Qualitative Data analysis Method**

Qualitative data is rare but it exists. Here are some of the following tools that you can use for qualitative data.

**Content Analysis**

Content analysis is the process of studying and analyzing the written content, instead of numbers and figures. Since the qualitative content doesn’t have any quantitative numbers, therefore the statistical tools would be useless. However, analysts of the content should be subjective and it should be free from personal biases.

For instance, different people read a letter and the interpretation of every person would be different. But there could be one unified analysis on which everyone would agree on.

**Narrative Analysis**

Narrative analysis is a type where you check and verify data from many sources like field surveys, interviews, or observation of the focus group. The purpose of narrative analysis is to study the views, experiences, and stories of people to answer your research questions.

**Discourse Analysis**

Just narrative analysis, in discourse analysis you study how people interact with one another. The purpose of discourse analysis is to analyze the social context when communication and interaction occur between the respondent and researcher. In discourse analysis, you analyze the day-to-day activities of the subject and use it for the research.

**Grounded Theory**

Grounded theory is also a type of qualitative data analysis where you create and give an explanation to the data. Here the researcher doesn’t use the statistical tools to analyze the data. However, you use inductive reasoning to study and analyze the data and think of the possible explanations.

Businesses use grounded theory when you don’t have quantitative data. One person provides a possible explanation, if it is acceptable, then the team starts collecting data based on the premise.

**Quantitative Data Analysis Method**

Some of the quantitative methods of data analysis are as follows;

**Average**

The average is the central point or number in the dataset. There are three ways to calculate and find out the average; means, median, and mode. It doesn’t matter whatever way you use to find the average, the result would provide you the answer. However, if you find out average, then you can smooth out your dataset and draw the conclusion from it. If you don’t have average, then you have no choice but to compare data like low numbers with high numbers.

**Mean**-mean value or average means that you add all the numbers and divide the result with the total numbers within the dataset.

**Median**-it means the central or the midpoint in the dataset.

**Mode**-mode is the number that is most frequently repeated in the dataset.

**Percentage**

The percentage is a ratio or a number that gives you a fraction of 100. For percentage, you use %, percent, or pct. For instance, you can say 45% or .45 is equivalent, and it’s the fraction.

**Frequency**

Frequency is similar to mode. It means how many times a number repeated in the dataset. In fact, frequency is a standard to calculate the average or the mode in the dataset.

Even if you don’t have to find out the frequency or the mode in the dataset, it is better to know the most frequently occurred number in the set, because it would tell you the repetitiveness in the event.

**Range**

The range is also a type of quantitative data analysis where you find the gap or the difference between the lowest and highest number. Range tells you how much or the amount of data is within the set.

The range is a very important standard or metric. Businesses usually use range in the event of fluctuating circumstances, where it helps in decision making.

**Regression**

Regression is also a tool of quantitative data analysis where you study the relationship between 2 or more dependent and independent variables. This analysis helps you to find out the most prominent factor and how it affects others.

For instance, the sale in the market depends on the lockdown. The longer it goes, the lesser the sales will be. Lockdown is affecting the performance of businesses through fewer sales. That’s how businesses and businesses and companies predict events by using the regression analysis.

**Correlation**

Correlation is a quantitative tool that tells you how strong a relationship is between two quantitative numerical variables. If the relationship between 2 variables is strong, then it means that the correlation would be stronger. Otherwise, it won’t be.

**Variance Analysis**

Variance analysis is a type where you analyze the difference between actual and planned numbers. When you add up all the variances, then the final summary would provide you the performance of the company in a certain time period. Businesses and companies check the favourability by contrasting the stand and actual cost.

**How to Choose Data Analysis Method**

** **Now, the question is what data analysis method you should choose. Since there are different types of data analysis methods and tools available. The tool of data analysis depends on the type of data you have and what you want to achieve with it. If the data you have is quantitative, then you should choose the quantitative method. Otherwise, you should choose the qualitative method.

In the case of quantitative analysis, you would have tools like average, mean, mode, median, regression, correlation, variance, and frequency. In the case of qualitative analysis, tools like content analysis, narrative analysis, discourse analysis, and ground theory available to you to analyze the data.

**Conclusion**

After studying various methods of data analysis, we have concluded that different types of data require different methods of data analysis. If you poorly choose the method, then it would jeopardize the authenticity of your research and you would get wrong results. Therefore, it’s important that you should carefully choose the right method of data analysis.