Studying data is amongst the everyday chores of researchers. It’s not a big deal for them to go through hundreds of pages per day to extract useful information from it. However, recent times have seen a massive jump in the amount of data available. While it’s certainly good news for researchers to get their hands on more data that could result in better studies, it’s also no less than a headache.
Thankfully, the rising trend of data science in the past years has also meant a sharp rise in data analysis techniques. These tools and techniques save a lot of time in hefty processes a researcher has to go through and allow them to finish the work of days in minutes!
As a famous saying goes,
“Information is the oil of the 21st century, and analytics is the combustion engine.”
– Peter Sondergaard, senior vice president, Gartner Research.
So, if you’re also a researcher or just curious about the most important data analysis techniques in research, this article is for you. Make sure you give it a thorough read, as I’ll be dropping some very important points throughout the article.
What is the Importance of Data Analysis in Research?
Data analysis is important in research because it makes studying data a lot simpler and more accurate. It helps the researchers straightforwardly interpret the data so that researchers don’t leave anything out that could help them derive insights from it.
Data analysis is a way to study and analyze huge amounts of data. Research often includes going through heaps of data, which is getting more and more for the researchers to handle with every passing minute.
Hence, data analysis knowledge is a huge edge for researchers in the current era, making them very efficient and productive.
What is Data Analysis?
Data analysis is the process of analyzing data in various formats. Even though data is abundant nowadays, it’s available in different forms and scattered over various sources. Data analysis helps to clean and transform all this data into a consistent form so it can be effectively studied.
Once the data is cleaned, transformed, and ready to use, it can do wonders. Not only does it contain a variety of useful information, studying the data collectively results in uncovering very minor patterns and details that would otherwise have been ignored.
So, you can see why it has such a huge role to play in research. Research is all about studying patterns and trends, followed by making a hypothesis and proving them. All this is supported by appropriate data.
Further in the article, we’ll see some of the most important types of data analysis that you should be aware of as a researcher so you can put them to use.

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Types of Data Analysis: Qualitative Vs Quantitative
Looking at it from a broader perspective, data analysis boils down to two major types. Namely, qualitative data analysis and quantitative data analysis. While the latter deals with the numerical data, comprising of numbers, the former comes in the nontext form. It can be anything such as summaries, images, symbols, and so on.
Both types have different methods to deal with them and we’ll be taking a look at both of them so you can use whatever suits your requirements.
Qualitative Data Analysis
As mentioned before, qualitative data comprises nontextbased data, and it can be either in the form of text or images. So, how do we analyze such data? Before we start, here are a few common tips first that you should always use before applying any techniques.
Familiarizing with the data  Get a basic overview of the data and try spotting any details manually, if possible. 
Defining objectives  Define your objectives and know what questions this data can answer. 
Make your plan  Figure out the broad ideas and assign them labels to structure the data. 
Find patterns  Start looking for patterns and connections in data using data analysis techniques. 
Now, let’s move ahead and see where the qualitative data analysis techniques come in. Even though there are a lot of professional ways to achieve this, here are some of them that you’ll need to know as a beginner.
Narrative Analysis
If your research is based upon collecting some answers from people in interviews or other scenarios, this might be one of the best analysis techniques for you. The narrative analysis helps to analyze the narratives of various people, which is available in textual form. The stories, experiences, and other answers from respondents are used to power the analysis.
The important thing to note here is that the data has to be available in the form of text only. Narrative analysis cannot be performed on other data types such as images.
Content Analysis
Content analysis is amongst the most used methods in analyzing quantitative data. This method doesn’t put a restriction on the form of data. You can use any kind of data here, whether it’s in the form of images, text, or even reallife items.
Here, an important application is when you know the questions you need to know the answers to. Upon getting the answers, you can perform this method to perform analysis to them, followed by extracting insights from it to be used in your research. It’s a fullfledged method and a lot of analytical studies are based solely on this.
Grounded Theory
Grounded theory is used when the researchers want to know the reason behind the occurrence of a certain event. They may have to go through a lot of different use cases and comparing them to each other while following this approach. It’s an iterative approach and the explanations keep on being modified or recreated till the researchers end up on a suitable conclusion that satisfies their specific conditions.
So, make sure you employ this method if you need to have certain qualitative data at hand and you need to know the reason why something happened, based on that data.
Discourse Analysis
Discourse analysis is quite similar to narrative analysis in the sense that it also uses interactions with people for the analysis purpose. The only difference is that the focal point here is different. Instead of analyzing the narrative, the researchers focus on the context in which the conversation is happening.
The complete background of the person being questioned, including his everyday environment, is used to perform the research.
Quantitative Analysis
Quantitative analysis involves any kind of analysis that’s being done on numbers. From the most basic analysis techniques to the most advanced ones, quantitative analysis techniques comprise a huge range of techniques. No matter what level of research you need to do, if it’s based on numerical data, you’ll always have efficient analysis methods to use.
There are two broad ways here; Descriptive statistics and inferential analysis.
However, before applying the analysis methods on numerical data, there are a few preprocessing steps that need to be done. These steps are used to make the data ‘ready’ for applying the analysis methods.
Make sure you don’t miss these steps, or you will end up drawing biased conclusions from the data analysis. IF you want to know why data is the key in data analysis and problemsolving, feel free to check out this article here. Now, about the steps for PREPROCESSING THE QUANTITATIVE DATA.
Data Validation  Making sure the data doesn’t come from invalid or fraudulent sources. 
Data Editing  Dealing with errors or missing values in the data. 
Data Coding  Assigning labels and codes to the data according to the specific situation. 
Descriptive Statistics
Descriptive statistics is the most basic step that researchers can use to draw conclusions from data. It helps to find patterns and helps the data ‘speak’. Let’s see some of the most common data analysis techniques used to perform descriptive statistics.
 Mean
Mean is nothing but the average of the total data available at hand. The formula is simple and tells what average value to expect throughout the data.
 Median
The median is the middle value available in the data. It lets the researchers estimate where the midpoint of the data is. It’s important to note that the data needs to be sorted to find the median from it.
 Mode
The mode is simply the most frequently occurring data in the dataset. For example, if you’re studying the ages of students in a particular class, the model will be the age of most students in the class.
 Standard Deviation
Numerical data is always spread over a wide range and finding out how much the data is spread is quite important. Standard deviation is what lets us achieve this. It tells us how much an average data point is far from the average.
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Inferential Analysis
Inferential statistics point towards the techniques used to predict future occurrences of data. These methods help draw relationships between data and once it’s done, predicting future data becomes possible.
 Correlation
Correlation s the measure of the relationship between two numerical variables. It measures the degree of their relation, whether it is causal or not.
For example, the age and height of a person are highly correlated. If the age of a person increases, height is also likely to increase. This is called a positive correlation.
A negative correlation means that upon increasing one variable, the other one decreases. An example would be the relationship between the age and maturity of a random person.
 Regression
Regression aims to find the mathematical relationship between a set of variables. While the correlation was a statistical measure, regression is a mathematical measure that can be measured in the form of variables. Once the relationship between variables is formed, one variable can be used to predict the other variable.
This method has a huge application when it comes to predicting future data. If your research is based upon calculating future occurrences of some data based on past data and then testing it, make sure you use this method.
A Summary of Data Analysis Methods
Now that we’re done with some of the most common methods for both quantitative and qualitative data, let’s summarize them in a tabular form so you would have something to take home in the end.
Quantitative Data Analysis Methods  
Descriptive Statistics  Mean  Finding average. 
Median  Midpoint of data.  
Mode  Most frequent data point.  
Standard Deviation  The spread of data.  
Inferential Analysis  Correlation  The statistical relationship between variables. 
Regression  The mathematical relationship between variables. 
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Conclusion
That’s it! We have seen why data analysis is such an important tool when it comes to research and how it saves a huge lot of time for the researchers, making them not only efficient but more productive as well.
Moreover, the article covers some of the most important data analysis techniques that one needs to know for research purposes in today’s age. We’ve gone through the analysis methods for both quantitative and qualitative data in a basic way so it might be easy to understand for beginners.