Difference Between Content, Statistical & Thematic Analysis

Data analysis is about cleaning, modifying, and modelling data to obtain helpful information. It is used to predict market trends and help with decision-making. Data analysis using thematic analysis aims to check out the helpful information from the collective data and make the appropriate decisions based on data analysis.

Whenever we are required to make any decision, data analysis can be used to determine the data trends to help support future decisions. It is nothing but an analysis of our past content and future thoughts for making decisions based on relevant data. Analysts can also do data analysis to determine the future of the research. Many techniques are used to analyse data. In this article, you will learn about the three types of data analysis processes and their differences. The main analysis methods that most researchers use are:

  1. Thematic Analysis
  2. Content Analysis
  3. Statistical Analysis

Thematic Analysis:

It is a way of analysing the type of data that is qualitative and descriptive. This type of data that the researcher collects is to see the themes in the data to solve a research problem. You will often analyse the data during research to see the different patterns and themes. This screening process allows the researchers to divide data according to various categories. It is a hectic job as the researcher often has to read and evaluate the data several times before completing the main themes of the study, known as immersion.

In addition to analysing data themes, there has to be a connection between the key themes that the researcher used in the final analysis. Building the final structure and the research logic will be challenging if the themes are not connected. Therefore, hiring a best dissertation writing service becomes essential in this regard.

Content Analysis:

Content analysis is a method used by the researcher to analyse the data that is either quantitative or qualitative. This type of analysis allows the researcher to see essential data from a vast data bank. When you talk about the term research, you have to collect the data in various ways. You can research from books, websites, pictures, etc. You get ideas from different sites and read the scholar’s books also.

Additionally, in the content analysis, the researcher analyses each data object of the research. Researchers used the coding method to identify and classify different data sets available in content analysis.

Content analysis is also used for analysing quantitative data compared to thematic analysis. After the content analysis process, the data is used to identify the data frequencies. For this reason, content analysis has become a popular data analysis strategy used in the radio and television program sectors.

Statistical Analysis:

Statistical analysis is used to evaluate mathematical terms meaning quantitative data. It collects and interprets data to reveal patterns and trends used in quantitative research. It is part of data analysis that we use for collecting research interpretations. When the data uses different statistical modeling and design surveys, the statistical analysis is constructive.

The statistical analysis aims to recognize the trends or techniques used in the research data. A wholesaler or retailer uses statistical analysis to detect the data that is not well structured and the customer data list in which they put the data randomly. This type of analysis is mainly for the sellers to identify data patterns, and in this way, their sales growth automatically increases.

The statistical analysis identifies the nature of the quantitative research data. Then find the connection between the research-based data. Then create a model to understand better how the data connects to the primary population. After this, the statistical analyzer must prove how effective the model is. Now apply the predictive analysis process to run the situations that are helpful to guide future decisions. There are two types of Statistical analysis:

Descriptive Analysis

The analyses of complete data are in numerical format. It gives results for continuous data in the form of deviation, whereas describing the categorized data in percentages. It describes the frequency of the different data sets.

Inferential Analysis   

It analyses the data sample or the complete data sets. This type of analysis can create different solutions by identifying different data samples. It measures the sample of the data sets you get from the experiments. When you need to compare the data sets about the larger population, you use inferential analysis.

Difference between Statistical, Content, and Content Thematic Analysis:

Experts of have discussed below what the similarities or differences between them are:

Type of Data:

Thematic analysis analyses qualitative data, whereas content analysis evaluates both qualitative and quantitative data sets. The statistical analysis uses only mathematical values, which means quantitative data.

Analysis Focus:

In thematic analysis, the researcher focuses on the structure or themes of the data. While in content, the researcher focuses on the frequencies of the data. In statistical analysis, the analysts identify the trends used in the different numeric data sets.


Thematic analysis helps create a logical structure of a large amount of qualitative data for better understanding. Whereas content analysis helps recognize the frequencies of the data sets. The statistical analysis finds patterns in unstructured customer data to create a positive response in customers’ eyes.

  Thematic analysis Content Analysis Statistical Analysis
Types of Data Qualitative Data Qualitative +  Quantitative Data Sets Quantitative Data
Analysis Focus Themes Of The Data Frequencies Of The Data Patterns In Unstructured Data
Benefits Logical Structure Recognize The Frequencies in the Information Patterns In Unstructured Data


It is essential to identify the nature of all the data analysis methods. They are beneficial for academic researchers to create a flow in or understand a large amount of data. This blog aims to provide you with knowledge about the most valuable research patterns and how they are different from one another.

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