Insights on Content Analysis – History, Types, Steps and its importance

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Introduction

It’s always easy to calculate the data in numbers through various statistical methods to show how many people were surveyed for the particular study, the number of questions and their response, but have you ever analyzed how to measure qualitative data too in figures? 

That’s where the content analysis comes into the picture. 

What is Content Analysis?

Content analysis is a research tool which helps to understand, quantify and analyze the presence, meanings and relationships of certain words, themes and concepts. 

In simple words, it’s basically a qualitative research tool which helps to quantify the online and offline data collected , varying from word, social media pictures, images, journals, articles, magazines, newspaper, text books, story books, feedbacks, movies and audio files  by sorting, comparing and converting the information into codes based on specific qualitative content pattern and then put together as a useful quantitative information.

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History of Content Analysis

Types of Content Analysis

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1. Conceptual Content Analysis

Conceptual analysis is more like counting the number of times the selected concept has occurred within the text and tallying its presence.

2. Relational Content Analysis

Relational Content Analysis is the same as conceptual content analysis, but the difference is it also explores the relationship between the concepts identified.

Steps of Content Analysis

As we have seen that content analysis is a qualitative tool there are few steps involved in converting the qualitative data into quantitative.

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1. Data Preparation

Data preparation is the important step in content analysis. One must be very clear about the objective of content as it is the crux based on which the questions will be framed. Before proceeding, we must check whether the verbal and observed collected data can be transcribed into information.

2. Identify the units/Keyword

Next step is to identify the main unit/keyword based on which the whole content is prepared. The identified unit/keyword must also be based on objectives of the content.

3. Developing the codes

Once the main unit/keyword is identified then it is classified into relative categories on the basis of objectives and then it is broken into codes for further analysis.

4. Pre-testing the code on sample

The code developed with transcribed data has to be tested with a sample to check whether it shows desired results and is in similar terms with the objective of content. If not, then again, they have to re-develop the code and test it again.

5. Assessment of codes employed

Once sample testing is done, consistency of the results has to be validated and has to be checked for its reliability and then the coding process will be applied for the whole data set.

6. Drawing inferences based on coding 

Next step after assessing the data is to draw inferences from the codes and categories classified. This helps to identify the relationship and uncover the patterns based on content objectives for further analysis.

7. Presentation of Results

After all the assessments and inferences of data, the conclusion has to be presented based on units/keywords classified with proper graphical, matrix or conceptual frameworks, so that the reader is able to understand and enjoy the content.

Example of Content Analysis

• Step 1: A researcher wants to write about, “How the Covid pandemic has changed the lives of people in relation to mental health”? This is a behavioral study as it involves mental health related to the losses that occurred in lives because of Covid 19. 

• Step 2: So, the researcher through various modes of online survey gets the qualitative data needed for the content.

• Step 3: The main key unit/keyword is identified as anxiety based on the answers given. 

• Step 4: The codes/categories are classified further like unemployment, loss of lives, loans to be paid and the business losses. 

• Step 5: Then through the coding process the qualitative data collected will be processed.

• Step 6: Finally, the data processed will be presented in numbers as graphs and metrics which in-turn helps to identify the relationship between anxiety and the related categories above. 

 In this way it helps the reader to understand the qualitative aspect of content better.

Importance of Content Analysis

As Content analysis helps us to convert the qualitative data into numerical, graphs and metrics it’s easy for the readers to understand what the whole content is all about. Most of all its usage is more in fields of psychology, political and social science, culture, Marketing ideas, Customer feedback through calls and in literature as all these areas cover the emotional and behavioral side of humans which cannot be calculated based on facts or numbers. Also, through relational content analysis, it plays a huge role in identifying and exploring the relationships and trends between concepts or patterns which is an important part in all these fields. 

Conclusion

Content analysis is more of a research tool which can be relied upon and it’s also cost friendly and less time consuming as the data are collected through social interaction of participants. Since it has a proper strategy to decode data into numbers, it can be understood very easily and helps for further analysis and research. It is very helpful for the development of Artificial Intelligence as technology plays a major role in the coding process and also for social interaction with the participants.

So guys please check out The Thinking Cloud page for more information on content writing, content strategy and content related problems!!!