Content analysis is a method that identifies certain themes and concepts in the data. The thematic analysis quantifies the data available in a qualitative form. This technique uses different data types, including speeches, conversations, text, etc. Different types of data may also analyze in a single study. To analyze the data, researchers create different codes. These codes help in analyzing the data. The researchers use this technique to find the objectives and effects of the content. It also helps in making inferences about the content and the respondents. It also helps in making inferences about the relationships between different codes. The researchers use this analysis to identify propaganda, intentions, and communication patterns. Content analysis gains a wide range of acceptance from different disciplines.
Types Of Content Analysis:
The content analysis further divides into two categories: conceptual analysis and relational analysis. Both types of analysis have their meaning, interpretations, and consequences.
The conceptual analysis examines the frequency of concepts in the given data. It observes that how much time a term or concept repeats in the data.
In relational analysis, we examine the concepts as we do in conceptual analysis. In relational analysis, we do not examine individual concepts. Instead, we observe the relationship between different concepts. Individual concepts have not worthy enough to give a strong verdict about something. Thus, it observes the link between concepts to take the data's wider picture.
There are different approaches to conducting thematic analysis. All these approaches help in analyzing and interpreting the data. But, they differ from one another on the basis of the coding scheme.
Conventional Content Analysis:
This approach is used when the subject doesn’t have enough research studies. Then, the researchers rely more on the data rather than rely on previous information. The researchers use this approach on a wider scale in qualitative studies.
Directed Content Analysis:
In this approach, researchers conduct their research on available theories. Their main purpose is to argue or add insights to the available theory. The researchers may use available codes or form new ones from given information.
Summative Content Analysis:
In this approach, researchers don’t rely on the quantity of the concepts. They also aim to understand the meanings of these concepts. The researchers find codes and quantify them. Then they interpret the codes to understand their underlying meanings.
When Do We Use Content Analysis?
Content analysis is a method that helps to analyse the communication trends of the data. You can take any politician's speech to analyze how he expresses his thoughts. You may also analyze what type of thoughts he shares in his speech. Content analysis allows you to analyze data at any level. You can also analyze the people's discussions or compare the views of different groups. It also allows you to understand and interpret different factors. Some of these factors are shared below:
The researchers use this method when they want to study people's behaviours. They may analyse the behaviour of an individual or a society as a whole. For example, the researcher wants to study the behaviours of school kids. He also wants to analyze how peers affect their behaviours. He can collect data from kids about their peers. This helps the researcher to understand their views about their peers. The characteristics that kids share may allow them to understand their behaviours. The researchers may study the behaviours by using content analysis.
The researchers may use content analysis to understand the viewpoint of people. The viewpoint doesn’t have any underlying meanings. The viewpoint is always specific and clear. It is a suitable technique for analyzing the viewpoints of people. For example, if researchers ask a question about the influence of the US in Afghanistan. People will share their opinion without any hesitation. The researcher may also ask about people's viewpoints on the Brexit deal. The respondents will share their opinion without holding any underlying meanings. It is the right technique to deal with data without underlying meanings. Also, this type of data is easy to quantify. The researcher can compare different opinions of the people.
People have different cultural values. Content analysis also helps to understand the values of the people. The researchers can also compare the values of different cultures. The researchers find the differences between the values by comparing them. Then provide recommendations for their research. The recommendations provide solutions for living in peace with others.
Content analysis helps in studying the emotions of people. Every human has emotions. They attach these emotions to different things. Understanding the emotions of people about specific events is very important. For example, ashes are not just cricket series. People are emotionally attached to this series. If we don't respect the emotional affiliations of others, it may harm society. It identifies the emotions of the people. It also helps in understanding the difference in emotional affiliation between people. After understanding these opposite emotions, the researchers suggest insights that can overcome these opposite emotions.
Advantages Of Content Analysis:
The content analysis uses both quantitative and qualitative methods. This mix increases the significance of the results. It has both interpretive and descriptive aspects. These aspects understand the latent meaning of data. Also, it provides a quantitative picture of the data (Lindgren, 2020). This technique provides statistical data to understand the hidden meaning of concepts.
Less Ethical Issues:
The content analysis uses information that is already available. That's why these analysis techniques have fewer ethical issues than others. You are going to visit the offices and places of the people. You are also not disturbing them during their office hours. Their personal lives don't affect their study. That's why this kind of study has fewer ethical problems.
Authenticity Of Data:
The researcher uses communications texts. The probability of alteration in data is very lower. Most of the content consists of formal information available on different platforms. Thus, the data for the study is authentic and trustworthy. The authenticity of data is a major advantage of content analysis.
Most of the data for the content analysis belongs to the previous data. It includes many significant historical insights. These insights enhance the credibility of the analysis. These historical insights help in comparison with real-time.
Content analysis allows the researcher to run statistical formulas on the codes. The statistical analysis is usually run on the codes generated from qualitative data. The statistical analysis increases the stance of the researchers. Statistical analysis of the codes is only possible in it.
Content analysis is an inexpensive technique. The researchers don’t have to spend much on travelling and other expenses. Most of the data is already available. They don’t ask for research grants or research scholarships for conducting research. It is a cost-effective technique in every manner.
It has significant importance because it collects data from different sources. This diverse data increases the authenticity and provides a wider perspective. It also helps in comparing and documenting the trends over time. Content analysis of historical material also gives good insights. Analysing diverse data with strong historical claims increases the authenticity of the study.
Disadvantages Of Content Analysis:
Content analysis deals with a large amount of diverse data. A large amount of diverse data may consume a lot of time. Data collection from different sources also consumes a lot of time. Time taking is the major drawback of it. Thus, novice researchers are not encouraged to use this analysis technique.
More Chances Of Error:
In content analysis, there are more chances of errors. When researchers conduct relational content analysis, there is more probability of errors. Interpretation of relational content analysis is not an easy task. The researchers make mistakes while doing it. Experience of many years is required to conduct it. Experience researchers can also make errors in it. So there is more probability of novice researchers making errors.
The content analysis uses a very liberal and subjective approach. Most of the time, researchers ignore the theoretical basis of the study. They try to draw inferences on the basis of subjective information. According to Graneheim (2017), a high level of subjectivity affects authenticity. Further, researchers only consider the relationships shown in the literature that affects the analysis. Ignoring the theoretical framework is not appreciable in the research. Besides considering subjective information, they must also relate it to theories.
In content analysis, most of the researcher uses a reductive approach. They have been dealing with complex data for a long time, so they want to simplify it. They also ignore a large amount of data for adopting this reductive approach. It’s a hectic task to deal with a large amount of data. This tiredness encourages researchers to adopt a reductive approach. In this approach, researchers lose a large amount of data. It may also affect the results of the research. Most of the results of content analysis are affected by this approach.
Most of the time, researchers consider the word count from the data. They make their inferences on the basis of word count. They ignore the underlying meaning of the data. The researchers may find results but don’t know what it means. Anyone can count the words from the data, but it's not a professional approach. Only a novice researcher can do this due to his less understanding of the technique. They think they are doing right, which is the right approach for content analysis. They are also doing this because they want to complete the work quickly.
Disregards The Context:
Most of the time, it works with data in a textual form. As a result, the researchers ignore the context of the data. Understanding the context of the data is an important part of the research. The data's context helps researchers gain a clear understanding of the data. It also helps in understanding the patterns and underlying meanings of it. Disregarding the context is the biggest drawback of content analysis. Without considering the context, research may fail to meet its objectives.
Content analysis is a technique to find the themes and concepts in qualitative data. In qualitative data, the researchers analyse different words and themes. He converts them into quantifiable data. Data may consist of speeches, field research, historical documents, and others. Further, it has two types: conceptual analysis and relational analysis. These two types show different results. The researchers use different approaches to conduct content analysis. Also, the researchers conduct this analysis to understand the people's attitudes and viewpoints.
It uses mix method approach that increases its worthiness. The use of secondary data in this technique increases its authenticity. Content analysis also has fewer ethical issues than others. In contrast, it has more chances of error because of the large and diverse form of data. It uses a subjective and reductive approach that affects their results. Further, irrespective of its pros and cons, content analysis is one of the most reliable and used methods.