What is content analysis and how can you use it in your research?

Content analysis is a research method used to identify patterns in recorded communication. To conduct content analysis, you systematically collect data from a set of texts, which can be written, oral, or visual:

  • Books, newspapers and magazines
  • Speeches and interviews
  • Web content and social media posts
  • Photographs and films

Content analysis can be both quantitative (focused on counting and measuring) and qualitative (focused on interpreting and understanding). In both types, you categorize or “code” words, themes, and concepts within the texts and then analyze the results.

What is content analysis used for?

Researchers use content analysis to find out about the purposes, messages, and effects of communication content. They can also make inferences about the producers and audience of the texts they analyze.

Content analysis can be used to quantify the occurrence of certain words, phrases, subjects or concepts in a set of historical or contemporary texts.

How to conduct content analysis

If you want to use content analysis in your research, you need to start with a clear, direct research question.

Example research question for content analysis

Is there a difference in how the US media represents male and female politicians in terms of trustworthiness?


Next, you follow these five steps.

1. Select the content you will analyze

Based on your research question, choose the texts that you will analyze. You need to decide:

  • The medium (e.g. newspapers, speeches or websites) and genre (e.g. opinion pieces, political campaign speeches, or marketing copy)
  • The criteria for inclusion (e.g. newspaper articles that mention a particular event, speeches by a certain politician, or websites selling a specific type of product)
  • The parameters in terms of date range, location, etc.

If there are only a small amount of texts that meet your criteria, you might analyze all of them. If there is a large volume of texts, you can select a sample.

To research media representations of male and female politicians, you decide to analyze news articles and opinion pieces in print newspapers between 2017–2019. Because this is a very large volume of content, you choose three major national newspapers and sample only Monday and Friday editions.

2. Define the units and categories of analysis

Next, you need to determine the level at which you will analyze your chosen texts. This means defining:

  • The unit(s) of meaning that will be coded. For example, are you going to record the frequency of individual words and phrases, the characteristics of people who produced or appear in the texts, the presence and positioning of images, or the treatment of themes and concepts?
  • The set of categories that you will use for coding. Categories can be objective characteristics (e.g. female, aged 40-50lawyer, mother) or more conceptual (e.g. trustworthy, corrupt, conservative, family oriented).


Your units of analysis are the politicians who appear in each article and the words and phrases that are used to describe them. Based on your research question, you have to categorize based on gender and the concept of trustworthiness. To get more detailed data, you also code for other categories such as the age, political party, and marital status of each politician mentioned.

3. Develop a set of rules for coding

Coding involves organizing the units of meaning into the previously defined categories. Especially with more conceptual categories, it’s important to clearly define the rules for what will and won’t be included to ensure that all texts are coded consistently.

Coding rules are especially important if multiple researchers are involved, but even if you’re coding all of the text by yourself, recording the rules makes your method more transparent and reliable.

In considering the category “female politician,” you decide which titles will be coded with this category (senator, governor, counselor, mayor). With “trustworthy”, you decide which specific words or phrases related to trustworthiness (e.g. honest and reliable) will be coded in this category.

4. Code the text according to the rules

You go through each text and record all relevant data in the appropriate categories. This can be done manually or aided with computer programs, such as QSR NVivo, Atlas.ti and Diction, which can help speed up the process of counting and categorizing words and phrases.

Following your coding rules, you examine each newspaper article in your sample. You record the characteristics of each politician mentioned, along with all words and phrases related to trustworthiness that are used to describe them.

5. Analyze the results and draw conclusions

Once coding is complete, the collected data is examined to find patterns and draw conclusions in response to your research question. You might use statistical analysis to find correlations or trends, discuss your interpretations of what the results mean, and make inferences about the creators, context and audience of the texts.

The results reveal that words and phrases related to trustworthiness appeared in the same sentence as a male politician more frequently than they did in the same sentence as a female politician. From these results, you conclude that national newspapers present male politicians as more trustworthy than female politicians, and infer that this might have an effect on readers’ perceptions of women in politics.

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