Content Analysis | A Step-by-Step Guide with Examples
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 categorise or ‘code’ words, themes, and concepts within the texts and then analyse 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 analyse.
Content analysis can be used to quantify the occurrence of certain words, phrases, subjects, or concepts in a set of historical or contemporary texts.
In addition, content analysis can be used to make qualitative inferences by analysing the meaning and semantic relationship of words and concepts.
Because content analysis can be applied to a broad range of texts, it is used in a variety of fields, including marketing, media studies, anthropology, cognitive science, psychology, and many social science disciplines. It has various possible goals:
- Finding correlations and patterns in how concepts are communicated
- Understanding the intentions of an individual, group, or institution
- Identifying propaganda and bias in communication
- Revealing differences in communication in different contexts
- Analysing the consequences of communication content, such as the flow of information or audience responses
Advantages of content analysis
- Unobtrusive data collection
You can analyse communication and social interaction without the direct involvement of participants, so your presence as a researcher doesn’t influence the results.
- Transparent and replicable
When done well, content analysis follows a systematic procedure that can easily be replicated by other researchers, yielding results with high reliability.
- Highly flexible
You can conduct content analysis at any time, in any location, and at low cost. All you need is access to the appropriate sources.
Disadvantages of content analysis
Focusing on words or phrases in isolation can sometimes be overly reductive, disregarding context, nuance, and ambiguous meanings.
Content analysis almost always involves some level of subjective interpretation, which can affect the reliability and validity of the results and conclusions.
- Time intensive
Manually coding large volumes of text is extremely time-consuming, and it can be difficult to automate effectively.
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.
Next, you follow these five steps.
Step 1: Select the content you will analyse
Based on your research question, choose the texts that you will analyse. 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 number of texts that meet your criteria, you might analyse all of them. If there is a large volume of texts, you can select a sample.
Step 2: Define the units and categories of analysis
Next, you need to determine the level at which you will analyse 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., aged 30–40, lawyer, parent) or more conceptual (e.g., trustworthy, corrupt, conservative, family-oriented).
Step 3: Develop a set of rules for coding
Coding involves organising 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.
Step 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 categorising words and phrases.
Step 5: Analyse 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.