Overview – Content analysis

Content analysis is a research method where researchers analyse the content of existing material – such as videos, books, images, or other media – to identify patterns and themes.

Content analysis can produce both qualitative data (interpretations of meanings, themes, or messages within content) and quantitative data (numerical measurements of how often something appears). It’s often used as a way to convert qualitative data into quantitative data that is easier to analyse and process.

Features of a content analysis

A content analysis takes qualitative data such as interviews, diaries, videos, images, observation records, etc. to identitfy themes and patterns. Unlike experiments or observations, researchers conducting a content analysis do not generate the data themselves – they take existing data and analyse that.

This information is often converted into quantitative data by creating coding categories and counting the frequency of specific words, behaviours, or themes. For example, a psychologist may analyse social media posts (qualitative data) to measure the frequency (quantitative data) of cyberbullying by counting abusive comments and offensive language.

Common features of content analysis include:

  • Analysis of existing material: Researchers study pre-existing content rather than collecting new data directly from participants. Such materials may include:
    • Newspaper articles
    • Television programmes and films
    • Social media posts
    • Advertisements
    • Diaries
    • Letters
    • Interviews
  • Qualitative data: Content analysis usually starts with qualitative data (e.g. text, images, or videos). Researchers will often convert this into quantitative data by counting the frequency of specific categories (e.g. counting the number of aggressive behaviours in a particular film).
  • Behavioural categories: Behaviours are operationalised into observable categories that can be counted or compared (e.g. defining ‘aggression’ as punching, kicking, shouting, and so on).
  • Systematic coding: Researchers use a coding scheme to consistently record when each category appears within the content.

Examples of content analyses

Examples of content analysis in psychology include:

  • Bandura et al (1961) Researchers analysed children’s behaviour after exposure to aggressive role models, coding different types of aggressive acts (e.g. hitting, kicking, verbal aggression).
  • Freud (1909): Researchers could analyse written records, letters, or case notes to identify recurring themes in psychological experiences.

Evaluation: Content analysis

Strengths of content analysis:

  • Converts qualitative data into quantitative data: Content analysis enables researchers to transform detailed qualitative material into quantitative/numerical data that can be easily compared and statistically analysed. For example, researchers analysing films can count the number of aggressive acts shown, which allows them to compare levels of aggression between different films or different time periods.
  • High replicability: Clear coding categories and standardised procedures allow other researchers to repeat the analysis and check whether similar findings are produced. For example, researchers analysing aggression in films could use the same coding scheme (e.g. counting physical violence, threats, and insults) to see if they produce similar results.
  • Reduced risk of demand characteristics: As researchers often analyse existing content, subjects are unlikely to alter their behaviour because they do not know they are being studied. For example, people posting on social media are unlikely to change what they write because they are unaware that their posts may later be analysed by researchers.

Weaknesses of content analysis:

  • Subjectivity in interpretation: Researchers may interpret the meaning of content differently, especially when analysing complex themes or emotions. For example, one researcher may classify a character’s behaviour in a film as aggressive, whereas another may interpret it as defensive or justified. However, clearly defined behavioural categories can reduce this risk.
  • Sampling bias: Researchers may select content that does not accurately represent the wider population or topic being studied. For example, analysing social media posts specifically may not represent the attitudes and behaviours of society in general (many of whom may not post on social media).
  • Can’t identify causation: Content analysis can identify patterns but cannot determine cause and effect relationships (unlike e.g. an experiment). For example, finding that aggressive language is common on social media does not prove that exposure to aggressive content causes people to become more aggressive.

How a content analysis is performed

To conduct a content analysis, researchers must collect, categorise, and analyse existing material. The process is similar to a structured observation in that researchers use behavioural categories and coding schemes to record specific features.

The main steps are:

  • Collect data: Researchers decide what (pre-existing) material they will study and gather it from relevant sources (e.g. video recordings, newspaper archives, social media posts, interview transcripts, etc.) and prepare it for analysis.
  • Operationalise the variables: Researchers create clear categories that define exactly what counts as the behaviour or phenomena they are studying (e.g. behavioural categories). For example, when studying aggression in films, researchers might define aggressive behaviour as:
    • Physical violence (punching, kicking, pushing)
    • Verbal aggression (threats, insults)
    • Weapon use
  • Coding scheme: Researchers create a standardised system for recording each occurrence of the catergories above. For example, each time aggression occurs, researchers may record:
    • Type of aggression shown (physical, verbal, weapon use)
    • The frequency of each aggressive act
    • The duration of aggressive acts
  • Analyse the data: Researchers compare frequencies or identify themes within the content. Quantitative analysis may involve counting how often categories appear, whereas qualitative analysis may involve interpreting meanings and patterns. For example, researchers studying aggression in films might compare the number of violent acts shown across different genres or time periods to identify patterns in how aggression is portrayed.

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