Content Analysis

• Content analysis= a way of analysing data by organising or summarising it into its common themes or patterns & is commonly associated with qualitative data, but can be used for quantitative data too.
• Content Analysis Steps:

  1. need a sample of data- must decide the criteria by which the sample will be selected, so that it is both representative & manageable- need to look thoroughly.
  2. need a way to categorise/ separate the data- called coding.
  3. decide how the data is going to be recorded (qualitative or quantitative).
• Coding= researchers' use of behavioural categories, ie they take the overall behaviour & separate it into elements then read. These can be pre-existing categories (you know what you're looking out for before you analyse the data) or emergent categories (categories or themes emerge when data is examined).
• Qualitative- describes examples in each category, often using quotes, excerpts or pictures etc.
• Quantitative- count the amount of times each category was mentioned, eg put into tables & graphs etc.
• Content Analysis Strengths:

  • Tends to have high ecological validity, as it is based on observations of what people actually do- real communications such as newspapers or books that people read.
  • Sources can be accessed by others (eg videos of people giving speeches), the content analysis can be replicated and therefore the observations can be tested for reliability.
• Content Analysis Weaknesses:

  • Observer bias reduces the objectivity & validity of findings because different observers may interpret the meaning of the behavioural categories differently.
  • Content analysis is likely to be culture biased, because interpretation of verbal or written content will be affected by the language & culture of the observers and the behavioural categories.