Thematic and content analysis

• There are different ways of analysing this data, depending on what you want to explore. The mains ways are:

  • Content Analysis = the content of the data, what is happening. Usually manifest in behaviour.
  • Thematic Analysis = the themes and therefore meaning present in the data.
• Content Analysis - Analysis performed on the content of the data, interested in what communication is happening. Usually focuses on behaviour either expressed through action or text.
• Content analysis:

  • Can be a form of observational research.
  • Concerned with people who are studied indirectly via communications or behaviour they have produced.
  • This data can include but is not limited to:

  1. Real-world [field] observations - TV, film and filmed advert
  2. Spoken
  3. Written text
  4. Visual ‘text’
  5. Transcripts of dialogue

  The aim of content analysis is to summarise in a systematic way so that conclusions can be drawn
• Content Analysis and Quantitative data:

  Initial stage in content analysis is CODING

  • Some data sets may be very large so information needs to be categorised into meaningful units

  1. How often does X appear
  2. Particular terms or descriptions
  3. How often something is shown

  • This allows us to quantify data.

  The outcome is therefore quantitative data.
• Thematic Analysis and Qualitative data:

  -Thematic analysis is a type of content analysis but looks for themes in the data.

  -The outcome of thematic analysis is therefore qualitative.

  • Initial stage in thematic analysis is also CODING
  • HOWEVER, this coding is then used to identify themes and ideas that are recurrent.
  • These can then be developed into broader categories.
  • Outcome is qualitative.
  • Themes are then reported through rich description.
• Applying thematic analysis to data:

  • The main process of thematic analysis involves identifying themes or ideas (either explicit or implicit) that is recurrent – it keeps cropping up – whether specially mentioned or hinted at.
  • For example, mental health issues might be referred to in newspapers as a ‘drain on nhs’. These themes can be developed such as: stereotyping, treatment or control. When researcher is satisfied that the themes developed covers aspects of the data they are analysing, they can then collect a new set of data to test validity of themes and categories.
• The process of content and thematic analysis

  
• Content analysis strengths:

  • Often don’t need to worry about ethical issues as most info is already in public domain and therefore consent not usually needed
  • High external validity because high levels of realism (the data already all occurs in the real world).
• Content analysis strengths:

  • Flexible – we can use it for qualitative or quantitative analysis
  • If sources are accessible by others, findings can be replicated = possible medium-level reliability
  • Content analysis allows us to quantify qualitative data.
  • Quantifying the data allows us to used descriptive statistics.
  • Multiple coders (inter-rater) can be used to make it more reliable
• Content Analysis limits:

  • People are studied indirectly – communications analysed outside of the context in which they are occurred.
  • Researcher may attribute opinions and motivations (observer bias).
  • Could lack objectivity.
  • Could be open to cultural bias - Cultural bias is the tendency to judge people in terms of one's own cultural assumptions.
• Thematic analysis strength:

  • Often don’t need to worry about ethical issues as most info is already in public domain and therefore consent not usually needed.
  • High external validity because high levels of realism (the data already all occurs in the real world).
  • If sources are accessible by others, findings can be replicated = possible medium-level reliability.
  • Multiple coders (inter-rater) can be used to make it more reliable.
  • Themes can allow for very rich descriptions and so very detailed accounts.
• Thematic analysis limits:

  • People are studied indirectly – communications analysed outside of the context in which they are occurred.
  • Researcher may attribute opinions and motivations (observer bias).
  • Could lack objectivity.
  • Could be open to cultural bias - Cultural bias is the tendency to judge people in terms of one's own cultural assumptions.
• Content Analysis

  • Analysis performed on the content of the data and is interested in what communication is happening. Usually focuses on behaviour either expressed through action or text.
• Codes/coding

  • Categorising data into meaningful units
• Behavioural Categories

  • Categorising data into meaningful units based on observed action
• Thematic Analysis

  • Analysis that brings out recurring ideas in the data and therefore tries to understand implicit meaning