content analysis and coding

• Content analysis


  An indirect observational method that is used to analyse human behaviour, investigating through studying human artefacts (the things people make).
• Content analysis is often on the written word (qualitative data), or write-ups of spoken words (transcripts). This is transferred into quantitative data.
• How to perform content analysis

  1. Decide a research question.
  2. Select a sample - from a larger quantity of all possible data (e.g. tweets)
  3. Coding - decide on categories/coding units to be recorded.
  4. Work through data - read the sample, and tally the number of times the pre-determined category appears.
  5. Data analysis - determine patterns through the quantitative data.
• Content analysis example

  • Lonely hearts - identifying the difference between what men and women want in each other. Content analysis can occur when picking categories which are common.
• Testing for reliability/assessing for consistency

  Test-retest reliability: run the content analysis again on the same sample and compare the two sets of data.

  Inter-rater reliability: a second rater also performs the content analysis, with the same set of data and the same behavioural categories. Compare the two sets of data.
• Strengths of content analysis

  + The 'artefacts' are usually not created for research but are taken from the real world - high external validity and generalisable.

  + Replicable using the same categories - high reliability.

  + More ethical - doesn't cause any harm to participant ect.

  + People don't directly know they are being observed - less chance of demand characteristics.

  + not time consuming
• Limitations of content analysis

  • researcher/observer bias - researchers tend to interpret the text in a way that supports their pre-existing views.
  • may lack validity since some research may not be accurate and completed under controlled conditions.
• Thematic analysis 


  Researchers start by attempting to identify the deeper meaning of the text by reading it first, and allowing themes to emerge.
• How to perform thematic analysis

  • Collect text
  • Read text to spot patterns that can be coded and collected.
  • Re-read the text looking for these emergent themes.
• Strengths of thematic analysis

  + Theories come from discovery of themes so it stops the researcher imposing their own bias on the analysis.

  + High external validity.

  + Easy to get a sample.

  + Easy to replicate.
• Limitations of thematic analysis

  • Subjective interpretation - researchers may decide to only count certain themes.
  • Data not created in controlled conditions.