content analysis

Question 1

what is content analysis A01,

Answer 1

• observation of human behaviour indirectly
• observing things we produce e.g. TV, programmes, adverts
• turns non- numerical data e.g written, visual, verbal into statistical data - quant

-sample must be representative- the content e.g. variety clips from differing time periods

• we categorise the data then make themes, boarder categories linked to latent meaning - hidden meaning

Question 2

how to perform a content analysis

Answer 2

how to perform
-top down approach- decide categories in advance to look for

• bottom up if investigating unfamiliar material, categories emerge from what is found as observed
• researcher creates a coding system breaking down info into coding units

frequency of key words/ content- manifest content

• manifest content needs to be meaningful, context key words/ content occurs within- latent content

Question 3

strengths of content analysis

Answer 3

• Ease of application – content analyses are relatively easy to perform. They are also a fairly inexpensive and non-invasive research method, as contact with participants is not needed.

-Reliability – findings from content analyses are easy to confirm as reliable(consistent), as such studies are easy to replicate (perform again exactly as before) because can easily find clips for example online and anyone can replicate

Validity -In depth qualitative analysis increases the validity. Content analyses tend to have high ecological validity because it is based on observations of what people actually do; real communications that are current and relevant such as recent newspapers or children’s books. Therefore, it also has high mundane realism.

As the artefacts that are being analyzed already exist, there is no chance of demand characteristics. The person who created the artefact did not know that what they created would be used in a content analysis, and therefore, this could not have affected them

Question 4

weaknesses

Answer 4

Descriptive – content analysis is purely descriptive and as such will not reveal underlying reasons for behaviours, attitudes, and so on. limiting validity, lack depth and detail

No causality – as content analyses are not performed under controlled conditions, as with laboratory experiments, they do not establish cause and effect relationships between variables

Observer bias - This can affect both the objectivity and validity of findings as different observers might interpret the meanings of the categories in the coding system differently.

may be bias in selection of content, selecting content that supports hypotheses

Reductionism- it could be argued that converting qualitative data to numbers is reductionist, meaning the richness and complexity of the qualitative data is lost.