7.1.5 Content Analysis

Content Analysis

This is a method used to analyse qualitative data by turning it into quantitative data
A content analysis quantifies qualitative data through the use of coding, although the outcome is usually written and then turned into quantitative data
Waynforth & Dunbar (1995) conducted a content analysis by analysing lonely heart adverts in newspapers to see if men and women were looking for different things in relationship
- They looked at 881 lonely heart adverts
- They found that men aimed their adverts at younger women and tended to cite their resources as being more important than their attractiveness
- Women aimed their adverts at older males and mentioned their attractiveness more than their resources
- The researchers codified the above themes and gave them numerical values so that the qualitative responses were transformed into quantitative data

The researcher chooses the research question
They select a sample of pre-existing qualitative research (some of which they may have conducted; some of which will have been conducted by other researchers) e.g. interview transcripts, diaries, video recordings, images
The researcher will decide on the coding of the categories/coding units
The researcher works through the data creating a tally which indicates which shows the categories/codes that are most common in the qualitative data

The researcher will then need to test for reliability via:
- Test-retest reliability: Run the content analysis again on the same sample and compare the results; if they are similar then this shows good test-retest reliability
- Inter-rater reliability: A second rater conducts the content analysis with the same coding categories and data and compares them; if the results are similar then this shows good inter-rater reliability

Strengths of Content Analysis	Limitations of Content Analysis
Reliability is established as a content analysis is easily replicable	Researcher bias can happen as the researcher has to interpret the data
It allows statistical analysis to be conducted	It may lack validity due to extraneous variables, for example, dairy entries tend to be highly subjective
It is not overly time-consuming compared to thematic analysis of qualitative data	The data is purely descriptive and so does not have explanatory power
It complements other research methods and can be used verify results from other research	Lacks causality as the the data was not collected under controlled conditions
	The results can often be flawed due to the over representation of certain events and using material that is already available. E.g. Negative events usually have a lot more coverage than positive and so this could skew the data to given an invalid representation of behaviour

This is a method used to analyse qualitative data
It allows researchers to identify, analyse and report common/key themes from a set of data

1. The researcher familiarising themselves with the data by reading it over and over again
2. Themes within the data emerge i.e. patterns, repeated or common ideas or concepts
3. The researcher reviews these themes and patterns to see if they can explain behaviour and answer the research question
4. The researcher then categorises and defines each theme
5. The researcher writes up the analysis into their formal report
Thematic analysis is an inductive method: themes emerge from the data, there is no hypothesis-testing involved

Strengths of Thematic Analysis	Limitations of Thematic Analysis
Thematic analysis provides the researchers with flexibility in the way that they approach the data	Researcher bias can happen as the researcher has to interpret the data
It uses a subjective approach so that the researcher can apply a range of theories to it	It may lack validity as the data has not been collected under controlled conditions
Thematic analysis allows the research to explore the data without any preconceptions which can help to generate real themes and patterns which increases validity	Thematic analysis is extremely time-consuming to undertake
	Thematic analysis researchers may find it difficult knowing what data to focus on