content analysis + coding

Content analysis: What is content analysis?

An observational technique that analyses qualitative data indirectly to identify patterns and themes.

E.g; written responses, interview transcript, audio/video recordings, newspaper articles + drawings.

What is the purpose of content analysis?

To classify qualitative data systematically + draw conclusions from it (turn into quantitative data).

Why must researchers form a research question before conducting content analysis?

To determine what the analysis will focus on + which category will be used.

What should researchers do before analysing it?

Become familiar with the data to ensure the coding system is appropriate + relevant.

Coding: What is coding?

The process of creating categories to classify qualitative data.

What are coding units?

Categories use to classify + count specific themes/behaviours in the data.

Why is coding useful?

Organises large amounts of qualitative data and allows patterns to be identified.

How does coding convert qualitative data into quantitative data?

By counting the frequency of coding units/categories.

What are the stages of content analysis?

• 1 - collect the data.

• 2 - read/examine the data to become familiar with with it.

• 3 - identify coding units/categories.

• 4 - apply the coding units to the data.

• 5 - count how often each coding unit appears.

Strengths of content analysis?

• +ve: content analysis=useful for sensitive topics - participants can provide written accounts rather than discussing experiences directly.

• +ve: ethically useful - can investigate topics using material already in public domain (newspapers) without requiring informed consent.

• +ve: can analyse both types of data - qualitative provides depth + validity, while quantitative allows pattens + comparisons to be identified.

• +ve: high ecological validity + reliability - uses real-life communications + naturally occurring behaviour; records o original material remain, allowing re-analysis + replication.

Weaknesses of content analysis?

• subjective findings - researcher bias, may interpret meanings differently based on their own assumptions/expectations.

• Cultural bias - language + behaviours may be interpreted differently, reducing validity.

• Loss of qualitative detail - richness, emotional meaning lost → reduce validity because the coded data may not fully represent the true meaning of the original data.