Content and Thematic analysis- RESEARCH METHODS Flashcards
RESEARCH METHODS
Primary data
- Psychologist collects data firsthand from participants directly
Evaluation of Primary data
STRENGTH:
- Fits your hypothesis
- Be sure of controlling extraneous variables
Weakness:
- Time- consuming
Secondary data
- Existing sources of data collected by the researcher
Evaluation of Secondary data
STRENGTH:
- Less- time consuming
Weakness:
- Might not fit hypothesis
- Might not control extraneous variables
Qualitative data
Data is words/ descriptions
Evaluation of Qualitative Data
STRENGTH:
- More depth
Weakness:
- It is harder to analyse (than quantitative data)
Quantitative Data
Data is numbers
Evaluation of Quantitative data
STRENGTH:
- It is easy to analyse (than qualitative data)
Weakness:
- Lacks depth
Content Analysis
- Research tool used to indirectly observe the presence of any recorded communication (media)
- It is a method of analysing qualitative data
General steps when conducting a content analysis:
Choose a sampling method (event or time)
1. Read through the data
2. Make notes on emerging themes and write behavioural categories
3. Read through the data again
4. Count the instances of each category (quantitative)
Evaluation of a content analysis
ADVANTAGE:
- Once you’ve carried out content analysis- allows you to identify patterns and make comparisons
- Ethical to investigate things that may otherwise be public
- Good ecological validity- real- world data
- More reliable than thematic analysis- less subjective
DISADVANTAGE:
- Lose depth and explanations behind behaviour
Thematic analysis
- Method of qualitatively analysing data
General steps when conducting a thematic analysis:
- Read the data and try to understand the meaning
- Make note of the emergent themes
- Describe/ report on the themes
Evaluation of Thematic analysis
ADVANTAGES:
- Ethical to investigate things that may otherwise be public
- Good ecological validity- real- world data
- Keep the detail and explanations behind behaviours while able to see patterns
DISADVANTAGES:
- Can become more subjective- bring own expectations to data- research is biased