KEY
C.C- classical conditioning
SLT- social learning theory
SD- systematic desensitisation
PPTS- participants
O.C- operant conditioning
ARRM- attention, retention, reproduction, motivation
NS- neutral stimulus
CR- conditioned response
CS- conditioned stimulus
UCS- unconditioned stimulus
UCR- unconditioned response
AN- anorexia nervosa
EV-extraneous variable
IV-independent variable
DV- dependent variable
HO- home office
,Assess thematic analysis (8m)
INTRO
- Thematic analysis is a method used to analyse qualitative data by identifying patterns
and themes within a data set.
- It allows for flexibility in what the researcher wants to find, without needing to link to a
specific theory.
- It provides rich, detailed data and can be used to analyse transcripts, secondary
data, media, etc.
- A data corpus is a large collection of qualitative texts being analysed, while a data set
refers to related sets of data that can be analysed as a whole.
STAGE 1+ 2/AO1
- Stage 1: Familiarisation with the data involves reading through the entire data
corpus. If the data is audio, it needs to be transcribed, and initial observations can be
noted.
- Stage 2: Generating initial codes means labelling parts of the data with words or
short phrases to identify key features.
- This can be done manually or using software, and colour coding or post-it notes are
helpful for highlighting possible themes.
STAGE 3+4/AO1
- Stage 3: Searching for themes is where codes are grouped into broader patterns of
meaning. Tools like mind maps or tables can be used.
- Stage 4: Reviewing themes involves refining them by combining, splitting, or
discarding to ensure they form a coherent pattern.
- The themes should reflect the data corpus and the aim of the research.
STAGE 5+6
- Stage 5: Defining and naming themes means giving each theme a concise name and
a clear definition that captures its essence. A detailed analysis should be done for
each theme.
- Stage 6: Producing the report involves writing up the final analysis in a way suited to
the audience, for example, a scientific journal or newspaper article.
AO3
- One strength is high inter-rater reliability.
- Two or more researchers can code the same data and compare results.
, - If they find the same themes, it shows that the findings are not just based on one
person’s opinion.
- This helps reduce subjectivity and bias.
CA
- A weakness is low validity if the themes don’t fully reflect what participants actually
meant.
- Sometimes researchers might over-simplify, misinterpret, or take quotes out of
context.
- This weakens how true the findings are to the data.
AO3
- Another strength is test-retest reliability.
- Because the process has clear, standardised steps, it can be repeated on a different
set of data.
- If similar themes come up, this shows consistency and makes the method more
reliable.
CA
- One weakness is that it can also lack generalisability.
- This is because thematic analysis takes time, it’s often done on small samples.
- Therefore the results may not apply to wider populations, especially in large-scale
studies.
AO3
- Another strength is that it also has high validity, as the qualitative data is rich and
detailed.
- This allows researchers to explore people’s real thoughts and emotions in depth,
which can lead to useful application,
- like understanding mental health issues or improving therapy.
CA
- However thematic analysis has researcher bias.
- Since identifying themes is subjective, the researcher’s own beliefs or expectations
might influence what they pay attention to.
- Thus, makes the process less objective and could affect the accuracy of the final
results.
, CONCLU/APPLICATION
- Overall, thematic analysis is a useful method for understanding rich, in-depth data.
- It's especially valuable in clinical or social psychology, where identifying patterns in
participant language can guide treatment or policy, though care must be taken to
avoid subjective.
C.C- classical conditioning
SLT- social learning theory
SD- systematic desensitisation
PPTS- participants
O.C- operant conditioning
ARRM- attention, retention, reproduction, motivation
NS- neutral stimulus
CR- conditioned response
CS- conditioned stimulus
UCS- unconditioned stimulus
UCR- unconditioned response
AN- anorexia nervosa
EV-extraneous variable
IV-independent variable
DV- dependent variable
HO- home office
,Assess thematic analysis (8m)
INTRO
- Thematic analysis is a method used to analyse qualitative data by identifying patterns
and themes within a data set.
- It allows for flexibility in what the researcher wants to find, without needing to link to a
specific theory.
- It provides rich, detailed data and can be used to analyse transcripts, secondary
data, media, etc.
- A data corpus is a large collection of qualitative texts being analysed, while a data set
refers to related sets of data that can be analysed as a whole.
STAGE 1+ 2/AO1
- Stage 1: Familiarisation with the data involves reading through the entire data
corpus. If the data is audio, it needs to be transcribed, and initial observations can be
noted.
- Stage 2: Generating initial codes means labelling parts of the data with words or
short phrases to identify key features.
- This can be done manually or using software, and colour coding or post-it notes are
helpful for highlighting possible themes.
STAGE 3+4/AO1
- Stage 3: Searching for themes is where codes are grouped into broader patterns of
meaning. Tools like mind maps or tables can be used.
- Stage 4: Reviewing themes involves refining them by combining, splitting, or
discarding to ensure they form a coherent pattern.
- The themes should reflect the data corpus and the aim of the research.
STAGE 5+6
- Stage 5: Defining and naming themes means giving each theme a concise name and
a clear definition that captures its essence. A detailed analysis should be done for
each theme.
- Stage 6: Producing the report involves writing up the final analysis in a way suited to
the audience, for example, a scientific journal or newspaper article.
AO3
- One strength is high inter-rater reliability.
- Two or more researchers can code the same data and compare results.
, - If they find the same themes, it shows that the findings are not just based on one
person’s opinion.
- This helps reduce subjectivity and bias.
CA
- A weakness is low validity if the themes don’t fully reflect what participants actually
meant.
- Sometimes researchers might over-simplify, misinterpret, or take quotes out of
context.
- This weakens how true the findings are to the data.
AO3
- Another strength is test-retest reliability.
- Because the process has clear, standardised steps, it can be repeated on a different
set of data.
- If similar themes come up, this shows consistency and makes the method more
reliable.
CA
- One weakness is that it can also lack generalisability.
- This is because thematic analysis takes time, it’s often done on small samples.
- Therefore the results may not apply to wider populations, especially in large-scale
studies.
AO3
- Another strength is that it also has high validity, as the qualitative data is rich and
detailed.
- This allows researchers to explore people’s real thoughts and emotions in depth,
which can lead to useful application,
- like understanding mental health issues or improving therapy.
CA
- However thematic analysis has researcher bias.
- Since identifying themes is subjective, the researcher’s own beliefs or expectations
might influence what they pay attention to.
- Thus, makes the process less objective and could affect the accuracy of the final
results.
, CONCLU/APPLICATION
- Overall, thematic analysis is a useful method for understanding rich, in-depth data.
- It's especially valuable in clinical or social psychology, where identifying patterns in
participant language can guide treatment or policy, though care must be taken to
avoid subjective.
