week 6 Flashcards
Qualitative data analysis
A process of deconstructing and reconstructing evidence
To produce meaningful interpretation
Multiple Qualitative Analyses
Qualitative analysis is intense, engaging, challenging, non-linear, contextualised and highly diverse.
Requires a qualitative stance and worldview.
Involves a variety of quite different approaches (different epistemologies).
Approaches to analysis tend to bring different assumptions with them.
Data analysis does not take place in a vacuum, but in the particular context of a research project.
Big Q
Processes tend to be flexible interpretative and subjective
Big Q rejects notions of objectivity and context
Contextual and situated nature of meaning
Small q
Use of qualitative techniques but within a hypothetical-deductive framework
Concern with accuracy and reliability of coding
Structured codebooks of coding frames-which are then applied to the data
Multiple codes working independently to code the data
Qualitative data analysis approaches
Grounded theory, Content analysis, Thematic analysis, Discourse analysis, Discursive psychology, narrative analysis, Conversation analysis, IPA- interpretative Phenomological analysis
Thematic analysis
An umbrella term for a set of approaches that share a focus on identifying themes
Discourse analysis:
An umbrella term for a range of methodological approaches that analyse the use and function of talk and text within social interaction
Interpretative phenomenological analysis
An approach with an idiographic focus, aiming to provide detailed examinations of personal lived experience and how participants are making sense of their personal and social world
Pluralistic approaches
TA, IPA and DA are tools for analysing data in psychology.
Grounded theory and Narrative Analysis are also commonly used in psychological studies.
Ongoing debate about the potential of adopting more than one method or methodology in any given research project
Analytical Pluralism
The application of more than one qualitative analytical method to a single data set.
Leads to a more comprehensive answer to the research question.
Avoids privileging any particular approach over another.
It enables researchers to develop rich, complex understanding and opens multiple possibilities of interpretation.
Many possible kinds of pluralism, including using multiple methods, data sources, theories, or researchers.
Common, Foundational Practices and Norms: 1
Analysing qualitative data is always a meaning-making process.
All starts with reading the data openly, with sensitivity to context.
Focus must be on participant’s intentions and meanings, even if this is understood in different ways.
Researchers must be reflexive, honest, and critical in describing their own presence and procedures.
Common, Foundational Practices and Norms: 2
Checking, revising, and refining interpretations by returning to the data.
Writing general account that both reflects and transcends the data:
Supporting knowledge claims with evidence.
Elaborating on the limits and open horizons of the research.
Collecting new data following analysis in iterative cycles.
Transparent accounting of procedures.
What is the role of the researcher in the research process
Reflexivity
Representing the other
How to establish trustworthiness
Quality evaluation criteria
Is there any software that may help with the analysis process
Computer-assisted
Qualitative data
Analysis software
Reflexivity
Critical reflection on the research process and one’s own role as a researcher.
Active acknowledgement and explicit recognition that their position may affect the research process.
Challenges the view of knowledge production as independent of the researcher producing it and of knowledge as objective.
Links to epistemology
Personal reflexivity
The way in which the research is shaped by the researcher’s own background, identities, interests, views
Discursive reflexivity
Detailed attention to the role of the researcher in the process
Epistemological reflexivity
The way in which the research is shaped by theoretical assumptions, research questions, methods of data collection
Researcher positioning
Gender, ethnic background, affiliation, age, sexual orientation.
Immigration status, personal experiences, linguistic tradition.
Beliefs, biases, preferences.
Theoretical, political and ideological stances.
Emotional responses to participant’s experiences.
Researcher’s beliefs, feelings, andpersonal experience.
How may these positions impact the research?
Access to the field
The nature of researcher-researched relationship, which in turn affects the information participants may be willing to share.
The findings and conclusions of the study.
But how can we demonstrate reflexivity
Make explicit any beliefs and experiences that may influence how you conduct the research.
Use of first-person language (e.g., I, We).
Provide a detailed and transparent report of decisions and their rationale.
Prolong our engagement with the field.
‘Data triangulation’: use multiple sources of data or multiple approaches to analysing data.
Peer review, forming of a peer support network.
Keep a diary or research journal for “self-supervision”.
Who has been constructed as “Other”?
Women.
People with mental health conditions.
People identifying as lesbian, gay, transgender, queer.
Economically disadvantaged people.
People with HIV/AIDS.
People from black and minority communities.
People with disabilities.
…..
How to deal with othering
We can, for example:
ask participants to evaluate the validity of our account of them.
listen to how the participants speak of “the research group” so that our own perspective is problematised.
listen to how other members of powerful groups speak about members of the group to which the participants belong.
Try to provide opportunities to create a dialogue between ourselves and the participants, so that no account is privileged.
Appraising qualitative research
Credibility- comprehensive trustworthy and sensible explanations of the data
Dependability- coherence between methods and findings, and auditable research process
Transferability- relevance of concepts and findings to other setting
Confirmability-=findings and interpretations that reflect the views of participants
What criteria should you use?
Whilst there is no consensus on how to best evaluate qualitative research, there is some agreement on the importance of:
Provision of contextualised accounts of the participants.
Detailed accounts of the analytical process.
An account of the researcher’s speaking position and how this influenced the analysis.
Consistent grounding of interpretations in research data.
Is there any software that may help me with the analysis process?
CAQDAS: Computer-Assisted Qualitative Data Analysis Software.
Compatible with several methods of qualitative analysis.
Tools for managing qualitative data, particularly during the coding/annotating phases and with large data sets:
Nvivo (available at YSJ)
Atlas.ti
Transana
LIWC
ELAN (open access).
