Transcript Second order analysis

PhD Success in Qualitative Research
Sten Ludvigsen
InterMedia
University of Oslo
PhD Success in Qualitative Research
 Empirical contexts – InterMedia
 Design experiments in schools (science, project work, social
science, art history, etc)
 Other naturalistic settings – workplaces (hospitals, computer
engineering, software development – knowledge management
system in action)
 Video-ethnography –
 observations – documents – video-recordings- interview – logs,
PhD Success in Qualitative Research
 Rigor in methods, strategies, review and
theory
 Relevance – first and second order
analysis
Members orientation
Systematic review
PhD success in …
 Research design and analytic strategies
 Design: theory, conceptual system,
methods, analytic strategies, data,
empirical results and findings
Design
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Experiments
Quasi-experiments
Design experiments
Field trials
Ethnographic studies
Design
 Theory-driven, but
 Status of empirical data
 Instruments-driven, but
 Status of frames of interpretation
 Explorative, hypothesis-testing, research question; theory
based, empirical based
Analytic strategies
 Coding, set of predefined categories
Structure and patterns
 Emerging talk – categories
Processes
Relationships
Structure
Assumptions and core ideas
 Framing
Turn to social practice
Social interaction
Tool
Materiality
Instruments
Analytic strategies
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Research questions
 How do participants talk about ……
 Do content- or process-based prompts leads to most effective learning?
 How do teachers organize the activities?
 Which objects transform the activities
 What's the relationship between the teachers actions and the students uptake?
 What's the students orientations; social, epistemological, institutional …
 Which concepts is used by students?
Analysing interactional data
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Activity – interaction
Interviews
Observation
Video recorded data
Automatic generated data
Analysing interactional data
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Theory as premises
Review
Empirical design
Data – how, what, ……
Unit of analysis
Levels of descriptions
The computer-based 3D models
The Situated and Historical Nature of
CSCL……….
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Extract 1: Scientific concepts in flux
Cornelia: I understood that we were going to build bricks and so on or build upward [in the 3D model]. I
understood that and looking for all of these [amino acids]. I did not understand what insulin or a protein is … what
a, why should we find these GTA and then it becomes Met and so on? That … I understood why we did that, but
not why or what it means, and so on.
Pat: No, neither did I.
Cornelia: And then I didn’t think there was any point to building that thing [the 3D model of the protein] when we
didn’t understand anything.
Mark: I don’t understand anything.
Fredric: Understand what?
Mark: Well, what, what, what is it supposed to be good for?
Fredric: What it is good for? You should help that guy! Because he...
Mark: Why is it like that? Yes, why is it like that, so to speak? I will never understand that. Why is it like that?
Pat: There should have been some links where it stood, so to speak, what you should do or what the different
things meant.
Teacher: Mmm.
Pat: So that you understood it better.
Fredric: Isn’t it just that way, so to speak...?
Model for analysing group
interaction
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Unfolding interaction with tools
 Particularization and categorization
 How to get a valid understanding
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Multiplicity as starting point
Interconnectedness
Sensemaking (members orientation)
Dynamic understanding of context
Multiple layers of context
Sequences – but not only
Historical influence
Analysing interactional data
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Step 1:
Overview over the corpus
Themes
Read many times – what do the participant
do and what do they try to achieve
Analysing interactional data
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Step 2:
Segments
Episodes
Time frames
Analysing interactional data
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Step 3:
Intuitive
Contra intuitive
Usual – unusual
How do the participants orient themselves in relation to
the others
 The content of the talk
 Specific terms, concepts,
Analysing interactional data
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Step 4:
Introduction of a theme – closure
Thematic shifts –
Semiotic resources
 Artifacts, language, history
 Resources that gives directions – or conceptual
Analysing interactional data
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Step 5:
Construction of time
Connection between types of data
Example: cut and paste – cognitive effort
Analysing interactional data
 Step 6:
 Key utterances – short sequences that create
direction for the activities
 Long sequences
 Example: I do not understand (student)
 Teachers interventions
 Uptake over time – perspectives
The Situated and Historical Nature of
CSCL……….
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Extract 1: Scientific concepts in flux
Cornelia: I understood that we were going to build bricks and so on or build upward [in the 3D model]. I
understood that and looking for all of these [amino acids]. I did not understand what insulin or a protein is … what
a, why should we find these GTA and then it becomes Met and so on? That … I understood why we did that, but
not why or what it means, and so on.
Pat: No, neither did I.
Cornelia: And then I didn’t think there was any point to building that thing [the 3D model of the protein] when we
didn’t understand anything.
Mark: I don’t understand anything.
Fredric: Understand what?
Mark: Well, what, what, what is it supposed to be good for?
Fredric: What it is good for? You should help that guy! Because he...
Mark: Why is it like that? Yes, why is it like that, so to speak? I will never understand that. Why is it like that?
Pat: There should have been some links where it stood, so to speak, what you should do or what the different
things meant.
Teacher: Mmm.
Pat: So that you understood it better.
Fredric: Isn’t it just that way, so to speak...?
Analysing interactional data
 Step 7:
 Summary so far:
Data level
Data-data level
First order analysis – members categories
and orientations
Analysing interactional data
 Step 8:
 Towards theory and analytic concepts
 Orientations
Question, answers, summary, explanations,
clarification, deepening, broadened,
confrontations, elaboration, conclusion, ……
Analysing interactional data
 Step 9:
 Analytical concepts
 Scaffolds, artifacts, resources, object,
tensions, break downs, tools, history,
community, rules, div. of labor, dialogue,
……..
Analysing interactional data
 Step 10:
 Back to research questions
 Step 11
 Interpretation based on the review
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 Interpretation based on theory – analytic concepts
Analysing interactional data
 Step 13:
 Discussion and conclusion
 Second order analysis
 Reliability
 Validity
 Type of generalizations (scale and scope)
Analysing interactional data
 Step 14:
 Levels of explanation:
 Empirical data – and the main level of explanation
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Ontogenesis
Micro genesis
Sociogenesis
Phylogenies
Analysing interactional data
 Step 15:
 Institutional – historical – cognition
 Premises – or outcome
 To be shown
Analysing interactional data
 Step 16:
 The relationship between structure – and
emerging talk
Analysing interactional data
 Step 18:
 In the family of socio-cultural perspective
tension between structural- and
phenomenological theories
PhD Success in Qualitative
Research
 Steps to be taken in a article
Data reduction
Data selection
Data analysis
Data presentation
PhD Success in Qualitative Research
 Summary
 Corpus
Transcripts ….
What it consist of
 Zooming in – (Roth, 200x)
 Zooming out
PhD Success in Qualitative Research
 Summary
The phenomena – instruments – planning –
Variation – in depth analysis
 Students engagement –
Everyday talk – more oriented towards concepts
PhD Success in Qualitative Research
 Summary
Learning – metaphors
Change of ……..
Levels of explanation