Qualitative method – Data analysis Flashcards
Please explain the main principles behind Goria’s inductive coding model
- Inductive theory development
- New concepts and linkage between them
Two cycles
- ‘‘1st-order’’ concepts - analysis (i.e., an analysis using informant-centric terms and codes) → High number of codes
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‘‘2nd-order’’ themes - analysis (i.e., one using researcher-centric concepts, themes, and dimensions) Reduce the number of codes
- In this 2nd-order analysis, we are now firmly in the theoretical realm, asking whether the emerging themes suggest concepts that might help us describe and explain the phenomena we are observing
- “Aggregate dimensions”. We investigate whether it is possible to distill the emergent 2nd-order themes even further into 2nd-order ‘‘aggregate dimension”
Lastly, in trying to finalize the analyses of the data, we invariably must deal with the issue of different authors interpreting some informant terms and passages differently. If agreements about some codings are low, we revisit the data, engage in mutual discussions, and develop understandings for arriving at consensual interpretations. We reconcile differing interpretations by developing consensual decision rules about how various terms or phases are to be coded.
What is coding?
What is coding:
- Central to qualitative analysis, coding reduces large amounts of empirical material and makes the data readily accessible for analysis, while at the same time increasing the quality of the analysis and findings.
- Codes identify a feature of the data (semantic content or latent) that appears interesting to the analyst (Braun & Clarke)
- Semantic content: Something the participants say on the surface A specific term or something the participant talk about
- Latent: Is something that can’t be seen More in-depth Ideology
What are the advantages and disadvantages of coding?
Advantages of coding - Linneberg & Korsgaard:
- -Acquire deep comprehensive and thorough insights into your data
- Simply reading through your data as if it were a book will not prevent you from overlooking potentially new and surprising data
- -Make the data easily accessible and retrievable
- Coding sorts the data into labeled segments. This enables quicker access to data and allows the researcher to retrieve it for another look.
- -Sorting and structuring your data
- -Ensuring transparency
- Can be enhanced by observing transparency in respect to how your conclusions are linked to your data
- -Ensuring validity
- Coding is an important step in moving from the raw data to the findings, as well as being a means to maintain coherence between the objective and the results. Coding is a way to ensure that the questions asked are the questions that have been answered.
- -Giving voice to your participants
- Codes are created as a means to understand the phenomenon and/or participants and their perspectives
Disadvantages of coding
- Coding does split your data, and there is a risk that in your analysis you focus solely on the relations between the codes at the expense of holistic and comprehensive understandings of the examples and phenomena you are studying
Which different coding methods can be used?
Deductive coding:
(1) Top-down (2) Working from a predefined list of codes (3) Derived from the conceptual framework (4) In structured research projects
Inductive coding:
(1) Bottom-up (2) Develop codes based on the data (participants) rather than the researcher (3) Capturing the complexity and diversity of the data (4) In explorative theory-building research projects
- An inductive approach (Bottom-up) means the themes identified are strongly linked to the data themselves
- Inductive analysis is therefore a process of coding the data without trying to fit it into a preexisting coding frame, or the researcher’s analytic preconceptions. In this sense, this form of thematic analysis is data-driven.
How to ensure quality of our codes/ coding procedure?
- In general, this is challenged in qualitative studies We need to convince the audience
- The process in data analysis is influenced by “us” (subjective) as a researcher because we are the main research instrument, however, there is something that can help us
- We need to show the audience that we make valid inferences and that we not just cherry-picking
- Triangulation – Different methods and different sources
- Reflexivity – this way the best way?
- Be transparent about the analytical process and with your data
- Member checks - Talk with your peers, respondent feedback, supervisors
- Memos - memos is used as a tool as early as possible in data collection. Repeatedly, we find that data collection and coding run in parallel, and the analytical memo is a great tool to help materialize ongoing reflections, much like a log that can both inform subsequent data collection and lead to richer explanations in the analysis later on.
- Audit trails - That explain the process and the arguments that we have gone through when doing the research
What is Thematic analysis?
Braun & Clarke: This paper is very good at describing the thematic coding process
- Thematic analysis should be seen as a foundational method for qualitative analysis. Thematic analysis is a method for identifying, analyzing and reporting patterns (themes) within data.
- It is important to recognize that qualitative analysis guidelines are exactly that / they are not rules, and, following the basic precepts, will need to be applied flexibly to fit the research questions and data. Moreover, analysis is not a linear process of simply moving from one phase to the next. Instead, it is a more recursive process, where movement is back and forth as needed, throughout the phases.
What is discourse analysis?
For example discourse analysis that makes meaning through studying language and language use. It is a bundle of methods that analyzes verbal communication, written report within organizations or outside organizations in terms of academic journals. It is therefore not useful when the aspects are not easily gathered or represented through language.
Owick mentions 4 main approaches to DA where the 1) is deconstruction where it entails close reading that opens up complexity, 2) foucauldian-inspired analysis where one identifies the rules which govern bodies of texts, 3) critical discourse analysis which embraces a critical epistemology and make visible and criticize connections between properties of texts and social relations, 3) intertextual analysis focuses on identifying and analyzing parts of a text in terms of earlier sources that are incorporated in the text.
Oswick: We can look at three epistemological commitments of the researcher in terms of being a positivist that uses DA as a means to establish a coherent reality, critical which looks at how and why certain views dominate while others are suppressed and poststructuralist that sees multiple interpretations of reality
Please explain “Components of Data Analysis: Interactive Model” by Miles et al
- Data collection leds to data display and data condensation
- Data condensation is the process of selecting, focusing, simplifying, abstracting and transforming data from e.g. field notes or interviews → coding and transcripts
- Data display is an organized, compressed assembly of information that allows conclusion drawing and action. It helps us understand what is happening and to do something (e.g. analyze further) based on the understanding → matrix
- Drawing and verifying conclusions here preliminary conclusions are drawn
In this view, qualitative data analysis is a continuous, iterative enterprise. Issues of data condensation, display, and conclusion drawing/verification come into play successively as analysis episodes follow each other. Such a process is actually no more complex, conceptually speaking, than the analysis modes quantitative researchers use. Like their qualitative colleagues, they must be preoccupied with data condensation (calculating means, standard deviations), with display (correlation tables, regression printouts), and with conclusion drawing/verification (significance levels, experimental/control group differences). But their activities are carried out through well-defined, familiar methods; are guided by canons; and are usually more sequential than iterative or cyclical. Qualitative researchers are in a more fluid and more humanistic position.
