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CHAPTER 12. INTERPRETIVE RESEARCH, CHAPTER 13. QUALITATIVE ANALYSIS,…

- - - - In positivist research, however, the
        researcher is considered to be external to and independent of the research context, and is not
        presumed to bias the data collection and analytic procedures.
- - - - Phenomenological inquiry requires that researchers eliminate any prior assumptions and personal biases, empathise with the participant’s
        situation, and tune into existential dimensions of that situation so that they can fully understand
        the deep structures that drive the conscious thinking, feeling, and behaviour of the studied
        participants.
      - Existential phenomenological
        research method In the data collection phase, participants embedded
        in a social phenomenon are interviewed to capture their subjective experiences and perspectives
        regarding the phenomenon under investigation.
  - - - The researcher’s choice of
        actions must be based on theory, which should explain why and how such actions may bring
        forth the desired social change. The theory is validated by the extent to which the chosen action
        is successful in remedying the targeted problem.
    - - Diagnosing involves identifying and defining a problem
        in its social context.
      - Action-planning involves identifying and evaluating alternative solutions to the
        problem, and deciding on a future course of action based on theoretical rationale.
      - The evaluation stage examines the extent to
        which the initiated action is successful in resolving the original problem—i.e., whether theorised
        effects are indeed realised in practice.
      - Action-taking is
        the implementation of the planned course of action.
      - In the learning phase, the experiences and feedback from
        action evaluation are used to generate insights about the problem and suggest future modifications
        or improvements to the action.
- - - - Because interpretive research is based on a different set of
        ontological and epistemological assumptions about social phenomena than positivist research,
        the positivist notions of rigor—such as reliability, internal validity, and generalisability—do not
        apply in a similar manner.
        
        Credibility. Interpretive research can be considered credible if readers find its inferences to be
        believable. This concept is akin to that of internal validity in functionalistic research.
        
        The credibility
        of interpretive research can be improved by providing evidence of the researcher’s extended
        engagement in the field, by demonstrating data triangulation across subjects or data collection
        techniques, and by maintaining meticulous data management and analytic procedures—such as
        verbatim transcription of interviews, accurate records of contacts and interviews—and clear
        notes on theoretical and methodological decisions, that can allow an independent audit of data
        collection and analysis if needed.
        
        Confirmability. Confirmability refers to the extent to which the findings reported in
        interpretive research can be independently confirmed by others—typically, participants. This is
        similar to the notion of objectivity in functionalistic research. Since interpretive research rejects
        the
        notion of an objective reality, confirmability is demonstrated in terms of
        ‘intersubjectivity’—i.e., if the study’s participants agree with the inferences derived by the
        researcher.
        
        Transferability. Transferability in interpretive research refers to the extent to which the
        findings can be generalised to other settings. This idea is similar to that of external validity in
        functionalistic research.
        
        The researcher must provide rich, detailed descriptions of the research
        context (‘thick description’) and thoroughly describe the structures, assumptions, and processes
        revealed from the data so that readers can independently assess whether and to what extent the
        reported findings are transferable to other settings.
        
        Dependability. Interpretive research can be viewed as dependable or authentic if two
        researchers assessing the same phenomenon, using the same set of evidence, independently arrive
        at the same conclusions, or the same researcher, observing the same or a similar phenomenon
        at different times arrives at similar conclusions.
        
        This concept is similar to that of reliability
        in positivist research, with agreement between two independent researchers being similar to
        the notion of inter-rater reliability, and agreement between two observations of the same
        phenomenon by the same researcher akin to test-retest reliability.
        
        To ensure dependability,
        interpretive researchers must provide adequate details about their phenomenon of interest and
        the social context in which it is embedded, so as to allow readers to independently authenticate
        their interpretive inferences.
- - - - Interpretive research should attempt to collect both qualitative and
        quantitative data pertaining to the phenomenon of interest, and so should positivist research as
        well. Joint use of qualitative and quantitative data—often called ‘mixed-mode design’—may lead to
        unique insights, and is therefore highly prized in the scientific community.
- - - - Second,
        the researcher identifies and applies rules to divide each text into segments or ‘chunks’ that can
        be treated as separate units of analysis. This process is called unitising. For example, assumptions,
        effects, enablers, and barriers in texts may constitute such units.
      - Third, the researcher constructs
        and applies one or more concepts to each unitised text segment in a process called coding. For
        coding purposes, a coding scheme is used based on the themes the researcher is searching for or
        uncovers as they classify the text.
      - First, when there are many texts to analyse—e.g.,
        newspaper stories, financial reports, blog postings, online reviews, etc.—the researcher begins
        by sampling a selected set of texts from the population of texts for analysis. This process is not
        random, but instead, texts that have more pertinent content should be chosen selectively.
      - Finally, the coded data is analysed, often both quantitatively and
        qualitatively, to determine which themes occur most frequently, in what contexts, and how they
        are related to each other.
    - - Schilling (2006)4 addressed this criticism
        by organising different content analytic procedures into a spiral model. This model consists of
        five levels or phases in interpreting text: convert recorded tapes into raw text data or transcripts
        for content analysis, convert raw data into condensed protocols, convert condensed protocols into
        a preliminary category system, use the preliminary category system to generate coded protocols,
        and analyse coded protocols to generate interpretations about the phenomenon of interest.
    - - First, the coding process is restricted to the
        information available in text form. For instance, if a researcher is interested in studying people’s
        views on capital punishment, but no such archive of text documents is available, then the analysis
        cannot be done.
      - Second, sampling must be done carefully to avoid sampling bias. For instance, if
        your population is the published research literature on a given topic, then you have systematically
        omitted unpublished research or the most recent work that is yet to be published.
- - - - Three coding techniques for analysing text data: open, axial,
        and selective.
        
        Open coding is a process aimed at identifying concepts or key ideas that are hidden
        within textual data, which are potentially related to the phenomenon of interest. The researcher
        examines the raw textual data line by line to identify discrete events, incidents, ideas, actions,
        perceptions, and interactions of relevance that are coded as concepts (hence called in vivo codes).
        
        Each concept is linked to specific portions of the text (coding unit) for later validation. Some
        concepts may be simple, clear, and unambiguous, while others may be complex, ambiguous, and
        viewed differently by different participants. The coding unit may vary with the concepts being
        extracted. Simple concepts such as ‘organisational size’ may include just a few words of text, while
        complex ones such as ‘organizational mission’ may span several pages.
        
        Concepts can be named
        using the researcher’s own naming convention, or standardised labels taken from the research
        literature. Once a basic set of concepts are identified, these concepts can then be used to code the
        remainder of the data, while simultaneously looking for new concepts and refining old concepts.
        While coding, it is important to identify the recognisable characteristics of each concept, such as
        its size, colour, or level—e.g., high or low—so that similar concepts can be grouped together later.
        This coding technique is called ‘open’ because the researcher is open to and actively seeking new
        concepts relevant to the phenomenon of interest.
        
        Next, similar concepts are grouped into higher order categories. While concepts may be
        context-specific, categories tend to be broad and generalisable, and ultimately evolve into
        constructs in a grounded theory. Categories are needed to reduce the amount of concepts the
        researcher must work with and to build a ‘big picture’ of the issues salient to understanding
        a social phenomenon.
        
        The second phase of grounded theory is axial coding, where the categories and subcategories
        are assembled into causal relationships or hypotheses that can tentatively explain the
        phenomenon of interest. Although distinct from open coding, axial coding can be performed
        simultaneously with open coding. The relationships between categories may be clearly evident in
        the data, or may be more subtle and implicit.
        
        In the latter instance, researchers may use a coding
        scheme (often called a ‘coding paradigm’, but different from the paradigms discussed in Chapter 3)
        to understand which categories represent conditions (the circumstances in which the phenomenon
        is embedded), actions/interactions (the responses of individuals to events under these conditions),
        and consequences (the outcomes of actions/interactions).
        
        As conditions, actions/interactions, and
        consequences are identified, theoretical propositions start to emerge, and researchers can start
        explaining why a phenomenon occurs, under what conditions, and with what consequences.
        
        The third and final phase of grounded theory is selective coding, which involves identifying a
        central category or a core variable, and systematically and logically relating this central category
        to other categories.
        
        The central category can evolve from existing categories or can be a higher
        order category that subsumes previously coded categories. New data is selectively sampled to
        validate the central category, and its relationships to other categories—i.e., the tentative theory.
        
        Selective coding limits the range of analysis, and makes it move fast. At the same time, the coder
        must watch out for other categories that may emerge from the new data that could be related to
        the phenomenon of interest (open coding), which may lead to further refinement of the initial
        theory.
        
        Hence, open, axial, and selective coding may proceed simultaneously. Coding of new data and theory refinement continues until theoretical saturation is reached—i.e., when additional
        data does not yield any marginal change in the core categories or the relationships.
- - - - However, software programs
        cannot decipher the meaning behind certain words or phrases or the context within which these
        words or phrases are used—such sarcasm or metaphors—which may lead to significant
        misinterpretation in large scale qualitative analysis.
- - - - After a grounded theory is generated, it must be refined for internal consistency and logic.
        Researchers must ensure that the central construct has the stated characteristics and dimensions,
        and if not, the data analysis may be repeated. Researcher must then ensure that the characteristics
        and dimensions of all categories show variation. For example, if behaviour frequency is one
        such category, then the data must provide evidence of both frequent performers and infrequent
        performers of the focal behaviour. Finally, the theory must be validated by comparing it with
        raw data. If the theory contradicts with observed evidence, the coding process may need to be
        repeated to reconcile such contradictions or unexplained variations.