VL 1

1. Natural setting: performed in the field, studying a phenomenon in the context in which it occurs.

2. Researchers as a key instrument: researchers collect data and information themselves, often through face-to-face interactions, observing behaviours, studying documents, or interviewing participants.

3. Multiple sources of data: researchers typically gather a variety of data of different sorts, from interviews to documents to observations and so forth.

4. Inductive analysis: emphasise bottom-up analysis of data and the build-up of patterns, themes, and concepts into increasingly abstract units from the data.

5. Evolutionary design: follow an evolutionary research process in which a research plan, a theory, data collection, or analysis can unfold and change over time as the research progresses.

Interviews: Conversations with key informants

Observations: observing phenomena/behaviors directly

Documentation: studying documents, plans, schemes etc

Triangulation: using multiple sources of data

Coding: assigning tags or labels as units of meaning to pieces or chunks of data collected

Memoing: writing subjective commentaries or reflections about what was happening at the time or place of the data collection

• Interviews

• Focus groups

• Observations

• Open ended surveys

• Archival analysis (review of documents or other media)

• Content from the Internet

• Case study

• Action Research

• Grounded Theory

• Most popular qualitative research method

• a method involving intensive research on a phenomenon (a case) within its natural setting (one or more sites) over a period of time.

• rely on multiple sources of evidence, where data needs to converge in a triangulating fashion

• Planning refers to identifying the research questions and other rationale for doing a case study.

• Designing refers to defining the unit of analysis, the number and types of cases to be studied, and the potential use of theory or propositions to guide the study.

• Preparing involves taking the necessary steps to conduct high-quality data collection

• Collecting means executing the case study protocol(s) and gathering data, preferably from multiple sources.

• Analysing consists of examining, categorising, coding, tabulating, testing or otherwise combining and studying the evidence collected to draw empirically based inferences and other conclusions.

• Sharing refers to bringing case study results and findings to a close by identifying and addressing relevant audiences and providing them with the findings through appropriate reporting or presentation.

• an interactive and interventive method of inquiry

• builds upon the idea of introducing changes or other sorts of interventions into a context and studying the effects of those actions

• The distinctive feature of action research is the deliberate introduction of interventions by the researcher

Advantages

• Provides an opportunity to contribute to both academic knowledge and to solving a real- world problem.

• Combines relevance and rigor in research.

Challenges

• Doing action and research together is a challenging act for anyone, let alone an inexperienced scholar.

• Assuming a position of a value-neutral, independent observer to the extent that it allows for critical reflection and analysis, while at the same time maintaining a role as an influencer and intervener .

• Access to participating organizations that put control to the researcher is hard to organize.

a type of qualitative research that relies on inductive generation (building) of theory based on (“grounded in”) qualitative data systematically collected and analysed about a phenomenon.

• The main purpose of the grounded theory method is theory building, not testing.

• Prior domain knowledge should not lead to pre-conceived hypotheses or conjectures about the research that the research then seeks to falsify or verify.

• The research process involves the constant endeavour to jointly collect and compare data, and to constantly contrast new data to any emerging concepts and constructs of the theory being built.

• All kinds of data are applicable, and are selected by the researcher through theoretical sampling.

Advantages

• Early and tight immersion into data analysis – unlike, say, quantitative research where data analysis is typically conducted at a much later stage of the research process.

• Encourages systematic and detailed data analysis

• Literature provides ample guidelines for conducting these steps.

Challenges

• Detailed and systematic bottom-up analysis of data: It is very easy to get bogged down in data analysis on a very low level of detail

• It is difficult to integrate data to higher levels of abstraction.

• Dependent on both excellent and rich data – collected typically before knowing what to look for

• Dependent on creative and critical thinking ability – a skill not easily learned or taught.

1. Research question

   1. Interesting

   2. novel

   3. relevant

   4. feasible

   5. ethical

2. Research Design

   1. Exploration

      – build an understanding of the phenomena that interests us

   2. Rationalisation

      – make sense of the puzzle or problem that interests us

   3. Validation

      – We subject our emergent or developed theory to

      rigorous examination

1. Data:

   – What type of data is required? Where can I collect observations or other forms of evidence? How will I sample the relevant data?

2. Risks:

   – What are the potential dangers associated with execution of the research design? For example, what is the likelihood of a case organisation not being available for study anymore? What are strategies available to minimise or mitigate these risks?

3. Theory:

   – Which and how much literature concerning the phenomena of interest is available? Where are gaps in the knowledge base? What findings have been produced to date that might have an impact on my work and influence choices in my research design?

4. Feasibility:

   – Can the research design be executed within the constraints associated with a doctoral study such as time limitations, resource limitations, funding, experience, geographic boundaries, and others?

5. Instrumentation:

   – how will my concepts of interest be measured or described? Will my operationalisation be appropriate given the choice of research methodology and set of data available?