Theory analysis involves looking at the concepts that emerge from your data. This is a process that requires flexibility because it is difficult to know how much data you will need to collect. The process of collecting and analyzing data continues until you reach theoretical saturation.
Researchers using grounded theory should include their coding techniques and the development of conceptual categories in the methodology section. This will help readers assess the validity of the research.
If you’re like me, you may have been confused by all those people who refer to themselves as data scientists or data analysts. This is because many of these job titles have ambiguous and overlapping definitions. Job descriptions often combine a half dozen skills into one title, which makes it difficult to sort out the different functions.
In a theory-based analysis, data experts look at a dataset and see the bigger picture of what is happening. They also use their expertise to identify what needs to be done to improve the data. They can also create data visualizations and explain them in ways that nontechnical audiences can understand.
Data expert fellows will work closely with a civil society organisation over the course of the nine month placement to support their resource constrained teams learn new skills and become more data-driven. They will also help the organisations to design organisational capacity building programmes and provide long term support on their data projects and advocacy.
Theories are a tool used by scientists to understand and explain the world. They are based on a collection of observations and evidence, and must meet a set of criteria for worthiness. These criteria include level of abstraction, conceptual clarity, and logical consistency. Theories must also have utility, meaning they should help to make data analysis easier.
In Grounded Theory (GT) research, the researcher ‘theorizes’ from their memos by extracting theoretical meaning from the coding process and identifying logical relationships between codes. They do this by sorting their memos into ‘types’, which contributes to theory building.
A common technique for analyzing literature is to apply a theory developed by a scholar or another expert to the source text under scrutiny. The theory acts as a lens that magnifies parts of the text according to the researcher’s interests. For example, a literary analyst might apply a Marxist theory of history to a novel. They might also apply a Freudian theory of personality development to a poem.
Many scholars discuss the relationship between theory and method. This includes theories that ground a particular methodological approach (e.g., phenomenology, ethnography, narrative), as well as epistemological paradigms that influence a study’s approach. However, the discussion often blurs the boundaries between these different types of theory. It is important to distinguish between theory as presupposed and theory as central.
Grounded theory-based analysis generally involves finding repeating themes in the data; coding the emergent themes with keywords; and categorizing them through relationship identification. This process is called open coding, and it forms the basis of grounded theory. Grounded theory researchers conduct this type of analysis until they reach theoretical saturation, which occurs when new data no longer contributes to the emerging theory. This is why it is important to be flexible with your research, and to continue collecting and analyzing data until you reach theoretical saturation. This flexibility allows you to identify and learn from negative or discrepant cases.
Evaluators must be aware of the range of theories that can be used in their work. They must also be able to identify when a specific theory will work best in their context and how they might use it. While they may not be the silver bullet for every evaluation, theory-based evaluation offers a viable alternative to the more traditional experimental designs.
In our interviews with BUILD evaluators, all but one of them described using theories to design their site-level evaluation studies. In particular, they were using utilization-focused evaluation theory to guide their analysis and to align with the consortium-level CEC’s utilization-focused evaluation model.
In theory-based evaluation, a evaluator’s goal is to make causal inferences by assessing how and why an intervention produces its expected results. To do so, a evaluator develops a theory of change that articulates the sequence of outputs, immediate outcomes, and intermediate outcomes expected owing to an intervention. The theory of change expands on the results chains in logic models to include assumptions, risks, and contextual factors that support or hinder the theory’s realization as observed outcomes.