ATLAS.ti is ideal for all sorts of research, including the deductive research approach. Let's look at the nature of the typical research structure, then examine some key differences between inductive reasoning and deductive reasoning. Finally, we'll look at methods that a deductive approach can employ and some of the tools that ATLAS.ti provides to help you with your research.
All scientific research involves the organization of knowledge generated from studies where researchers have analyzed empirical data. For researchers to develop a particular theory, they use their empirical observations to generate a conceptualization that can be broken down into discrete elements. An existing theory is merely any conceptualization that the larger community of researchers agrees upon to develop further research.
A deductive research approach is common in contexts where researchers conduct similar experiments based on existing theory or new experiments intended to develop competing theories.
Essentially, researchers pursue deductive research to confirm an existing theory. On the other hand, inductive research can assert a new causal relationship or a theoretical framework through a grounded theory approach or analyzing data for patterns arising from the data.
There are shortcomings to both deductive reasoning and inductive reasoning. Deductive logic alone cannot account for relatively unfamiliar phenomena that cannot be explained by existing theory, while relying exclusively on inductive study can unnecessarily repeat research efforts in contexts where confirmatory inquiry is more beneficial.
Holistically, deductive and inductive approaches go hand in hand, as social science research is highly situational. The degree of theoretical coherence in the context you want to study will largely determine whether deductive reasoning or inductive reasoning is more appropriate for your research. You should consider incorporating deductive reasoning in your study design if previous researchers have already provided a relevant theory for your inquiry. Otherwise, highly novel and unexplored research will require new theories that inductive reasoning can provide.
ATLAS.ti can allow researchers to conduct analysis through inductive and deductive approaches. Data analysis software can provide a place to organize and code your data for effective analysis. What separates ATLAS.ti from other platforms is its machine-learning capabilities that can make the coding and analytical processes faster and more efficient. Especially when researchers have an existing theory they can apply to their data, ATLAS.ti can facilitate the deductive approach to analysis quickly and with a minimal learning curve.
Consider an example where there is an existing theory about the mental health outcomes of young people. Let's say that a study employing inductive reasoning has identified several criteria for good mental health, including social relationships, financial well-being, and positive environmental factors such as time spent outdoors and adequate physical activity. Let's imagine that researchers have conducted qualitative interviews and specific observations with young people to develop this theory, with the general conclusion that a young person who satisfies most of these criteria likely benefits from good mental health.
Up to this point in our imaginary example, inductive research has allowed researchers collecting data to assert that each criterion shares a causal relationship with the broader theme of mental health. However, other researchers may be interested in challenging this framework through deductive reasoning. A researcher employing deductive reasoning tests hypotheses proposed by researchers employing inductive reasoning to strengthen the existing theory or propose an alternative framework for the same social phenomenon.
For example, a deductive approach can collect data from a new set of research participants who assert good mental health. Deductive reasoning typically views the established theory as a lens on that new data. If the criteria in that existing framework can be found in the data, then the deductive researcher has successfully affirmed that theory.
A deductive research strategy first requires that the researcher reads existing theories that a deductive argument can rely on. A persuasive researcher in any case needs to demonstrate that they are knowledgeable about the scholarship in the particular theory they want to develop.
In the absence of an existing theory, the research inquiry should adopt an inductive approach, particularly as there is no framework to apply to the data.
If the theory is already established, the researcher can create a set of codes from the criteria from that theory. In the example above, a deductive study has predetermined codes such as "financial well-being" or "positive environment" that can be applied in the case that they are present in the data.
The type of theory can also determine what sort of study to pursue. Theories that rely on the perspectives of research participants will largely dictate the use of qualitative interview or quantitative survey data where the researcher elicits stated opinions. On the other hand, theories whose developed hypotheses look at behaviors or actions require specific observations.
Deductive reasoning applies to the data analysis phase of research. In other words, any research method involved in collecting data can lead to analysis employing a deductive approach. That said, while researchers employ an inductive study with the goal of developing hypotheses, deductive investigations begin with a theory or hypothesis that a researcher studies and wants to develop or critique.
Qualitative coding can apply both inductive and deductive approaches. Under deductive reasoning, researchers take the theory they want to test and create a set of codes that represent the different elements of that theory.
The Code Manager in ATLAS.ti can help you organize your deductive and inductive codes for your project. In other words, you can conduct deductive research with one set of codes to test an existing theory and conduct inductive research with another set of codes that can further develop theory. Codes in ATLAS.ti can be divided into code groups and labeled with different colors to help you keep track of relatively large and complex projects for effective data analysis.
Quantitative methods are helpful when employing deductive reasoning. ATLAS.ti includes tools such as Code-Document Table and Code Co-Occurrence Table that count codes applied to data, while Text Search and Query Tool are ideal for determining frequencies and patterns in your project. Of course, ATLAS.ti can accommodate both inductive and deductive strategies in any data analysis.
When using theories that rely on what people say, the Text Search function is ideal for analyzing interviews and focus group discussions through deductive reasoning. Text Search can look for a word or phrase relevant to an established theory to determine whether research participants reflect the criteria in that theory.
This tool tallies the codes you have applied to the different documents in your project. Suppose you have a project where your interviews are divided into discrete documents. The Code-Document Table can then tell you how often the parts of the theory you are using are apparent in each interview. This tool can confirm whether the theory applies to your study or if you need additional theoretical development in aspects of your project where the existing theory is not present.
The Code Co-Occurrence Table works similarly to the Code-Document Table, except you can cross-reference codes with each other. This is helpful to researchers when they are looking for elements of theory in other social phenomena. Suppose there are cases when the criteria for mental health are present in observations where research participants do not feel they are in good mental health. In that case, what other circumstances exist or co-occur with those aspects, necessitating further research inquiry? The Code Co-Occurrence Tool can facilitate the deductive research approach while identifying new directions for your research.
The Query Tool can provide the deductive reasoning with deeper analysis to confirm existing theory while also developing it. Particularly where parts of the same theory co-occur with each other, it might be illuminating to explore the potential of causal relationships between otherwise discrete criteria. The Query Tool can help you identify segments of your data that match a defined set of criteria based on a combination of codes. You can then look through the results to better understand why your data segments meet your criteria. This can aid the deductive research approach by identifying areas of theoretical development.