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Necessary condition analysis (NCA) is a research approach and tool employed to discern "necessary conditions" within datasets. [1] These indispensable conditions stand as pivotal determinants of particular outcomes, wherein the absence of such conditions ensures the absence of the intended result.
In biological research, experiments or tests are often used to study predicted causal relationships between two phenomena. [1] These causal relationships may be described in terms of the logical concepts of necessity and sufficiency. Consider the statement that a phenomenon x causes a phenomenon y.
A research design typically outlines the theories and models underlying a project; the research question(s) of a project; a strategy for gathering data and information; and a strategy for producing answers from the data. [1] A strong research design yields valid answers to research questions while weak designs yield unreliable, imprecise or ...
In some cases, independent variables cannot be manipulated, for example when testing the difference between two groups who have a different disease, or testing the difference between genders (obviously variables that would be hard or unethical to assign participants to). In these cases, a quasi-experimental design may be used.
It may be the case that several sufficient conditions, when taken together, constitute a single necessary condition (i.e., individually sufficient and jointly necessary), as illustrated in example 5. Example 1 "John is a king" implies that John is male. So knowing that John is a king is sufficient to knowing that he is a male. Example 2
However, there remain applications to which this design is relevant, for example, in calculating time-savings from an intervention which improves access to amenities. In addition, there may be cases where non-experimental designs are the only feasible impact evaluation design, such as universally implemented programmes or national policy ...
The reversal design is the most powerful of the single-subject research designs showing a strong reversal from baseline ("A") to treatment ("B") and back again. If the variable returns to baseline measure without a treatment then resumes its effects when reapplied, the researcher can have greater confidence in the efficacy of that treatment.
Randomized controlled trial [5]. Blind trial [6]; Non-blind trial [7]; Adaptive clinical trial [8]. Platform Trials; Nonrandomized trial (quasi-experiment) [9]. Interrupted time series design [10] (measures on a sample or a series of samples from the same population are obtained several times before and after a manipulated event or a naturally occurring event) - considered a type of quasi ...