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Pragmatic validity
Pragmatic validity in research looks to a different paradigms from more traditional, (post)positivistic research approaches. It tries to ameliorate problems associated with the rigour-relevance debate, and is applicable in all kinds of research streams. Simply put, pragmatic validity looks at research from a prescriptive-driven perspective. Solutions to problems that actually occur in the complex and highly multivariate field of practice are developed in a way that, while valid for a specific situation, need to be adjusted according to the context in which they are to be applied.
The term "validity" is often seen as a sort catch-all for the question whether the knowledge claims resulting from research are warranted. The confusion might arise from the mingling of the terms ‘internal validity’ and ‘external validity’, where the former refers to proof of a causal link between a treatment and effect, and the latter is concerned with generalizability. (In this discussion I maintain the term ‘generalizability’ rather than external validity mainly to avoid any possible confusion between the two terms.) During this discussion I consider that validity is reflected in the question, “did we measure the right thing?”, or, in other words, can the researcher prove that the effect he observed was actually a result of the cause? Positivistic research approaches this question in a different way than pragmatic research, which is based in a different paradigm. Design Science Research is one example of research firmly situated in a pragmatic perspective.
==Validity in (post)positivist research==
Postpositivist research typically strives to numerically report upon empirical observations made within a controlled environment in order to arrive at a universal truth about a causal effect between a limited number of variables. This statement relates what much of the epistemology of Positivistic science is based on: isolating singular variables in order to come to a conclusion that is free of context. Laboratory experiments and quantitative models are the preferred methods for observing and reporting. These are considered to rule out any rival plausible explanations and thus help to guarantee validity.
抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』
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