Positivism vs. Interpretivism in research
Positivism
Positivism claims that knowledge is achievable by observing and measuring natural phenomena. Humans and machines can extract truth (or knowledge) by collecting data with their sensors then analyzing it. Humans and machines discover the truth by logical interpretations and finding patterns in data. We can add knowledge to the world’s puzzle until we find the “theory of everything.” Science is always measurable, and if not, we should quantify that (e.g., IQ test to quantify intelligence).
Interpretivism
Interpretivism believes that in social phenomena, we can not measure everything. Thus we should look at phenomena with different lenses. Many factors such as language, culture, viewpoints, and beliefs form alternative truths for groups or individuals. Researchers overlook their bias while quantifying social concepts. They argue that some rationales work for society, and the same explanations might work differently in another community. Thus, we should not look for a global and optimum answer for each problem.
In Research
“If we are to be consummate researchers, we need to have a deep understanding of the strengths and weaknesses of different research methods and data-analysis techniques. We also need to have a deep understanding of the different sorts of knowledge we obtain using different research methods […] In my view, obtaining this understanding is inhibited rather than facilitated by the current but longstanding positivist versus interpretive rhetoric. [2]”
References
1- Macionis, John J.; Gerber, Linda M. Sociology (7th Canadian ed.). Toronto: Pearson Canada. p. 32. ISBN 978–0–13–700161–3.
2- Weber, R. (2004). Editor’s Comments: The Rhetoric of Positivism versus Interpretivism: A Personal View. MIS Quarterly, 28(1), page xi
