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Reviewing the logic of social scientific claims
Author(s) -
Stephen Gorard,
Yiyi Tan
Publication year - 2021
Publication title -
technium social sciences journal
Language(s) - English
Resource type - Journals
ISSN - 2668-7798
DOI - 10.47577/tssj.v24i1.4781
Subject(s) - causation , judgement , causality (physics) , epistemology , observational study , causal inference , descriptive statistics , psychology , computer science , mathematics , econometrics , philosophy , statistics , physics , quantum mechanics
This paper considers three different claims to knowledge, namely, “fully descriptive”, “generally descriptive” and causal claims. These are all common in social science, and each type of claim requires more assumptions than the previous one. After discussing their methodological and logical foundations, this paper describes some of the limitations in the nature of these three claims. Fully descriptive claims suffer from non-random errors and inaccuracies in observations, and can be queried in terms of utility. Generally, in addition to observational errors, descriptive can be questioned because of the long-standing problem of induction. Even the notion of falsification might not be able to help with this. Finally, causal claims are the most problematic of the three. While widely assumed, causation cannot be observed directly. The paper combines and develops three models of what causation might be, and discusses their implications for causal claims. It points out that so far our belief in causation is still a kind of religious one, and that neither theory nor inferential statistics can help in proving or observing its existence. Finally, the paper provides some suggestions for avoiding being misled by false knowledge and reporting our research findings with tentative care and judgement.

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