This document summarizes key concepts in logic and scientific reasoning as they relate to approaches in social scientific communication research. It discusses the importance of conceptual analysis and explication of concepts. It outlines two approaches of reasoning - deduction and induction - and examples of models that use each. It suggests that social scientific research could benefit from more emphasis on inductive approaches and careful conceptualization to establish validity and reliability.

1. LOGICAL ISSUES IN THE SOCIAL
SCIENTIFIC APPROACH OF
COMMUNICATION RESEARCH
Q. J. YAO, PH.D., ASSOCIATE PROFESSOR
DEPARTMENT OF COMMUNICATION & MEDIA, LAMAR UNIVERSITY, BEAUMONT, TX 77710
QYAO@LAMAR.EDU (409) 880-7656
PRESENTED TO THE PHILOSOPHY OF COMMUNICATION DIVISION AT THE 107TH ANNUAL
CONVENTION OF THE NATIONAL COMMUNICATION ASSOCIATION, SEATTLE, WA, 18-21, 2021

2. COULD THE RELATIVITY THEORY BE WRONG?
• In 2019, some academics at the 106th annual Indian Science
Congress dismissed the findings of Albert Einstein and Isaac
Newton (BBC, 2019).
• That criticism of Einstein’s relativity theory has been seen as
ludicrous worldwide.
• If religion is followers’ science and science is scientists’ religion, Einstein was
at least a cardinal bishop and scientists cannot imagine him being
questioned.
• But logically, can the relativity theory be wrong?
• Yes, because Einstein used the hypotheticodeductive (H-D) model to test his
theory—let’s explain this from the fundamental part of logic.

3. CONCEPTS, PROPOSITION, AND THEORY
• Concepts are the starting point of logic and bricks to build any science, and they need to be explicated
thoroughly (Chaffee, 1991). Vague concepts generate confusion and meaningless debates
(Wittgenstein, 1922/2007).
• Conceptual analysis (at least for key concepts in the filed) & definition:
• Distillation (intention or connotation) v. List (extension or denotation)
• Constructs (Kerlinger & Howard, 1999) are more latent concepts (normally multi-dimensional); variables
(Dubin, 1978) are indicators to measure concepts and are developed through conceptualization and
operationalization.
• Conceptual analysis is harder for social sciences than for natural sciences, which partly contributes to the lower
replicability of the former (Open Science Collaboration, 2015 ), making them “harder sciences.”
• Examples of conceptual confusions in communication research: age (Chaffee, 1991), framing & priming (Yao, Liu,
& Stephens, 2020), social reality (McLeod & Chaffee, 2017), uncertainty (Dunwoody, 2005 ).
• Propositions are connections of two concepts (through reasoning) and evolve into hypotheses and
theories along passing more testing. Models (Dubin, 1978) are a set propositions connecting a group of
three or more variables.

4. TWO APPROACHES OF REASONING:
DEDUCTION & INDUCTION
• Deduction: from the general to the particulars (Aristotle, 1984 ); when the premises are
true, the conclusion must be true; with more mathematical form and beauty of rigor.
• One form: p  q, p, q; or p  q, ¬q, ¬p. Fallacy: p  q, ¬p, ¬q; p  q, q, p.
• Models of scientific research:
• Popper’s Falsification Model (p  q, ¬ q, then ¬ p); Lakatos’ MSRP method (1978)
• The Deductive-Nomological (D-N) Model (Hempel, 1965): law-like statement & statement of
antecedent  a deductive conclusion.
• The hypotheticodeductive (H-D) model (Popper, 1959): problem  hypothesis  testable
consequences (also confusingly called hypotheses in the literature)  testing; essentially,
either p  q, q, p (Einstein's way of testing the theory of relativity); or p  q, ¬q, ¬p.

5. TWO APPROACHES OF REASONING:
DEDUCTION & INDUCTION, CONT.
• Induction: from the particulars to the general (Aristotle, 1984; Bacon, 2000); when the
premises are true, the conclusion may be true; less rigorous and criticized by Hume
(1748/1999), Russel (1946), and even rejected as a logic reasoning method by Popper
(1959).
• Forms: statistical referencing/data mining/meta-analysis (samples  population) or p  q,
q, p (abduction or retroduction; Peirce, 1898/1992).
• Models of scientific research:
• Sociological theory building model (Homans, 1951): observation  conceptualization &
generalization  confirmation & improvement
• Hempel’s (1965) inductive-statistical (I-S) model & Salmon’s (1971) statistical relevance (S-R)
model: statistical law-like statement & statement of antecedent  a deductive conclusion
• O’shaughnessy (1992) and Copi et al’s (2010) inductive models: observation  categorization 
generalization & hypothesizing  testing  confirmation or rejection.

6. CONCLUSION
• Conceptual analysis is a critical but skipped step in
communication and some other social science research.
Efforts like AERA, APA, and NCME’s joint committee’s
(2014) Standards for Educational and Psychological Testing
should be encouraged in multiple areas of social sciences.
• Pure deduction is impossible in scientific research; the H-D
model falls in either the falsification model or the
abduction model.
• Some increasingly popular concepts of research
methodology, such as statistical inferencing, data, mining,
meta-analysis, are inductive in nature.

7. SUGGESTIONS TO SOCIAL SCIENTIFIC
APPROACH OF COMMUNICATION
RESEARCH
• Our current world is largely based on the dividends
of the scientific revolution and the consequential
industrial revolutions, all essentially inductive
products (Kuhn, 1996). Inductive research should be
more encouraged.
• Go back to review our key concepts and explicate
them more carefully and establish their validity and
reliability. Then inductively test their relationships.
• The higher the level of measurement, the better
(gender, for instance, may be measured with an
interval scale of muscularity-femininity continuum in
lieu of a nominal variable).

8. REFERENCES
• American Educational Research Association, American Psychological Association,
National Council on Measurement in Education, Joint Committee on Standards for
Educational & Psychological Testing (US). (2014). Standards for educational and
psychological testing. Washington, DC: American Educational Research Association.
• Aristotle. (1984). The organon. In J. Barnes (Ed.) The complete works of Aristotle:
The revised Oxford translation (v. 1). Princeton, NJ: Princeton University Press.
• Bacon, F. (2000). The new organon (Eds. L. Jardine & M. Silverthorne). Cambridge,
UK: Cambridge University Press.
• BBC. (2019, January 7). India scientists dismiss Einstein theories. Available at:
https://www.bbc.com/news/world-asia-india-46778879
• Chaffee, S. (1991). Explication. Newbury Park, CA: Sage Publications.
• Copi, I., Cohen, C., & McMahon, K. (2010). Introduction to Logic, 14th ed. Upper
Saddle River, NJ: Prentice-Hall.
• Dubin, R. (1978). Theory building, revised edition. New York: The Free Press.
• Dunwoody, S. (2005). Explicate, please. MAPOR News, fall issue, 4.
• Hempel, C. (1965). Aspects of scientific explanation and other essays in the
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• Hume, D. (1748/1999). An enquiry concerning human understanding (Ed. T. L.
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Belmont, CA: Wadsworth Publishing.
• Kuhn, T. (1996). The Structure of scientific revolutions. Chicago, IL: The University of
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• Lakatos, I. (1978). The methodology of scientific research programmes. In J. Worrall
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