Applying Conceptual Analysis and Inductive Research in Social Science

This summary is provided by the IPR Measurement Commission

Summary
The IPR Measurement Commission and the Joint Committee on Testing Practices of the American Educational Research Association (AERA), the American Psychological Association (APA), and the National Council on Measurement in Education (NCME) (2014) have long been dedicated to the development of measurement and science in our field. Many social science studies, however, still suffer from conceptual confusion (Bringmann, Elmer, & Eronen, 2022; Chaffee, 1991; Yao, Liu, & Stephens, 2020), over reliance on deduction (Locke 2007), low replicability (Open Science Collaboration, 2015), and inadequate modeling (Kuhn, 1996). This study analyzes and explains the logical foundations for these challenges.  

Method
This study conceptually reviewed standard discussions on concepts, deduction, induction, and their applications in the social sciences. Conceptual reviews build the “very basis” for empirical studies and can address issues that empirical studies cannot (Bringmann, Elmer, & Eronen, 2022, p. 344). The relevant literature was gathered through Google Scholar, the author’s personal library collection, peer recommendations, and citations in available articles.

Insights & Implications
Scientific concepts must be defined before being used in research; this is referred to as explication. The significance of explication (Chaffee, 1991) has been emphasized by philosophers ranging from Immanuel Kant to Rudolf Carnap (1963). Concepts should first be conceptualized, and then put into operation with one or more measurable variables (Dubin, 1978). These measures, with sufficient validity and reliability, are better when developed collectively (AERA, APA, NCME, 2014), reviewed regularly, and used commonly across social sciences.

A hypothesis explains a relationship between two concepts derived from existing knowledge or data. After withstanding multiple tests, a hypothesis can become a theory. Hypotheses can be generated through deduction or induction. Deduction, the inference from the general to the particular, whose conclusion is guaranteed to be true with the truth of its premises, has been favored by scientists (Hurley & Watson, 2018). Its dominance, however, is mythical because its three representative reasoning models are not practical or purely deductive: Karl Popper’s (1963) falsification model is not practical, as statistical research tools are unsuitable for finding counterevidence and falsifying a theory does not advance knowledge; Carl Hempel’s (1965) deductive-nomological (D-N) model is not adopted because it requires a universal law that cannot be validated and generates no new knowledge; the popular hypotheticodeductive (H-D) model, “the official doctrine” (Locke, 2007, p. 872), turns to either likely falsification when testing rejects the hypothesis, or a deductive fallacy called abduction (Kistruck & Shantz, 2022; Hurley & Watson, 2018), when testing supports the hypothesis.

Induction, the inference from the particulars to the general, whose conclusion is not absolutely guaranteed true by the truth of its premises (Hurley & Watson, 2018), generates new knowledge and is credited with initiating the Scientific Revolution, which might have been delayed by the dominance of deduction during the medieval era (Kuhn, 1996). Statistical analysis, “big data,” and meta-analysis all infer from samples to populations and are essentially inductive. Inductive models, such as grounded theory (Charmaz & Thomberg, 2021) and the process for inductive theorization (Locke & Lathem, 2020), have also been developed.

Communication scholars and institutions are encouraged to conduct more inductive research to identify patterns among concepts with collectively accepted measurements.

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References:

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Yao, Q.J., Liu, Z., & Stephens, L.S. (2020). Exploring the dynamics in the environmental discourse: the longitudinal interaction among public opinion, presidential opinion, media coverage, policymaking in 3 decades and an integrated model of media effects. Environment Systems and Decision, 40(1), 14-28. https://doi.org/10.1007/s10669-019-09746-y.