The quantification of natural or social phenomena was not a familiar method among thinkers and philosophers of ancient civilizations throughout history. Except for some passing references in certain historical phases, particularly with some Greek philosophers, it has been established among researchers that physics followed by chemistry were the fields most inclined towards quantitative methods. However, I am more inclined to believe that Chinese philosophy, especially in its medical aspect (particularly the study of the mutual influence between the Yin-Yang duality and Wu-Xing, aimed at revealing the mechanisms of harmony between nature, the universe, and humanity) was based on a cognitive and methodological integration that anticipated some aspects of quantification, pre-dating some of the Greeks and contemporaneous with others.
The use of quantitative methods began its journey from what is known as the “hard sciences.” However, the important lesson we can learn from this examination of the hard sciences is that they did not emerge as solid sciences. Socrates’ theory on the relationship of motion and what he called momentum (Impetus) were merely “how” theories. Physics remained non-quantitative for more than 1,500 years until it transformed into a complete quantitative science with Descartes in the study of optics, and with an even greater influence from Galileo in the study of motion over four centuries ago, paving the way for Einstein’s theory of relativity and Planck’s quantum theory regarding black body radiation. One researcher points out that the 1905 volume of the Physical Review contained not a single non-quantitative study, which also happened in chemistry following the works of Lavoisier and Dalton in the eighteenth century.
Subsequently, the quantitative method infiltrated social sciences, with statistics being the first to penetrate, particularly in psychology, followed by mathematical models (as evidenced by Richardson, Klingberg, and earlier by Kondratiev, etc.). A study published by a researcher at the University of Ottawa indicates that more than half of the statistical techniques used in psychology have been in use for less than 40 years, and empirical studies have exponentially increased since the mid-1980s. The crossing of quantitative methods into various fields of “soft sciences” began, with hard sciences focusing on matter in its various forms, while soft sciences center on “behavior.” Between them lie fields like geography and economics, which are in a state of ambiguity.
This points to a specific issue: there is a clear coherence between the evolution of quantitative methods and technological development. The more the human mind immerses itself in developing tools, expanding their application, and transforming them, the more it leans toward understanding phenomena “technically.” With the growth of interdisciplinary approaches, the quantitative method has become entrenched.
In political science, which I believe to be one of the duller sciences (a claim supported by Buckminster Fuller in his quantitative simulation World Game, which indicated that political scientists are the least capable among all social and human disciplines at accurately predicting phenomena within their field), the quantitative method has increased, albeit with noticeable dullness (4.5% of political science research employs quantitative methods based on the analysis of 67,000 scholarly articles from one hundred high-impact journals during the period from 2000 to 2019). However, the revolution in this field has gained momentum with the emergence of measurement methods in political science in recent years, which reflect technological advancement. This gradually solidifies Auguste Comte’s call to approach social phenomena with the same methodology used for natural phenomena.
It appears that the field of international relations is ahead of political science in employing quantitative methods, yet quantitative indicators in this area confirm a rapid increase in the use of these methods following the expansion of the internet and computer technologies, along with the abundance of statistical analysis programs. Political future studies lead in the utilization of quantitative methods, followed by political economy and studies of political discourse (content analysis and others), in addition to studies of systems, wars, and stability, albeit at varying rates. Political philosophy ranks at the bottom of this list, despite undergoing some content analysis techniques.
The last point is the accuracy of the quantitative data that political researchers rely upon. Often, sources of this data are accused of being biased or malicious. However, my experience in this field and the study I conducted comparing data from various sources showed that the convergence rate was no less than 88% (and I was careful to select data sources from the country or countries in question and from Western sources, whether American, German, etc., and from Chinese, Russian, or international or regional organizations, and I found no significant differences). However, some researchers do not pay attention to the variations in measurement methods (for example, there is a significant difference between measuring Gross Domestic Product (GDP) on a nominal basis versus purchasing power parity). If one does not pay attention to the measurement methodology, one can easily fall into error or find discrepancies in the number of major indicators or the number of sub-indicators, as is the case with models measuring globalization, democracy, political stability, or the Gini index for wealth distribution in society, political corruption, voting models in international or regional organizations, or measuring political distance, etc. Notably, the increase in the use of quantitative methods in contemporary Chinese studies, measured across 1,800 journals in seven branches of social sciences, has surged by a factor of 21 from 1978 (the beginning of the four modernizations) to 2018. If we connect this extremely high rate with the development of China’s international status, we will find a significant correlation between the two phenomena, further reinforcing the contribution of the quantitative method (alongside other variables) to this development.
The increasing precision of future study results is inseparable from the growing use of the quantitative method, which in turn is linked to technological advancement. Otherwise, how can we explain that the accuracy rate in the predictions of the Likert model over 40 years was 100%, or that the accuracy rate in Johan Galtung’s predictions was 8 out of 10, and that the accuracy rate in Alvin Toffler’s predictions ranged between 7 and 8 out of 10 (there is disagreement among researchers about the accuracy of some of his predictions), and many others as well.
Once again, I return to Auguste Comte in his call to benefit—not to monopolize—the quantitative method in political science, provided that the benefit is not “like a night-time woodcutter.
