The social sciences today are more necessary than ever. Yet, more than ever, we are recognizing some limits of those sciences. In this article, we’ll briefly review some opportunities and constraints of our collective ability to advance those sciences; and, suggest an innovative approach to accelerating our sciences.
First, and most broadly, let’s talk science. Science is a process of research (using a variety of methods) in order to gain knowledge. That knowledge is often expressed as theories, models, hypotheses, and even conjectures. That knowledge is disseminated through publications. In time, we build on research and publication to improve our collective knowledge. With better knowledge, we can make better decisions to reach our goals (improve our lives, community, and planet).
Although we have more research and publications than ever, it is not always clear that the results are highly useful. For our purposes, the usefulness of that knowledge may be measured on three dimensions: relevance, data, and structure. We’ll cover each of these in turn.
The Pillar of Relevance
One pillar of the social sciences is its relevance (sometimes referred to as meaning). The knowledge developed from research applies directly to understanding and improving the human condition from psychology to economics, and everywhere in between.
And, on the surface, that relevance seems pretty clear. We are pretty good at choosing and using knowledge that fits the appropriate situation or context. If we want to help individuals understand and improve themselves, we use theories of self-development. If we want to understand the world of finance, we use economic theory.
That surface simplicity becomes more complex upon reflection. For example, in terms of my personal development, I should wonder about how my development might be somehow tied to the global economy? If everything is connected, then everything must be (to a greater or lesser extent) relevant.
That difficulty arises because, although we are pretty sure that everything is connected, we’re not always sure how it is connected. So, if we start tracing all the interconnected pieces (e.g. from personal development to the global economy) we quickly realize that to fully understand anything, we must understand everything.
That presents a difficulty because none of us has the time to master all of the social sciences. I barely had time to get one doctorate; I don’t have time to get all of them! So, we accept the limits of our human capacity and move forward as best we can.
Science is not about accepting our limits. Science is about extending them.
While some researchers will certainly (and appropriately) work alone investigating tightly focused topics, those who are investigating more complex topics (e.g. community development, alleviating poverty, health, conflict, etc.) might consider a new approach to measuring the range of relevance of their research.
Here, having broader expertise as part of a research project counts as having greater relevance. So including an expert in psychology (for example) serves as an indicator that the research will address the psychological dimension. Having an expert from one field means the project has validity as “level one” research. Bring on an expert from another field (business, for example) with the appropriate input on the design of the project, and the validity goes to level two.
One suggestion of this article is that by evaluating research proposals (and results) by levels of relevance, editors and funders have a “quick and dirty” metric for choosing between the many proposals and submissions. I, for one, would much rather read (and use) level ten research instead of level two.
In providing a simple, quantitative indicator of relevance, we clarify the validity of our research and encourage interdisciplinary collaboration.
The Pillar of Data
Of course, increased relevance may lead to an increase in data. And (of course) it is generally accepted that more data is better. Data, then is the second pillar of the social sciences.
It is said that we live in an age of data. We have an explosion of data (and/or we face the messy situation of data smog and disciplinary fragmentation). All that data has provided the social sciences with unprecedented opportunity to improve our knowledge – and so our efficacy and influence.
Something strange seems to be happening. Or, perhaps more accurately, something is not happening.
Back in 1997, we had about 12K petabytes. That is 12 exabytes. There are 1,000 exabytes in each zettabyte. And, today we have about 10 zetabytes of data in the world. So, we have (very roughly) a thousand times as much data available today as we did 20 years ago. Of course, not all that data is highly useful. So, let’s look at a better indicator. Although it is only a rough indicator, we can search Google Scholar for the term “social science.” Up until the year 2000, there were about 150 thousand publications. All told, there are now about 3.5 million.
Has the effectiveness, usefulness, or influence of the social sciences improved a thousand times in the past twenty years (as we might expect from the increase in electronic data)? Has it increased twenty times (as we might expect from the increased number of publications generally representing knowledge)? Not that I’ve noticed.
The social sciences has the appearance of strength, supported by the two pillars of relevance and data. While necessary, however, those two do not seem sufficient for advancing our sciences to deal with the ever-growing problems of our planet. In order to have greater strength and stability, we need something more.
The Pillar of Structure
In addition to relevance and supporting data, the usefulness of our knowledge may also be seen in its structure. For knowledge, the “brick house” metaphor is quite appropriate here. Relevance may be seen in the human/social element of the house. Is it in the best location? Is it comfortable? The bricks represent the data (more data means more bricks). How those bricks are connected is the structure.
A pile of loose bricks in a beautiful location does equate to a livable home.
Studies in human development and education show that as we learn and grow, as we learn more things, our knowledge becomes more structured; we understand how the pieces fit together. Studies in political psychology (extended later to business) have shown that individuals and teams with more structured knowledge make better decisions and so more successfully reach their goals.
When studying theories (as forms of knowledge) we can evaluate the structure of each theory using Integrative Propositional Analysis (IPA) which gives us a measure of “how structured” each theory is. That measure (assuming it is supported by relevance and data) provides a new way to evaluate “how useful” a theory is likely to be in practical application.
In the following figure, we can see the structure of one theory of physics as it evolved from ancient times through the scientific revolution. Its low level of structure in ancient times reflected its limited usefulness. Its increase during the scientific revolution shows improved understanding. And, building on each other’s research, a theory was eventually developed with 100% structure – a theory that has remained highly useful in practical application to this day.
Theories of electrostatic attraction:
How structured, you may ask, are the theories of the social sciences?
The following figures provide a few examples from various social sciences. While this is a somewhat eclectic sample (from sociology, psychology, entrepreneurship, and program evaluation), the results are illustrative and concerning.
From sociology, we have theories of conflict:
As you may see, the trend line is nearly flat. Despite, we assume, adequate relevance and adequate data, we do not see a science which is advancing. We do not see theories improving in structure and so becoming more useful in practical application for understanding and resolving conflict.
Moving on to the field of psychology, the next figure shows nine highly cited theories of motivation. Over the past century, we do see some improvement in the structure of those theories. For those interested in extrapolating the trend line, it seems we may have a revolution of this field in about ten centuries.
Theories of motivation from psychology:
The next figure represents 15 theories form the field of program evaluation. Here, we see an increase from about 5% structure to about 15% structure in only about 20 years. To extrapolate, we could have a highly useful theory of evaluations (enabling must more effective evaluations leading to improvements in human/social services in less than a century).
Theories of program evaluation:
Finally, theories of entrepreneurship were seen declining in structure. Not a good direction!
In general, theories of the social sciences seem to weigh-in at about 20% structure. Their limited effectiveness or usefulness for understanding and resolving the problems of the world stands in contrast to our knowledge from physics that is both highly useful and highly structured.
Summary and Conclusion
When it comes to developing more useful theoretical knowledge, we seem to have a pretty good sense of what is relevant. We don’t use theories of electronics to inform our methods of therapy. And, we are striving to improve that relevance through interdisciplinary collaborations. So, we can measure “how relevant” our research is by simply enumerating the variety of disciplines providing substantive input to research design and results. We also have a good sense of what counts as high quality data – and we are continually striving to improve the quantity and quality of that data.
In contrast to very rapid advances in data and some advances in interdisciplinarity, the social sciences do not seem have a good sense of structure; we seem to be advancing there only very slowly. We can change this by using IPA to evaluate the structure of our theories and to synthesize/integrate those theories within and between disciplines. By collaborating across disciplines to evaluate and improve the relevance, data, and structure of theoretical knowledge in the social sciences, we can create “social science accelerators” to more rapidly improve the usefulness and impact of our research. The argument here is that such an approach is highly desirable in a world desperate for the benefits that the social science has promised for so long.
About the Author
Dr. Wallis is a Fulbright alumnus, international visiting professor, award-wining scholar, and Director of the Foundation for the Advancement of Social Theory; researching and consulting on theory, policy, and strategic planning. An interdisciplinary thinker, his publications cover a range of fields including psychology, ethics, science, management, organizational learning, entrepreneurship, policy, and program evaluation with dozens of publications, hundreds of citations, and a growing list of international co-authors. In addition, he supports doctoral candidates at Capella University in the Harold Abel School of Psychology. Following a career in corrosion control engineering, he earned his PhD at Fielding Graduate University and took early retirement to pursue his passion – leveraging innovative insights on the structure of knowledge to accelerate the advancement of the social/behavioral sciences for improved practices and the betterment of the world. His textbook, with Bernadette Wright, “Practical Mapping for Applied Research and Program Evaluation” (Sage Publications) provides unique and effective approaches for developing new knowledge in support of sustainable success for businesses, government, and non-profits programs working to improve measurable results, individual lives, and whole communities.
Dr Wallis is also a Research Associate at the SSRC