The UK is currently implementing a major, new academic initiative to address, at the undergraduate level, the underdeveloped methodological skills of students majoring in the social sciences, particularly in the areas of quantitative method and statistics.
The QM is a very impressive goal, something the states and other countries would likewise do well to think about implementing. When one considers, for example, the methodological training the average social science major receives, particularly in comparison to students in the computational sciences, applied mathematics or various areas within the natural sciences, such as physics, it is not much. For example, as a professor in the states, I have spent the past few years implementing advanced data and research methods courses into our undergraduate sociology program--by the way, I want to give a "shot out" to my Kent State Ashtabula students!
Now, I must say, it has not been an easy endeavor. But, to my surprise, I have found the students to be very receptive. There is, however, a very specific reason why: I present to them a wider definition of quantitative method than just statistics.
If we, as social scientists, took a few minutes to look at what students on the other side of our campuses are learning, we would find that statistics is only a small part of it. Quantitative method, today, is all about BIG DATA, e-science, data mining, GIS analysis, machine/artificial intelligence, control theory, agent-based modeling, network science, information visualization, and so forth. I can just keep going and going... But, why would my social science students be interested in such things? Because, even when lacking a background in such methods, they intuitively "get the point" of such methods. Common now, think about it. Our students, live, after all, in a wild new, electronic world, a virtual reality permanently integrated into their everyday reality, data streaming everywhere, 24/7, dynamic in a virtual time/space: smart cell phones telling them where their friends are at any moment, emails, texts, tweets, kicks, Google maps, endlessly streaming music, Pandora radio, electronic banking, iTunes, social media networks, LinkedIn, YouTube, online television and videos--on and on and on and on it goes.
Explain to me, what does statistics have to do with such things? I will tell you, not much. Now, I will say, in all fairness, students still need to learn statistics; and, as such, I make statistics a prerequisite to students taking more advances modeling strategies. And, in my own work, I rely heavily on statistics, particularly factor analysis, multidimensional scaling and cluster analysis.
But, at the end of the day, students want (yes, I did say that word "want") to learn about (even if they get only a sampling) the other incredible methods that computer scientists, applied mathematicians, computational scientists, e-scientists, physicists, geographers, and others are developing. So, to such a desire to learn, I say Wow!, and a Double Wow!
And, such a desire to learn about these new methods goes to the point recently made by the UK sociologist and complexity scientist, David Byrne. In a recent article, UK Sociology and Quantitative Methods: Are We as Weak as They Think? Or Are They Barking up the Wrong Tree? Byrne, an expert in quantitative method, makes the case that, given the massively increased complexity of daily social life, statistics alone is just not going to "cut it," as they say. We need to learn from the complexity sciences. And, we need to bring the theoretical and empirical strengths of sociology and the social sciences to help with the epistemological and theoretical challenges of studying complex social systems.
I agree wholeheartedly, and I think that students intuitively do as well. If the social sciences seek to be on par with the methodological abilities of the natural and computational sciences and applied mathematics, they must understand that these disciplines--while facile in statistical analyses--have moved far, far beyond such methods.
In other words, we have a lot of catching up to do, and focusing on statistics, while necessary, is not sufficient. Add to this fact the reality that, in the sciences and in the labor market our students will work in research teams comprised of groups and networks of peers from a variety of academic backgrounds, where cross-disciplinary communication will be vital to success, we have a great need to create a much larger, more methodologically sophisticated vocabulary and tool set for our students.