Abstract:
Understanding social science domains is difficult because of the complex nature of the domains and the consequential challenges in capturing and analyzing data. To address such challenges, many analytic techniques have been and continue to be developed [1]. This means that social science fields like education, despite the perception that they are “soft” sciences, may be the hardest fields to research [2–4]. Within social sciences data can be either numeric or text, requiring statistical and qualitative techniques of data collection and analysis [5–7]. Perhaps post-structuralist or post-modern approaches [8] to data cannot or should not be automated or statistically analyzed. However, while analysis of qualitative data is normally undertaken by humans, automation of the analytic process for these data is being developed (e.g., statistical discourse analysis; [9]). Such developments suggest that the future of social science research may bring greater synchronization between these approaches to data. Consequently, this opinion piece focuses upon the teaching of quantitative and statistical methods in the doctoral degree. In this piece, I first provide evidence for the complexity of social science research. Then I consider the challenges and issues in our current arrangements in doctoral education in the field. I conclude with some tentative solutions for improving doctoral education in quantitative methods. Unsurprisingly, it is my opinion that we need to admit that it is not evident how to balance the quantitative research methods curriculum such that it both prepares a wide variety of doctoral students for their careers as analysts, researchers, or scholars and does justice to the complex nature of reality and scientific investigation.
