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Our goal in this book has been to examine the production of knowledge in the social science disciplines with an eye to improvements that might be implemented, now or in the foreseeable future. To bring this matter into view, we adopted a systemic (macro-level) framework – as distinguished from the micro-level framework usual to social science methodology, which focuses primarily on the production and vetting of individual studies.
In recent years, methods of data collection in the social sciences have expanded in range and sophistication. New data sources (many of them hosted on the worldwide web) and data harvesting techniques (e.g., web crawlers) have been discovered, leading to big-data projects of a sort previously unimaginable (Steinert-Threlkeld 2018).
Whilst a great deal of progress has been made in recent decades, concerns persist about the course of the social sciences. Progress in these disciplines is hard to assess and core scientific goals such as discovery, transparency, reproducibility, and cumulation remain frustratingly out of reach. Despite having technical acumen and an array tools at their disposal, today's social scientists may be only slightly better equipped to vanquish error and construct an edifice of truth than their forbears – who conducted analyses with slide rules and wrote up results with typewriters. This volume considers the challenges facing the social sciences, as well as possible solutions. In doing so, we adopt a systemic view of the subject matter. What are the rules and norms governing behavior in the social sciences? What kinds of research, and which sorts of researcher, succeed and fail under the current system? In what ways does this incentive structure serve, or subvert, the goal of scientific progress?
In sciences such as biomedicine, researchers and journal editors are well aware that progress in answering difficult questions generally requires movement through a research cycle: Research on a topic or problem progresses from pure description, through correlational analyses and natural experiments, to phased randomized controlled trials (RCTs). In biomedical research all of these research activities are valued and find publication outlets in major journals. In political science, however, a growing emphasis on valid causal inference has led to the suppression of work early in the research cycle. The result of a potentially myopic emphasis on just one aspect of the cycle reduces incentives for discovery of new types of political phenomena, and more careful, efficient, transparent, and ethical research practices. Political science should recognize the significance of the research cycle and develop distinct criteria to evaluate work at each of its stages.