The QMMR section sponsored five short courses at APSA’s annual meeting. The courses were held on Tuesday September 8 and Wednesday September 9.  

Please see below for the descriptions.


Instructors: Ben Read, University of California Santa Cruz; Naunihal Singh, Naval War College; Ora Szekely, Clark University

Field research can be both daunting and exhilarating. Scholars learn a great deal about how to conduct fieldwork by doing so, yet there is also much value in reflecting on the practices of other field researchers and talking through each other’s experiences. This course provides high-impact concepts, tips, and guidelines that participants can adapt and apply in their own research. It is based on the premise that designing research, collecting data, and analyzing data are overlapping and inter-dependent processes that begin before a scholar enters the field, continue while she is there, and extend beyond her return. Throughout, we provide strategies to help researchers (1) consider how ethical principles affect the conduct of field research; (2) convert a research design into a “Data Collection Plan”; (3) access elusive data and data sources; (4) evaluate data’s evidentiary value; (5) organize and manage data; and (5) analyze data both in and out of the field. Although fieldwork is usually associated with “studying politics abroad,” we discuss techniques that may be applied inside and outside the U.S. The course includes lecture, Q/A, and small-group components. Participants will also be directed to useful document templates, such as spreadsheets for organizing fieldwork, sample correspondence, etc. The course is valuable for students planning dissertation projects, for scholars who would like to develop or improve their fieldwork skills, and for those who teach classes on research methods.


Instructor: Jennifer Cyr, University of Arizona

This short course explores strategies that political scientists can use to collect and generate data while conducting fieldwork. Many scholars engage in multiple types of data gathering and use mixed analytic methods. With this in mind, this course will introduce techniques that generate qualitative data in two different ways: interviews and focus groups. We will discuss strategies for recruitment, the development of question protocols, and data analysis for both methods and address concerns about power dynamics between researchers and their subjects. We will also consider when to use interviews versus focus groups as a data collection strategy and discuss how to integrate these into mixed methods research designs. Although fieldwork is usually associated with “studying politics abroad,” the techniques considered here may be applied inside and outside the U.S. The course includes lecture, Q&A, and practice exercises. Participants are encouraged to bring specific questions or concerns to the course to consider as a group. The course is valuable for students planning dissertation projects, for scholars who would like to develop or improve their fieldwork skills, and for those who teach classes on research methods.



Instructor: Sebastian Karcher, Syracuse University

Research data management entails developing a data management plan and handling research materials systematically throughout the research lifecycle. Effectively managing data makes research more robust, allows data to be useful over a longer period of time, and facilitates sharing data with the broader research community. This short course equips participants with a range of strategies for effectively managing qualitative data. Hands-on exercises allow participants to practice basic data management tasks in the context of their own projects. The short course particularly emphasizes writing data management plans (DMPs), as required by the National Science Foundation (NSF), for research involving qualitative and multi-method data. We also consider the benefits and challenges of sharing data and demonstrate appropriate techniques for mitigating them, again with the help of exercises and tools that participants will be able to use with their own research. Finally, the short course introduces and briefly discusses new techniques for making qualitative research more transparent, including developing interview methods appendices and tables, documenting analysis performed in qualitative data analysis (CAQDAS) software, and employing Annotation for Transparent Inquiry (ATI).



Instructor: Derek Beach, University of Aarhus

The study of causal mechanisms is ubiquitous in the social sciences. Mechanism-focused research using in-depth case studies enables us to gain a better understanding of how things work and under what conditions using real-world cases instead of gaining knowledge about mean causal effects across cases based on experimentally manipulating treatments in controlled populations. However, the potential gains of mechanism-focused research have not been fully reaped in the social sciences because of the tendency to reduce mechanisms to counterfactuals which are then investigated using cross-case comparisons.

Inspired by recent developments in mechanism-focused research in medicine and policy evaluation (Clarke et al, 2014; Cartwright and Hardie, 2012), the first session of the course will discuss the standards developed in the natural sciences for what constitutes a ‘good’ mechanistic explanation (e.g. Craver and Darden, 2013), and how these can be translated into social science theorization. The second session will then present the developing standards in the natural sciences for what constitutes ‘good’ mechanistic evidence, and again how these can be translated into the social sciences. The final session discusses practical applications, including how mechanism-focused research can be used as an adjunct method to improve social science experiments in designing the experiment and interpreting the data.



Instructors: Andrew Bennett, Georgetown University; Tasha Fairfield, London School of Economics; Jeff Checkel, European University Institute

This course will cover the underlying logic and best practices of process tracing, which is a within-case method of developing and testing causal explanations of individual cases.

We will briefly summarize the philosophy of science behind explanation via reference to hypothesized causal mechanisms and then outline the logic of process tracing in terms of Van Evera’s “hoop tests,” “smoking gun tests,” “doubly decisive tests,” and “straw in the wind tests.” We will then explicate an explicitly Bayesian approach to process tracing, which entails asking whether the evidence we find would be more or less plausible if a given hypothesis is true as compared to a rival.

Throughout the session we will emphasize best practices and applications to exemplars of process tracing research. Students will practice applying Bayesian reasoning in small group exercises. As time allows, and depending on the number of students, the instructors will also ask students to discuss how they plan to use process tracing in their current research project. This will allow the instructors and fellow students to offer constructive advice on how best to carry out process tracing in each student’s project.