QUALITATIVE AND
MULTI-METHOD RESEARCH SHORT COURSES
AT APSA 2020
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.
FIELD RESEARCH I: DESIGNING/PREPARING FIELDWORK &
OPERATING IN THE FIELD
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.
FIELD RESEARCH II: TECHNIQUES FOR COLLECTING AND
GENERATING DATA
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.
MANAGING AND SHARING QUALITATIVE DATA
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).
STUDYING CAUSAL MECHANISMS USING IN-DEPTH CASE STUDIES
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.
PROCESS TRACING
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.