QUALITATIVE AND MULTI-METHOD RESEARCH SHORT COURSES AT APSA 2017
The QMMR section is sponsoring six short courses at
APSA’s annual meeting in San Francisco. The courses are scheduled to be held on
Wednesday August 30th.
Please see below for the descriptions. Registration for
the QMMR short courses is managed centrally by APSA, and can be accessed at: http://web.apsanet.org/apsa2017/short-courses/
If you have any questions about one or more of the short
courses, please contact the instructors listed below.
SC05: DESIGNING AND CONDUCTING FIELD RESEARCH (QMMR
1) 9:00 a.m. – 1:00 p.m.
Instructors: Naazneen Barma, Naval Postgraduate School (nhbarma@nps.edu); Ben Read, University of
California, Santa Cruz (bread@ucsc.edu);
Naunihal Singh, Air War College (naunihal@aya.yale.edu)
Fieldwork can be both daunting and exhilarating. Scholars
generally learn it by doing it, yet there is much value in reflecting on the
practices of veteran 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. Our
approach to fieldwork is relevant to those using qualitative techniques (we
give special attention to interviewing, which is near-ubiquitous among
political scientists) as well as quantitative techniques such as surveys and
experiments, and assume that many scholars will use multiple methods.
Throughout, we provide strategies to help anticipate and address challenges
such as (1) converting a research design into a “to get” list; (2) accessing
elusive data and data sources; (3) evaluating data’s evidentiary value; (4)
organizing and managing data; and (5) analyzing 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.
SC09: MANAGING AND SHARING QUALITATIVE DATA AND
QUALITATIVE RESEARCH TRANSPARENCY (QMMR 2)
9:00 a.m. – 1:00 p.m.
Instructors: Diana Kapiszewski, Georgetown University (dk784@georgetown.edu); Sebastian
Karcher, Syracuse University (skarcher@maxwell.syr.edu)
This short course has three central goals. First, the
course provides guidance to help scholars manage data through the research
lifecycle. We show how participants can meet funders’ data-management
requirements and improve their own research by creating a data management plan.
We discuss strategies for effectively documenting data throughout the research
process to enhance their value to those who generated them and to other
scholars. We also provide practical advice on keeping data secure to protect
against data loss as well as illicit access to sensitive data. Second, we
consider the multiple benefits of sharing data, the various uses of shared data
(e.g. for evaluating scholarly products, for secondary analysis, and for pedagogical
purposes), the challenges involved in sharing qualitative research data
(including copyright and human participants-related concerns), and various ways
to address those challenges. Finally, we discuss transparency in qualitative
research. Production transparency and analytic transparency facilitates the
effective interpretation and evaluation of scholarly products. Production
transparency is achieved by clearly describing how the information that
underpins a study was collected and how data were generated from that
information. Analytic transparency is achieved by clearly demonstrating how
data and analysis support the empirical claims and inferences in published work
in a manner appropriate to that work’s research design. We also introduce
participants to ways to achieve production and analytic transparency in
qualitative research, paying particular attention to work that uses narrative
causal approaches supported by individual data sources.
SC11: PROCESS TRACING (QMMR 3) 9:00 a.m. – 1:00 p.m.
Instructors: Andrew Bennett, Georgetown University (bennetta@georgetown.edu) and Tasha Fairfield, London School of Economics (T.A.Fairfield@LSE.ac.uk)
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. The first session of the
course will briefly summarize the philosophy of science behind explanation via
reference to hypothesized causal mechanisms. It will then outline the logic of
process tracing in terms of Bayesian methods of inference, including the application
of “hoop tests,” “smoking gun tests,” “doubly decisive tests,” and “straw in
the wind tests.” The second session of the course will focus on best practices
and examples of process tracing, including the more inductive use of process
tracing for theory development as well as its deductive use for theory testing.
As time allows, and depending on the number of students, the instructors will
ask students to outline briefly 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.
SC06: DESIGNING NATURAL EXPERIMENTS (QMMR 4) 1:30 p.m. – 5:30 p.m.
Instructor: Daniel Hidalgo, Massachusetts Institute of
Technology (dhidalgo@mit.edu)
This course provides a practical and example-driven guide
to the design and analysis of natural experiments. The course will emphasize
the role of qualitative inquiry in the discovery of natural experiments and use
of case knowledge in the justification of these designs’ core assumptions.
Natural experiments typically depend on a deep qualitative understanding of how
causal factors are assigned to units in the population of interest. This
qualitative understanding is critical for both discovery and analysis of
natural experiments, but how precisely to incorporate this type of data into a
concrete research project is often overlooked in standard methodological texts
and courses. Questions that will be addressed include: What types of
qualitative data are most useful for strengthening natural experiments? What
makes the use of qualitative evidence in a given application convincing? How
can qualitative evidence be effectively presented in the context of a research
article? How can we raise standards for the use of qualitative data in the
design and analysis of natural experiments?
The course will begin with a presentation of the basic
causal model and assumptions often employed in the design of natural
experiments, but the bulk of the course will be structured around a detailed
examination of the nuts and bolts of recent political science examples. The
course will integrate hands-on analysis of real data with the conceptual
material. In addition to the analysis of the quantitative data typically
presented research articles, we will also examine the raw qualitative data that
is often used in the discovery and justification of natural experiments. These
exercises will involve the examination of primary source documents, interview
transcripts, and archival sources.
SC12: SET-THEORETIC MULTIMETHOD RESEARCH: PRINCIPLES AND
IMPLEMENTATION (QMMR 5) 1:30 p.m. – 5:30
p.m.
Instructor: Ingo Rohlfing, University of Cologne (i.rohlfing@uni-koeln.de)
Multimethod Research: Principles and implementation (QMMR
5) Set-theoretic multi-method research (MMR) is a novel approach aiming for the
generation of integrated theory. It combines Qualitative Comparative Analysis
(QCA) that is mainly operating on the cross-case level with process tracing
performed on the within-case level. In this short course, participants are
familiarized with the principles and practices of set-theoretic MMR. Our focus
rests on three issues: It is explained what types of single-case and
comparative case studies are viable based on the results of a truth table
analysis and what they are good for. The focus is on types of cases that are
analyzed for improving the theoretical and causal model underlying the QCA
study; Participants learn how the types of cases in set-theoretic MMR are
related to the established inventory of cases and comparisons in qualitative
research and nested analysis; We present formalized criteria for choosing the
best available cases for process tracing. The presentation of fundamentals is
combined with the discussion of their implementation and the introduction of
the R package SetMethods. This package, among other things, assists empirical
researchers in doing reproducible set-theoretic MMR. We use published studies
to illustrate the principles of set-theoretic MMR and the operation of the
package. After the course, participants will be equipped with the knowledge
necessary to interpret and evaluate published set-theoretic MMR studies and
have acquired the basic knowledge to implement their own multi-method analysis.
SC03: CAUSAL CASE STUDIES (QMMR 6) 1:30 p.m. – 5:30 p.m.
Instructor: Derek Beach, University of Aarhus (derek@ps.au.dk)
After more than two decades after the publication of
Designing Social Inquiry (King, Keohane and Verba, 1994), the field of
qualitative, case-based research methods has reached a level of maturity where
it is no longer necessary to define case study methods purely in terms of how
they differ from quantitative, variation-based methods. Increasingly, the
debate has shifted towards defining the nature and uses of different causal
case study methods on their own terms. This short course aims to exploring the
state-of-the-art, focusing both on debates about the ontological and
epistemological foundations of different case-based methods, along with
developing a set of more practical guidelines for their use in alignment with
different foundational assumptions. Three widely used case-based methods are
discussed in this course are: small-n comparative methods, congruence methods,
and process-tracing methods.
The goal of this course is twofold: 1) to provide
participants with a better understanding of the debate about the logical
foundations of different case study methods, in particular the different
understandings of the nature of causal relationships that different method aim
to analyze; and 2) to provide a set of practical guidelines that will enable
scholars to utilize each of the causal case study methods in their own research
that also enables their combination in logical consistent ways. The course
exposes how case-based methods are similar and different to each other in ways
that have not been widely recognized, both as regards their ontological and
epistemological foundations relating to different types of causal relationships
(counterfactuals or mechanisms), the research situations in which they can be
utilized, and the guidelines and best practices for their use.
If you find these short course descriptions helpful, you may also be interested in some of the symposia in previous issues of the Section's newsletter, which may be accessed via this link.