Designing and Conducting Field Research
Diana Kapiszewski, Georgetown University and Lauren MacLean, Indiana University
This module considers the design, planning, and execution of field research. A basic premise underlying the module is that fieldwork entails shifting among research design, data collection, and data analysis. We will consider how researchers can develop a "field research design" in tandem with their research design, and how they can retool both if they encounter significant challenges while in the field. We will also discuss -- and practice using -- multiple data collection techniques such as interviewing, focus groups, and surveys. Participants will also be provided with useful tools to structure and organize their fieldwork, as well as strategies for addressing intellectual and inter-personal challenges that conducting field research involves. In addition, we will consider how field researchers can begin to engage in analysis while still in the field, and how they can successfully navigate re-entry to their home institutions.
Qualitative Methods for Causal Analysis
James Mahoney, Northwestern University and Gary Goertz, University of Notre Dame
Participants will cover a range of classic and standard topics of qualitative methodology, with a special focus on within-case causal inference and multimethod research. The sessions use logic and set theory as a foundation for discussing and elucidating qualitative methods. The topics covered will include conceptualization, counterfactual analysis, comparative-historical analysis, and case-selection.
Qualitative Comparative Analysis
Carsten Schneider, Central European University and Eva Thomann, University of Exeter
This module provides an overview of Qualitative Comparative Analysis (QCA) and fuzzy sets, including instruction in its use within RStudio. QCA is inherently multi-method, combining within-case and cross-case analysis. It is specifically designed for studies with moderate-sized Ns; however it is also sometimes used on large datasets. Within the limitations facing empirical data, QCA is best seen as a tool for unraveling causal complexity, with different configurations of causally relevant conditions leading to the same outcome. Module topics include: necessary/sufficient causation, causal complexity, counterfactual analysis, crisp-set and fuzzy-set configurational analysis using truth tables, and post-QCA case selection for process tracing.
Interpretation and History
Thomas Dodman, Columbia University, and Daragh Grant, Harvard University
The first part of the module introduces participants to methods of discourse analysis employed by political theorists and historians of political thought and to critical approaches to intellectual history. The sessions cover different approaches to “reading” texts, and examine debates over meaning, concepts, context, and the explanation of historical change, as well as engaging with ongoing debates about the politics of historiography. We discuss the techniques of the Cambridge school and the German tradition of Begriffsgeschichte (concept history). The second part of the module covers interpretive methods for archival and historical research, introducing students to the challenges of working with materials drawn from different social, cultural, and historical settings, and exploring creative interpretive strategies for addressing these challenges.
Jason Seawright, Northwestern University
This module looks at how to productively combine qualitative and quantitative methods when the overall goal is causal inference. The module will be structured around a discussion of multi-method designs that use regression-type methods as the quantitative component of the causal inference. For example, one session looks closely at combining case studies with regression, offering research designs for testing assumptions connected with measurement, confounding, and the existence of a hypothesized causal path. Another will investigate case selection, asking how cases should best be selected from a larger population. Participants will also investigate how multi-method research works in the context of random (or as-if random) assignment, exploring how to design case studies in conjunction with experimental or natural-experimental research. Another session will consider what tools from statistics and machine learning can add to causal inferences based on process tracing.
Andrew Bennett, Georgetown University, Tasha Fairfield, London School of Economics, and David Waldner, University of Virginia
This module examines the inferential logic of process tracing, which is used extensively in qualitative case studies. We identify Bayesian probability as one foundation for causal inference in process tracing, which entails assessing which hypothesis or theory provides the best explanation for the evidence at hand. We will present practical advice for conducting process-tracing research as well as best practices for applying Bayesian reasoning in case study analysis. In separate sessions, we will introduce causal graphs as a way to model causal processes and we will introduce the “Completeness Standard”—composed of causal graphs, event-history maps, and invariant causal mechanisms—as a way to assess process tracing. We will consider the extent to which successful execution of this standard supports valid claims of unit-level causal inferences. Finally, we introduce typological theorizing as a way to address interaction effects and an aid in selecting cases for process tracing, and we discuss examples of typological theories proposed in students own work as well as in published research.
Causal Inference from Causal Models
Alan M. Jacobs, University of British Columbia
This module explores the use of causal models to design and implement qualitative and mixed-method empirical strategies of causal inference. A great deal of recent methodological progress in the social sciences has focused on how features of a research design – such as randomization by the researcher or by nature - can allow for causal identification with minimal assumptions. Yet, for many of the questions of greatest interest to social scientists and policymakers, randomization or its close equivalents are unavailable. We are, in short, often forced to rely on beliefs about how the world works - that is, on models. Based on a book-in-progress by Macartan Humphreys and Alan Jacobs, this module will examine how we can engage in systematic model-based causal inference. Specifically, we will explore how researchers can encode their prior knowledge in a probabilistic causal model (or Bayesian network) and an associated directed acyclic graph (DAG), use the model to make research design choices (including selecting cases and choosing observations), and draw inferences about causation at the level of both individual cases and populations, using both qualitative and quantitative data.
Timothy Pachirat, University of Massachusetts, Amherst and Fred Schaffer, University of Massachusetts, Amherst
This module investigates how sustained attention to meaning making in the research world contributes to the study of politics. We address the promises, and perils, of social research that invites the unruly minutiae of lived experience and conceptual lifeworlds to converse with, and contest, abstract disciplinary theories and categories. The module explores the promises and pitfalls of ethnographic approaches to the political with specific attention to their potential to subvert, generate, and extend understandings of politics and power. One part of the module examines the family of practices that characterize ethnographic interviewing and explores in more depth one type of ethnographic interviewing: ordinary language interviewing. Ordinary language interviewing is a tool for uncovering the meaning of words in everyday talk. By studying the meaning of words, the promise is to gain insight into the various social realities these words name, evoke, or realize. In another part of the module, attendees will explore the practice of participant observation, with special emphasis on jottings, fieldnote writing, and the ethics of fieldwork. In their fieldsite groups, participants conduct participant-observation exercises in pre-selected sites.
Thad Dunning, University of California Berkeley, and Daniel Hidalgo, Massachusetts Institute of Technology
The module reviews the concept of natural experiments and discusses their strengths and limitations through a survey of recent examples from political science and economics. We introduce a common formal framework for understanding and assessing natural experiments. The assessment involves using an evaluative framework based on (1) the plausibility of as-if random assignment and (2) the credibility of causal and statistical assumptions. We discuss formal tools for assessing designs on these criteria, such as sensitivity analyses, non-parametric bounds, and robustness tests. In a separate session, we highlight the essential role of qualitative methods in the analysis of natural experiments. We present examples that illustrate how qualitative evidence can bolster the credibility of causal assumptions and aid in the interpretation of quantitative results. We then discuss how to bolster the credibility of natural experiments in the design-stage. In particular, we will focus on the role of “ex-ante” approaches to increasing the credibility of our inferences, such as the use of pre-analysis plans, results-blind review, and sample splitting. How can qualitative methods be integrated into efforts to increase research transparency? We end the module by evaluating the promise and obstacles to the use of multi-method research in the analysis of natural experiments, with a focus on external validity. We discuss how qualitative methods can help address some of the criticisms of natural experiments, as well as how natural experiments can bolster the inferences drawn from qualitative evidence.
Archival Research and Elite Interviews
James Goldgeier, Andrew Moravcsik, and Elizabeth Saunders
This module focuses on how to collect and manipulate primary-source records of policy-making—interviews, archival documents and published primary sources—in social science research. This includes how one prepares for, structures, conducts, and manages the information flow from archival visits, interviews or structured examination of published materials. The module focuses on practical research skills scholars can use and judgments they must make in everyday research. Participants discuss how to work in different types of archives and repositories, how to obtain previously classified information (e.g. use the Freedom of Information Act), how to maximize the output of interviews, and how qualitative scholars can best cope with—and benefit from—high standards of research transparency. In addition, the module includes a hands-on demonstration of strategies that use digital technology to collect, organize, and store your oral and documentary data so that you can easily access and systematically manipulate it as you move to the analysis and writing phase of your project.
Managing and Sharing Qualitative Data
Diana Kapiszewski, Georgetown University, Sebastian Karcher, Syracuse University, and Dessislava Kirilova, 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 module 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. 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 module 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).
Interpretivism and Discourse Analysis
Lisa Wedeen, University of Chicago and William Mazzarella, University of Chicago
This module provides students with an introduction to different modes of discourse analysis. Participants will learn to "read" texts while becoming familiar with contemporary thinking about interpretation, narrative, and social construction. Approaches covered will include Foucault’s “interpretive analytics” and Wittgenstein’s understanding of language as activity and its relevance to ordinary language-use analysis (including theories of performativity).
Comparative Historical Analysis
Markus Kreuzer, Villanova University
We live in a constantly emerging world in which studying changes across time are just as crucial as analyzing differences across cases to understand our contemporary politics. Comparative historical analysis (CHA) has long studied such historical changes and made important contributions to our understanding of how to use time to study the past. It goes back to the 19th century classics and shares more recently its ambitions with American Political Development, historical institutionalism, and a long historical tradition in international relations. These approaches all point out that time is to the past what grammar is to language and maps are to space: an essential tool of analysis. This module explores three distinct contributions that CHA makes to our understanding of time. First, it identifies distinct temporal building blocks that make time analytically tractable. Time scales specify how far into the past we look, chronologies specify the events we analyze, and periodizations make historical contexts comparable. These three building blocks constitute a historical notion of time that asks how different the past is from the present. CHA complements this historical notion of time with three elements of physical, clock-like time: duration, tempo and sequences which help to identify variations in the unfolding of the past. Second, CHA uses these temporal building blocks for time spotting, that is foregrounding temporal and historical dynamics that many existing explanations background. In foregrounding time, CHA poses a series of macro-historical questions about the origins as well as continuous transformation of the state, political regimes, markets, war, and global structures. Third, CHA answers these macro-historical questions with the help of a range of causal mechanisms that explain the unfolding of historical processes through time. These mechanisms involve tipping points, diffusion, causal mechanisms, causal effects related to sequencing patterns, increasing and decreasing returns, and intercurrence (i.e. interaction among concurrent historical processes). Overall, the model encourages students to spot elements of time that are hidden in their fields of research and explore how CHA can help them think about such elements more systematically, and thus enrich their analysis.
Geographic Information Systems(GIS) as a Qualitative Research Method
Jonnell Robinson, Syracuse University
The module will introduce participants to GIS as a tool for qualitative research, present basic GIS terminology and concepts and the basic functions of ESRI’s ArcGIS software suite, particularly those functions that are most commonly used by social scientists. A subsequent session will explore basic visualization and analytical functions such as building and querying attribute tables, selecting map features, and symbolizing data. The module will also review the types and sources of data that are available for GIS users working in both data rich and data poor settings, the ethics of using mapping in research, how metadata can be used to communicate qualitative information, and data overlay analysis. Other sessions will introduce open source geovisualization and analysis tools including Open Street Map, Google My Maps, and QGIS; demonstrate valuable data collection techniques for archival research, field work, participatory and community‐based mapping, as well as the availability and accessibility of spatial data through data repositories; and provide an overview of basic map design, integrating narrative and photos with GIS, and a discussion about why, how and where to further hone GIS skills.
Social Network Analysis
Jenn Larson, Vanderbilt University
This module serves as an introduction to the empirical study of social networks. It begins with the very basics, covering ways to describe positions within networks and networks as a whole. We introduce the terminology of social network analysis, focusing on node- and network-level features, both how to describe them precisely and what they mean substantively. Next, the module surveys recent empirical research that explores the role of networks in outcomes such as development, protests, and conflict, and identifies many open questions across the social sciences. Finally, it presents an overview of tools that researchers can use to gather original network data via surveys in field settings. It covers a variety of design issues, including which ties to measure and how to do so.