The Institute for Qualitative and Multi-Method Research (IQMR)

Since its founding sixteen years ago, the Institute for Qualitative and Multi-Method Research has welcomed well over two thousand graduate students and junior faculty.

The institute seeks to enable participants to create and critique methodologically sophisticated qualitative research designs, including case studies, tests of necessity or sufficiency, and narrative or interpretive work. It explores the techniques, uses, strengths, and limitations of these methods, while emphasizing their relationships with alternative approaches. Short descriptions of the modules being offered in 2018 can be found at the bottom of this page

IQMR attendees have the opportunity to receive constructive feedback on their own qualitative research designs. 

CQRM member institutions will use their own meritocratic criteria to select participants to attend the 2018 institute, and must notify CQRM of their choices by February 23rd, 2018. 

Students, fellows and junior faculty who were not sure if they would have be selected, or who attend non-member organizations, should have applied directly to CQRM through the open pool.The deadline for applications has passed for the 2018 IQMR. Applicants will be notified of the outcome in December. CQRM will cover the costs of participation, lodging, and meals for successful applicants. Attendees will be responsible for their own transportation costs to and from Syracuse University.

Participants at the 2018 institute will arrive on Sunday June 17th, and depart on Friday June 29th, or Saturday June 30th. Sessions will commence on Monday June 18th. The institute will run until the afternoon of Friday June 29th, with a two day break on June 23rd and 24th.


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.

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.

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.

William Lowe, Princeton University.
The module covers the use of computers to systematically analyze text, typically as precursor, successor, or complement to a qualitative analysis. Participants will discuss and practice classical dictionary-based content analysis, learn about topic modeling and document classification, and show how to project texts into interpretable rhetorical or ideological spaces for understanding and visualization.  Along the way, attendees will scrape texts from the web, and discuss good ways to integrate text analysis into a variety or research designs. The module presumes a grasp of basic mathematical and statistical concepts and a willingness to follow along with the computational parts. The module mostly uses R and its packages. Expertise in R is not required, although some prior experience will be helpful.

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.  

Charles Ragin, University of California, Irvine and Ingo Rohlfing, University of Cologne.
This module provides an overview of Qualitative Comparative Analysis (QCA) and fuzzy sets, including instruction in use of fsQCA software and RStudio.  Topics include: necessary/sufficient causation, causal complexity, counterfactual analysis, and crisp-set and fuzzy-set configurational analysis using truth tables. Particular attention is given to the phenomenon of limited diversity and how different QCA solutions deal with limited diversity.

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.

Diana Kapiszewski, Georgetown University and Lauren MacLean, Indiana University.
This module considers the design, planning, and execution of field research. The module examines multiple less-interactive and more-interactive data collection techniques, including interviewing, focus groups, and surveys, and provides students with useful tools to structure and organize their fieldwork. The module also offers strategies for addressing the intellectual, logistical, and inter-personal challenges that conducting field research involves. A basic premise underlying the module is that fieldwork entails shifting among research design, data collection, and data analysis.  Thus the module also discusses how researchers can retool their design if they encounter significant challenges, and describes how they can begin to engage in analysis while still in the field.

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.

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).

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.

Lisa Wedeen, 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).

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. 

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.

Markus Kreuzer, Villanova University.
This module surveys three key elements that comparative historical analysis (CHA) uses to analyze macro-structural processes. First, CHA employs a variety of concepts to describe longer-term historical processes that rest on distinct temporal units of analysis. Scholars varyingly conceptualizes the historical process as a chronology of unconnected occurrences, an episodic process consisting of distinct periods, a teleological process with fixed stages, events forming a continuous process, or multiple events resulting in concurrent but also interacting processes. Second, CHA differentiates time itself in terms of more specific characteristics. It talks about duration, tempo, sequencing, acceleration, and direction (i.e. reversibility) of time that are based on more objective, clock-like attributes of time. Most CHA scholars use these attributes to further differentiate the dynamics of the broader historical processes. Some focus on those clock-like attributes independent of historical processes. Third, CHA stipulates a range of causal mechanisms to explain the unfolding of historical processes through time. The mechanisms involve tipping points, diffusion, causal effects related to sequencing patterns, increasing & decreasing returns, and intercurrence (i.e. interaction among concurrent historical processes). Students will be asked to compare CHA works in terms of how effectively these use these three elements.

The schedule and reading list from the 2017 institute is available through the link on the left. Please note, however, that this syllabus will be revised for the 2018 institute, and should be viewed with this in mind.