IQMR 2026 Modules

Below please find descriptions of the module sequences that are currently confirmed for IQMR 2026. The module sequences are listed in alphabetical order (which does not match the order in which they will be offered).

Bayesian Inference for Qualitative Research

Tasha Fairfield

The way we intuitively approach qualitative research is similar to how we read detective novels. We consider different hypotheses to explain what happened—whether democratization in South Africa, or the death of Samuel Ratchett on the Orient Express—drawing on the literature we have read (e.g. theories of regime change, or other Agatha Christie mysteries) and any other salient knowledge we have. As we gather evidence and discover clues, we update our views about which hypothesis provides the best explanation—or we may introduce a new alternative that we think up along the way. Bayesianism provides a logically rigorous and intuitive framework that governs how we should revise our views about which hypothesis is more plausible, given our relevant prior knowledge and the evidence that we find during our investigation. Bayesianism is enjoying a revival across many fields, and it offers a powerful tool for improving inference and analytic transparency in qualitative research. The principles we will cover in this module can be applied to single case studies (within-case analysis), comparative case studies (cross-case analysis), and multi-method research that draws on both qualitative evidence and quantitative data. Throughout, we will work with examples and exercises drawn from published social science research.

Community Engaged Research

Jamila Michener

Comparative Historical Analysis

Marcus Kreuzer

We live in a constantly emerging world in which the contexts in which we explain social phenomena change across time and place. Comparative historical analysis (CHA) has long been attentive to the temporal and spatial dynamics shaping social phenomena. It complements traditional methods that often background such dynamics behind historical and geographic scope conditions. CHA includes 19th century social science classics, American Political Development, historical institutionalism, global and diplomatic history, post-colonial studies, political geography, place-based analysis, or any intellectual agenda interested in macro-historical questions. These approaches share an ontological self-awareness necessary in problem-driven research that emphasizes the proper alignment of methods with questions.

The module discusses four key elements of CHA:

• First, CHA identifies the temporal and spatial building blocks used to construct context. It does not treat context as residual noise or spurious co-variations. Instead, CHA draws on history to think of time as sequential events that either replicate or transform the past. It also draws on geography to think of space as interconnected places that either remain exceptional or become more uniform.

• Second, CHA is problem driven and therefore engages in exploratory topic analysis. Exploration is messy and needs to be translated into description and patterns that help formulate new research questions. CHA employs two description strategies. Eventful analysis employs thick, historical description and identifies patterns of continuity and discontinuity across time as well as convergence and divergence across space. Longue durée analysis employs time series and data visualization to graph longitudinal or cross-spatial trends.

• Third, CHA emphasizes the importance of theory in building causal arguments. It regularly employs abductive reasoning to update existing explanations or develop new ones. Theories only have a small empirical footprint and background a wide range of causal factors. CHA uses theory to foreground overlooked causal factors, identify confounders, and generate more test-worthy hypotheses.

• Fourth, CHA uses causal graphs to make testing transparent. CHA is committed to visualizing its arguments in causal graphs. Such graphing improves causal inferences by making transparent causal pathways, locating confounders, constructing strong tests, specifying scope conditions, and identifying types of causality.

Overall, the module encourages students to spot elements of time and space that are hidden in their fields of research and explore how CHA can help them think about such elements more systematically to enrich their analysis.

Designing and Conducting Fieldwork

Diana Kapiszewski, Lauren MacLean, and Robert Mickey

This module sequence discusses strategies for designing, planning, and conducting fieldwork in the social sciences. We begin by considering the multiple aspects of preparing for field research, and then discuss some practical elements — with intellectual implications — of operating in the field. On the second day we consider several “more-interactive” forms of data collection: surveys, focus groups, and interviews, and also consider the types of observation in which all social scientists who conduct field research engage. The third module considers the conduct of archival research, and the various ways in which scholars iterate on their research design and field research design as they conduct fieldwork. Each session of each module is conducted with the understanding that participants have carefully read the assigned materials. The instructors present key points drawing on the assigned readings, other published work on field research, and the experiences they and others have had with managing fieldwork’s diverse challenges. Interaction and discussion in small and large groups is encouraged.

Ethnographic Methods

Sarah E. Parkinson & Kanisha Bond

How can ethnographic methodologies and methods inform the study of politics? The immersive, meaning-centric, everyday-life focus that ethnographic methodologies offer affords social scientists insight into processes, practices, and understandings of political worlds that might otherwise remain hidden or obscure. Sometimes portrayed as “a craft” rather than “a method” or caricatured as “just hanging out,” the research approaches we explore in the Ethnographic Methods sequence explore ethnography’s immense potential in the study of power and politics. It addresses questions such as: What commitments does an ethnographic researcher make to herself, her interlocutors, the communities she studies, and to the discipline? What are the various ways in which a researcher can situate herself in a research practice where she is fundamentally the instrument? How does a researcher develop a robust ethnographic approach to a project, whether as a guiding methodology for an entire book or as part of a multi-method endeavor? What types of data or evidence do those leveraging ethnographic methods generate, through which methods, and how do they analyse them? How have ethnographers challenged, expanded, and innovated upon the presumed fundamentals of ethnography?

Geographic Information Systems

Jonnell Robinson

The module sequence introduces participants to Geographic Information Systems (GIS) spatial data visualization and analysis. Six sessions provide participants with hands-on experience using ESRI’s ArcGIS software suite and a variety of open-source mapping programs including QGIS, Open Street Map, and Google My Maps. Participants will learn to locate and generate high quality spatial data, display mapped data using professional cartographic principles, perform basic spatial data analysis, and how to further hone their GIS skills. The modules also introduce critical GIS and reviews important ethical concerns when mapping socially constructed data. Participants are welcome to work with their own data during the mapping exercises. Participants will leave the module with the skills and confidence to create simple yet powerful maps.

Integrating Qualitative Methods and Natural Experiments

Chris Carter and Guadalupe Tuñón

Recent years have seen a rise in the use of natural experiments in published political science research. Qualitative methods are fundamental in identifying and evaluating natural experiments. In this module sequence, we introduce natural experiments and discuss their strengths and limitations by surveying recent and classic examples from political science. We provide a common framework for understanding and assessing natural experiments based on the credibility of causal assumptions. We discuss instrumental variables, true natural experiments, and regression-discontinuity designs. We examine both qualitative and quantitative approaches to these designs, and demonstrate that they often complement rather than substitute for one another. The course is designed to offer practical advice on identifying and evaluating natural experiments through a close examination of applied examples.

Interpretation and History

Amel Ahmed

What is historical interpretation? In one sense interpretation is a part of all historical analysis. Typically we cannot observe history directly; we learn of it only through documents and artifacts that we have to make sense of. Historical interpretation is not separate from other modes of historical analysis but lies on a continuum. Emphasizing the interpretive aspects of historical analysis means that we do not take at face value the documentary evidence of history we encounter. We question the text as well as its source, we compare narratives, placing them in their historical context, we look for silences and gaps in evidence, as well as voices that may not be heard as easily. Importantly, we also interrogate our own objectives in questioning history and examine the ways in which they may shape our own narratives. Historical interpretation shares with other interpretive methods the search for meaning in subjects’ actions and utterances. But with historical interpretation, the distance of the researcher from the subject matter produces distinctive epistemological challenges and requires a methodological orientation aimed at achieving understanding without the possibility of direct engagement or immersion. In this module we will grapple with some of the dilemmas of historical interpretation including reading history, questioning history, analyzing history, and writing history. We will also engage with enduring epistemological debates about the nature of historical inquiry as well as the challenges of discerning historical lessons.

Interpretive Methods

Lisa Wedeen and Yuna Blajer de la Garza

This two-module sequence provides students with an introduction to interpretive methods through a discussion of various modes of discourse analysis and ideology critique. Students will learn to “read” texts and other artifacts while becoming familiar with contemporary thinking about interpretation, narrative, genre, and criticism. In the first four sessions we shall explore the following methods: Wittgenstein’s understanding of language as activity and its practical relevance to ordinary language-use analysis; Foucault’s “interpretive analytics” with hands-on exercises applying his genealogical method; and various versions (two sessions) of cultural Marxism—with specific attention to “ideology critique.” The last two classes will consider the differences between interpretivism and positivism, attending to the ways in which an ethnographic sensibility uses empirical research to elucidate how people interpret, navigate, and challenge the social and political worlds they inhabit.

Logic of Qualitative Methods

James Mahoney and Gary Goertz

These three modules introduce qualitative methodology by using logic as a foundation for understanding qualitative research. The modules cover many classic and standard topics of qualitative methodology: necessary and sufficient conditions, small-N comparative methods, sequence analysis, concepts, typologies, process tracing, causal mechanisms, counterfactuals, critical junctures, and case selection. The modules also provide the basic tools for a complete research design for a Ph.D. dissertation or book project.

The first module (led by James Mahoney) provides an introduction to logic and set theory, summarizes classic small-N methodologies, discusses a regularity theory of causality, and presents counterfactual analysis as a tool for evaluating hypotheses. The second module (led jointly by Gary Goertz and James Mahoney) focuses on concepts, typologies, within-case analysis, and critical event analysis. The final module (led by Gary Goertz) consists of three sessions on Large-N Qualitative Analysis (LNQA). This methodology involves process tracing, i.e., within-case causal inference, with a goal of systematic causal generalization across the cases.

Multi-Method Research

Jaye Seawright

This course explores the productive integration of qualitative and quantitative methods, encompassing case studies, statistical modeling, experiments, and machine learning.

Module 1: Foundations of Multi-Method Design The first session establishes the groundwork for mixed-method research, focusing on designs that pair case studies with regression-based statistical techniques. We will analyze the various roles qualitative components play in strengthening quantitative findings, specifically regarding research designs for testing assumptions related to measurement, confounding, and hypothesized causal paths. Additionally, we will evaluate best practices for selecting cases from a larger population to ensure inferential validity.

Module 2: Experiments and Natural Experiments The second session examines multi-method research within the context of random (or “as-if” random) assignment. We will discuss how to effectively design and pair case studies with experimental and natural-experimental frameworks. This session further refines case selection strategies, adapted specifically for the constraints and opportunities provided by experimental data.

Module 3: Machine Learning and Advanced Integration The final session introduces machine learning tools as both a centerpiece of mixed-method designs and as a support for case studies. We will explore how statistics and machine learning can bolster causal inferences derived from process tracing, as well as their utility in non-causal research goals, such as concept formation and measurement.

Each module includes hands-on activities (ranging from in-room focus groups to guided exercises in R) and practical applications to bridge theory and practice.

Process Tracing and Typological Theories

Andrew Bennett

This module begins with the philosophy of science of causal mechanisms and the basics of case study research design. These lay the foundations for exploring the inferential logic of process tracing, a key form of within-case analysis. We will use Bayesian probability as the underlying logic of process tracing, which entails assessing which hypothesis or theory provides the best explanation for the evidence at hand. We will cover practical advice for conducting process-tracing research as well as best practices for applying Bayesian reasoning in case study analysis. Finally, the module introduces typological theorizing as a way to address interaction effects and an aid in selecting cases for process tracing, and we will discuss examples of typological theories proposed in students’ own work as well as in published research.

Qualitative Causal Inference: From Fundamentals to Process Tracing Applications

David Waldner

This module introduces students to Qualitative Causal Inference (QCI), a hybrid model of inference that has important implications for how we think about and implement Process Tracing. In Part I, David Waldner outlines the fundamentals of QCI, contrasting it to two alternative models of inference: design-based causal inference, associated with the ‘credibility revolution’ in quantitative social science, and the detective model of causal inference championed by some Process Tracing methodologists. In these sessions students will become familiar with the distinct understanding of ‘causal mechanisms’ that underpins QCI. They will also learn to specify causal graphs to achieve unit-level causal inference, with attention to the mitigation of three potential sources of bias: (1) spuriousness and endogeneity via the construction of a ‘front-door path’ that indicates causal continuity between X and Y; (2) confounding variable bias by checking for the presence of a ‘back-door path’ that produces a non-causal association between X and Y; and (3) measurement error by checking for the presence of a ‘side-door path’ that complicates taking the pre-treatment value of the outcome variable as a proxy measure of the counterfactual (and inherently unobservable) value of the outcome under control. In Part II, Ezequiel Gonzalez-Ocantos examines QCI’s implications for Process Tracing. The sessions (a) offer practical modelling guidelines, with a focus on the construction of ‘front-door paths;’ (b) discuss how causal graphs orient and discipline data collection; (c) present techniques for dealing with missing data, a key challenge to meet the ‘front-door’ criterion; and (d) propose mitigation strategies for bias introduced by the ‘back and side-door paths.’

Research Ethics

Diana Kapiszewski and Amanda Weiss

Re-thinking Small-N Comparisons

Erica Simmons and Nicholas Rush Smith

Why do we compare? Typically, in political science research, causal inference is taken as the primary goal. Similarly, research that is generalizable to as many cases as possible tends to be valued more than research which can explain only a few. This unit will push past these assumptions in two ways. First, it will provide logics for generalization not rooted in ideas of statistical generalizability or mechanical reproduction. Second, it will expand the goals of comparison from causal inference to alternative practices like creative redescription or conceptual development. Third, we will explore how we can leverage strategies for rethinking comparison to address the practical challenges and unexpected discoveries that often upend pre-established research designs. When a “crisis of research design” strikes, how can researchers cope with partially implemented data collection plans to still generate meaningful theoretical and empirical insights? How can scholars salvage their research designs while maintaining methodological rigor? Finally, we will critique short research designs that will be provided in advance. Among other questions, we will ask ourselves: What kinds of claims can the author make with this research design and why? What are the limits on the kinds of claims they can make? How convincing is this research design? If you were on the selection committee of a funding agency, how would you rate this research design?

Text as Data

Fiona Shen-Bayh

What does it mean to transform texts into data? How do computers read and analyze qualitative information quantitatively? Do computational analyses of texts map onto qualitative understanding of human language? This unit explores these questions and more by introducing students to the “text as data” pipeline, beginning with the curation of digital texts and concluding with the measurement of political concepts in lexical terms. Our first lab-based session will examine what it means to transform a collection of documents into machine readable texts, after which we will cover step-by-step how to build and clean a digital corpus in Python. The next three lab-based sessions will examine a variety of quantitative approaches to analyzing a digital corpus, including counting, vectorizing, and embedding techniques. Our final session will conclude with a group discussion of text-based measurement strategies wherein we critically question whether such methods can produce reliable and valid measures of the concepts political scientists care about.

Unified Sessions

Jaye Seawright, David Waldner, Lisa Wedeen

These “Unified Sessions,” which all IQMR participants attend, welcome them to the Institute and introduce some of its intellectual foci. Three faculty who have taught at IQMR since its inception will consider the epistemological diversity that underpins qualitative and multi-method research, consider some of the core methods used by scholars in the QMMR community, and think about ways that qualitative and quantitative methods can be combined. The sessions serve as general introductions to various of the module sequences that participants can take at IQMR.