IQMR 2025

Multi-Method Research (Modules 30, 34)

Thursday, June 26; Friday, June 27

Jaye Seawright (Northwestern University)

This module sequence looks at how to productively combine qualitative and quantitative methods. For example, one session looks closely at adding case studies to 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. We will also consider mixed-method designs aimed at concept formation and measurement.

Book to Purchase: Seawright, Jason. 2016. Multi-Method Social Science: Combining Qualitative and Quantitative Tools. Cambridge: Cambridge University Press.

Participants may enter the module sequence after it has begun, but their doing so is discouraged.

Multi-Method Research I (M30, June 26)

This module works through multiple ideas about how to combine qualitative and quantitative research techniques within a single project, working through these concepts with an eye to applications that use regression and similar techniques (e.g., logit, probit, multilevel models) as the quantitative side of an overall design. The goal is to explore optimal research design choices, consider potential weaknesses of such designs, and encounter ideas at the cutting edge of methodological thought in the relevant research traditions.

8:45am - 10:15am – Multi-Method Design: General Principles

This session introduces major paradigms of mixed- and multi-method research, including iteration, triangulation, integration, and more. We will discuss the foundational beliefs of each paradigm regarding qualitative and quantitative research and their interrelation, as well as the pragmatic implications of each approach for combining methods.

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1:30pm - 3:00pm – Combining Case Studies and Regression

This session discusses what is known about the strengths and weaknesses of regression-type research and process-tracing qualitative case studies for causal inference. It then explores specific research design strategies for combining these methods in ways that minimize these weaknesses while enhancing the strengths of each method.

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3:30pm - 5:00pm – Case Selection

This session introduces a range of methods that have been suggested for selecting cases from an available population. We will discuss these methods, and then analyze them in terms of their suitability for a range of different goals, with the objective of deriving guidelines for which methods to use for each objective.

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Suggested readings:

Multi-Method Research II (M34, June 27)

This module extends the ideas about mixed- and multi-method design to contexts beyond regression, including natural experiments and laboratory/survey/field experiments; description, concept formation, and measurement; and theory-building.

8:45am - 10:15am – Multi-Method Design with Experiments

This session asks how multi-method design can work with research where the quantitative component involves some kind of experimental research. Such projects are an increasingly important part of social science, and the design implications are different in interesting ways from those raised by regression. This session explores designs that engage with those differences, including designs focused around ideas of experimental realism, network and equilibrium effects, and selecting/designing a treatment.

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1:30pm - 3:00pm – Multi-Method Designs Centering Case Studies

This session asks what multi-method research can add to studies that are basically qualitative case studies. We will consider exploratory designs where statistical approaches help broaden the range of ideas explored; ways that statistical text-as-data methods can provide support in summarizing and providing context for documents analyzed within qualitative research; approaches for using multi-method designs to facilitate movement across levels of analysis within a case study; and the use of experiments embedded within case studies.

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3:30pm - 5:00pm – Multi-method Designs for Concept-formation, Theory-building, and Measurement

This session explores the long-standing, parallel qualitative, quantitative, and statistical/machine learning literatures on description, measurement, concept formation, and theory-building, and asks whether and how these traditions can be mixed in practice to produce better description, measurements, concepts, and theories. Can this earliest stage of research benefit from the same multi-method paradigms that we earlier applied to causal inference?

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