C02-02PM: Comparative Historical Analysis

Comparative historical analysis (CHA) traces its roots to Durkheim, Marx, Weber and continues as historical sociology, comparative history, historical institutionalism and other strands of historical social science. It anchors a wide range of problem-driven research that studies capitalist development, social policy, democratization, identity formation, post-colonialism, globalization, and other macro-historical phenomena, that is, phenomena embedded in changing historical contexts. Despite its heterodoxy, CHA employs a systematic toolkit for studying macro-historical questions. This course is built around “The Grammar of Time. A Toolkit for Comparative Historical Analysis” (Cambridge 2023)—the first methodological synthesis of CHA.

CHA’s problem-driven research requires aligning methods with questions and this alignment, in turn, entails broadening the methodological toolkit from testing techniques to various pre-testing activities. These pre-testing activities involve sleuthing, description, conceptualization, data visualization, exploratory topic analysis, theorizing, and causal graphing. They are central to problem-driven research because they assist in figuring out what is going on, what questions to pose, when to update concepts, how to trace causal processes, and how to analyze temporal dynamics. These pre-testing activities, together with testing techniques, are ultimately necessary for translating mere testing results into robust answers.

The course is suitable for students interested in problem driven research and historical change. It asks students to apply the CHA tools to their research projects. No prior knowledge of CHA is required.

Comparative historical analysis (CHA) studies problem-driven, macro-historical questions. Such questions are complex because they often are not fully researched, context dependent, subject to historical change, geographically heterogeneous, and marked by complex causal dynamics. Answers to such questions become testable only after extensive prior, pre-testing research. Standard textbooks often ignore these pre-testing activities and subscribe to the seemingly immaculate conception of hypotheses. 

The course is structured around five modules that, following CHA, pay equal attention to the pre-testing and testing stages of the research process. 

Day 1: Historical Thinking: expands the exploratory terrain

Problem-driven research requires close contextual attention to properly understand a phenomenon, identify its key issues, and update research questions. CHA borrows from historians so-called “historical thinking” to explore contextual complexities. It draws on historians’ long-standing skepticism that facts can speak for themselves, and that context exists independently of theoretical priors. Historical thinking makes transparent the theoretical construction of different possible contexts. It explores how theories construct the temporal, spatial and other units of analysis (e.g. temporalities, spatialities, scale) that scholars often unwittingly bring to their analysis. Historical thinking relaxes these temporal and spatial assumptions to permit a less theory-laden and less experience-distant analysis of empirical phenomena. Relaxing units of analysis, turning de facto variable back into proper names, foregrounds more contextual complexities, expands the exploratory terrain, and thereby the potential for updating research questions.

Days 2 & 3: Exploratory Topic Analysis: turns exploration into discoveries

Exploratory topic analysis involves strategies for translating explorations into discoveries, that is, patterns that raise new research questions. These exploratory strategies are linked to the three strands of CHA: eventful, longue durée, and macro-causal analysis. Eventful analysis employs historical description, periodization, and casing to identify two types of patterns: transformations over time and varieties across space. Longue durée analysis uses time series data and visualization to identify longitudinal and cross-sectional trend patterns. Macro-causal analysis explores context to identify overlooked causal patterns either in the form of confounders and inductive insights.

Day 4: Abductive Theorizing: updates theories with inductive insights

Exploratory topic analysis provides the bridge between describing empirical phenomena, to figure out what is going on, and theorizing, to identify causal factors and update existing theories. Theorizing involves building causal arguments and thus is different from treating theories as largely static, context-independent purveyors of testable implications. CHA emphasizes the abductive quality of theorizing in which explanations evolve across multiple research cycles by bring old test results in conversation with new inductive insights. It also underscores the de-confounding role that theory plays in problematizing existing explanations to identify causal factors hidden behind parameters, scope conditions or other simplifications that theories impose on social reality. Abductive theorizing aims to generate more test-worthy hypotheses.

Day 5: Causal Graphing: makes testing transparent 

CHA’s credo to align methods with questions commits to employing different causal identification strategies ranging from analytical narratives, to process tracing, set theory, all the way to multi-variate regression analysis. CHA, however, is committed irrespective of the causal identification strategy to visualize its arguments in causal graphs. Such graphing improves causal inferences by making transparent causal pathways, locating potential confounders, constructing strong tests, specifying scope conditions, and identifying types of causality. Causal graphing increases confidence in causal inferences to assuring that test results are converted into genuine answers by being warranted with robust causal theories.

The course introduces these CHA tools and provides additional resources to students interesting in learning more than can be covered in a single day. These CHA tools lack the formalization and check-list quality of standard variance-based research designs because they must be adapted to the research question at hand. The deployment of CHA tools hence involves a certain context-dependent bricolage. The course simulates this bricolage by inviting students to use their research projects for the daily exercises and peer-to-peer working groups. 

Prerequisites

The course is most suitable to students already conversant with any of the strands of historical social analysis or who are working on problem-driven projects that cannot be easily shoehorned into the theory testing template. No background in CHA is required, but curiosity and heterodox thinking are highly welcome. 

This description is subject to change at the discretion of the Instructor

2 Credits

For completion of all work before and during the course, as outlined by the Instructor, and 90% participation and attendance of the course.

2 Additional Credits

Course specific extra assignments. These can include submitting assignments before the course, daily assignments, and/or a final assignment to be completed after the course as decided by the Instructor.  This description is subject to change at the discretion of the Instructor.