| dc.description.abstract | This paper presents a new method inspired by evolutionary biology for analyzing longer sequences of requisites for the emergence of particular outcome variables across numerous combinations of ordinal variables in social science analysis. The approach involves repeated pairwise investigations of states in a set of variables and identifying what states in the variables that occur before states in all other variables. We illustrate the proposed method by analyzing a set of variables from version 6 of the V-Dem dataset (Coppedge et al. 2015a, b). With a large set of indicators measured over many years, the method makes it possible to explore long, complex sequences across many variables in quantitative datasets. This affords an opportunity, for example, to disentangle the sequential requisites of failing and successful sequences in democratization. For policy purposes this is instrumental: Which components of democracy are most exogenous and least endogenous and therefore the ideal targets for democracy promotion at different stages? | sv |
| dc.description.sponsorship | This research project was supported by Riksbankens Jubileumsfond, Grant M13-0559:1, PI: Staffan I. Lindberg, V-Dem Institute, University of Gothenburg, Sweden; by Swedish Research Council, Grant C0556201, PIs: Staffan I. Lindberg, V-Dem Institute, University of Gothenburg, Sweden and Jan Teorell, Department of Political Science, Lund University, Sweden; by Knut and Alice Wallenberg Foundation to Wallenberg Academy Fellow Staffan I. Lindberg, Grant 2013.0166, V-Dem Institute, University of Gothenburg, Sweden; as well as by internal grants from the Vice-Chancellor’s office, the Dean of the College of Social Sciences, and the Department of Political Science at University of Gothenburg. We performed simulations and other computational tasks using resources provided by the Notre Dame Center for Research Computing (CRC) through the High Performance Computing section and the Swedish National Infrastructure for Computing (SNIC) at the National Supercomputer Centre in Sweden. We specifically acknowledge the assistance of In-Saeng Suh at CRC and Johan Raber at SNIC in facilitating our use of their respective systems. | sv |
