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- Knutsen, C. H., et al.
(författare)
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Introducing the Historical Varieties of Democracy dataset: Political institutions in the long 19th century
- 2019
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Ingår i: Journal of Peace Research. - : SAGE Publications. - 0022-3433 .- 1460-3578. ; 56:3, s. 440-451
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Tidskriftsartikel (refereegranskat)abstract
- The Historical Varieties of Democracy dataset (Historical V-Dem) contains about 260 indicators, both factual and evaluative, describing various aspects of political regimes and state institutions. The dataset covers 91 polities globally - including most large, sovereign states, as well as some semi-sovereign entities and large colonies - from 1789 to 1920 for many cases. The majority of the indicators come from the Varieties of Democracy dataset, which covers 1900 to the present - together these two datasets cover the bulk of 'modern history'. Historical V-Dem also includes several new indicators, covering features that are pertinent for 19th-century polities. We describe the data, coding process, and different strategies employed in Historical V-Dem to cope with issues of reliability and validity and ensure intertemporal and cross-country comparability. To illustrate the potential uses of the dataset we describe patterns of democratization in the 'long 19th century'. Finally, we investigate how interstate war relates to subsequent democratization.
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| 2. |
- Marquardt, Kyle L., 1983, et al.
(författare)
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IRT Models for Expert-Coded Panel Data
- 2018
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Ingår i: Political Analysis. - : Cambridge University Press (CUP). - 1047-1987 .- 1476-4989. ; 26:4, s. 431-456
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Tidskriftsartikel (refereegranskat)abstract
- Data sets quantifying phenomena of social-scientific interest often use multiple experts to code latent concepts. While it remains standard practice to report the average score across experts, experts likely vary in both their expertise and their interpretation of question scales. As a result, the mean may be an inaccurate statistic. Item-response theory (IRT) models provide an intuitive method for taking these forms of expert disagreement into account when aggregating ordinal ratings produced by experts, but they have rarely been applied to cross-national expert-coded panel data. We investigate the utility of IRT models for aggregating expert-coded data by comparing the performance of various IRT models to the standard practice of reporting average expert codes, using both data from the V-Dem data set and ecologically motivated simulated data. We find that IRT approaches outperform simple averages when experts vary in reliability and exhibit differential item functioning (DIF). IRT models are also generally robust even in the absence of simulated DIF or varying expert reliability. Our findings suggest that producers of cross-national data sets should adopt IRT techniques to aggregate expert-coded data measuring latent concepts.
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