Kyle L. Marquardt

Is Putin's Popularity Real?

AbstractVladimir Putin has managed to achieve strikingly high public approval ratings throughout his time as president and prime minister of Russia. But is his popularity real, or are respondents lying to pollsters? We conducted a series of list experiments in early 2015 to estimate support for Putin while allowing respondents to maintain ambiguity about whether they personally do so. Our estimates suggest support for Putin of approximately 80%, which is within 10 percentage points of that implied by direct questioning. We find little evidence that these estimates are positively biased due to the presence of floor effects. In contrast, our analysis of placebo experiments suggests that there may be a small negative bias due to artificial deflation. We conclude that Putin’s approval ratings largely reflect the attitudes of Russian citizens.

V-Dem Methodology V6

Part I sets forth the V-Dem conceptual scheme. Part II discusses the process of data collection. Part III describes the measurement model along with efforts to identify and correct errors.

IRT Models for Expert-Coded Panel Data

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. In this article, 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 real and simulated data. Specifically, we use expert-coded cross-national panel data from the V–Dem data set to both conduct real-data comparisons and inform ecologically-motivated simulation studies. 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 of latent concepts.