Dynamic Patterns of Human Rights Practices

Abstract

The science of human rights requires valid comparisons of repression levels across time and space. Though extensive data collection efforts have made such comparisons possible in principle, statistical measures based on simple additive scales made them rare in practice. This article uses a dynamic measurement model that contrasts with current approaches by (1) accounting for the fact that human rights indicators vary in the level of information they provide about the latent level of repression, (2) allowing realistic descriptions of measurement uncertainty in the form of credible intervals and (3) providing a theoretical motivation for modeling temporal dependence in human rights levels. It presents several techniques, which demonstrate that the dynamic ordinal item-response theory model outperforms its static counterpart.

Uncovering Patterns Among Latent Variables: Human Rights and De Facto Judicial Independence

“…To incorporate the information from these country-year distributions, we follow recommendations from Schnakenberg and Fariss (2014) by duplicating our dataset 1,000 times and assigning a random draw from the posterior distribution of the latent variable to each country-year observation. We use this new value as the outcome measure.…”

“…We then estimate a set of 1,000 OLS models, combining the results across the the multiple sets of data to create one set of coefficient and standard error estimates. This procedure is substantively important because it allows us to to relax the assumption that theoretically important variables are measured perfectly and without error (Mislevy, 1991;Schnakenberg and Fariss, 2014). The equation used to combine the estimates from each of the 1,000 OLS models was developed by Rubin (1987) to combine estimates from multiply imputed datasets.…”

“…The equation used to combine the estimates from each of the 1,000 OLS models was developed by Rubin (1987) to combine estimates from multiply imputed datasets. Mislevy (1991) and Schnakenberg and Fariss (2014) discuss this approach in the context of latent variable models. Figure 2 presents the results.…”

“…Uncertainty is important substantive information necessary for comparing the relationship between complex theoretical concepts across political contexts and over time (Fariss, 2015;Schnakenberg and Fariss, 2014). The latent variable model provides a principled method for estimating the uncertainty of the country-year units.…”

Going the Distance: The Price of Projecting Power

“…A lagged dependent variable does not enter the equation since the model is estimated from independent MIDs and not time-series or panel data. Note that we estimated the GEE model with the errors clustered (Fariss and Schnakenberg 2013;Schnakenberg and Fariss 2012;Treier and Jackman 2008). This research builds on insights from historical research on the development of diesel engines and gas turbines periods Smil (2007).…”