Mismeasuring Militias: Limitations of Advocacy Group Data and of State‐Level Studies of Paramilitary Groups

The errors associated with measuring the number of militia and patriot groups may cast doubt on conclusions drawn from prior studies of the spatial variation of these movements. Most studies of militias have been qualitative investigations of a single group, state, or region. A growing number of studies, however, have used quantitative techniques to assess the hypothesis that the number of militia groups by state covaries with structural and cultural forces. We outline a number of concerns with the validity of the counts, conducted by the Anti‐Defamation League and the Southern Poverty Law Center, used by these studies. We re‐estimate models from previous studies using the four alternative measures of these groups employed in prior studies. We find that many inferences drawn for identical theoretical models differ based upon the measure used. These discrepancies apply not simply to tangential control variables but to indicators of key theoretical constructs. In other words, the decision as to whether or not a particular theoretical framework receives empirical support often depends upon which measure of the dependent variable is used. This suggests that the inconsistent findings in prior research may be due to measurement error and makes it difficult to assess the validity of the conclusions drawn from these studies. It is important to be aware of these weaknesses since scholars studying political crimes and related phenomena often use information from similar sources, making this specific example relevant to a more general area of research.     

The Impact of Pro-Government Militias on Human Rights Violations

New data show that between 1982 and 2007, in over 60 countries governments were linked to and cooperated with informal armed groups within their own borders. Given the prevalence of these linkages, we ask how such links between governments and informal armed groups influence the risk of repression. We draw on principal-agent arguments to explore how issues of monitoring and control help understanding of the impact of militias on human rights violations. We argue that such informal agents increase accountability problems for the governments, which is likely to worsen human rights conditions for two reasons. First, it is more difficult for governments to control and to train these militias, and they may have private interests in the use of violence. Second, informal armed groups allow governments to shift responsibility and use repression for strategic benefits while evading accountability. Using a global dataset from 1982 to 2007, we show that pro-government militias increase the risk of repression and that the presence of militias also affects the type of violations that we observe.     

The development and pilot testing of a family treatment court best practices assessment: The model standards implementation scale

In 2019, the family treatment court (FTC) best practice standards (the Standards) were published to clarify attributes of FTC programs associated with superior child, parent, and family outcomes. The Standards cover the breadth of FTC operations including program structure and leadership, substance use treatment and complementary services, and behavioral responses to participants. This study aimed to develop an instrument (the Model Standards Implementation Scale; “MSIS”) that stakeholders can use to assess implementation of the Standards by individual FTCs. The MSIS balances usability with scientific validity. Interrater reliability (IRR), internal consistency, and several types of validity were assessed. Results indicated moderate to strong IRR, high internal consistency, mixed known groups validity depending on Standard, and high convergent and divergent validity. Initial findings suggest good validity and usability of the MSIS for evaluating FTC Standards' implementation. Notably, the process of using the tool functioned to educate FTC team members on the Standards. Although implementation of the MSIS is a resource-intensive process, the opportunity to receive constructive feedback proved to be an effective incentive for initial and subsequent participation in the evaluation among FTCs. Future research is needed to examine predictive validity, including association between Standards' implementation and family outcomes in FTCs.     

The British general election: Synthetic forecasts

Election forecasting work, in Britain and elsewhere, has been confined mainly to traditional approaches – statistical modeling or poll-watching. We import a new approach, which we call synthetic modeling. These models, developed out of forecasting efforts from the American 2012 presidential election, we are currently testing in a comparative European context. In this exercise, we apply the strategy to forecasting British general elections, in particular the upcoming 2015 contest. These synthetic forecasts come from hybrid models blending structural knowledge with contemporary public opinion. We generate ongoing nowcasts, from six months prior, to one month before, Election Day itself.     

Forecasting the 2015 British election through party popularity functions

To forecast the May 7, 2015 British General Election, we develop party popularity models based on Continuous Monitoring Survey (CMS) data from April 2004 to February 2015. Our models predict party vote shares three months prior to the election, using previous support levels, national economic evaluations, macro-partisanship and political measures. Our Seemingly Unrelated Regression (SUR) methodology allows us to predict support for the Tories, Labour, Liberal Democrats and “other” parties, separately, yet simultaneously, by constraining total support for all parties to 100%. Our model, estimated with data from February 2015, predicts that Labour will win the highest vote share in Great Britain, but that no party will win a majority of seats in parliament.     

Forecasting the 2015 British general election: The 1992 debacle all over again?

This article introduces and reviews a set of twelve academic forecasts of the 2015 British general election. Along with the vast majority of others including journalists and betting markets, they failed by a big margin to predict that the Conservatives would emerge with an overall majority of seats. Several suffered from the 1992 scale inaccuracies of the vote-intention opinion polls. Forecasts based on other data sources typically did a bit better, but also fell short. Nonetheless, this was not 1992 all over again. The dramatic collapse of the Liberal Democrats and rise of the SNP, UKIP and Greens were successfully anticipated. Also this collection includes numerous methodological advancements, with several new methods and developments to established approaches.     

Does the economy really matter for satisfaction with democracy? Longitudinal and cross-country evidence from the European Union

Research focusing on the relationship between the economy and satisfaction with democracy often presents mixed results. This article argues that this uncertainty is mainly due to model specification, number of surveys and measurement. After discussing why the role of the economy should not be overlooked, by using an empirical strategy that applies Bayesian cross-classified mixed models to 572 national surveys in 28 European countries from 1973 to 2013 drawn from the Eurobarometer, it is shown that objective macro-economic indicators and a subjective indicator seem to substantially affect citizens’ satisfaction with democracy in Europe. The findings are robust when controlling for various institutional and political variables and using alternative model specifications.     

Public confidence in the judiciary: the interaction between political awareness and level of democracy

Apart from the studies that focus on public attitudes toward higher courts in advanced democracies, we know little about the factors that can explain public confidence in the judiciary in a comparative setting. In this regard, the goal of this study is to explain whether, and to what extent, the country's level of democracy moderates the impact of political awareness on public confidence in the judiciary. This study uses hierarchical linear models to analyse the interaction between individual and country level factors by using the World Values Survey (2005–2009) data for 49 countries and various other data sources. Our empirical results show that in advanced democracies political awareness variables like education and political participation have a positive impact on public confidence in the judiciary, whereas in countries with weak levels of democracy higher political awareness leads to increased cynicism about the judiciary. These results suggest that a one-size-fits-all approach to explain confidence in the judiciary is not possible when we are dealing with a wide range of societies that vary in terms of many characteristics, both institutional and cultural.     

Loess:: a nonparametric, graphical tool for depicting relationships between variables

Loess is a powerful but simple strategy for fitting smooth curves to empirical data. The term “loess” is an acronym for “local regression” and the entire procedure is a fairly direct generalization of traditional least-squares methods for data analysis. Loess is nonparametric in the sense that the fitting technique does not require an a priori specification of the relationship between the dependent and independent variables. Although it is used most frequently as a scatterplot smoother, loess can be generalized very easily to multivariate data; there are also inferential procedures for confidence intervals and other statistical tests. For all of these reasons, loess is a useful tool for data exploration and analysis in the social sciences. And, loess should be particularly helpful in the field of elections and voting behavior because theories often lead to expectations of nonlinear empirical relationships even though prior substantive considerations provide very little guidance about precise functional forms.     

Competing Risks, Persistence, and Desistance in Analyzing Recidivism

A statistical procedure is developed to analyze recidivism in samples whichare subject to the presence of desisters and to multiple modes ofreconviction. This allows for a more accurate study of individuals'transition and hazard in the type and timing of offenses following aspecific type of conviction. The use of a nonparametric approach forinvestigating failure in the presence of other acting causes is shown;initial estimators of the probabilities of reconviction for different typesof offenses are obtained, and the method can be used both to display thedata and to choose an appropriate parametric family for the survivaltimes. An exponential mixture model for competing risks is presented insuch a way that it allows us to adjust for concomitant variables and toassess their effects on the probabilities both of reconviction forpredetermined types of offenses and desistance and of the hazards ofreconviction; a method for assessing calibration of predicted survivalprobabilities is suggested. A 21-year follow-up of persons convicted ofindecent assault on a female in 1973 illustrates the methods; we find ahigh probability of sexual reconviction for individuals with previoussexual convictions and evidence of diversity and a raised hazard ofreconviction for young chronic offenders.