Parties’ responsiveness to voters’ positions in a direct democratic setting

This study proposes the use of Bayesian item response theory models to measure aggregate public support for European integration. This approach addresses the limitations of other indicators and produces valid estimates of public attitudes over long time periods, even when available indicators change over time or present interruptions. I compare Bayesian item response theory models with alternative approaches used in the study of support for European integration, and demonstrate that they produce more accurate estimates of latent public opinion. The estimates are validated by showing their association both to alternative public opinion measures and to the vote share of Eurosceptic parties across Europe. I show that Bayesian models solve unaddressed issues like ensuring cross-country comparability of the estimates and modelling responses with multiple answer options.

Are political parties in young democracies responsive to the policy preferences of the public? Compared to extensive scholarship on party responsiveness in established democracies, research on party responsiveness in young democracies is limited. We argue that weaker programmatic party–voter linkages in post-communist democracies create incentives for parties to respond to their supporters rather than the more general electorate. Such responsiveness occurs in two ways. First, parties follow shifts in the mean position of their supporters. Second, drawing on the research on party–voter congruence, we argue that parties adjust their policy positions to eliminate previous incongruence between themselves and their supporters. Analyses based on a comprehensive dataset that uses expert surveys, parties’ manifestoes and election surveys to measure parties’ positions, and several cross-national and national surveys to measure voters’ preferences provide strong support for this argument.

Bayesian dynamic item response models have been successfully used for educational testing data; these models are especially useful for individually varying and irregularly spaced longitudinal testing data. However, because of the complexity of the models and the large size of the data sets, computation time is excessive for carrying out full data analyses in practice. Here, we introduce a parallel Markov chain Monte Carlo method to speed the implementation of these Bayesian models. Using both simulation data and real educational testing data for reading ability, we demonstrate that computation time is greatly reduced for our parallel computing method versus full data analyses. The estimated error of our method is shown to be small, using common distance metrics. Our parallel computing approach can be used for other models in the Educational and Psychometric fields, including Bayesian item response theory models. Copyright © 2017 John Wiley & Sons, Ltd.

Item response theory (IRT) models provide an appropriate alternative to the classical ordinal confirmatory factor analysis (CFA) during the development of patient-reported outcome measures (PROMs). Current literature has identified the assessment of IRT model fit as both challenging and underdeveloped (Sinharay & Johnson, 2003; Sinharay, Johnson, & Stern, 2006). This study evaluates the performance of Ordinal Bayesian Instrument Development (OBID), a Bayesian IRT model with a probit link function approach, through applications in two breast cancer-related instrument development studies. The primary focus is to investigate an appropriate method for comparing Bayesian IRT models in PROMs development. An exact Bayesian leave-one-out cross-validation (LOO-CV) approach (Vehtari & Lampinen, 2002) is implemented to assess prior selection for the item discrimination parameter in the IRT model and subject content experts’ bias (in a statistical sense and not to be confused with psychometric bias as in differential item functioning) toward the estimation of item-to-domain correlations. Results support the utilization of content subject experts’ information in establishing evidence for construct validity when sample size is small. However, the incorporation of subject experts’ content information in the OBID approach can be sensitive to the level of expertise of the recruited experts. More stringent efforts need to be invested in the appropriate selection of subject experts to efficiently use the OBID approach and reduce potential bias during PROMs development.

There are a growing number of item response theory (IRT) studies that calibrate different patient-reported outcome (PRO) measures, such as anxiety, depression, physical function, and pain, on common, instrument-independent metrics. In the case of depression, it has been reported that there are considerable mean score differences when scoring on a common metric from different, previously linked instruments. Ideally, those estimates should be the same. We investigated to what extent those differences are influenced by different scoring methods that take into account several levels of uncertainty, such as measurement error (through plausible value imputation) and item parameter uncertainty (through full Bayesian IRT modeling). Depression estimates from different instruments were more similar, and their corresponding confidence/credible intervals were larger when plausible value imputation or Bayesian modeling was used, compared to the direct use of expected a posteriori (EAP) estimates. Furthermore, we explored the use of Bayesian IRT models to update item parameters based on newly collected data.

Studies have shown that a foreign policy position of a member of Congress is often distinct from a domestic one. Despite this, measures commonly used to determine the foreign policy positions of members of Congress are based on congressional votes on domestic as well as foreign policy matters. As foreign policy votes take up only a small portion of all congressional votes, these measures conflate a member’s foreign policy position with his or her domestic policy position. While there are other measures based exclusively on foreign policy votes, these are also problematic because they tend to use a small number of controversial votes and thus inflate extremism. To address these shortcomings, I present a new measure by applying a Bayesian item response theory model to all foreign policy votes. This paper demonstrates the similarities, differences, and advantages of this measure by comparing it with the existing measures in a series of analyses of foreign policy positions of political parties and individual legislators.

ABSTRACTRecent research in American politics demonstrates that despite gender-based partisan sorting, gender gaps in policy preferences persist within political parties—particularly among Republicans. Republican women report significantly more moderate views than their male counterparts across a range of policy areas. These gaps are largely attributable to gender differences in beliefs about the appropriate scope of government and attitudes toward gender-based inequality. Arguably, gender has become a more salient feature of American elections in recent years, and this heightened salience raises questions about whether these within-party gender gaps are stable over time or vary across campaign contexts. We use survey data from the 2012 and 2016 American National Election Study to evaluate whether gender gaps in policy preferences are stable across elections or if the 2016 election context affected the magnitude of gender differences in policy preferences. We find that gender gaps in policy preferences within political parties are fairly stable across the two electoral periods.

BackgroundTraditional concordance metrics have shortcomings based on dataset characteristics (e.g., multiple attributes rated, missing data); therefore it is necessary to explore supplemental approaches to quantifying agreement between independent assessments. The purpose of this methodological paper is to apply an Item Response Theory (IRT) -based framework to an existing dataset that included unidimensional clinician and multiple attribute patient ratings of symptomatic adverse events (AEs), and explore the utility of this method in patient-reported outcome (PRO) and health-related quality of life (HRQOL) research.MethodsData were derived from a National Cancer Institute-sponsored study examining the validity of a measurement system (PRO-CTCAE) for patient self-reporting of AEs in cancer patients receiving treatment (N = 940). AEs included 13 multiple attribute patient-reported symptoms that had corresponding unidimensional clinician AE grades. A Bayesian IRT Model was fitted to calculate the latent grading thresholds between raters. The posterior mean values of the model-fitted item responses were calculated to represent model-based AE grades obtained from patients and clinicians.ResultsModel-based AE grades showed a general pattern of clinician underestimation relative to patient-graded AEs. However, the magnitude of clinician underestimation was associated with AE severity, such that clinicians’ underestimation was more pronounced for moderate/very severe model-estimated AEs, and less so with mild AEs.ConclusionsThe Bayesian IRT approach reconciles multiple symptom attributes and elaborates on the patterns of clinician-patient non-concordance beyond that provided by traditional metrics. This IRT-based technique may be used as a supplemental tool to detect and characterize nuanced differences in patient-, clinician-, and proxy-based ratings of HRQOL and patient-centered outcomes.Trial registrationClinicalTrials.gov NCT01031641. Registered 1 December 2009.

Many scholars have argued that primary elections are an important factor in the polarization of the American Congress. Yet little research measures change in the policy preferences of primary electorates to evaluate the connection directly. We create the first explicit measures of the preferences of primary voters over the last 60 years using a Bayesian item-response theory model. Although the overall distribution of population preferences has changed little, the preferences of primary voters are now much more related to the party of the primary that they attend. We show that liberals are much more likely to turn out in Democratic primaries and conservatives are much more likely to turn out in Republican primaries. We estimate that the divergence of primary from general electorates is six times larger in 2012 than in 1958 owing to this “primary sorting”. This trend began with the emergence of the Southern Republicans. As the Republican party became viable, conservative Southerners switched to Republican primaries leading to a leftward shift in Democratic primary electorates. Nationwide, primary sorting began sometime after it began in the South. We speculate that Southern realignment played a clarifying role that contributed to subsequent sorting of primary electorates nationwide.

Parties face a trade-off between motivating partisans to participate in the election and appealing to issue-oriented middle-of-the-road voters. We show that, consequently, parties may diverge from the median voters’ preferred policy by sending ambiguous messages to voters which include announcements of alternative platforms. Moreover, surprisingly, an increase in the size of a partisan constituency may lead to platform convergence towards the median voters’ preferred policy. We identify two conditions for this outcome. First, the electorate is sufficiently divided such that full convergence does not occur and, second, the majority of the non-partisan voters is more inclined to the party with increased support of partisans.

We propose a novel nonparametric Bayesian item response theory model that estimates clusters at the question level, while simultaneously allowing for heterogeneity at the examinee level under each question cluster, characterized by a mixture of binomial distributions. The main contribution of this work is threefold. First, we present our new model and demonstrate that it is identifiable under a set of conditions. Second, we show that our model can correctly identify question-level clusters asymptotically, and the parameters of interest that measure the proficiency of examinees in solving certain questions can be estimated at a rate (up to a log term). Third, we present a tractable sampling algorithm to obtain valid posterior samples from our proposed model. Compared to the existing methods, our model manages to reveal the multi-dimensionality of the examinees' proficiency level in handling different types of questions parsimoniously by imposing a nested clustering structure. The proposed model is evaluated via a series of simulations as well as apply it to an English proficiency assessment data set. This data analysis example nicely illustrates how our model can be used by test makers to distinguish different types of students and aid in the design of future tests.

Bayesian item response theory models have been widely used in different research fields. They support measuring constructs and modeling relationships between constructs, while accounting for complex test situations (e.g., complex sampling designs, missing data, heterogenous population). Advantages of this flexible modeling framework together with powerful simulation-based estimation techniques are discussed. Furthermore, it is shown how the Bayes factor can be used to test relevant hypotheses in assessment using the College Basic Academic Subjects Examination (CBASE) data.

Interlocal agreements are becoming a popular policy tool for facilitating intergovernmental coordination and cooperation in Canada and the United States. Indigenous and local governments are also turning to these agreements despite long histories of colonialism, exploitation and dispossession by the settler State toward Indigenous communities. To what extent do interlocal agreements between Indigenous and municipal governments require stringent accountability measures to facilitate intergovernmental coordination? Using a hierarchical Bayesian item response theory model, we explore this question by analyzing 317 interlocal agreements between Indigenous and municipal communities in Canada. We find that accountability strength varies significantly across agreements, contrary to our expectation that accountability requirements would be strong across agreements due to the long history of colonialism. We also find that some of the variation may be a function of the policy area addressed by each agreement, although this finding is likely the result of measurement uncertainty in our estimates.

Photovoltaic (PV) panels, increasingly popular due to decarbonization, are expected to increase household resilience during natural hazard-triggered blackouts. Their effectiveness is also expected to improve with the addition of storage batteries. However, PV ownership does not necessarily lead to electricity use in blackouts; even when used, the actual use of electrical appliances is unclear. We examined households that owned PV panels and experienced a natural hazard-triggered blackout to determine what barriers and facilitating factors existed for using PV electricity, which electrical appliances could be used with PV, and whether the benefits of PV changed with storage batteries. A questionnaire survey targeted households in Japan that experienced blackouts caused by the 2018 Hokkaido Eastern Iburi earthquake and the 2019 Typhoon Faxai, respectively, and their responses (n = 282 and 259, respectively) were analyzed. Descriptive statistics showed that while it was sometimes necessary to switch PV systems to stand-alone operation mode during blackouts, most respondents who did not use PV systems did not know how to operate them, which was a barrier to their use. The main facilitating factors were preparation like advance explanations from the sales staff and the regular manual check for use in case of blackouts. Bayesian item response theory models demonstrated that PV electricity enabled households to use a variety of electrical appliances, including those related to food and communication (i.e., refrigerators and cell phone chargers), whose needs increased during the blackouts. The probability of each appliance being used increased in households with batteries. The above results were commonly confirmed regardless of hazard type. These findings will improve the disaster resilience of households equipped with PV panels and batteries, as well as spread the installation of such equipment by highlighting their benefits.

After a bit more than four years in Enschede, the final product of my work is there.As any PhD project, this has been quite a process, and i will use this section to thank all the people that helped me get to this point.I really enjoyed working in the department of research methodology, measurement and data analysis (OMD).First of all I would like to thank Jean-Paul Fox, my supervisor, for everything he taught me and his inspirational enthusiasm for mathematical models.Rinke, thank you for your support in the first two years: for your patience in helping me with Fortran, for being a sparring partner and a listening ear, for the "across the hall" conversations, but also for continuing to be there when i needed an external view on things and for your feedback in the last weeks before finishing this thesis, and most of all for being a great friend.Furthermore i would like to thank Marianna and Iris for being wonderful office mates; Connie, Qi Wei, Hanneke, Muirne and Caroline for the many lunch walks we made; Stephanie for the all the advice and inspirational discussions; Sebi for supporting me in my teaching experience and Cees