This article examines the perceived discrimination of immigrants – a group for whom experiences of discrimination can be damaging for their long‐term commitment and identification with the national core group. Taking its point of departure in the literature on national identity, the article argues that perceived discrimination should be strongest among immigrants in host national societies with an exclusive self‐image. This hypothesis is examined by use of multilevel regressions on cross‐national survey data from 18 Western European countries. It is found that where exclusive attitudes are widespread in the host population, the percentage of immigrants who perceive themselves to be part of a group discriminated against is significantly greater, all else being equal. In addition, there is a cross‐level interaction effect of host national inclusivity and ethnic minority identity which suggests that individual‐level determinants of perceived discrimination do not ‘work’ in the same way in normatively different contexts. In terms of the implications of these findings, the article points to the importance of contextualising individual‐level accounts of perceived discrimination, with particular focus on the power of a society's attitudinal milieu to affect individual feelings of inclusion and exclusion.

This article examines support for radical left ideologies in 32 European countries. It thus extends the relatively scant empirical research available in this field. The hypotheses tested are derived mainly from group‐interest theory. Data are deployed from the 2002–2010 European Social Surveys (N = 174,868), supplemented by characteristics at the country level. The results show that, also in the new millennium, unemployed people and those with a lower income are more likely to support a radical left ideology. This is only partly explained by their stronger opinion that governments should take measures to reduce income differences. In contrast to expectations, the findings show that greater income inequality within a country is associated with reduced likelihood of an individual supporting a radical left ideology. Furthermore, cross‐national differences in the likelihood of supporting the radical left are strongly associated with whether a country has a legacy of an authoritarian regime.

Volunteer rates vary greatly across Europe despite the voluntary sector’s common history and tradition. This contribution advances a theoretical explanation for the variation in volunteering across Europe—the capability approach—and tests this approach by adopting a two-step strategy for modeling contextual effects. This approach, referring to the concept of capability introduced by Sen (Choice, welfare and measurement, Oxford University Press, 1980/1982), is based on the claim that the demand and supply sides of the voluntary sector can be expected to vary according to collective and individual capabilities to engage in volunteering. To empirically test the approach, the study relied on two data sources—the 2015 European Union (EU) Survey on Income and Living Conditions (EU-SILC), including an ad hoc module on volunteering at the individual level, and the Quality of Government Institute and PEW Research Center macro-level data sets—to operationalize economic, human, political, social, and religious contextual factors and assess their effects on individuals’ capability to volunteer. The results support the capability hypothesis at both levels. At the individual level, indicators of human, economic, and social resources have a positive effect on the likelihood of volunteering. At the contextual level, macro-structural indicators of economic, political, social, and religious contexts affect individuals’ ability to transform resources into functioning—that is, volunteering.

This article analyses the influence of national context on civil society strength based on four key dimensions: level of democracy, political stability, rule of law and economic development. Whereas existing studies mainly focus on Western and post-communist countries, we explicitly include developing countries in our analysis. We use associational membership as proxy for civil society strength and include data of 53 countries. Rule of law, economic development and (to a lesser extent) political stability emerge from our multilevel regression models as the main factors affecting civil society membership. Unlike previous studies, we show that these relations are quadratic instead of linear. This means that where existing theories predict a drop in memberships in developing countries, we find a rise. In other words, harsh conditions actually strengthen civil society in terms of membership levels. We argue that this could be the case because reasons for CSO membership are essentially different in the developed and in the developing world. Contrary to theoretical assumptions, democratic rights do not appear critically important for civil society membership.

Previous studies have shown that social enterprises can improve the health conditions of socially disadvantaged people through qualitative approaches. As income-related health inequality has grown, the role of social enterprises in addressing this issue has become more significant. This study examined whether social enterprises could positively affect the self-rated health of South Korean low-income residents using multilevel models. The results showed that government-certified social enterprises were associated with positive self-rated health among low-income residents. On the other hand, preliminary social enterprises with insufficient profitability and weak corporate governance showed mixed results. Based on the empirical results, this study suggests relevant policy implications.

Housing co-operatives host miniature versions of civil society. They vitalize a social system that is shaped by formal regulations, economic functions and a population of private housing units. The study examines factors that influence a person’s willingness to volunteer in civic society using a multilevel analysis based on survey data from 32 co-operatives and 1263 members. To do so, the social exchange theory is extended to include the member value approach, which connects social engagement with the fulfillment of a range of needs, thus going beyond a narrow economic cost–benefit analysis. Study results show that volunteer engagement largely depends on the degree to which members can expect to experience their own achievement. This finding provides an explanation for significant differences in the engagement levels beyond factors that have already been determined (age, level of education). On an organizational level, the study reveals that the age of an organization influences volunteer engagement, but that the size and the degree of professionalization do not have an effect on it.

This article investigates the dynamics of support for income redistribution in Europe. With European Social Survey data spanning 2006 to 2012, it assesses whether the Great Recession resulted in substantial parallelism or increasing polarisation in preference change across various sub‐publics. After introducing hypotheses based on claims that social groups are affected differently by economic insecurity, the article proceeds in two empirical sections. First, whereas prior research suggests that hard times fuel diverging attitudinal patterns, it is found that income groups, ideological groups and educational groups did not shift differently over time during the first years of the crisis, thus providing strong evidence for the ‘parallel publics’ hypothesis in the European context and in times of economic turmoil. Next, the article addresses the extent to which change in aggregate support for redistribution came from changes in small minorities of the population, supposed to be more responsive to their economic environment. Using multilevel analysis, it is shown that the most educated significantly contributed to the overall change more than the others. As a result, they may have been partly driving the economic mood during the first years of the Great Recession.

Recent trends of mass‐level euroscepticism seriously challenge Deutsch's transactionalist theory that increased transnational interactions trigger support for further political integration. While transnational interactions have indeed proliferated, public support for European integration has diminished. This article aims to solve this puzzle by arguing that transnational interaction is highly stratified across society. Its impact on EU support therefore only applies to a small portion of the public. The rest of the population not only fails to be prompted to support the integration process, but may see it as a threat to their realm. This is even more the case as, parallel to European integration, global trends of integration create tensions in national societies. The following hypotheses are proposed: first, the more transnational an individual, the less she or he is prone to be eurosceptical; and second, this effect is more pronounced in countries that are more globalised. A multilevel ordinal logit analysis of survey data from the 2006 Eurobarometer wave 65.1 confirms these hypotheses.

Which new parties entered national parliaments in advanced democracies over the last four decades and how did they perform after their national breakthrough? This article argues that distinguishing two types of party formation (that facilitate or complicate party institutionalisation) helps to explain why some entries flourish, while others vanish quickly from the national stage. New parties formed by individual entrepreneurs that cannot rely on ties to already organised groups are less likely to get reelected to parliament after breakthrough than rooted newcomers. This hypothesis is tested on a newly compiled dataset of new parties that entered parliaments in 17 advanced democracies from 1968 onwards. Applying multilevel analyses, the factors that shape newcomers' capacity to reenter parliament after breakthrough are assessed. Five factors have significant effects, yet affect party performance only in particular phases: both a party's electoral support at breakthrough and its operation in a system with a strong regional tier increase the likelihood of initial reelection. In contrast, a distinct programmatic profile, the permissiveness of the electoral system and easy access to free broadcasting increase a party's chance of repeated reelection. Only formation type significantly affects both phases and does so most strongly, substantiating the theoretical approach used in this article.

Democratic innovations (DIs), such as deliberative mini-publics and referenda, are gaining traction in Europe, but their legitimacy depends on public support and their ability to address democratic discontent. While prior research focuses on individual-level drivers, structural conditions remain understudied. This study uniquely integrates the regional economic context into the analysis, combining survey data (N = 16,000) with economic indicators from ninety-one regions in thirteen European countries. Findings show that DIs receive slightly more support in poorer regions. Additionally, in these regions, economic hardship fuels demand for DIs by amplifying economic deprivation and political disaffection (‘enraged’ mechanism), whereas in wealthier regions, political interest is the key driver of public support for DIs (‘engaged’ mechanism). By incorporating economic conditions into the study of DIs, this research refines two key theories of DI support and offers a more nuanced understanding of when and why citizens support institutional change, thereby informing more context-sensitive participatory policies.

The strategy of tuberculosis (TB) contact investigation is essential for enhancing disease detection. We conducted a cross-sectional study to evaluate the yield of contact investigation for new TB cases, estimate the prevalence of TB, and identify characteristics of index cases associated with infection among contacts of new cases notified between 2010 and 2020 in São Paulo, Brazil. Out of 186466 index TB cases, 131055 (70.3%) underwent contact investigation. A total of 652286 contacts were screened, of which 451704 (69.2%) were examined. Of these, 12243 were diagnosed with active TB (yield of 1.9%), resulting in a number needed to screen of 53 and a number needed to test of 37 to identify one new TB case. The weighted prevalence for the total contacts screened was 2.8% (95% confidence interval [CI]: 2.7%–2.9%), suggesting underreporting of 6021 (95% CI: 5269–6673) cases. The likelihood of TB diagnosis was higher among contacts of cases identified through active case-finding, abnormal chest X-ray, pulmonary TB, or drug resistance, as well as among children, adults, women, individuals in socially vulnerable situations, and those with underlying clinical conditions. The study highlights significant TB underreporting among contacts, recommending strengthened contact investigation to promptly identify and treat new cases.

We propose a hierarchical Bayesian model for analyzing multi-site experimental fMRI studies. Our method takes the hierarchical structure of the data (subjects are nested within sites, and there are multiple observations per subject) into account and allows for modeling between-site variation. Using posterior predictive model checking and model selection based on the deviance information criterion (DIC), we show that our model provides a good fit to the observed data by sharing information across the sites. We also propose a simple approach for evaluating the efficacy of the multi-site experiment by comparing the results to those that would be expected in hypothetical single-site experiments with the same sample size.

Human performance in cognitive testing and experimental psychology is expressed in terms of response speed and accuracy. Data analysis is often limited to either speed or accuracy, and/or to crude summary measures like mean response time (RT) or the percentage correct responses. This paper proposes the use of mixed regression for the psychometric modeling of response speed and accuracy in testing and experiments. Mixed logistic regression of response accuracy extends logistic item response theory modeling to multidimensional models with covariates and interactions. Mixed linear regression of response time extends mixed ANOVA to unbalanced designs with covariates and heterogeneity of variance. Related to mixed regression is conditional regression, which requires no normality assumption, but is limited to unidimensional models. Mixed and conditional methods are both applied to an experimental study of mental rotation. Univariate and bivariate analyzes show how within-subject correlation between response and RT can be distinguished from between-subject correlation, and how latent traits can be detected, given careful item design or content analysis. It is concluded that both response and RT must be recorded in cognitive testing, and that mixed regression is a versatile method for analyzing test data.

Classical factor analysis assumes a random sample of vectors of observations. For clustered vectors of observations, such as data for students from colleges, or individuals within households, it may be necessary to consider different within-group and between-group factor structures. Such a two-level model for factor analysis is defined, and formulas for a scoring algorithm for estimation with this model are derived. A simple noniterative method based on a decomposition of the total sums of squares and crossproducts is discussed. This method provides a suitable starting solution for the iterative algorithm, but it is also a very good approximation to the maximum likelihood solution. Extensions for higher levels of nesting are indicated. With judicious application of quasi-Newton methods, the amount of computation involved in the scoring algorithm is moderate even for complex problems; in particular, no inversion of matrices with large dimensions is involved. The methods are illustrated on two examples.

Van Breukelen offers a promising method for modeling both response speed and response accuracy. However, the underlying conception of both dependent measures is somewhat flawed, leading the author to conclude that the approach possesses limitations that, under revised assumptions, may not hold. The central misconception, and a set of related misconceptions, is addressed, and it is suggested that this approach holds a good deal of promise for application in the perceptual and cognitive sciences.

We extend dynamic generalized structured component analysis (GSCA) to enhance its data-analytic capability in structural equation modeling of multi-subject time series data. Time series data of multiple subjects are typically hierarchically structured, where time points are nested within subjects who are in turn nested within a group. The proposed approach, named multilevel dynamic GSCA, accommodates the nested structure in time series data. Explicitly taking the nested structure into account, the proposed method allows investigating subject-wise variability of the loadings and path coefficients by looking at the variance estimates of the corresponding random effects, as well as fixed loadings between observed and latent variables and fixed path coefficients between latent variables. We demonstrate the effectiveness of the proposed approach by applying the method to the multi-subject functional neuroimaging data for brain connectivity analysis, where time series data-level measurements are nested within subjects.

This study employs social cognitive theory to examine the dynamics of ethical climate, environmental passion, and low-carbon behaviours among Malaysian public servants based on data from 407 employees across 37 departments. Although ethical climate did not have a direct impact on low-carbon behaviour, a significant association with environmental passion was observed. Additionally, environmental passion exhibited a noteworthy relationship with low-carbon behaviour, and emerged as a mediator between ethical climate and low-carbon behaviour, with green mindfulness moderating this relationship. These findings underscore the importance of nurturing environmental passion and green mindfulness to promote low-carbon behaviour among employees and aid organisations in addressing environmental challenges. By addressing these empirical gaps, this study contributes to the literature on low-carbon behaviour and offers both theoretical insights and practical implications for sustainability initiatives.

Trust in national and local institutions is an essential component of democracy. The literature has dealt mainly with the former, while less attention has been given to the latter. This paper advances a novel theoretical approach to inquire about trust in local institutions, which is also used to test national ones. We posit that trust is affected by the perceptions individuals have of the physical space where they live. Both a) the perceived quality of life in the neighbourhood where individuals live and b) the neighbourhood (perceived) peripherality are hypothesized to affect trust in local (and to a lesser extent) national institutions. We test our hypotheses in Italy, over a large representative sample of more than 40.000 respondents. We show that both variables are crucial predictors of local trust, but only the perceived quality of life predicts national trust. Equally important, social, cultural and economic individual capital does not modify the relation.