Using a multilevel analysis including 207 volunteers and paid workers nested within 51 nonprofit organizations (NPOs), this study examines the effect of individual and group attitudinal and behavioral commitment on their assessment of organizational effectiveness. Drawing on classical attitude theories, our results indicate that individuals with higher affective organizational commitment tend to assess their NPO’s effectiveness higher, while individuals staying because of the lack of alternatives assess it lower. However, in line with behavioral commitment theories, both relationships are mediated by the effect of teamwork behavioral commitment. We also found a negative effect of normative attitudinal commitment partially nested at the group level. Overall, our results suggest that encouraging volunteers and paid workers to participate in concrete teamwork behaviors on a daily basis constitutes a twofold benefit: it adds to the effect of affective attitudinal commitment at the individual level, while counter balancing the negative effects related to normative individual and collective resistances.

Many studies show that policy makers react to the policy choices made in other jurisdictions, but we still know relatively little about the factors driving interdependent policy making, especially about how context shapes interdependence. Theoretical arguments suggest that contextual factors, such as stable institutions and geographic location, explain variation in interdependence. However, there is a lack of empirical research investigating contextual heterogeneity in interdependent policy making, mainly because it cannot be analysed with standard spatial econometric methods. This article introduces multilevel modeling that allows the study of contextual variation in interdependence and illustrates the method with the analysis of uneven tax competition in Switzerland. The findings of fine‐grained data show that cantonal governments compete more strongly with their competitors the closer a unit is located to a metropolis with comprehensive public good provision. The analysis demonstrates that we can better understand the mechanisms of interdependent policy making by studying its contextual drivers.

Are citizens in consensus democracies with developed direct democratic institutions more satisfied with their political system than those in majoritarian democracies? In this article, individual‐level data from the second wave of the Comparative Study of Electoral Systems and an updated version of Lijphart's multivariate measure of consensus and majoritarian democracy covering 24 countries are used to investigate this question. The findings from logistic multilevel models indicate that consensual cabinet types and direct democratic institutions are associated with higher levels of citizens' satisfaction with democracy. Furthermore, consensus democracy in these institutions closes the gap in satisfaction with democracy between losers and winners of elections by both comforting losers and reducing the satisfaction of winners. Simultaneously, consensus democracy in terms of electoral rules, the executive–legislative power balance, interest groups and the party system reduces the satisfaction of election winners, but does not enhance that of losers.

Some nonprofits easily attract resources, while others struggle to survive. However, little is known about what characteristics account for the difference in nonprofit organizations’ capacity to mobilize resources, especially in authoritarian countries. Using multilevel modeling techniques and a national sample of 3344 philanthropic foundations in 31 regions of mainland China, this research seeks to address this knowledge gap by examining the effect of both organizational and contextual factors on foundations’ revenues, paying special attention to the government’s role. Results show that the distribution of resources is highly unbalanced in China’s foundation sector and that foundations with particular characteristics are systematically favored. By exploring what factors give foundations the edge in mobilizing resources, this study reveals how the Chinese government has used a more sophisticated, indirect method than direct control to shape resource distribution and regulate the development of nonprofits. Social organizations can survive and even thrive, but only certain types.

The purpose of this paper is to demonstrate and evaluate the use of Bayesian dynamic borrowing (Viele et al, in Pharm Stat 13:41-54, 2014) as a means of systematically utilizing historical information with specific applications to large-scale educational assessments. Dynamic borrowing via Bayesian hierarchical models is a special case of a general framework of historical borrowing where the degree of borrowing depends on the heterogeneity among historical data and current data. A joint prior distribution over the historical and current data sets is specified with the degree of heterogeneity across the data sets controlled by the variance of the joint distribution. We apply Bayesian dynamic borrowing to both single-level and multilevel models and compare this approach to other historical borrowing methods such as complete pooling, Bayesian synthesis, and power priors. Two case studies using data from the Program for International Student Assessment reveal the utility of Bayesian dynamic borrowing in terms of predictive accuracy. This is followed by two simulation studies that reveal the utility of Bayesian dynamic borrowing over simple pooling and power priors in cases where the historical data is heterogeneous compared to the current data based on bias, mean squared error, and predictive accuracy. In cases of homogeneous historical data, Bayesian dynamic borrowing performs similarly to data pooling, Bayesian synthesis, and power priors. In contrast, for heterogeneous historical data, Bayesian dynamic borrowing performed at least as well, if not better, than other methods of borrowing with respect to mean squared error, percent bias, and leave-one-out cross-validation.

Performance-targeted interventions are an important tool in improving educational outcomes and are often applied at the school level, where low-performing schools are selected for participation. In this paper, we aim to identify low-performing schools in Cambodia that are in need of support on improving students’ abilities in formulating math problems. Using data from the PISA for Development project, we present an application of a structured multilevel mixture item response theory (IRT) model that utilizes strategic constraints in order to achieve our research aims. The approach utilized in this application draws on psychometric traditions in multilevel IRT modeling, mixture IRT modeling, and constrained mixture IRT modeling. Results support classifications of Cambodian schools participating in PISA-D as low- and non-low-performing schools, as well as provide insight into these schools various contexts. Implications for future school interventions in Cambodia as well as future extensions to this modeling approach are discussed.

With decades of advance research and recent developments in the drug and medical device regulatory approval process, patient-reported outcomes (PROs) are becoming increasingly important in clinical trials. While clinical trial analyses typically treat scores from PROs as observed variables, the potential to use latent variable models when analyzing patient responses in clinical trial data presents novel opportunities for both psychometrics and regulatory science. An accessible overview of analyses commonly used to analyze longitudinal trial data and statistical models familiar in both psychometrics and biometrics, such as growth models, multilevel models, and latent variable models, is provided to call attention to connections and common themes among these models that have found use across many research areas. Additionally, examples using empirical data from a randomized clinical trial provide concrete demonstrations of the implementation of these models. The increasing availability of high-quality, psychometrically rigorous assessment instruments in clinical trials, of which the Patient-Reported Outcomes Measurement Information System (PROMIS®) is a prominent example, provides rare possibilities for psychometrics to help improve the statistical tools used in regulatory science.

Research questions that address developmental processes are becoming more prevalent in psychology and other areas of social science. Growth models have become a popular tool to model multiple individuals measured over several time points. These types of models allow researchers to answer a wide variety of research questions, such as modeling inter- and intra-individual differences and variability in longitudinal process (Molenaar 2004). The recently published book, Growth Modeling: Structural Equation and Multilevel Modeling Approaches (Grimm, Ram & Estabrook 2017), provides a solid foundation for both beginners and more advanced researchers interested in longitudinal data analysis by juxtaposing both the multilevel and structural equation modeling frameworks for several different models. By providing both sufficient technical background and practical coding examples in a variety of both commercial and open-source software, this book should serve as an excellent reference tool for behavioral and methodological researchers interested in growth modeling.

This study relied on the affective events theory and the social exchange theory to develop a framework that explains how situational factors (daily micro-interruptions) enhance affective reactions (negative affect) and, in turn, impair health conditions (mental health) at work. We further delineate theoretical arguments to propose the pet-human’s health effect by demonstrating that pets are boundary conditions that attenuate this relation, and as such are protective conditions for employees’ mental health. We conducted a 5-day diary study with two groups of participants, one with participants who owned pets (N = 82 x 5 = 410), and the other who did not own pets (N = 87 x 5 = 435). The multilevel results showed an indirect effect of daily micro-interruptions on individuals’ mental health through negative affect, with a daily backdrop of poorer mental health for those who did not own a pet (compared to those who owned a pet). These results evidence the benefits of owning a pet for individuals’ mental health, even at work, and as such provide recommendations for teleworking practices. Moreover, this study resorts to an innovative and robust data collection method to demonstrate the pet-human’ health effect. This study expands knowledge on the role of pets in working daily routines and shows that pets may be a personal resource for individuals while working.

Who joins extremist movements? Answering this question is beset by methodological challenges as survey techniques are infeasible and selective samples provide no counterfactual. Recruits can be assigned to contextual units, but this is vulnerable to problems of ecological inference. In this article, we elaborate a technique that combines survey and ecological approaches. The Bayesian hierarchical case–control design that we propose allows us to identify individual-level and contextual factors patterning the incidence of recruitment to extremism, while accounting for spatial autocorrelation, rare events, and contamination. We empirically validate our approach by matching a sample of Islamic State (ISIS) fighters from nine MENA countries with representative population surveys enumerated shortly before recruits joined the movement. High-status individuals in their early twenties with college education were more likely to join ISIS. There is more mixed evidence for relative deprivation. The accompanying extremeR package provides functionality for applied researchers to implement our approach.

Attempts to formalize inspection and monitoring strategies in industry have struggled to combine evidence from multiple sources (including subject matter expertise) in a mathematically coherent way. The perceived requirement for large amounts of data are often cited as the reason that quantitative risk-based inspection is incompatible with the sparse and imperfect information that is typically available to structural integrity engineers. Current industrial guidance is also limited in its methods of distinguishing quality of inspections, as this is typically based on simplified (qualitative) heuristics. In this paper, Bayesian multi-level (partial pooling) models are proposed as a flexible and transparent method of combining imperfect and incomplete information, to support decision-making regarding the integrity management of in-service structures. This work builds on the established theoretical framework for computing the expected value of information, by allowing for partial pooling between inspection measurements (or groups of measurements). This method is demonstrated for a simulated example of a structure with active corrosion in multiple locations, which acknowledges that the data will be associated with some precision, bias, and reliability. Quantifying the extent to which an inspection of one location can reduce uncertainty in damage models at remote locations has been shown to influence many aspects of the expected value of an inspection. These results are considered in the context of the current challenges in risk based structural integrity management.

The putative bilingual executive advantage has been argued to stem from lifelong experience with executively demanding language behaviors, such as switching between the two languages. However, studies testing for possible associations between language switching frequency and EF in bilinguals have yielded inconsistent results. One reason for this could lie in the methods used that have evaluated the frequency and type of language switches with retrospective self-reports, as well as in problems in reliability and convergent validity of the executive tasks. By using Ecological Momentary Assessment (EMA) as a reference point for self-reports of language switches, we examined the validity of general retrospective self-reports of language switching. Additionally, we examined associations between language switching and EF using multilevel models. Our results indicated that the commonly used retrospective self-reports of language switching may lack convergent validity. However, we found tentative evidence that contextual language switches, assessed with EMA, may be associated with better inhibitory control, set shifting, and working memory.

This study addresses why small parties nominate candidates to run in the district elections and how nomination of district candidates could influence small parties’ share of party votes in Taiwan. Previous studies on party's strategic entry in the mixed electoral system demonstrate the existence of ‘contamination effect’ in various Western democracies. While ‘contamination effect’ suggests that party would gain more proportional representation (PR) seats by increasing its number of candidate nomination in the single-member-district (SMD) races, we contend that small parties should also take the strength of nominated candidates into consideration. Nominating strong candidates in SMD competitions could generate positive ‘spillover effect’ to party's PR tier. By focusing on the 2016 Taiwan legislative election, our findings suggest that first, small parties need to fulfill the institutional requirements in order to qualify for running in the party-list election; second, the ‘contamination effect’ exists in Taiwan, but it is conditional; and finally, candidates’ strength creates positive ‘spillover effect’ on party's proportional seats.