Attention deficit/hyperactivity disorder (ADHD) is associated with an increased risk of cardiovascular diseases (CVDs). However, whether this is a causal relation and how ADHD may predispose to a higher risk of CVD needs to be determined. We aimed to assess the causal association between ADHD and both coronary artery disease (CAD) and heart failure (HF), and to quantify the mediating effects of potential modifiable mediators. We conducted a two-step, two-sample Mendelian randomization (MR) study using SNPs as genetic instruments for exposure and potential mediators. Leveraging summary data on the latest genomewide association studies for ADHD, proposed mediators (i.e., metabolic factors, inflammatory factors, lifestyle behaviors, psychiatric disorders, and educational attainment), CAD and HF, we decomposed the total effect of ADHD on each outcome into direct and indirect effects through multiple mediators. Genetically predicted ADHD was associated with increased odds of CAD (OR 1.13; 95% CI [1.07, 1.19]), with educational attainment (EA) being the largest contributor (32.27% mediation, 95% CI [18.33%, 56.93%]). Body mass index (BMI), type 2 diabetes (T2D), EA, smoking initiation (SI), and depression jointly explained 83.59% (95% CI [63.95%, 101.49%]) of the association. Genetically predicted ADHD was associated with increased odds of HF (OR 1.11; 95% CI [1.05, 1.19]), with SI being the largest contributor (35.87% mediation, 95% CI [13.75%, 100.14%]). BMI, T2D, and SI jointly explained 82.39% (95% CI [45.90%, 131.60%]) of the association. The findings support a causal relationship between ADHD and both CAD and HF. Several modifiable risk factors substantially mediate these associations, suggesting potential targets for interventions aimed at reducing CVD risk in individuals with ADHD.

Previous scholarship suggests that rising inequality in democracies suppresses trust in institutions. However, the mechanism behind this has not clearly been identified. This paper investigates the proposition that income inequality leads to increased democratic distrust by depressing perceptions of external efficacy. Based on time‐series cross‐sectional survey data from the European Social Survey, we find that changes in income inequality have a negative effect on changes in political trust and external efficacy. Causal mediation analysis confirms that inequality affects trust through lower efficacy. Further analyses show that this efficacy‐based mechanism does not depend on political orientation. As a direct effect remains among left‐wing respondents, our empirical results indicate that inequality affects trust via both a mechanism of substantive output evaluation and a process‐based evaluation that measures of external efficacy can capture. These findings highlight the empirical and theoretical relevance of this so far neglected mechanism and provide a potential solution for the puzzle that inequality depresses trust also among those for whom inequality is not politically salient.

Studies of variable lenition patterns converge on two phonetic properties as characteristic of lenition: reduced duration and increased intensity. However, the causal precedence of the two factors remains unclear. We focus on the causal structure of variable lenition. Study 1 examines the relationship between three correlates of lenition—speech rate, stress, and low information content—and their effect on reduced duration and increased intensity. We find that though increased intensity is more prototypically viewed as the core aspect of lenition, the effect of the three correlates on intensity is mediated by duration. Study 2 shows that all frequent lenition processes in the Buckeye corpus involve durational reduction. The contribution of this article is a proposal with a fairly simple principle, with few auxiliary assumptions: reduced duration precedes increased intensity in variable lenition.

Existing explanations of individual preferences for decentralisation and secession focus on collective identity, economic considerations and party politics. This paper contributes to this literature by showing that preferences for fiscal and political decentralisation are also driven by concern about the quality of government in the face of corruption. It makes two claims. Firstly, information on national‐level corruption decreases satisfaction with national politicians, and subsequently increases preferences for decentralisation and secession. Secondly, information on regional‐level corruption pushes citizens of highly corrupt regions to prefer national retrenchment and unitary states. The effects of this political compensation mechanism crosscut national identities and involve regions that are not ethnically or economically different from the core. We test our argument using a survey experiment in Spain and confirm its cross‐national generalisability with data from the European Values Study.

Process data, in particular, log data collected from a computerized test, documents the sequence of actions performed by an examinee in pursuit of solving a problem, affording an opportunity to understand test-taking behavioral patterns that account for demographic group differences in key outcomes of interest, for instance, final score on a cognitive item. Addressing this aim, this article proposes a latent class mediation analysis procedure. Using continuous process features extracted from action sequence data as indicators, latent classes underlying the test-taking behavior are identified in a latent class mediation model, where an examinee’s nominal latent class membership enters as the mediator between the observed grouping and outcome variables. A headlong search algorithm for selecting the subset of process features that maximizes the total indirect effect of the latent class mediator is implemented. The proposed procedure is validated with a series of simulations. An application to a large-scale assessment highlights how the proposed method can be used to explain performance gaps between students with learning disability and their typically developing peers on the National Assessment of Educational Progress (NAEP) math assessment.

Exposure to multiple languages may support the development of Theory of Mind (ToM) in neurotypical (NT) and autistic children. However, previous research mainly applied group comparisons between monolingual and bilingual children, and the underlying mechanism of the observed difference remains unclear. The present study, therefore, sheds light on the effect of bilingualism on ToM in both NT and autistic children by measuring language experiences with a continuous operationalization. We measure ToM with a behavioral, linguistically simple tablet-based task, allowing inclusive assessment in autistic children. Analyses revealed no difference between monolingual and bilingual NT and autistic children. However, more balanced exposure to different languages within contexts positively predicted first-order false belief understanding in NT children but not autistic children. Mediation analysis showed that the impact in NT children was a direct effect and not mediated via other cognitive skills.

In the past decade, researchers have been increasingly interested in understanding the process of language learning, in addition to the effect of instructional interventions on L2 performance gains (i.e., learning products). One goal of such investigations is to reveal the interplay between learning conditions, processes, and outcomes where, for example, certain conditions can promote attention to the learning targets, which in turn facilitates learning. However, the statistical modeling approach taken often does not align with the conceptualization of the complex relationships between these variables. Thus, in this paper, we introduce mediation analysis to SLA research. We offer a step-by-step, contextualized tutorial on the practical application of mediation analysis in three different research scenarios, each addressing a different research design using either simulated or open-source datasets. Our overall goal is to promote the use of statistical techniques that are consistent with the theorization of language learning processes as mediators.

Research shows that meritocratic recruitment (MR) in public administration is positively related to improved government performance and developmental outcomes. However, the mechanisms behind these improvements remain understudied theoretically and empirically. This paper addresses this gap by theorising and testing two simultaneous pathways through which MR influences development outcomes. First, by prioritising competence over nepotism or political expedience, MR enhances the epistemic quality of bureaucratic personnel (the competence mechanism). Second, by creating incentive misalignment between bureaucrats and politicians, it enables bureaucrats to resist undue political influence, prioritise public interests in governance, and ultimately contribute to development (the impartiality mechanism). Applying mediation analysis to fourteen years of cross-national data, we examine whether changes in recruitment systems are associated with competence- and impartiality-laden indicators of government performance and developmental outcomes. The findings provide robust empirical support for these mechanisms, advancing theoretical understanding and empirical insights into the effects of MR.

This study was designed to explore the mediating role of serum 25-hydroxyvitamin D (25(OH) D) in Triglyceride–glucose (TyG) index and hypertension (HTN). Study participants were selected from the 2001 to 2018 National Health and Nutrition Examination Survey. Firstly, we estimated the association between TyG index and serum 25(OH)D with HTN using a weighted multivariable logistic regression model and restricted cubic spline. Secondly, we used a generalised additive model to investigate the correlation between TyG index and serum 25(OH)D. Lastly, serum 25(OH)D was investigated as a mediator in the association between TyG index and HTN. There were 14 099 subjects in total. TyG index was positively and linearly associated with HTN risk, while serum 25(OH)D had a U-shaped relationship with the prevalence of HTN. When the serum 25(OH)D levels were lower than 57·464 mmol/l, the prevalence of HTN decreased with the increase of serum 25(OH)D levels. When serum 25(OH)D levels rise above 57·464 mmol/l, the risk of HTN increases rapidly. Based on the U-shaped curve, serum 25(OH)D concentrations were divided into two groups: < 57·464 and ≥57·464 mmol/l. According to the mediation analysis, when serum 25(OH)D levels reached < 57·464 mmol/l, the positive association between the TyG index and incident HTN was increased by 25(OH)D. When serum 25(OH)D levels reached ≥ 57·464 mmol/l, the negative association between the TyG index and incident HTN was increased by 25(OH)D. There was a mediation effect between the TyG index and HTN, which was mediated by 25(OH)D. Therefore, we found that the association between serum 25(OH)D levels and TyG index may influence the prevalence of HTN.

Extended redundancy analysis (ERA) is a statistical approach to component-based multivariate regression modeling that explores interrelationships among multiple sets of while incorporating regression with a data-reduction technique. The extant models that utilize ERA have assumed the outcome variables with the same data type. Also, ERA models focused on estimating direct pathways only without explicitly addressing mediation effects. In this paper, ERA is extended to handle multiple mediators and mixed types of outcome variables by adopting a Bayesian framework, taking into account correlation structure among all of the outcome variables. The proposed method develops an algorithm that derives the joint posterior distribution of parameters using a Markov chain Monte Carlo algorithm. Simulations and an empirical dataset are provided to illustrate the usefulness of the proposed method.

Mediation analysis constitutes an important part of treatment study to identify the mechanisms by which an intervention achieves its effect. Structural equation model (SEM) is a popular framework for modeling such causal relationship. However, current methods impose various restrictions on the study designs and data distributions, limiting the utility of the information they provide in real study applications. In particular, in longitudinal studies missing data is commonly addressed under the assumption of missing at random (MAR), where current methods are unable to handle such missing data if parametric assumptions are violated.

In this paper, we propose a new, robust approach to address the limitations of current SEM within the context of longitudinal mediation analysis by utilizing a class of functional response models (FRM). Being distribution-free, the FRM-based approach does not impose any parametric assumption on data distributions. In addition, by extending the inverse probability weighted (IPW) estimates to the current context, the FRM-based SEM provides valid inference for longitudinal mediation analysis under the two most popular missing data mechanisms; missing completely at random (MCAR) and missing at random (MAR). We illustrate the approach with both real and simulated data.

A social network comprises both actors and the social connections among them. Such connections reflect the dependence among social actors, which is essential for individuals’ mental health and social development. In this article, we propose a mediation model with a social network as a mediator to investigate the potential mediation role of a social network. In the model, the dependence among actors is accounted for by a few mutually orthogonal latent dimensions which form a social space. The individuals’ positions in such a latent social space are directly involved in the mediation process between an independent and dependent variable. After showing that all the latent dimensions are equivalent in terms of their relationship to the social network and the meaning of each dimension is arbitrary, we propose to measure the whole mediation effect of a network. Although individuals’ positions in the latent space are not unique, we rigorously articulate that the proposed network mediation effect is still well defined. We use a Bayesian estimation method to estimate the model and evaluate its performance through an extensive simulation study under representative conditions. The usefulness of the network mediation model is demonstrated through an application to a college friendship network.