For two-treatment randomized trials with clustering in one of the treatment arms and a continuous outcome, designs are presented that minimize the number of subjects or the amount of research budget, when aiming for a desired power level. These designs optimize the treatment-to-control allocation ratio of study participants but also optimize the choice between the number of clusters (such as therapy groups) versus the number of persons per cluster (therapy group) in the arm with clustering. Optimal designs require prior knowledge of parameters from the analysis model, which are unknown during the design stage. We present maximin designs which address this by ensuring a pre-specified power level for plausible ranges of the unknown parameters, while maximizing the power for worst-case values of these parameters. Maximin designs are also derived when the number of clusters, or the cluster size is fixed due to practical constraints. An empirical example illustrates how to calculate sample sizes for such practical designs and shows how much these maximin designs can reduce the required research budgets compared to designs with equal subject numbers in treatment and control. A user-friendly R Shiny app facilitates these sample size calculations.

Measurement invariance is a common interest in behavioral research to ensure that scores are comparable across groups or time. Evidence for measurement invariance is traditionally evaluated by comparing fit index differences between constrained and unconstrained models to cutoffs like ΔCFI > -0.01. However, traditional fit index difference cutoffs have been noted to have limited generalizability and potentially poor performance outside of conditions used to derive cutoffs. Dynamic measurement invariance (DMI) cutoffs were proposed to address generalizability concerns by re-simulating cutoffs for each model so that the cutoffs are custom-tailored to the researcher's model and data characteristics. However, a notable limitation of the extant fit index difference cutoff literature is that only multiple-group factor models for two-group comparisons have been considered. These models are appropriate for evaluating invariance of independent groups (e.g., demographic characteristics, countries, languages), but they cannot accommodate dependent groups with correlated responses across groups. Common examples of dependent groups include longitudinal data(i.e., groups are timepoints) or dyadic data (e.g., romantic partners, parents, and children). In such cases, models outside the multiple-group framework that incorporate between-group correlations are needed to evaluate invariance, but there is currently no guidance for interpreting fit index difference cutoffs in these models. Therefore, this paper proposes extending DMI cutoffs for dependent groups (DG-DMI). After describing the method, three empirical examples are provided to show how DG-DMI can inform conclusions about dyadic or longitudinal measurement invariance (including data with three or more timepoints). Open-source software is provided to facilitate the application of the method.

Resource variability shapes the ecology of social information and collective sensing.

Abstract The humanitarian impact of armed conflict remains a significant international issue, with an estimated 2 billion people residing in fragile or conflict‐affected settings. Despite increasing attention and study of armed conflict and its impact on human populations, few studies have evaluated the methods necessary to assess such relationships: how to use disaggregated and granular conflict data, measure and operationalize conflict, etc. In this study, we identify important considerations for conducting armed conflict and health research, including how data structures and decisions might impact conclusions. We discuss the particular characteristics of existing armed conflict datasets and the types of biases that may be present in data drawn from conflict‐affected areas. Further, we demonstrate how data and measurement choices can result in different conclusions and, if handled improperly, even spatial misclassification, bias, and spurious conclusions. Lastly, we illustrate some of these data and measurement choices in an empirical example where we assess the impact of conflict on women's contraceptive use in Nigeria. Using conflict event data from the Uppsala Conflict Data Program and health data from the Nigerian Demographic and Health Survey in 2018, we show how the relationship between exposure and outcomes varies across different spatiotemporal dimensions of conflict exposure measurement.

In this article, the author attempts to assess selected components of social capital in the context of stimulating social innovation in peripheral areas. The Middle Pomerania region serves as an empirical example. It is the region that is specific in terms of history and structure, which, despite the lack of formal delimitation in the administrative division, exhibits relative economic and social cohesion, while at the same time displaying high intra-regional diversity. The main objective of the article is to diagnose and assess the innovation potential of local communities operating within social organisations, which are main players in the creation of a regional innovation system. The results of the empirical research indicate that the peripherality of the region, although considered rather a dissimulating feature, can in itself be a catalyst for local activity. The inhabitants declare that the Middle Pomerania region, although peripheral, has sufficient social capital resources to create changes and social innovation. In her conclusions, the author suggests constant monitoring of the social capital in peripheral regions and encourages the building of relationships in the region through, among other things, supporting the activities of social organisations. These organisations can and should become stimulators of development for marginalised regions.

Abstract This article reveals late Ottoman (1876–1908) debates over agrarian policy in the empire’s Arab provinces that set the parameters for a fully articulated discourse of national economy emerging after the constitutional revolution of 1908. Debates between imperial officials, regional capitalists and foreign consular agents produced protectionist restrictions on the newly constructed agrarian land market, especially in an extended geography encompassing Palestine and the Hijaz Railway. Ottoman officials viewed the Arab provinces both as an untapped resource and as a possible alternative base in the event of Anatolia’s occupation. Restrictions on the land market constructed Muslim cultivators as ideal landholders and non-Muslim subjects, both Christian and Jewish, as potentially suspect and unfit for landholding. Protectionist and exclusionary agrarian policies responded to a wider context of imperial capitalism in which Ottoman officials occupied a subordinate, but still sovereign, position. These policies had an unrecognized legacy in the colonial and postcolonial Middle East, creating a durable state domain that aimed to shield large swaths of land as territory from foreign investment and occupation. The much-discussed work of Frederick List on national economy focused on practices of import substitution and tariffs with empirical examples culled mainly from the United States and Germany. In the Ottoman Empire, in contrast, agrarian policy played a crucial role in practices of national economy because of the urgent prerogative to maintain territorial sovereignty in the face of imminent colonial expansion. The article suggests a reappraisal of the contributions of Ottoman policy debates to the broader history of capitalism.

Abstract Multiplexity is a promising concept in International Relations (IR) to describe the emerging world order. For Amitav Acharya, a multiplex world is a world of complex interconnectedness and interdependence, with cross-cutting international orders and globalisms led by non-western actors. In a multiplex world, the West will have ‘to negotiate accommodation’ with these global South voices and influence ‘to salvage aspects of the liberal order’. This article interrogates the theoretical foundations of multiplexity by arguing that its claims of foregrounding the significance of the global South—which is, in turn, democratizing power relations—are problematic. Multiplexity as a concept ends up broadening the basis of oligarchy rather than democratizing power relations and empowering ordinary people. We show how this co-optation dynamic is implicit in the concept of multiplexity, where the deep-rooted commitment to the western neo-liberal ideology remains central. In the post-Second World War context, we use specific empirical examples such as the New International Economic Order, the BRICS and the G20, where the much-trumpeted arrival of a new multiplex world was co-opted by great powers. Ultimately, we call for critical multiplexity. The article synthesizes the insights of Gramscian hegemonic projects and Kautskyian ultra-imperialism, and focuses on the struggles of the people rather than on elite prerogatives. Critical multiplexity is a world that is attentive to the challenges against entrenched political and economic power relations and identity politics, without which multiplexity remains in closer communion with a superficially ‘diverse’ but still elitist and class-based international order.

The present study employs penalized structural equation modeling (PSEM) to conduct 1-step analyses of latent class models with covariates. Through the utilization of simulation studies, it is demonstrated that the 1-step approach yields unbiased parameter estimates, which are comparable to those obtained through the application of multi-step bias-correction methods, including the three-step, and BCH procedures. In contradistinction to multi-step approaches, the PSEM 1-step method necessitates only a single estimation step to incorporate covariates into the latent class model, thereby obviating the necessity for additional procedures to adjust for classification error. Moreover, we illustrate the practical application of the PSEM 1-step approach through an empirical example and provide Mplus syntax as a reference for applied researchers.

Purpose The paper aims to explore the often-overlooked career challenges faced by men from disadvantaged backgrounds, particularly in Western contexts. It seeks to highlight the underutilization of intersectionality frameworks in studying men's vocational and career experiences. By focusing on how gender norms, disadvantages, as well as technological and demographic shifts intersect, the paper intends to uncover unique barriers these men face. It ultimately aims to provide recommendations for organizational and policy interventions and identify avenues for future research to better support disadvantaged men's career development and integration. Design/methodology/approach The paper employs a critical approach, drawing on interdisciplinary literature from sociology, psychology, management, and cultural studies. It integrates an intersectional framework with a social structural perspective, analyzing existing empirical studies predominantly from Western countries. The authors conduct a broad literature review covering career stages such as vocational preparation and career entry and consider demographic and technological trends affecting disadvantaged men. Empirical examples and critical theoretical concepts are used to illustrate how masculinity intersects with variables such as socio-economic status, race, or migrant status to create unique career hurdles for men. Findings The paper reveals that men from disadvantaged backgrounds face multiple barriers throughout multiple career stages considered in this paper, rooted in gender norms, socio-economic inequities and systemic biases. These men experience educational disadvantages and career misalignment due to labor market shifts. Structural changes favor cognitive and social skills associated with upper-class masculinity, making physical strength less valued and diminishing opportunities in traditionally male-dominated sectors. These challenges contribute to social exclusion and barriers to joining emerging occupations. Research limitations/implications The paper primarily relies on data and studies from Western, affluent countries, mainly the USA, UK and parts of the EU, which limits its generalizability globally. Additionally, much of the research cited focuses on specific intersections and contexts; thus, findings may not apply universally. The authors acknowledge the scarcity of intersectional research on disadvantaged men, calling for more studies across diverse geographical and cultural settings to validate and extend current knowledge. Future research should explore the nuanced interactions of gender, class, race and other identity variables relevant to other regions, and to examine institutional practices affecting men's careers worldwide. Practical implications The paper proposes targeted organizational and policy interventions to support disadvantaged men's vocational and career development. Recommendations include enhancing educational programs that address boys' academic challenges, increasing the visibility of male role models in education and in female-dominated professions, and promoting inclusive workplace cultures that challenge harmful gender norms. These initiatives aim to broaden employment opportunities for disadvantaged men while contributing to gender equity and workforce diversity. Social implications The paper highlights the social consequences of neglecting disadvantaged men's career challenges, including increased risks of social exclusion, political backlash, and susceptibility to radicalization. It underscores how feelings of victimhood and insecurity linked to masculinity and socio-economic disadvantage can fuel hostility towards marginalized groups and lead to the rise of extremist and antisocial movements. Addressing men's unique career barriers is thus not only a matter of individual well-being but also crucial for social cohesion, equity, and the prevention of social unrest. The research encourages policies that foster inclusion and address intersectional inequities. Originality/value This paper makes a valuable and original contribution by bringing attention to an underexplored area: the career disadvantages faced by men from underprivileged backgrounds. It challenges prevailing assumptions that men universally benefit from organizational and societal structures, emphasizing the complexity of masculine situations and identities shaped by class, race, and other factors. By advocating for a social structural and intersectional framework, it broadens the scope of gender and career research and provides a foundation for future studies. The integration of technological and demographic megatrends further enriches the understanding of men's evolving career experiences and potential for future growth.

Abstract Large-scale surveys routinely rely on complex sample designs, necessitating special consideration of sampling variance estimation in multilevel models (MLM). While the sandwich estimator is widely used for this purpose, its implementation, particularly regarding stratification and weighting, remains challenging. Alternatively, the lesser-known replication methods provide a valid alternative; but they are often misunderstood as being only suitable for single-level models and are not widely supported by software packages. This paper clarifies key aspects of implementing both methods under two-level MLM common in large-scale surveys. We provide practical guidance on incorporating sample weights, correctly identifying variance strata for sandwich estimation, and applying replication-based variance estimation in MLM. Two simulation studies evaluate the performance of each method under correct and incorrect specifications, including omission of informative level-1 weights. Results demonstrate that the sandwich estimator and replication methods yield comparable variance estimates when implemented correctly and highlight the consequences of common misapplications. An empirical example using TIMSS 2015 Australia data is used to illustrate these issues in practice. This work contributes to improved methodological soundness in multilevel modeling and calls for expanded software support for replication methods in MLM.

Often in educational and behavioral randomized trials, researchers discover that subgroups of individuals respond differently to the same intervention due to underlying individual differences. Previous studies have investigated such heterogeneous treatment effects (HTE) across different latent classes for both cross-sectional and longitudinal (repeated measures) outcomes, but not in the context of longitudinal mediation analysis. This study develops Bayesian (nonlinear) growth mixture mediation models, B(N)GMMMs, to assess the HTE of the intervention variable X on the longitudinal dependent variable Y , via the longitudinal mediator M . We consider Y following linear or nonlinear trajectories and incorporate class predictive and growth predictive covariates. Monte Carlo simulations were conducted to evaluate model performance, and an empirical example demonstrates the model’s application.

Abstract Scholars, as well as practitioners, are well aware that engaging in politics is far from a gender-equal business, and they are still grappling with the question of how to understand the web of gendered constraints that continue to privilege men. This article seeks to provide conceptual and analytical tools that facilitate the analysis of the gendered playing field in politics. Based on empirical examples and feminist institutionalist theory, we provide a typology that facilitates the analysis of different types of gendered institutional constraints. Taking the concept of gender-equal political inclusion as our point of departure, we argue that institutional constraints in various nested spheres of society—the social, the political, and the subpolitical—need to be accounted for to adequately capture the complex web of formal as well as informal obstacles in a given context. The usefulness of this typology will be demonstrated with empirical illustrations.

Subgroup analysis is an important tool for studying treatment effect moderation. However, when a subgroup has a relatively small proportion (referred to as “focal subgroup”), standard subgroup analysis could encounter practical difficulties (e.g., low estimation precision). In this study, we propose an incremental subgroup analysis approach, which considers how the treatment effect would change as the proportion of focal subgroup gradually increases. The proposed approach provides estimates and confidence intervals for incremental subgroup effects, allowing visualization of the effect moderation trend with a continuous curve along with the corresponding confidence band. For estimation with baseline covariates, we extend a doubly robust method that can incorporate machine learning approaches for relaxing modeling assumptions, while allowing quantification of uncertainty for the effect estimate (e.g., via confidence intervals). Simulations are conducted to evaluate the performance of the estimation method. We illustrate the application of the proposed approach in an empirical example, assessing the moderation in the effect of a preventive intervention based on a relatively small subgroup. We hope that the proposed subgroup analysis approach provides an alternative or complementary method for studying effect moderation by subgroups.

In this paper, we propose a continuum of asymptotic stochastic dominance criteria as a novel rule for comparing and ranking prospects in long term decision-making contexts. The new criteria encompass the established asymptotic first-degree and second-degree stochastic dominance criteria, and between the two integer degrees, they effectively characterize the preferences of decision makers who are predominantly risk averse but do not categorically dislike all forms of risk, that is, those whose utility functions exhibit local convexities. To this end, we first introduce the concept of asymptotic fractional degree stochastic dominance, and then derive its equivalent conditions under the assumption of lognormal distributions. Furthermore, to enhance the tractability of asymptotic fractional degree stochastic dominance, under an additional condition on decision makers’ utility functions that marginal utilities are finite, we introduce a variant of asymptotic fractional degree stochastic dominance, referred to as operational asymptotic fractional degree stochastic dominance, and derive its corresponding equivalent distributional characterizations. The (operational) asymptotic fractional degree stochastic dominance offers a more comprehensive criterion for ranking prospects in long term decision-making contexts. This study elucidates how various constraints on marginal utilities of decision makers shape the equivalent distributional conditions of asymptotic stochastic dominance criteria. Empirical examples also illustrate that the newly proposed (operational) asymptotic fractional degree can be effectively utilized in practice. Funding: This work was supported by the National Natural Science Foundation of China [Grant 72271113] and the Natural Science Foundation of Jiangxi Province [Grants 20232ACB201003 and 20232BAB201022].

Abstract Mediation analysis in large-scale assessments often involves a multilevel structure, where students are nested within classrooms or schools. In such a context, multilevel structural equation modeling (MSEM) provides a flexible framework for estimating and testing the mediation process. Plausible values (PVs), however, present unique challenges for mediation analysis in large-scale assessments, yet methodological guidance remains limited. In particular, standard pooling procedures complicate the inference of indirect effects, which relies on the construction of confidence intervals. To address these gaps, we conducted a Monte Carlo simulation study comparing three modeling methods (aggregation, two-step approach, and MSEM) and three confidence interval methods (delta, distribution of the product, and Monte Carlo) in the context of 2–2–1 mediation with PVs. We evaluated their performance in terms of relative bias, confidence interval coverage, and power across a range of realistic conditions. Simulation results suggest that the MSEM-Monte Carlo combination performs best when sample size requirements were met. An empirical example is also provided to illustrate the practical implementation of 2–2–1 mediation analysis with PVs.

In psychological research, a common factor model is the most popular measurement model for scale items. However, there is increasing awareness that alternative measurement models, such as formative models, may make more theoretical sense for many kinds of psychological data. We demonstrate the nesting structure of three models specified in a structural equation modeling framework: a reflective confirmatory factor analysis (CFA), a formative Henseler-Ogasawara confirmatory composite analysis, and a formative pseudo-indicator model. Unlike CFA, Henseler-Ogasawara confirmatory composite analysis and pseudo-indicator model allow for the specification of composites in the structural equation modeling framework. In this article, we establish both theoretically and empirically that these three models are nested within one another, as long as the structural part of each model is saturated. As such, the three models can be compared via a chi-square difference test and other fit indices developed for nested models. We report on the results of a small simulation to evaluate whether the chi-square difference test and the root-mean-square error of approximation (RMSEA) based on it (RMSEAD) can reliably discern whether data were sampled from a CFA or a formative measurement model, varying sample size, indicator weights, and the strength of the correlation with another concept. In two empirical examples, we illustrate how tools for nested model comparison can be used to distinguish among reflective and formative measurement models. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

Understanding how effect heterogeneity across subgroups is mediated by multiple mediators is important yet under-studied. Despite the growing popularity of causal mediation analysis, existing methods rarely address the mediation of moderated treatment effects, particularly when multiple mediators are of interest. This study develops a causal inference approach for mediated moderation analysis with multiple mediators, decomposing a moderated treatment effect into mediated moderation effects and remaining moderation. We present causal estimands and extend a multiply robust estimator that can incorporate machine learning techniques to relax modeling assumptions. Simulations were conducted to evaluate the method’s performance. An empirical example about adolescent mental health illustrates the application. We hope this study provides a novel causal inference-based approach to understanding multiple mediating mechanisms underlying subgroup heterogeneity in treatment effects.

ABSTRACT Climate change intensifies droughts and floods, altering the supply and demand of water resources in ways that may produce or reproduce water injustice. Understanding where, when, and how these injustices emerge is critical to ensure fair water governance. Using a systemic literature review, this paper seeks to advance research on water governance by compiling and analysing empirical examples of water injustice in a changing climate across OECD river basins. The review finds a predominant focus on water scarcity. Manifestations of water injustice vary across contexts, and responses are insufficient to address their root causes. Water injustice is for the most part operationalised in ways that integrate distributional, procedural, and recognitional dimensions. The literature highlights four distributive water injustices, including maladaptive outcomes, unequal adaptive capacities, unjust energy transitions, and state and industry priorities. Two procedural water injustices emerge, namely historical exclusion from water governance and practical barriers to participation. Participatory interventions are put forward as a response. The literature on recognitional water injustices points to the misrecognition of Indigenous ontologies and epistemologies, local knowledge, and root causes of water injustice. These findings lead to four recommendation for future research and practice: (1) take the local context seriously when researching and addressing water injustice, (2) adopt a systems perspective, (3) accelerate societal transformations to tackle the root causes of water injustice, and (4) address underexplored research areas including unintended consequences emerging from participatory interventions, epistemic injustices in water governance, social vulnerability and water (in)justice, and implications of maladaptation for water injustice.

ABSTRACT Some studies have expressed concern that the Gaussian‐inverse Wishart–Haar prior typically employed in estimating sign‐identified VAR models may be unintentionally informative about the prior for the structural responses. We discuss what features to look for in this prior in the absence of specific prior information about the responses, building on the notion of weakly informative priors in Gelman et al. (2013), and in the presence of such information. Empirical examples illustrate that the Gaussian‐inverse Wishart–Haar prior need not be unintentionally informative. Even when it is, there are empirically verifiable conditions under which this fact becomes immaterial for the substantive conclusions.

ABSTRACT Human resource management (HRM) is inherently complex. It involves systems of principles, practices, and activities operating at individual, group, organizational, and macro levels, which are interlinked through complex processes. Yet, empirical research has not kept pace with this conceptual richness. We hope to inspire HRM scholars to address complexity. We draw on theoretical frameworks that explicitly conceptualize systems as systems , processes as processes and systems as processes complemented by empirical examples (mainly from adjacent fields) to illustrate how these modes of thinking have helped solve complex problems and how this could be applied to HRM. Embracing complexity in HRM is not about making research harder for its own sake but about seeing the field as it truly is dynamic, interconnected, and evolving. We need to shift our attention from isolated variables toward patterns, processes, and interdependencies across time. This requires richer data, deeper collaboration, and methodological boldness, and opens new possibilities for understanding the patterns and dynamics that shape people and organizations. Complexity in HRM invites us to see connections rather than fragments, to uncover the dynamics beneath the surface, and to design insights that matter. Perhaps the future of HRM research lies not in simplifying complexity, but in embracing it.