This study examines an evaluation that used Schwandt's post-normal evaluation design to create a structure that would capture the uncertainties and complexities encountered in implementing a novel professional development (PD) program at four university teacher preparation programs. Three distinct levels of evaluation were addressed in the study: (1) the evaluation of the PD implementation (RQ 1-2), the evaluation within the project conducted by the PD team internally (RQ 3-4), and an evaluation of the evaluation design itself (RQ 5-6). The PrimeD framework guided the PD implementation and all levels of the evaluation. PrimeD supported continuous collaboration among and within evaluator and implementation teams, which strengthened the focus and consistency of the project vision and goals across all four institutions. Monthly Networked Improvement Community (NIC) meetings provided a forum for participants to share ideas and develop plans for improving their professional practice using Plan-Do-Study-Act (PDSA) cycles. Conclusions, best practices, and contextual challenges were shared across institutions by the participants (teacher candidates, program alumni, classroom mentor teachers, and field experience supervisors). Observations of NIC meetings and focus groups of participants provided the data used to adjust approaches to the implementation and evaluation. Throughout this 5-year PD project, evaluation and research grew to become an integral part of everyday lesson development conversation. The PD implementation team also shared findings across institutions, resulting in modifications of the teacher preparation and early career programs across all four sites that led to better alignment across programs while retaining necessary contextual differences.
This study examines how retailer share repurchases interact with supply chain contract design in expanding markets, focusing on retailers' market development efforts, suppliers' pricing strategies, and the choice between wholesale price and revenue-sharing contracts. We construct a supply chain contract model incorporating a retailer's share repurchases, incentive-compatible mechanisms, market expansion, and control premium costs. We compare wholesale price and revenue-sharing contracts to evaluate their effects on risk-reward allocation and upstream-downstream coordination. Share repurchases can reduce agency conflicts and make retailers' market-expansion commitments more observable. However, they may also generate control premium costs and weaken channel coordination. Revenue-sharing contracts linked to retailer share repurchases can align the interests of original shareholders, private equity institutions, and suppliers while mitigating control premium effects and tunneling incentives. Retailer share repurchases are not merely capital-market transactions; their supply chain value depends on whether buyback-induced financial incentives can be translated into coordinated pricing, effort, and operational decisions. Retailers should align repurchase decisions with market development and ordering strategies. Suppliers can use revenue-sharing contracts to balance pricing incentives and downstream revenue allocation. Private equity institutions should calibrate governance mechanisms to support collaborative operations rather than excessive ownership-based control.
This paper addresses identification challenges in social sciences using observational data through tailored examples from crime and criminal justice research. The paper highlights how structural causal models and directed acyclic graphs make the assumed causal structure explicit and help diagnose potential pitfalls such as collider bias and the use of inadmissible instruments or covariate sets. By distinguishing identification from estimation, the paper emphasizes the importance of specifying a causal model that makes identifying assumptions explicit and supports valid inference under those assumptions. The case studies demonstrate how structural causal models can sharpen identification strategies and improve the credibility of causal claims in applied crime research.
Evidence synthesis research has great potential for informing decision-making, as it provides a holistic understanding of an entire body of literature. Meta-analyses are particularly important because they estimate intervention effects that can be used to inform both policy and practice. However, outside the field of healthcare, there has been little evidence that policymakers and practitioners use findings from meta-analyses in their work. A key barrier to the use of meta-analyses is that researchers often report advanced statistical estimates that non-research audiences struggle to comprehend. A critical first step to closing this gap is for researchers to translate meta-analytic average effect sizes in ways that may be more accessible to non-research audiences. This guide provides evidence synthesis researchers with easily accessible effect size transformations that can be performed on meta-analytic estimates of average standardized mean difference (SMD) effect sizes from random-effects models. The guide includes calculations, R code, statistical interpretations, and interpretations for non-research audiences for the average SMD (and its corresponding confidence interval and prediction interval), along with four transformations: Cohen's U3, overlapping coefficient, common language effect size, and probability of study effect superiority. Importantly, we summarize literature exploring how well non-research audiences comprehend these transformations so that researchers can be informed when choosing which transformation(s) to report (if any). We also note where more research is needed to better understand how non-researchers interpret these different transformations. Overall, this guide provides practical tools for researchers to present effect sizes in ways that may enhance the accessibility of evidence synthesis findings.
The rapid growth of crowdfunding markets has created an important managerial challenge for entrepreneurs: when raised capital substantially exceeds funding targets, how should excess funds be allocated to maximize efficiency, meet stakeholder expectations, and mitigate financial risk? Past research focuses on achieving funding goals, lacking systematic guidance for preferred strategy selection amid overfunding. Furthermore, differing crowdfunding typologies (reward-based, equity-based, debt-based) involve distinct stakeholder relationships and capital obligations, while varying overfunding degrees lead to qualitatively different challenges. Nevertheless, past research has not established decision frameworks that integrate typology and the degree of overfunding, leaving entrepreneurs without contextualized strategic guidance. The present study develops a hierarchical compromise decision-support framework to evaluate excess capital allocation strategies while determining the preferred option across nine contexts (three typologies × three gradients). The study identifies critical factors affecting strategy evaluation and determines five strategic options (scaling project, enhancing quality, accelerating development, designating reserves, returning funds) across contexts. The findings reveal three insights: (1) typologies exhibit fundamental strategic divergences: reward-based crowdfunding transitions from capital preservation to quality enhancement as overfunding decreases; equity-based consistently prioritizes capital retention to meet long-term investor expectations; while debt-based adopts a conservative orientation, returning funds under high overfunding to reduce leverage risks; (2) critical factors vary with overfunding: high overfunding highlights stakeholder relationship management, moderate emphasizes operational execution, and mild focuses on foundational capabilities; (3) the interaction effect between typology and degree is significant, requiring entirely different responses across typologies. The present study contributes to crowdfunding research and practice as follows. Its primary contribution is a structured, expert-based decision-support framework that, to our knowledge, is among the first to compare preferred post-success allocations across typologies and gradients, which indicates that preferred strategies depend on contextual combinations rather than on single factors. The study identifies nonlinear overfunding effects, verifying that moderate overfunding facilitates capability building, while extreme overfunding leads to stakeholder management burdens. Practically, the study provides a foundation for platforms to design differentiated advisory services and offers contextualized frameworks for entrepreneurs and consultants, with essentials implications for strengthening resource allocation efficiency in crowdfunding ecosystems.
Scholarly publications in social sciences have increased sharply and continue to expand in the era of big data and artificial intelligence. However, comprehensive syntheses of quantitative research methods remain scarce, presenting significant challenges for early-career researchers and graduate students in selecting appropriate research methods for their studies. To address this gap, we propose a conceptual framework that provides a structured guide to quantitative techniques, thereby enhancing methodological literacy and supporting informed decision-making in academic publishing. By integrating applied statistics and operations research-spanning basic to advanced analytics along a static-dynamic continuum, this framework empowers novice scholars to navigate technique selection effectively and mitigates the dominance of a few methodological choices in economics, business, and management research.
We offer a practical roadmap for implementing Counsel at First Appearance (CAFA) in the United States using evidence from Texas. CAFA consists of early access to legal representation for individuals facing bail adjudications after arrest, a stage of the case in which defendants do not enjoy a federal constitutional right to counsel. Drawing on randomized studies conducted in Texas and observations from jurisdictions that adopted CAFA, the paper outlines a step-by-step approach to implementation. It identifies stakeholders, including judges, public defenders, prosecutors, court administrators, and policymakers-and emphasizes the importance of early coordination and shared objectives. Alongside procedural guidance, the paper presents a set of questions for jurisdictions to consider during planning and rollout. By providing actionable strategies, the paper serves as a resource for jurisdictions and policymakers aiming to enhance fairness and efficiency through CAFA.
This paper aims to demonstrate how evaluation methods can inform the development of a program, using the example of a novel housing mobility program. The limited availability of Housing Choice Vouchers (HCVs) and other affordable housing assistance restricts the ability to address US national housing needs; millions more households qualify for HCVs than receive them. Those with HCVs encounter challenges in using vouchers in so-called high-opportunity areas. To address the dearth of HCVs and difficulty using them, a collaboration between faculty at Ohio State University and the community developed a new housing mobility program to increase the affordable housing supply, engaging stakeholders to understand the needs of low-income renters who are less likely to obtain an HCV. This paper outlines the Families Flourish program's development using both developmental and formative evaluations, examining the progress of the implementation and immediate outcomes using mixed methods. The developmental evaluation process required collaboration with a wide variety of stakeholders, partners, and the project team. According to the formative evaluation of the pilot program, participants' experiences suggested that enhanced housing security was associated with improvements in physical and mental health for parents and their children, and that a more stable and safe neighborhood environment was similarly associated with gains in employment, education, and income. Throughout the process of evaluation, particularly the developmental evaluation, the evaluator's communication and collaboration skills were essential to the methodology. The study concludes with lessons learned, as well as recommendations for policy and directions for future research.
The evidence-based policy movement assumes that rigorously evaluated interventions replicate across contexts to produce similar effects. This universalist assumption may be unwarranted for complex social programs, where context shapes effectiveness. This study examines generalizability using 15 cash transfer programs evaluated across 12 Middle Eastern and North African countries through four complementary approaches: leave-one-out cross-validation, permutation tests, equivalence testing, and fragility indices. Leave-one-out analysis demonstrates robust stability, with 100% of key relationships maintaining a magnitude within 25% of full-sample estimates. The correlation between transfer size and poverty reduction (r = 0.711, p < .001) shows strong consistency, and permutation tests yield p = .001, indicating genuine signals rather than chance patterns. Equivalence testing reveals that programs with nearly identical designs can produce statistically distinguishable outcomes, with confidence intervals excluding practical equivalence in 67% of matched comparisons. Fragility indices of 2 to 3 for most relationships indicate moderate robustness. These findings suggest that some design-outcome relationships show stability while context matters substantially for program effectiveness. This study offers methodological tools for assessing evidence robustness in small-sample comparative research and argues for epistemic humility in cross-context predictions.
Brokers serve as key connectors linking academic researchers who might otherwise remain unconnected, to co-authorship networks. This study examines whether more complex interdisciplinary co-authorships yield greater scholarly impact than ties with authors from the same discipline, which are facilitated by cognitive similarity. It also tests the extent to which such collaborations influence the academic performance of female researchers. The study analyzed 594 authors and 271 papers in the field of social learning. Despite their complexity, regression analyses confirm the benefits of novel sources and combinations of interdisciplinary knowledge. The higher coordination and communication costs of interdisciplinary collaboration are offset by the potential of the new knowledge generated. Interdisciplinary collaboration is associated with higher performance among female scholars, contributing to increased recognition and reputation. This pattern suggests that collaboration strategies and institutional support should reduce coordination barriers in interdisciplinary co-authorship and facilitate women's access to high-impact brokerage opportunities.
The evidence base for parent support groups in child welfare systems is limited but growing. Evaluation of such programs requires flexible and context-specific designs that capture both outcomes and mechanisms of change. This exploratory mixed-methods evaluation examined a peer-led support group program for parents who experienced child removal in a large Florida county to better understand how these programs operate and benefit parents working toward reunification. A total of 22 parents participated in weekly sessions facilitated by peer specialists with prior child welfare involvement. Quantitative assessments measured changes in psychosocial functioning and engagement in child welfare services, while semi-structured interviews (n = 8) explored participant perceptions of group processes and benefits. Significant decreases were observed in depression, trauma, and loneliness, alongside increases in perceived emotional support. Thematic analysis identified four interrelated themes that informed an empirically grounded conceptual framework describing the functions and benefits of support groups.This evaluation study demonstrates how mixed-methods approaches can yield actionable evidence from small scale community-based interventions and offers insight into the mechanisms that may inform future implementation and evaluation of peer-led models.
Recent advancements in Generative AI (GenAI) demonstrate strong potential for enhancing student evaluation and assessment practices through complementary and symmetric human-AI collaboration. This study examines the dispositions, perceived benefits, and challenges that teachers experience when they and GenAI complement one another in providing formative assessment and feedback. Partnering with a leading GenAI-powered EdTech platform, we conducted a case study in a Norwegian secondary school using Learny, a GenAI-supported feedback tool. Qualitative data were collected from teachers who employed Learny in their feedback practices. The findings indicate that teachers were generally curious and open to using GenAI, while emphasizing the need to maintain professional responsibility. They valued GenAI's capacity to save time, offer inspiration, and deliver timely feedback, yet raised concerns about feedback quality, lack of relational and contextual awareness, and the risk of student over-reliance on such feedback. The study contributes to the growing literature on hybrid intelligence in education by identifying conditions under which GenAI can effectively support, rather than replace, human educators. It underscores the centrality of teacher agency in AI-supported feedback and assessment practices and outlines key implications for pedagogy, future GenAI system design, and sustainable implementation.
By exploiting the panel data of 100 countries over the period 2006 to 2022, I show how climate change policies and green growth within a circular economy affect entrepreneurship. Higher energy intensity predicts a lower rate of new firm creation. In contrast, higher green growth correlates with a higher number of new businesses. The interaction between energy intensity and green growth indicates that environmental support can partially offset the adverse effects of energy-intensive economic structures. Using genetic distance to the United Kingdom as an instrumental variable, I establish a causal relationship. In particular, the findings suggest that well-designed environmental policies can act as a catalyst for new firm formation, especially in circular economies facing structural and developmental constraints.
As artificial intelligence (AI) systems assume greater responsibility in educational assessment, questions surrounding fairness, transparency, and trust have become central to their ethical and pedagogical legitimacy. Yet, little empirical work has examined how specific design features shape students' trust in AI-driven assessment, particularly in contexts where algorithmic decisions carry meaningful academic consequences. This study examines how transparency, ethical framing, and user agency influence students' trust in an AI-based assessment platform. Using a 2 × 2 × 2 between-subjects experimental design with 240 undergraduate participants, the study isolates the main and interaction effects of these variables on trust, perceived fairness, perceived control, and adoption intention. Findings indicate that transparency is the most influential predictor of trust, while user agency functions as a compensatory mechanism in low-transparency conditions. Ethical framing, although theoretically salient, showed limited impact once users interacted with the system directly and shifted their attention toward the more concrete procedural cues embedded in the interface. A significant interaction between transparency and agency underscores the importance of aligning epistemic clarity with procedural control to foster behavioral commitment. These results support a multidimensional model of trust that incorporates emotional security, procedural justice, and behavioral intent. Overall, the study underscores that trust in AI assessment is not a byproduct of system accuracy alone but a reflection of students' perceived legitimacy of the evaluative process.
Knowing how to implement emergency material scheduling and transportation during emergency rescues, such as major and critical emergencies, has become a research hotspot in academia and industry in recent years. To leverage the speed and terrain-insensitive advantages of aviation, the weak limitations of geographical conditions must be addressed, and the material scheduling efficiency of aviation rescue centers in disaster-stricken areas needs to be improved. In this study, CRITIC and cloud model theory were integrated to evaluate the urgency of emergency material demands in different flood-stricken areas under catastrophic flood disaster risks. Furthermore, a mathematical model for a single aviation emergency rescue center to dispatch emergency materials to multiple disaster-stricken sites was designed based on the optimized ant colony algorithm. A penalty function was then incorporated to formulate a multi-objective aviation scheduling model, aiming to minimize both total rescue time and total cost. The model was solved using an improved genetic algorithm. Taking rainstorm-induced flood disasters in the megacity of Zhengzhou, China, in 2021 as the empirical research case, the operating paths for the aviation emergency rescue center to serve multiple demand points were optimized. The impact of material demand urgency on scheduling decisions was analyzed. Results revealed that when material demand urgency is considered, aircraft complete deliveries according to urgency rankings and return to the center. All tasks can be completed within the required time via four routes. Although total time increases, economic cost is significantly reduced, and disaster loss is mitigated. The findings obtained from this study provide a decision-making reference for improving the efficiency of aviation emergency rescue and enhancing urban risk management capabilities in response to major and critical emergencies.
The aim of this study was to investigate the impact of the shift in recent years from passive to active parental consent for youth' participation in research. A longitudinal study that took place during this change provided the unique opportunity to analyze which adolescent (demographic, alcohol use, norms) and parental factors predict the recipient of consent. The sample consisted of 691 adolescents between 12 and 17 years old (M-age = 14.22; SD = 1.03; 44.9% boys). Factors for each and across five domains (socio-demographic, alcohol use, individual, group/peers, and parental factors) were included in a multiple logistic regression analysis to predict non-consent at follow-up. Across domains, results showed that adolescents who are older (OR = 0.60, p < .001), female (OR = 0.60, p < .001) and those perceived less strict rules about alcohol (OR = 1.26, p = .05), have a higher odds of not having consent for participation. These findings indicate that a specific selection of adolescents were given permission to participate in research, yet this was not particularly an at-risk group. Implications of these findings, such as the balance between autonomy and protection, are discussed.
This study addresses the problem of attrition bias in longitudinal education RCTs by comparing multiple attrition correction methods. Our analysis of three early reading experiments in Zambia, Ethiopia, and South Africa shows that treatment status is generally not associated with attrition, while baseline reading skills are negatively correlated with it. We estimate bounds under alternative assumptions about the attrition process. Our main contribution lies in proposing guidelines for selecting appropriate correction methods based on data characteristics, including the extent of treatment-effect heterogeneity and the correlation of attrition with baseline characteristics. These guidelines incorporate a novel diagnostic for assessing the plausibility of the stochastic-dominance assumption. The findings offer practical direction for researchers on how to design, implement, and interpret evaluations where attrition poses risks to causal validity.
Labor relations in Vietnam are undergoing a dramatic transition, moving from a system dominated by a single state-led trade union federation toward one that permits the formation of more independent unions. Understanding the impact of trade unions on labor market outcomes is therefore increasingly urgent to support this transition. However, empirical evidence remains scarce due to data limitations. This study provides the first evidence on the effects of trade unions on workers' wages across the wage distribution in the private sector, using a large, nationally representative dataset. Employing the Unconditional Quantile Regression approach, we estimate union effects across the wage distribution and find consistently negative coefficients at all quantiles for men. To further examine the distributional structure of the wage gap, we implement an Oaxaca-Blinder-type decomposition based on the Recentered Influence Function. The decomposition results reveal that, for both men and women, the wage gap becomes increasingly negative from the middle toward the upper end of the wage distribution. While the total explained component displays some instability at the lower quantiles, its absolute magnitude rises substantially at higher quantiles. This research contributes to a deeper understanding of union roles in contexts where political institutions play a dominant role in shaping union activities.
This paper evaluates the impact of racial and low-income quotas on the academic performance of senior students in Brazilian colleges and universities. Using longitudinal data from Brazil's Higher Education Census and the National Examination of Student Performance (ENADE), and employing a fixed effects approach, the study examines the influence of these quotas on student outcomes. The results show that neither racial nor low-income quotas significantly impact the academic performance of either quota or non-quota students. This finding holds across different groups of majors, indicating that the inclusion of quota students does not detract from overall student achievement.
Negative menstruation experiences adversely affect schoolgirls' social participation, education, and overall health, yet comprehensive menstrual health interventions are limited in Tanzanian schools. This study evaluated the feasibility, acceptability, cost, and potential impact mechanisms of a comprehensive school-based menstrual, sexual, and reproductive health (MSRH) intervention. The intervention, piloted in four secondary schools, included education sessions for girls and boys, pain management, distribution of menstrual kits (reusable pads and menstrual cups), WASH improvements, and stakeholder engagement. A mixed-methods process evaluation assessed acceptability, fidelity, cost, context, and potential impact mechanisms using qualitative methods (in-depth interviews, focus group discussions, and WASH observations) and quantitative methods (survey questionnaires, structured observation of education sessions, costing, and monitoring data). The intervention was well received by students, teachers, and local government authorities; MSRH education reached 86% of schoolgirls and 72% of schoolboys, while over 93% of girls received menstrual kits. Total implementation cost across the four schools was 111,347,467 TZS (38,003 GBP), approximately 39.05 GBP per student, lower than comparable initiatives in East Africa. Findings indicate the intervention is feasible and acceptable in school settings and can inform future menstrual health and hygiene programs, though further research is needed to assess broader effectiveness and sustainability.
