Evaluation Review最新文献

The impact of pro-environmental behavior on policymaking has been an exciting area of research. While the relationship between pro-environmental behavior and policymaking has been explored in numerous studies, there needs to be more synthesis on this topic. This is the first text-mining study of pro-environmental effects in which policymaking is a significant factor. In response, this study, for the first time, takes a novel approach by using text mining in R programming to analyze 30 publications from the Scopus database on pro-environmental behavior in policymaking, highlighting major research themes and prospective research areas for future investigation. Results from text mining yielded 10 topic models, which are presented with a synopsis of the published research and a list of the primary authors, as well as a posterior probability via latent Dirichlet allocation (LDA). Additionally, the study conducts a trend analysis of the top 10 journals with the highest impact factor, considering the influence of each journal's mean citation. The study offers an overview of the impacts of pro-environmental behavior in policymaking, showing the most relevant and frequently discussed themes, introduces the scientific visualization of papers published in the Scopus database, and proposes future study directions. These findings can help researchers and environmental specialists better understand how pro-environmental behavior can be fostered more effectively through policymaking.

Stroke is the leading cause of death and disability among people in China, and it leads to heavy burdens for patients, their families and society. An accurate prediction of the risk of stroke has important implications for early intervention and treatment. In light of recent advances in machine learning, the application of this technique in stroke prediction has achieved plentiful promising results. To detect the relationship between potential factors and the risk of stroke and examine which machine learning method significantly can enhance the prediction accuracy of stroke. We employed six machine learning methods including logistic regression, naive Bayes, decision tree, random forest, K-nearest neighbor and support vector machine, to model and predict the risk of stroke. Participants were 233 patients from Sichuan and Chongqing. Four indicators (accuracy, precision, recall and F1 metric) were examined to evaluate the predictive performance of the different models. The empirical results indicate that random forest yields the best accuracy, recall and F1 in predicting the risk of stroke, with an accuracy of .7548, precision of .7805, recall of .7619 and F1 of .7711. Additionally, the findings show that age, cerebral infarction, PM 8 (an anti-atrial fibrillation drug), and drinking are independent risk factors for stroke. Further studies should adopt a broader assortment of machine learning methods to analyze the risk of stroke, by which better accuracy can be expected. In particular, RF can successfully enhance the forecasting accuracy for stroke.

In 2003, Bloom, Hill, and Riccio (BHR) published an influential paper introducing novel methods for explaining the variation in local impacts observed in multi-site randomized control trials of socio-economic interventions in terms of site-level mediators. This paper seeks to improve upon this previous work by using student-level data to measure site-level mediators and confounders. Development of asymptotic behavior backed up with simulations and an empirical example. Students and training providers. Two simulations and an empirical application to data from an evaluation of the Health Professions Opportunity Grants (HPOG) Program. This empirical analysis involved roughly 6600 participants across 37 local sites. We examine bias and mean square error of estimates of mediation coefficients as well as the true coverage of nominal 95-percent confidence intervals on the mediation coefficients. Simulations suggest that the new methods generally improve the quality of inferences even when there is no confounding. Applying this methodology to the HPOG study shows that program-average FTE months of study by month six was a significant mediator of both career progress and long-term degree/credential receipt. Evaluators can robustify their BHR-style analyses by the use of the methods proposed here.

Program evaluations often investigate complex or multi-dimensional constructs, such as individual opinions or attitudes, by means of ratings. A different interpretation of the same question may affect cross-country comparability, leading to the Differential Item Functioning problem. Anchoring vignettes were introduced in the literature as a way to adjust self-evaluations from this interpersonal incomparability. In this paper, we first introduce a new nonparametric solution to analyse anchoring vignette data, recoding a variable based on a rating scale to a new corrected-variable that guarantees comparability in any cross-country analysis. Then, we exploit the flexibility of a mixture model introduced to account for uncertainty in the response process (the CUP model) to test if the proposed solution is effectively able to remove this reported heterogeneity. This solution is easy to construct and has important advantages compared with the original nonparametric solution adopted with anchoring vignette data. The novel indicator is applied to investigate self-reported depression in an old population. Data that will be analysed come from the second wave of the Survey of Health, Ageing and Retirement in Europe, collected in 2006/2007. Results highlight the need of correcting for reported heterogeneity comparing individual self-evaluations. Once interpersonal incomparability resulting from the different uses of response scales is removed from the self-assessments, some estimates are reversed in magnitude and signs with respect to the analysis of the collected data.

Economic corridors unlock new economic opportunities and tourism development in the region to achieve sustainable development goals. Green economic growth is conducive to environmental sustainability. Economic mega-projects of CPEC promote tourism that leads to communities' well-being and better quality of life. Modern infrastructure development contributes significantly to economic growth and tourism activities. This study's objectives emphasize exploring tourism and sustainable development pursuits under OBOR economic projects that open doors to improving residents' quality of life. The growing world is an eyewitness to a continuous rise in emissions and its severe consequences for humankind. It is necessary to show off the leading factors that result in tourism and economic activities causing environmental pollution rather than blame policymakers. Undoubtedly, many studies previously focused on demonstrating the influence of socio-economic factors that lead to better environmental quality. However, the empirical literature on tourism, social well-being, foreign direct investment, and the Environment in Belt and Road developed economies needed improvement. This research applied a series of advanced estimators that help demonstrate the study's probable results. This study explores the role of Social well-being (HDI), tourism development, FDI, renewable energy, information & communication technology (ICT), and urbanization on CO2 emissions in Belt and Road (BRI) developed economies.Estimated results exhibited the significant contribution of ICT and renewable energy to sustainability. Besides, FDI contributes to emissions reduction after its threshold level. Conversely, urbanization and tourism activities contribute to environmental pollution. The study outcomes stated inverted/EKC U-shaped hypotheses related to specified economies. Finally, the analysis based on the D-H panel causality test constructs exciting results.The present study concludes that economic corridor plays a vital role in tourism development, the community's well-being, and SDGs goals (sustainable development) impact on environmental safety. The findings suggest essential and applicable policies to attain the desired sustainability level. Findings contribute to the literature on tourism, well-being, and sustainability. Further studies can use insights using this methodology.

Randomized experiments involving education interventions are typically implemented as cluster randomized trials, with schools serving as clusters. To design such a study, it is critical to understand the degree to which learning outcomes vary between versus within clusters (schools), specifically the intraclass correlation coefficient. It is also helpful to anticipate the benefits, in terms of statistical power, of collecting household data, testing students at baseline, or relying on administrative data on previous cohorts from the same school. We use data from multiple cluster-randomized trials in four Latin American countries to provide information on the intraclass correlations in early grade literacy outcomes. We also describe the proportion of variance explained by different types of covariates. These parameters will help future researchers conduct statistical power analysis, estimate the required sample size, and determine the necessity of collecting different types of baseline data such as child assessments, administrative data at the school level, or household surveys.

This article describes a conceptual and empirical approach for estimating a human capital production function of child development that incorporates mother- or child-fixed effects. The use of mother- or child-fixed effects is common in this applied economics literature, but its application is often inconsistent with human capital theory. We outline the problem and demonstrate its empirical importance with an analysis of the effect of Head Start and preschool on child and adult outcomes. The empirical specification we develop has broad implications for a variety of applied microeconomic analyses beyond our specific application. Results of our analysis indicate that attending Head Start or preschool had no economically or statistically significant effect on child or adult outcomes.

In this paper, based on the established Vitae Researcher Development Framework (RDF), we introduce a new framework, tailor-made specifically for Vietnamese educational researchers, namely, Vietnam's Framework for Educational Researchers (VFER). VFER is expected to serve as a tool for Vietnamese educational researchers to self-evaluate their skills and support them in developing their career qualities from junior to senior career ladders. The framework includes four domains with ten subdomains and 28 indicators. To date, VFER has been implemented in some Vietnamese universities of pedagogical education. We expect that other research fields will look to VFER as a reference to build their own research capacity framework.

When individuals are released from prison, they typically enter a period of post confinement community supervision. While under community supervision, their behaviors are subject to special conditions requiring them to report to supervisors and prohibiting certain behaviors such as drug and alcohol use. Many supervisees are returned to prison because they violate those special conditions, or because they commit minor crimes that would not result in prison were they not being supervised. But others are returned to prison for serious new crimes. We distinguish the two as nuisance behaviors (the former) and pernicious behaviors (the latter). Our research applies competing events survival analysis to distinguish a structural model that accounts for nuisance behaviors from a structural model that accounts for pernicious behaviors. We demonstrate that returning offenders to prison for technical violations and minor crimes may reduce the incidence of major crimes because the occurrence of nuisance behaviors and pernicious behaviors are highly correlated. Our findings support the theory that nuisance behaviors signal the likelihood of pernicious behaviors.