Refinement and validation of a comprehensive scale for measuring HR practices aimed at performance-enhancement and employee-support

The purpose of this paper is to refine and validate a Human Resource practices (HRP) scale to measure employees' perceptions and test a two-tier model structured in eight practices and two bundles. In a sample of 554 employees, an EFA (Exploratory Factor Analysis) offered six factors that explained about 70% of the variance. Then, with 1647 employees (from 41 Spanish organizations), first- and second-order models were tested with Confirmatory Factor Analysis (CFA). The former encompasses eight practices. The latter grouped the practices in two bundles, one on enhancing performance and the other on supporting employees. The Cronbach's alpha, Rho coefficient (Composite Reliability Coefficient), Omega coefficient, and Spearman-Brown split half coefficient showed good reliability. Validity evidence was found for construct, criterion, convergent, content, discriminant, and predictive validity. Moreover, the paper integrates different ways of approaching the study of HR management based on employees' perceptions, using a two-tier approach. The two-bundle model showed better fit, pointing out the importance of paying attention to multiple outcomes for employees and organizations. The study makes a relevant theoretical contribution about the role and aims of HRM practices for organizational success and employees' performance and well-being.     

Risk Analysis: Celebrating the Accomplishments and Embracing Ongoing Challenges

As part of the celebration of the 40th anniversary of the Society for Risk Analysis and Risk Analysis: An International Journal, this essay reviews the 10 most important accomplishments of risk analysis from 1980 to 2010, outlines major accomplishments in three major categories from 2011 to 2019, discusses how editors circulate authors’ accomplishments, and proposes 10 major risk-related challenges for 2020–2030. Authors conclude that the next decade will severely test the field of risk analysis.     

Coalition for a Healthier Community: Lessons learned and implications for future work

The Coalition for a Healthier Community (CHC) initiative was implemented to improve the health and well-being of women and girls. Underpinning CHC is a gender-based focus that uses a network of community partners working collaboratively to generate relevant behavior change and improved health outcomes. Ten programs are trying to determine whether gender-focused system approaches are cost-effective ways to address health disparities in women and girls. Programs implemented through coalitions made up of academic institutions, public health departments, community-based organizations, and local, regional, and national organizations, are addressing health issues such as domestic violence, cardiovascular disease prevention, physical activity, and healthy eating. Although these programs are ongoing, they have made significant progress. Key factors contributing to their early success include a comprehensive needs assessment, robust coalitions, the diversity of populations targeted, programs based on findings of the needs assessments, evaluations taking into consideration the effect of gender, and strong academic–community partnerships. A noteworthy impact of these programs has been their ability to shape and impact public, social, and health policies at the state and local levels. However, there have been challenges associated with the implementation of such a complex program. Lessons learned are discussed in this paper.     

The value of being precise

This study investigates how individuals assess imprecise information. We focus on two essential dimensions of decision under uncertainty, outcomes and probabilities, and their respective precision. We believe the precision of information is highly relevant in the investment setting, as reflected in the well-known “home (familiarity) bias”, and the outcome and probability dimensions, separately or jointly, may affect investors’ knowledge of uncertainty and perceived risk of the investment options, and subsequently affect investors’ choices. To test this conjecture, we conducted three experiments. Our results show that 1) participants demonstrate a pattern of preference for precision and aversion of extreme vagueness and associate vagueness with higher perceived risk and lower investment (experiments one and two); 2) participants prefer vague outcome information to vague probability information (experiment two); 3) familiarity indeed positively affects the precision of estimated values, but this association is stronger for the outcome dimension than for probabilities (experiment three). Our results confirm that precision in information, especially in the outcome dimension has an impact on investors’ resource allocation choices.     

A New Methodology for Before–After Safety Assessment Using Survival Analysis and Longitudinal Data

The widely used empirical Bayes (EB) and full Bayes (FB) methods for before–after safety assessment are sometimes limited because of the extensive data needs from additional reference sites. To address this issue, this study proposes a novel before–after safety evaluation methodology based on survival analysis and longitudinal data as an alternative to the EB/FB method. A Bayesian survival analysis (SARE) model with a random effect term to address the unobserved heterogeneity across sites is developed. The proposed survival analysis method is validated through a simulation study before its application. Subsequently, the SARE model is developed in a case study to evaluate the safety effectiveness of a recent red‐light‐running photo enforcement program in New Jersey. As demonstrated in the simulation and the case study, the survival analysis can provide valid estimates using only data from treated sites, and thus its results will not be affected by the selection of defective or insufficient reference sites. In addition, the proposed approach can take into account the censored data generated due to the transition from the before period to the after period, which has not been previously explored in the literature. Using individual crashes as units of analysis, survival analysis can incorporate longitudinal covariates such as the traffic volume and weather variation, and thus can explicitly account for the potential temporal heterogeneity.     

对新课程实施中学生评价方法的哲学思考——标准化考试与档案袋评定的整合

评价既可能成为课程改革的动力 ,也可能成为其实施的障碍 ,它是课程改革成败的关键。为使基础教育课程改革达到预期目的 ,必须将评价改革作为课程改革的一个重要部分 ,探索出与新课程理念相一致的评价方法。目前国外较为流行的评价学生的方法是档案袋评定 ,这种评价理念与我国的新课程理念相一致 ,它强调学生参与 ,注重学生发展。然而档案袋评定能够作为标准化考试的替代评价法吗 ?笔者从不同哲学观进行分析 ,最后得到结论 :档案袋评定和标准化考试是看似对立、实质互补的两种评价方法 ,教师在实际评价中必须将两种方法结合运用     

企业家素质量化评价方法的研究

首先概括了企业家素质的重要作用和企业家素质对企业能力的重要影响 ,在此基础上提出在评价企业家素质时企业家素质与企业能力的重要关联性 ,然后提出了一种以企业家素质对企业能力的影响为考核对象的企业家素质评价方法     

浅析电子商务中的信息安全风险评估

本文结合电子商务自身特点,从对信息资产风险对象的确定、风险评估方法选择、评估的具体指标评价和结果的分析后,确定信息资产的风险控制策略。同时从投入与收益的比较选择中,简单分析控制措施选择与成本预算方面的关系。     

Chemical risk communication through the Internet in Spain

In this work, some corporate websites of the chemical industry of Tarragona (Spain) are studied, analysing how the chemical trade associations and companies present information and/or encourage dialogue with the community on issues relating to the chemical risk and their environmental, health and safety (EHS) performance. The results suggest that the chemical industry in Tarragona uses the corporate websites mainly to disseminate information about its EHS commitments and performance, but they do not encourage dialogue with the community through the Internet.     

A modified random-effect procedure for combining risk difference in sets of 2×2 tables from clinical trials

Summary Meta-analyses of sets of clinical trials often combine risk differences from several 2×2 tables according to a random-effects model. The DerSimonian-Laird random-effects procedure, widely used for estimating the populaton mean risk difference, weights the risk difference from each primary study inversely proportional to an estimate of its variance (the sum of the between-study variance and the conditional within-study variance). Because those weights are not independent of the risk differences, however, the procedure sometimes exhibits bias and unnatural behavior. The present paper proposes a modified weighting scheme that uses the unconditional within-study variance to avoid this source of bias. The modified procedure has variance closer to that available from weighting by ideal weights when such weights are known. We studied the modified procedure in extensive simulation experiments using situations whose parameters resemble those of actual studies in medical research. For comparison we also included two unbiased procedures, the unweighted mean and a sample-size-weighted mean; their relative variability depends on the extent of heterogeneity among the primary studies. An example illustrates the application of the procedures to actual data and the differences among the results. This research was supported by Grant HS 05936 from the Agency for Health Care Policy and Research to Harvard University.