城市化进程中农民满意度的Logistic回归分析

农民满意度是反映城市化进程中失地农民的自身利益需求与社会保障之间差异的重要指标.本文通过构造补偿实际比和相对生活改善率来定义一种新的农民满意度指标,并且对洛阳市及其周边地区的调查数据建立了Logistic回归方程,回判正确率为85%,结果表明所建立的农民满意度指标模型是基本可行的.     

物流系统中的客户服务与企业竞争力

首先介绍客户服务的概念、特征、构成要素及标准;然后简要介绍客户关系管理,阐述企业竞争力的内涵,论述物流企业中客户服务对企业赢得竞争优势的重要性;最后指出客户服务水平绩效评价要素及如何不断改进企业客户服务水平以适应客户不断发展变化的需求．     

中长期消费信贷的影响因素及发展路径——基于安徽省城镇居民调查的多分类Logistic回归模型分析

中长期消费信贷是消费信贷的重要组成部分,但在信贷约束和收入支出不确定性的条件下,消费者往往容易产生短视行为,出现"消费的过度敏感性",从而对中长期消费信贷形成制约。在安徽省城镇居民消费信贷调查的基础上,构建多分类Logistic回归模型,分析城镇居民中长期消费信贷的影响因素。结果表明,年龄、职业类型、五年期以上贷款利率等变量的显著性水平均为10%,具有统计学意义,而文化程度、婚姻状况、月供/月消费没有统计学意义,城镇居民愿意选择中长期消费信贷的概率为66.8%,预测正确率为95.1%,并提出发展中长期消费信贷的路径。     

基于层次分析法的物流企业竞争力模糊综合评价

评价物流企业竞争力是培育与发展物流企业竞争力的基础和必要环节。从物流企业竞争力评价方法研究现状分析入手,建立了基于物流企业竞争力层次模型的评价指标体系,并运用了基于层次分析法的模糊综合评价法对物流企业竞争力加以评价分析,为物流企业培育与提高竞争力指明方向。     

高校后勤队伍的“人本发展”与“人本服务”

高校后勤管理服务在量的扩张、质的提升、服务手段等方面呈现出新特点,而后勤管理队伍却存在人员结构复杂、专业程度与文化素质不高、培养与使用不相协调等问题,针对上述问题就如何进行后勤队伍的"人本发展",更好地推进以师生为本的"人本服务",提出了相应的措施,并对两者之间的辩证关系展开论述。     

The differential effects of preschool: Evidence from Virginia

This study investigated the differential and persistent effects of a state-funded pre-K program, the Virginia Preschool Initiative (VPI). We analyzed data from a cohort of over 60,000 students nested in approximately 1000 schools from the beginning of kindergarten to the end of first grade using two-level hierarchical logistic regression models. While controlling for student- and school-level variables, we found that attending a VPI-funded program was beneficially associated with a lower likelihood of repeating kindergarten and improved probabilities of meeting or exceeding minimum literacy competencies. The benefits of attending pre-K were greatest upon kindergarten entry and persisted until the end of the first grade for Hispanic and Black students, as well as for students with disabilities, despite VPI-funded program attendees going to schools with higher concentrations of poverty.     

Grades and Graduation: A Longitudinal Risk Perspective to Identify Student Dropouts

ABSTRACT

Studies of student risk of school dropout have shown that present predictors of at-risk status do not accurately identify a large percentage of students who eventually drop out. Through the analysis of the entire Grade 1–12 longitudinal cohort-based grading histories of the class of 2006 for two school districts in the United States, the author extends past longitudinal conceptions of dropout to a longitudinal risk perspective, using survival analysis, life tables, and discrete-time hazard modeling to appropriately account for student graduation, transfer, or dropout. The risk of dropout began in Grade 7, with the most hazardous years at Grades 8 and 11. A novel calculation of teacher-assigned grades, noncumulative GPA, is identified as a strong predictor of student dropout.     

Exploring differential bundle functioning in mathematics by gender: the effect of hierarchical modelling

Researchers interested in exploring substantive group differences are increasingly attending to bundles of items (or testlets): the aim is to understand how gender differences, for instance, are explained by differential performances on different types or bundles of items, hence differential bundle functioning (DBF). Some previous work has modelled hierarchies in data in this context or considered item responses within persons, but here we model the bundles themselves as explanatory variables at the item level potentially explaining significant intra-class correlation due to gender differences in item difficulty, and thus explaining variation at the second item level. In this study, we analyse DBF using single- and two-level models (the latter modelling random item effects, which models responses at Level 1 and items at Level 2) in a high-stakes National Mathematics test. The models show comparable regression coefficients but the statistical significances of the two-level models are smaller due to the larger values of the estimated standard errors. We discuss the contrasting relevance of this effect for test developers and gender researchers.     

International determinants of private school attendance

The current study uses Programme for International Student Assessment (PISA) 2006 data to investigate international determinants of private school attendance. In particular, we seek to understand whether student achievement and home background factors such as socioeconomic status (SES) or motivational and goal-oriented factors are more predictive of private school attendance – in line with the concept of “cream-skimming’. Using baseline category logit models for nominal responses, our findings indicate that SES, rather than achievement or attitude, is the strongest, most consistent international predictor of attending a school managed or funded in large part by the private sector. Instead of traditional cream-skimming, our analysis suggests that a sorting effect of another kind exists – private schools (managed or funded) tend to lure students from better socioeconomic backgrounds, while public schools tend to attract the most engaged students.     

因子分析在上市公司财务预警中的应用

本文以我国A股票市场上因＂财务状况异常＂而被特别处理的制造类上市公司为研究对象,运用logistic回归分析法构建了我国制造类上市公司财务危机发生前三年的预警模型,并检验了该模型的有效性。