Bayesian analysis for confirmatory factor model with finite-dimensional Dirichlet prior mixing

Confirmatory factor analysis (CFA) model is a useful multivariate statistical tool for interpreting relationships between latent variables and manifest variables. Often statistical results based on a single CFA are seriously distorted when data set takes on heterogeneity. To address the heterogeneity resulting from the multivariate responses, we propose a Bayesian semiparametric modeling for CFA. The approach relies on using a prior over the space of mixing distributions with finite components. Blocked Gibbs sampler is implemented to cope with the posterior analysis. Results obtained from a simulation study and a real data set are presented to illustrate the methodology.     

Paternal Risk Factors for Child Maltreatment and Father's Participation in a Primary Prevention Program in Germany

ABSTRACT

Across the globe, the research into the influence of paternal risk and protective factors on the family, as well as on the involvement of fathers in family-related interventions, is lacking. This study utilized 506 families and examined the characteristics of fathers in psychosocially stressed families and associations between paternal risk factors (PRFs: mental health disorder, physical health disorder, young paternity, unemployment, absence of father) and family risk factors (FRFs: problematic financial situation, problematic housing situation, social isolation) for child maltreatment. The identification of PRFs and FRFs was carried out using information from consultations with social education workers and from a risk inventory completed by the mothers. The data analysis revealed an increased incidence of economic problems found in families with fathers having mental health issues, fathers under the age of 22, and unemployed fathers or single mothers. Other factors such as social isolation occurred more often in families with a single mother. The present study underlines the importance of a father's involvement for young families. Future research and program recommendations should always systematically include the father's role in family-related interventions.     

品牌形象认知与品牌选择关系的实证研究

消费者的品牌形象认知对其品牌选择行为有重要影响。在消费者的品牌形象认知指标中,从对其品牌选择影响性质的差异来看,存在＂维护因子＂与＂增长因子＂两类指标。＂维护因子＂是品牌选择行为,也是品牌价值产生的必要条件;＂增长因子＂是品牌价值产生的充分条件,但必须在＂维护因子＂的基础上起作用。通过对不同品牌形象认知指标与消费者品牌选择行为之间的关系分析,有助于品牌管理者合理分配资源,提高品牌管理效率。     

基于因子分析法的我国区域人类发展实证研究

本文介绍并引进了以因子分析法为基础构建的人类发展指数(HDI)。通过指标体系的构建、评估方法的选择及与联合国开发计划署制订的人类发展指数比较发现,基于因子分析法的人类发展指数较之传统的方法更适合测算与度量我国区域人类发展水平。     

论我国养老保险的筹资模式

如何选择最佳的筹资方式是依托社会保障体系化解我国人口老龄化压力的关键所在。作为最基本的养老金筹资方式,现收现付制与个人帐户制的特征与对经济的影响不同,但并不能简单地由此分出两者谁优谁劣,不是由现收现付制完全转为个人帐户制所有问题就会迎刃而解。未来我国养老保险筹资方式的上佳选择是建立多支柱、多层次的社会保障体系。在此前提下,现收现付制与个人帐户制相互协调,共同存在较之相互替代更有助于缓解不可避免的养老金支付困难。     

女性性别意识及其影响因素———以福建省为例

利用2000年全国第二期妇女社会地位抽样调查福建样本的相关统计资料,描述女性性别意识的时期变动及其与男性性别意识的差异,并在综述前人研究的基础上,提出解释性别意识的理论模型。多元回归分析结果表明,个人的年龄、受教育程度、职业性质、对工作的满意度、社会流动经历以及家庭婚姻状况对女性性别意识产生显著的影响。     

人口城镇化对人力资本和物质资本效用发挥影响的分析

通过在对生产函数中引入人口城镇化变量的人力资本外部性内生增长模型进行检验分析,揭示了人口城镇化对人力资本、物质资本等生产要素在经济增长中的具体作用和作用途径。检验分析发现:在过去的20多年里,人口城镇化对人力资本和物质资本在经济增长中的作用发挥都有积极的促进;以人口城镇化所反映人口聚集对人力资本和物质资本的作用也是不同的,主要体现于物质资本的人口聚集效应,而人力资本的人口聚集效应还未充分发挥,在未来城镇化进程中应该注重人力资本要素的效应。     

新疆塔塔尔族人口死亡研究

长期以来,对塔塔尔族人口的研究很少有人涉猎。从第五次人口普查的相关数据,可以分析出塔塔尔族人口的死亡水平、影响因素以及死亡原因。     

甘肃省不同地区出生缺陷致病因素分析

为提高人口素质,预防出生缺陷的发生。我们根据甘肃地理特点及经济状况,按随机分层整群抽样方法,抽取当地出生的5岁内婴幼儿作为调查对象。以出生缺陷患儿为病例组,以同年、同性别、居住地相近的正常儿作为对照,对其家庭进行调查。结果显示甘肃省不同地区出生缺陷共有的易患因素为:母亲孕早期服药史;母亲孕早期感染史;父亲年龄在35岁以上亦是产生出生缺陷的危险因素。提示实施出生缺陷干预工程应大力进行孕前、孕早健康教育,深化优生指导,以达到降低出生缺陷发生率的目的。     

Clustering to reduce regional heterogeneity: A spanish case-study

Statistical methods of dimension reduction and classification are used to obtain homogeneous local-area clustering with regard to the most relevant demographic parameters. The dimension reduction is conducted in two stages using Principal Component Analysis and a modified k-mean procedure is proposed to determine the final clusters. This clustering will be useful in future demographic studies at a local level, in particular to obtain forecasts of demographic rates and population projections. The region of Castile and León in Spain is used to illustrate the method. A Poisson model is used to explore the advantages of the new clustering over the more conventional classification based on provinces.