The role of social and cognitive factors in individual gambling: An empirical study on college students

Most of the studies on the determinants of individual gambling behaviour rely on cognitive theories. In our study, we argue that, besides cognitive factors, several social factors might play an important role as well. We analyse data from an ad hoc webmail survey conducted on about 2000 undergraduate students enrolled in a large public university in the Northern Italy in the academic year 2012–13. Using a variety of statistical techniques (standard regression models, boosted regression trees and structural equations models), we show that social variables affect both participation in gambling in the past year and latent gambling propensity. In particular, controlling for several proxies for individual cognitive ability and understanding of probability, gambling propensity is positively affected by the degree of gambling in the social surrounding (parents, peers, neighbourhood) and the acceptability of gambling activities to the individual. Moreover, in our sample of college students the role of social factors appears to be larger than that of cognitive factors, and this is consistent across different types of models and specifications.     

Adjustment for nonresponse with variables from different sources: Bias correction and precision loss,with application to the Swiss European Social Survey 2012

ABSTRACT

Adjustment for nonresponse should reduce the nonresponse bias without decreasing the precision of the estimates. Adjustment for nonresponses are commonly based on socio-demographic variables, although these variables may be poorly correlated with response propensities and with variables of interest. Such variables nevertheless have the advantage of being available for all sample units, whether or not they are participating in the survey. Alternatively, adjustment for nonresponse can be obtained from a follow-up survey aimed at sample units which did not participate in the survey and from which the variables are designed to be correlated with response propensities. However, information collected through these follow-up surveys is not available for people in the sample who participated neither in the survey nor in its nonresponse follow-up. These two sets of variables when used in a nonresponse model for the Swiss European Social Survey 2012 differ only slightly with regard to their effect on bias correction and on the precision of estimates. The variables from the follow-up are performing slightly better. In both cases, the adjustment for nonresponse performs poorly.     

Inappropriate Comparisons: The LGB Population's Missing Reference Group

ABSTRACT

Given that many professional organizations emphasize the need for comparing assessment results to appropriate reference groups in their codes of ethics, we consider this practice through an ecological perspective as it applies to the lesbian, gay, and bisexual (LGB) population. The purpose of norming and types of assessment bias is discussed with respect to LGB population, which frequently lack specific norming data. The authors discuss whether the LGB population has characteristics similar to other commonly normed for groups and explore how group-shared constructs such as minority stress and internalized homophobia may introduce bias and affect the validity of assessments. A social anxiety assessment as well as a personality disorder inventory are examined for bias as case examples explaining how these assessments may exhibit bias when used with the LGB population. Increases in validity and effects on treatment planning are discussed as benefits of providing LGB-specific norms. Risks of providing these norms, such as minimizing subgroup differences and possibly increasing the risk of discrimination are also addressed before providing clinicians with recommendations as to how they may minimize the risk of bias in their assessment process.     

Merging data from multiple sources: pretest and shrinkage perspectives

In this article, we have developed asymptotic theory for the simultaneous estimation of the k means of arbitrary populations under the common mean hypothesis and further assuming that corresponding population variances are unknown and unequal. The unrestricted estimator, the Graybill-Deal-type restricted estimator, the preliminary test, and the Stein-type shrinkage estimators are suggested. A large sample test statistic is also proposed as a pretest for testing the common mean hypothesis. Under the sequence of local alternatives and squared error loss, we have compared the asymptotic properties of the estimators by means of asymptotic distributional quadratic bias and risk. Comprehensive Monte-Carlo simulation experiments were conducted to study the relative risk performance of the estimators with reference to the unrestricted estimator in finite samples. Two real-data examples are also furnished to illustrate the application of the suggested estimation strategies.     

A class of exponential-type ratio estimators of a general parameter

This paper addresses the problem of estimating a general parameter using information on an auxiliary variable X. We have suggested a class of exponential-type ratio estimators for the parameter and its properties are studied. It is identified that the estimators due to Upadhyaya et al. [Journal of Statistical Theory and Practice (2011), 5(2), 285–302] and Yadav and Kadilar [Revista Columbiana de Estadistica, (2013), 36(1), 145–152] are members of the proposed estimator. We have also shown that the suggested estimator is more efficient than the estimators of Upadhyaya et al. (2011 Upadhyaya, L.N., Singh, H.P., Chatterjee, S., Yadav, R. (2011). Improved ratio and product exponential type estimators. J. Stat. Theo. Pract. 5 (2): 285–302.[Taylor &; Francis Online] , [Google Scholar]) and Yadav and Kadilar (2013 Yadav, S.K., Kadilar, C. (2013). Improved exponential type ratio estimator of population variance. Revis. Colum. de Estadist. 36(1): 145–152. [Google Scholar]). Numerical illustration is provided in support of the present study.     

学前儿童性别教育的研究现状及其启示

学前儿童性别教育的有关研究表明,社会上还存在着根深蒂固的性别歧视和性别偏见,身处特定环境中的学前教育教师以及图画书、教学媒体等仍然带有较强的性别歧视和性别偏见等性别刻板印记。为此,国家需要在学前儿童性别教育研究的基础上重视学前教育阶段的性别平等教育,制定相关的政策,建立性别教育的终身教育体系,构建教师性别平等教育的培训网络平台,以现代性别教育理念为指导,基于对儿童生理差异的尊重,设计出适合学前儿童身心发展特点的性别教育课程,全面有效地实施性别平等教育。     

中国投资者股利偏好行为的实证分析

运用超额收益法对深圳和上海证券市场的股利政策市场效应进行实证研究,探析不同类型股利的市场效应及投资者行为。结果表明,中国证券投资者在投资决策过程由于受心理因素的影响,对于不同类型的股利政策做出了不同的市场反应,并表现出一定的股利偏好行为,即证券投资者对现金股利政策,尤其稳定的现金股利政策表现出明显的喜好行为,而对于不分配股利政策却表现出了漠视效应行为。     

留守经历对新生代农民工工作流动的影响--基于2015年北京市数据的实证检验

本文基于2015年北京市新生代农民工的微观调查数据，在利用倾向得分倒数加权法纠正样本的选择性偏差以后发现：（1）新生代农民工过高的工作流动频率在一定程度上与其普遍的留守经历有关；（2）不同类型的留守形式和留守时间对工作流动的影响有所不同，完全留守、长期留守者表现出更高的工作流动性；（3）相同的留守行为对工作流动的影响存在明显的性别差异，男性农民工更易受到童年时期留守经历的影响。上述结论的政策启示在于：在关注留守经历及其所带来的影响差异的同时，也应避免留守问题的代际传承。     

Estimation of a discriminant function from a mixture of two inverse Weibull distributions

The classification of a random variable based on a mixture can be meaningfully discussed only if the class of all finite mixtures is identifiable. In this paper, we find the maximum-likelihood estimates of the parameters of the mixture of two inverse Weibull distributions by using classified and unclassified observations. Next, we estimate the nonlinear discriminant function of the underlying model. Also, we calculate the total probabilities of misclassification as well as the percentage bias. In addition, we investigate the performance of all results through a series of simulation experiments by means of relative efficiencies. Finally, we analyse some simulated and real data sets through the findings of the paper.     

First‐order bias correction for fractionally integrated time series

Most of the long memory estimators for stationary fractionally integrated time series models are known to experience non‐negligible bias in small and finite samples. Simple moment estimators are also vulnerable to such bias, but can easily be corrected. In this article, the authors propose bias reduction methods for a lag‐one sample autocorrelation‐based moment estimator. In order to reduce the bias of the moment estimator, the authors explicitly obtain the exact bias of lag‐one sample autocorrelation up to the order n⁻¹. An example where the exact first‐order bias can be noticeably more accurate than its asymptotic counterpart, even for large samples, is presented. The authors show via a simulation study that the proposed methods are promising and effective in reducing the bias of the moment estimator with minimal variance inflation. The proposed methods are applied to the northern hemisphere data. The Canadian Journal of Statistics 37: 476–493; 2009 © 2009 Statistical Society of Canada