现代远程教育《秘书实务》课程学习支持模式研究

从学习支持服务的学习者、学习支持内容、学习支持策略等要素入手,在远程教育"教"与"学"时空分离的情况下,结合《秘书实务》课程重实践、重操作的课程特点,探索现代远程教育《秘书实务》课程学习支持模式的构建。     

INFLUENTIAL OBSERVATION IDENTIFICATION IN THE GROWTH CURVE MODEL WITH RAO'S SIMPLE COVARIANCE STRUCTURE

ABSTRACT

In this paper we discuss the identification of influential observations in a growth curve model with Rao's simple covariance structure. Based on the generalized Cook-type distance and the volume of a confidence ellipsoid, a variety of influence measures are proposed in terms of the case-deletion technique. Also, the influence of observations on a linear combination of regression coefficients is considered. For illustration, a practical example is analyzed using the proposed approach.     

Slowing Down Mobilities: Passengering on an Inter-war Ocean Liner

Abstract

This article contributes to recent work on the figure of the passenger and ‘passengering’ in mobilities research. It does so by considering the experiences of one passenger over two journeys on an ocean liner undertaken in 1930. The article ‘follows’ the passenger over the course of the two journeys in order to open up the processuality of passengering. The article makes two core contributions. First, it demonstrates the need to lend attention to the specific socialities and materialities of mobile modalities that work on and form the passenger. It is shown how specific assemblages come together at sea to create atmospheres that shape the experiences of long-distance travel over water. Second, by following the passenger over the course of journeying, it demonstrates the emergence of the passenger in relation to shifting affective atmospheres, not as a static figure but one of becoming passenger, as a shifting set of subjectivities, and, in turn, shows the value of ‘following’ a journey within mobilities research.     

The nearest proportional to size sampling design

The construction of a proportional to size sampling design nearest to a given design is introduced. The realization of the new design can be easily made with the help of the original one.     

An empirical goodness-of-fit test for multivariate distributions

An empirical test is presented as a tool for assessing whether a specified multivariate probability model is suitable to describe the underlying distribution of a set of observations. This test is based on the premise that, given any probability distribution, the Mahalanobis distances corresponding to data generated from that distribution will likewise follow a distinct distribution that can be estimated well by means of a large sample. We demonstrate the effectiveness of the test for detecting departures from several multivariate distributions. We then apply the test to a real multivariate data set to confirm that it is consistent with a multivariate beta model.     

Internationalizing Social Work Education Using Blackboard 6: INHOLLAND University,NL and James Madison University,USA

SUMMARY

Creating, delivering, and evaluating a pilot course in two countries and at two universities via Blackboard 6 presents pedagogical, cultural, and technological challenges as well as many rewards. The developmental process and negotiations required by both universities to bring the course to fruition are explored, including faculty workload, interactivity requirements connected to the course and credit assignment. The concepts of community building and cultural awareness are explored from faculty and students perspectives. Evaluation by the students and next steps from the developers' perspectives are important components supporting the efficacy of this approach.     

Classification with discrete and continuous variables via general mixed-data models

We study the problem of classifying an individual into one of several populations based on mixed nominal, continuous, and ordinal data. Specifically, we obtain a classification procedure as an extension to the so-called location linear discriminant function, by specifying a general mixed-data model for the joint distribution of the mixed discrete and continuous variables. We outline methods for estimating misclassification error rates. Results of simulations of the performance of proposed classification rules in various settings vis-à-vis a robust mixed-data discrimination method are reported as well. We give an example utilizing data on croup in children.     

The overlapping coefficient as a measure of agreement between probability distributions and point estimation of the overlap of two normal densities

The overlapping coefficient is defined as a measure of the agreement between two probability distributions. Its relationship to the dissimilarity index and its propertie are described. An extensive treatment of maximum-likelihood estimation of the overlap between two normal distributions is presented as an example of estimating the overlapping coefficient from sample data.     

An improved statistical test for historical linguistics

Historical linguistics needs procedures to evaluate the similarity between languages through the comparison of specific word lists drawn from the whole vocabulary. The main issue is to evaluate a fair threshold for the number of similar items beyond which it is sensible to reject the hypothesis of chance similarity. After a short review of papers dealing with that problem, in this paper an extension of those methods is proposed which exploits available data in a more efficient way. In particular, the exact distribution of the new test statistics is calculated and the power of the new procedure is compared with the power of the existing method.     

Bayesian inference for the proportion of true null hypotheses using minimum Hellinger distance

It is important that the proportion of true null hypotheses be estimated accurately in a multiple hypothesis context. Current estimation methods, however, are not suitable for high-dimensional data such as microarray data. First, they do not consider the (strong) dependence between hypotheses (or genes), thereby resulting in inaccurate estimation. Second, the unknown distribution of false null hypotheses cannot be estimated properly by these methods. Third, the estimation is affected strongly by outliers. In this paper, we find out the optimal procedure for estimating the proportion of true null hypotheses under a (strong) dependence based on the Dirichlet process prior. In addition, by using the minimum Hellinger distance, the estimation should be robust to any model misspecification as well as to any outliers while maintaining efficiency. The results are confirmed by a simulation study, and the newly developed methodology is illustrated by a real microarray data.