Hague Conference Conventions and the United States: A European View

From a European perspective, international cooperation in litigation does not primarily require the safeguarding of governmental interests, but the equitable balancing of the interests of plaintiffs and defendants. A European view of the role of US procedures in Hague Conference conventions is presented

Turkish in the European Union:Macro and micro perspectives

The increasing importance of Turkish as a European language can be viewed from macro or micro perspectives. Citizens of Turkish origin although they form a relatively large group of European countries that found themselves within the borders of the country is not a specific region, they feel connected with Turkey. As the Turkish community, they define their identity locally, independent of the nation to which they are subject today. The elements of the Turkish they speak are also translated in the local context, while “Istanbul Turkish” continues to be a reference. In this article, we will take a short look at these issues. Bir Avrupa dili olarak Türkçe’nin artan önemine makro ya da mikro perspektiflerden bakılabilir. Türk kökenli vatandaşlar bulundukları Avrupa ülkesinde görece geniş bir grup oluşturdukları halde, kendilerini ülke sınırları içinde belirli bir bölgeyle değil, Türkiye ile bağlantılı düşünüyorlar. Türk topluluğu olarak kimliklerini, bugün tâbi oldukları ulustan bağımsız, yerel olarak tanımlıyorlar. Konuştukları Türkçe’nin unsurları da yerel bağlamda çevriliyor, öte yandan “İstanbul Türkçesi” yine de bir referans olmaya devam ediyor. Bu makalede, bu meselelere kısa bir göz atacağız

combining multiple imputation and hidden markov modeling to obtain consistent estimates of employment status

Abstract Recently, a method was proposed that combines multiple imputation and latent class analysis (MILC) to correct for misclassification in combined data sets. A multiply imputed data set is generated which can be used to estimate different statistics of interest in a straightforward manner and can ensure that uncertainty due to misclassification is incorporated in the estimate of the total variance. In this article, MILC is extended by using hidden Markov modeling so that it can handle longitudinal data and correspondingly create multiple imputations for multiple time points. Recently, many researchers have investigated the use of hidden Markov modeling to estimate employment status rates using a combined data set consisting of data originating from the Labor Force Survey (LFS) and register data; this combined data set is used for the setup of the simulation study performed in this article. Furthermore, the proposed method is applied to an Italian combined LFS-register data set. We demonstrate how the MILC method can be extended to create imputations of scores for multiple time points and thereby show how the method can be adapted to practical situations

Estimating the number of serious road injuries per vehicle type in the Netherlands by using multiple imputation of latent classes

Statistics that are published by official agencies are often generated by using population registries, which are likely to contain classification errors and missing values. A method that simultaneously handles classification errors and missing values is multiple imputation of latent classes (MILC). We apply the MILC method to estimate the number of serious road injuries per vehicle type in the Netherlands and to stratify the number of serious road injuries per vehicle type into relevant subgroups by using data from two registries. For this specific application, the MILC method is extended to handle the large number of missing values in the stratification variable ‘region of accident’ and to include more stratification covariates. After applying the extended MILC method, a multiply imputed data set is generated that can be used to create statistical figures in a straightforward manner, and that incorporates uncertainty due to classification errors and missing values in the estimate of the total variance

Using Qualitative Methods for the Analysis of Adult Immigrants’ L2 Needs: Findings from a Research Project in Greece Focusing on School-Parents Communication

In the Greek context of economic crisis and of emerging xenophobic ideas and discourse, this article presents some findings from a research project which had the ambition to give voice to immigrants in Greece about their own language and communication needs. The target group of the project were immigrant parents, whose children attend public schools in the area of Volos. Communication between schools and immigrant families is fragmentary or non-existent, causing frustration for parents and teachers. The ELMEGO project used focus groups in order to construct social meaning related to migrant discourse, and shed light on it from an interdisciplinary perspective, combining insights from social anthropology, applied linguistics and the sociology of education. After the first stage of needs analysis, the practical outcomes of the project were the design of teaching material and educational activities, the implementation of pilot courses and their evaluation, and the creation of a resource pack. The results of the project question the validity of specific-purposes-approaches to language needs, stress the importance of conditions for learning the migrants’ second language, and associate social, cultural and institutional settings with identity and emotional choices

Estimating classification error under edit restrictions in combined survey-register data using Multiple Imputation Latent Class modelling (MILC)

Both registers and surveys can contain classication errors. These errors can be estimated by making use of information that is obtained when making use of a combined dataset. We propose a new method based on latent class modelling that estimates the number of classification errors in the multiple sources, and simultaneously takes impossible combinations with other variables into account. Furthermore, we use the latent class model to multiply impute a new variable, which enhances the quality of statistics based on the combined dataset. The performance of this method is investigated by a simulation study, which shows that whether the method can be applied depends on the entropy of the LC model and the type of analysis a researcher is planning to do. Furthermore, the method is applied to a combined dataset from Statistics Netherlands