The Impact of Items and Anchors on Parent–Child Reports of Problem Behavior

Research has repeatedly demonstrated that parent and child reports of child behavior and emotional functioning often do not agree in terms of symptom severity or even symptom presence. Given the potential clinical impact that discrepant reports may have on the therapeutic process, a significant amount of research has addressed the factors influencing this observed discrepancy. Traditionally, this research has evaluated the impact of demographic factors on mean differences and obtained correlation coefficients between parent and child reports. The current research improves upon previous research in this area in two ways. First, the current research uses parent and child measures with identical rather than similar items as is commonly found in previous research. Second, the current research moves beyond identifying demographic influences on discrepant reports by explaining observed discrepancies in terms of differences in parent and child perceptions of typical child behavior.David Carlston and Benjamin Ogles are affiliated with Department of Psychology, Ohio University, Athens, USA.Funding for this project was provided by the Ohio Department of Mental Health.     

Assimilation as multiracialism: The case of Singapore's Malays

The myths of meritocracy and multiracialism ‘explain’ between them both the ‘fairness’ of the Singapore system and the subordinate role of the non-Chinese minority races. They also purport to assure the minorities that they enjoy full status as members of the nation-building project and that their cultural and religious mores are embraced and protected within its framework. Using the Malay minority as its case study, and arguing from archival, oral, official government and secondary sources, this paper argues that the Singapore systems of meritocracy and multiracialism have not been concerned primarily with intercommunal tolerance since the 1970s, but are now programmes of assimilation of the racial minorities into a Chinese-dominated society.     

Divergence-based estimation and testing with misclassified data

The well-known chi-squared goodness-of-fit test for a multinomial distribution is generally biased when the observations are subject to misclassification. In Pardo and Zografos (2000) the problem was considered using a double sampling scheme and ø-divergence test statistics. A new problem appears if the null hypothesis is not simple because it is necessary to give estimators for the unknown parameters. In this paper the minimum ø-divergence estimators are considered and some of their properties are established. The proposed ø-divergence test statistics are obtained by calculating ø-divergences between probability density functions and by replacing parameters by their minimum ø-divergence estimators in the derived expressions. Asymptotic distributions of the new test statistics are also obtained. The testing procedure is illustrated with an example.     

MODEL-ASSISTED HIGHER-ORDER CALIBRATION OF ESTIMATORS OF VARIANCE

In survey sampling, interest often centres on inference for the population total using information about an auxiliary variable. The variance of the estimator used plays a key role in such inference. This study develops a new set of higher‐order constraints for the calibration of estimators of variance for various estimators of the population total. The proposed strategy requires an appropriate model for describing the relationship between the response and auxiliary variable, and the variance of the auxiliary variable. It is therefore referred to as a model‐assisted approach. Several new estimators of variance, including the higher‐order calibration estimators of the variance of the ratio and regression estimators suggested by Singh, Horn & Yu and Sitter & Wu are special cases of the proposed technique. The paper presents and discusses the results of an empirical study to compare the performance of the proposed estimators and existing counterparts.     

Generalized additive models for location, scale and shape

Summary.  A general class of statistical models for a univariate response variable is presented which we call the generalized additive model for location, scale and shape (GAMLSS). The model assumes independent observations of the response variable y given the parameters, the explanatory variables and the values of the random effects. The distribution for the response variable in the GAMLSS can be selected from a very general family of distributions including highly skew or kurtotic continuous and discrete distributions. The systematic part of the model is expanded to allow modelling not only of the mean (or location) but also of the other parameters of the distribution of y , as parametric and/or additive nonparametric (smooth) functions of explanatory variables and/or random-effects terms. Maximum (penalized) likelihood estimation is used to fit the (non)parametric models. A Newton–Raphson or Fisher scoring algorithm is used to maximize the (penalized) likelihood. The additive terms in the model are fitted by using a backfitting algorithm. Censored data are easily incorporated into the framework. Five data sets from different fields of application are analysed to emphasize the generality of the GAMLSS class of models.     

Proportional reversed hazard rate model and its applications

The purpose of this paper is to study the structure and properties of the proportional reversed hazard rate model (PRHRM) in contrast to the celebrated proportional hazard model (PHM). The monotonicity of the hazard rate and the reversed hazard rate of the model is investigated. Some criteria of aging are presented and the inheritance of the aging notions (of the base distribution) by the PRHRM is studied. Characterizations of the model involving Fisher information are presented and the statistical inference of the parameters is discussed. Finally, it is shown that several members of the proportional reversed hazard rate class have been found to be useful and flexible in real data analysis.     

South African Indian migration in the twenty-first century: towards a theory of ‘triple identity’

This paper is inspired by three issues, namely ongoing research on South Africans of Indian origin, the anecdotal evidence that accumulates through ongoing discussions on a casual level with such people and the increasing interest among researchers about how globalization and transnational movements are impacting upon identity formation among minorities who are seeking employment or a new life in the developed economies of the ‘big five’ English-speaking countries, i.e. the USA, Canada, Australia, New Zealand and the UK. It is in countries such as these that a third identity emerges and develops to instill a sense of triple identity. The information here suggests that, when people of Indian origin migrate to one of these countries, they continue to cling to South Africa because it is their country of birth, as well as to India, more for sentimental rather than practical reasons. The topic of this paper reflects a contemporary phenomenon not just among the Indians in South Africa, but also among other diasporas such as Chinese in Latin America, whose new and final emigration destination always seems to be the US. In addition, people of Chinese origin in the countries of Indochina, such as Vietnam, Laos and Cambodia, experienced similar patterns of becoming diasporas in Europe, North America and Oceania in the 1970s. However, migration implores them to renegotiate their identities in order to adapt and settle in accordance with their newly adopted host territories. This gives rise to an identity that straddles three countries, which induces the need to examine new ways of identity building in a global transnational economy.