The longitudinal effects of parental monitoring and self-control on depression in Korean adolescents: A multivariate latent growth approach

The purpose of this study was to examine the longitudinal effects of parental monitoring and self-control on depression. To address this purpose, this study investigated the interrelationships among depression, perceived parental monitoring, and self-control—as well as their developmental changes from the eighth to the twelfth grades—by repeated assessment of 3449 Korean adolescents. The data from Korea Youth Panel Survey (KYPS) of the eighth graders who were followed for five years were analyzed using latent growth modeling. The univariate latent growth models showed that adolescents experience an increase in both parental monitoring and self-control but a decline in depression over the five years. In addition, the multivariate latent growth model suggested that the initial level of parental monitoring had significant effects, both directly and indirectly through self-control, on the initial level of depression. The linear changes in parental monitoring were associated with the linear changes in self-control; likewise, the linear changes in self-control were related to those in depression. These results imply that parental monitoring and self-control need to be emphasized as a strategy in order to prevent or alleviate adolescents' depression.     

Family group decision making: Measuring fidelity to practice principles in public child welfare

This study describes the development and validation of a short, self-administered fidelity measure used to capture the perspectives of a range of individuals participating in a family group decision making conference. Exploratory factor analyses suggested either a two or a three factor solution. Findings from confirmatory factor analysis supported a three factor model that includes sets of practices yielding: (1) Productive Decision Making; (2) Family Group Inclusion; and (3) Professional Supportiveness. These results provide direction to public child welfare practitioners and policymakers about the importance and the feasibility of attending to multiple perspectives in assessing program delivery.     

“Outnumbered yet still on top,but for how long?” Theorizing about men working in the feminized field of public relations

The under-researched phenomenon of men working in the feminized field of public relations is investigated using the theory of gendered organizations and feminist and masculinity studies lenses. Survey, interview, and focus group findings illuminate the field's gender paradox wherein men report negative effects of gender-minority status at entry- and mid-levels and worry about a future when women will replace them at public relations’ highest management levels.     

The Clash between Social Policy and Traditional Values: Unmet Welfare Needs Sustained by the Culture of Familism in South Korea

Drawing from a wider study of the ideas of poverty and inequality in South Korea, this paper reports on the lived experiences of disadvantaged South Koreans and their relationship to the National Basic Livelihood Security System (NBLSS), a flagship social assistance program aimed at addressing poverty and inequality. Utilizing a cultural approach to understanding and analysing policy responses to poverty, the impact of familism in the application of the NBLSS safety net is examined. Research data is presented through the testimonies, obtained through qualitative research interviews, of disadvantaged research participants and those who work with them, as well as policy‐makers' perspectives, revealing key flaws in the current NBLSS policy objectives. It is clearly evident that, by clinging to outdated Confucian conceptualizations of familial piety as a justification for highly selectivist access to what is no more than a subsistence level of financial support for poor people, the proposition of a poverty safety net is compromised and is widely missing its aim of reducing poverty and inequality in contemporary South Korea.     

A reference model for collaborative design in mould industry

The mould industry is an indispensable foundation for mass production by manufacturer and influences the time to market of new product. It is difficult to integrate the new product development processes and information systems among the mould companies which are mainly small and medium enterprises. The design chain operations reference-model (DCOR) provides a well-recognised methodology to standardise the management process for a product development project and enables the integration for mould industry. The DCOR focuses on a primary and preliminary product development process and is necessary to extend the detail templates for the industrial requirements. Hence, the purpose of this research is to develop an approach that extends the DCOR to the mould industry. The framework of the developed model, which is called the design chain operations reference-model for the mould industry, is adopted for a case mould company for a feasibility discussion.     

Approximation algorithms for connected facility location problems

We study Connected Facility Location problems. We are given a connected graph G=(V,E) with nonnegative edge cost c _e for each edge e∈E, a set of clients D⊆V such that each client j∈D has positive demand d _j and a set of facilities F⊆V each has nonnegative opening cost f _i and capacity to serve all client demands. The objective is to open a subset of facilities, say , to assign each client j∈D to exactly one open facility i(j) and to connect all open facilities by a Steiner tree T such that the cost is minimized for a given input parameter M≥1. We propose a LP-rounding based 8.29 approximation algorithm which improves the previous bound 8.55 (Swamy and Kumar in Algorithmica, 40:245–269, 2004). We also consider the problem when opening cost of all facilities are equal. In this case we give a 7.0 approximation algorithm.     

Simplified Maximum Likelihood Inference Based on the Likelihood Decomposition for Missing Data

In this paper, we propose an estimation method when sample data are incomplete. We decompose the likelihood according to missing patterns and combine the estimators based on each likelihood weighting by the Fisher information ratio. This approach provides a simple way of estimating parameters, especially for non‐monotone missing data. Numerical examples are presented to illustrate this method.     

Directional Dependence of Genes Using Survival Truncated FGM Type Modification Copulas

Ordinal data, such as student's grades or customer satisfaction surveys, are widely used in daily life. We can fit a probit or logistic regression model to the ordinal data using software such as SAS and get the estimates of regression parameters. However, it is hard to define residuals and detect outliers due to the fact that the estimated probabilities of an observation falling in every category form a vector instead of a scalar. With the help of latent variable and latent residuals, a Bayesian perspective of detecting outliers is explored and several methods were proposed in this article. Several figures are also given.     

Bayesian False Discovery Rate Wavelet Shrinkage: Theory and Applications

Statistical inference in the wavelet domain remains a vibrant area of contemporary statistical research because of desirable properties of wavelet representations and the need of scientific community to process, explore, and summarize massive data sets. Prime examples are biomedical, geophysical, and internet related data. We propose two new approaches to wavelet shrinkage/thresholding.

In the spirit of Efron and Tibshirani's recent work on local false discovery rate, we propose Bayesian Local False Discovery Rate (BLFDR), where the underlying model on wavelet coefficients does not assume known variances. This approach to wavelet shrinkage is shown to be connected with shrinkage based on Bayes factors. The second proposal, Bayesian False Discovery Rate (BaFDR), is based on ordering of posterior probabilities of hypotheses on true wavelets coefficients being null, in Bayesian testing of multiple hypotheses.

We demonstrate that both approaches result in competitive shrinkage methods by contrasting them to some popular shrinkage techniques.     

A Regression Model for the AUC of Clustered Ordinal Test Results and Working Independent Optimal Weights

We study a regression model on the area under the receiver operating characteristic curves (AUC) for clustered (or repeatedly measured) test results. To account for cluster information, we consider a weighted estimating equation for Dodd and Pepe (2003 Dodd , L. , Pepe , M. ( 2003 ). Semiparametric regression for the area under the receiver operating charateristic curve . Journal of the American Statistical Association 98 : 409 – 417 .[Taylor & Francis Online], [Web of Science ®] , [Google Scholar])'s regression model with working independence weights. We find the optimal weight in the given class of working independence weights to minimize the variance (or MSE) of regression estimators. We apply the proposed procedure to analyzing our recent experiment on diagnosing a liver disorder. In this experiment, we investigated MRI images of patients having symptoms of potential liver disorder to compare the performance of different MRI picturing methods in testing for liver disorders.