Quality of Life in College Students with and without Social Phobia

Prior studies demonstrating quality of life impairment in phobia and anxiety disorders have relied upon epidemiological samples or clinical data. Using the same quality of life scale, the Short Form 36-item Health Survey (SF-36), in Iranian college students allowed us to study the impact of social phobia (SP) on quality of life among the college students. This report summarizes findings from a cross sectional study on Iranian students with social phobia studying at Shahed University. Quality of life was measured using the Short Form 36-item Health Survey (SF-36) which is a widely used and valid questionnaire to measure quality of life in cross-sectional and longitudinal studies. Three standard instruments were used to measure social phobia severity, namely Social Phobia Inventory, Social Interaction Anxiety Scale, and Brief version of the Fear of Negative Evaluation Scale. The sample consisted of 202 college students, 72 with SP and 130 without SP. The main finding of this study was that students with social phobia reported significantly lower quality of life, particularly in general health (P = 0.02), vitality (P < 0.0001), social functioning (P < 0.0001), role functioning—emotional (P < 0.0001), and Mental health (P = 0.001) dimensions. Standardized summed scores for mental health components of the SF-36 showed that 36.2% of all the s with SP were severely impaired while 16.0% of the students in control croup were severely impaired. Findings demonstrated that Iranian socially anxious college students reported extensive functional disability, and lower well-being compared to those without SP. These findings should encourage education officers to implement systematic efforts to prevent and treat social anxiety among students.     

Uncertainty Reduction Approaches,Uncertainty Coping Approaches,and Process Performance in Financial Services

Developing a better understanding of the impact of uncertainty on process performance has been recognized as an important research opportunity in service design ( Hill, et al., 2002 ). Within this general research stream, our study focuses on the question of what managers can do to most effectively address operational uncertainty and mitigate its negative effects. To begin to address this question, we report on an exploratory study using a sample of professionals in the financial‐services industry who acted as informants on 108 financial‐services processes. These professionals were sampled from a population of graduates of a university in the northeastern region of the United States who were employed in the financial‐services industry. Based on these processes, we empirically examine the relationship between responses to operational uncertainty and process performance after controlling for customer mix, other uncertainty sources, and process type characteristics. Our findings suggest that process improvement—an uncertainty reduction approach related to the internal functioning of the process—as well as several uncertainty coping approaches are associated with better performing processes. However, uncertainty reduction approaches related to customer involvement with, and demands on, the process are not associated with better performing processes. We discuss the implications of our findings for determining what actions managers can take to reduce the negative performance effects of operational uncertainty and how managers can decide which of these actions to take. We conclude with a discussion of the limitations of our study.     

Making Competitive Cities in the Light of the Meta-SWOT Tool: A Case Study of Tehran,Iran

ABSTRACT

While there is a growing body of scholarship on regional competitiveness, little attention has been paid to the planning methods that can pursue this objective. Public planners are mainly relying on conventional methods that have come under increasing criticism. This study aims to introduce Meta-SWOT as a novel planning tool in urban and regional studies. Meta-SWOT provides competitive and realistic strategies for planners. It systematically tries to present strategies rooted in internal resources and capabilities while acknowledging the challenges posed by the external environment. Nevertheless, it has several limitations that should be considered when applying to urban and regional planning.     

On simultaneous confidence intervals based on rank-estimates with application to analysis of gene expression data

Abstract

Inferential methods based on ranks present robust and powerful alternative methodology for testing and estimation. In this article, two objectives are followed. First, develop a general method of simultaneous confidence intervals based on the rank estimates of the parameters of a general linear model and derive the asymptotic distribution of the pivotal quantity. Second, extend the method to high dimensional data such as gene expression data for which the usual large sample approximation does not apply. It is common in practice to use the asymptotic distribution to make inference for small samples. The empirical investigation in this article shows that for methods based on the rank-estimates, this approach does not produce a viable inference and should be avoided. A method based on the bootstrap is outlined and it is shown to provide a reliable and accurate method of constructing simultaneous confidence intervals based on rank estimates. In particular it is shown that commonly applied methods of normal or t-approximation are not satisfactory, particularly for large-scale inferences. Methods based on ranks are uniquely suitable for analysis of microarray gene expression data since they often involve large scale inferences based on small samples containing a large number of outliers and violate the assumption of normality. A real microarray data is analyzed using the rank-estimate simultaneous confidence intervals. Viability of the proposed method is assessed through a Monte Carlo simulation study under varied assumptions.     

Inference on the asymptotic behavior of covariance operator of first-order periodically correlated autoregressive Hilbertian processes

This paper is devoted to a study on the structure of tensorial products of periodically correlated autoregressive (PCAR) processes with values in separable Hilbert spaces. It will be demonstrated that the resulting processes are PCAR with values in the space of Hilbert–Schmidt operators. These processes are applied while studying the convergence rate, limiting behavior and asymptotic distribution of the empirical estimators of the covariance operators of PCAR processes.     

A new generalized odd log-logistic family of distributions

We introduce and study general mathematical properties of a new generator of continuous distributions with three extra parameters called the new generalized odd log-logistic family of distributions. The proposed family contains several important classes discussed in the literature as submodels such as the proportional reversed hazard rate and odd log-logistic classes. Its density function can be expressed as a mixture of exponentiated densities based on the same baseline distribution. Some of its mathematical properties including ordinary moments, quantile and generating functions, entropy measures, and order statistics, which hold for any baseline model, are presented. We also present certain characterization of the proposed distribution and derive a power series for the quantile function. We discuss the method of maximum likelihood to estimate the model parameters. We study the behavior of the maximum likelihood estimator via simulation. The importance of the new family is illustrated by means of two real data sets. These applications indicate that the new family can provide better fits than other well-known classes of distributions. The beauty and importance of the new family lies in its ability to model real data.     

The Internal Validity of the Trade-Off Method of Conjoint Analysis

The discovery of consumers' preferences for multiattribute products/services has recently become possible with the development of conjoint measurement techniques. Conjoint analysis uses an individual's preference ranking or rating of some deliberately manipulated constructs of a concept to determine his/her numerically valued preferences for levels of those attributes that comprise the concept. Generally speaking, there are two methods of data collection for conjoint analysis: the full profile and the trade-off methods. This paper calls into question the internal validity of the preferences obtained via the trade-off method. An empirical analysis, which is based on the subjects' preferences for selecting a hospital, clearly shows that the estimates derived from the trade-off method may not reflect consumers' true preferences.     

Marriage and Family Formation of the Second‐Generation Afghans in Iran: Insights from a Qualitative Study

In 2006, around 1.2 million documented Afghans remained in Iran, of whom half were second generation. This paper is based on the results of a qualitative study conducted in three settings: Tehran, Mashhad and Isfahan, and draws on data collected via 80 in‐depth interviews and six focus group discussions with second generation Afghans in Iran. The aim of the paper is to explore the adaptation of second‐generation Afghans in Iran, particularly in relation to marriage and family formation. The results show that educational achievements and occupational skills of the second‐generation Afghans in Iran facilitated their adaptation to the host society, and inspired different marriage and family behaviors and aspirations in comparison with the first generation.     

Integer programming approach to static monopolies in graphs

A subset M of vertices of a graph is called a static monopoly, if any vertex v outside M has at least \(\lceil \tfrac{1 }{2}\deg (v)\rceil \) neighbors in M. The minimum static monopoly problem has been extensively studied in graph theoretical context. We study this problem from an integer programming point of view for the first time and give a linear formulation for it. We study the facial structure of the corresponding polytope, classify facet defining inequalities of the integer programming formulation and introduce some families of valid inequalities. We show that in the presence of a vertex cut or an edge cut in the graph, the problem can be solved more efficiently by adding some strong valid inequalities. An algorithm is given that solves the minimum monopoly problem in trees and cactus graphs in linear time. We test our methods by performing several experiments on randomly generated graphs. A software package is introduced that solves the minimum monopoly problem using open source integer linear programming solvers.