大学外语教学改革的价值取向:从对“法”的拘囿转向对人的关注

外语教学长期拘囿于寻找“最佳的教学方法”。我国大学外语教学领域存在着追赶国外“最新教学法”的现象，将外语学习和教学简单化、模式化，忽视了学习者和教师的认知与情感。Kumaravadivelu首次提出了“后方法”的概念，提出特定性、实践性和可能性三个基本参数的外语教学原则和十大宏观教学策略。教师在教学策略的指导下，结合三个基本参数转化成特定的教学措施，创造出各种不同的微观策略，应用于教学实践。“后方法”教学理念主张动态开放的外语教学，一方面关注对外语学习者的研究，倡导“以学生为中心”；另一方面注重教师对教学理论的反思与创设。教学法研究的重点从“法”的拘囿转向人的自主和自由。教师和学生的主体作用越来越受到关注。     

Disaster Management from a POM Perspective: Mapping a New Domain

We have reviewed disaster management research papers published in major operations management, management science, operations research, supply chain management and transportation/logistics journals. In reviewing these studies, our objective is to assess and present the macro level “architectural blue print” of disaster management research with the hope that it will attract new researchers and motivate established researchers to contribute to this important field. The secondary objective is to bring this disaster research to the attention of disaster administrators so that disasters are managed more efficiently and more effectively. We have mapped the disaster management research on the following five attributes of a disaster: (1) Disaster Management Function (decision‐making process, prevention and mitigation, evacuation, humanitarian logistics, casualty management, and recovery and restoration), (2) Time of Disaster (before, during and after), (3) Type of Disaster (accidents, earthquakes, floods, hurricanes, landslides, terrorism and wildfires etc.), (4) Data Type (Field and Archival data, Real data and Hypothetical data), and (5) Data Analysis Technique (bidding models, decision analysis, expert systems, fuzzy system analysis, game theory, heuristics, mathematical programming, network flow models, queueing theory, simulation and statistical analysis). We have done cross tabulations of data among these five parameters to gain greater insights into disaster research. Recommendations for future research are provided.     

Conflict Management of Cooperative Organizations in Contemporary Iran: A Review Analysis

Emergence of conflicts-of-interests and of disputes in organizations, especially within and among work team systems, is inevitable. Cooperatives are self-governing organizations with democratic characters aimed at meeting the members’ needs and promoting social equity. Yet, internal organizational conflicts arise as conflicts-of-interests develop. Such conflicts can cause serious damages to the parties involved as well as harm the reputation of cooperatives as social and economic entities that tend to pursue social justice and fair distribution of income in society. This paper addresses conflict management in contemporary cooperative organizations. It is based on a preliminary research performed on factors causing conflicts in various types of cooperatives in Iran by analyzing 253 complaints made in 224 housing, consumer, transportation, producer procurement, and credit cooperatives within the central province of Tehran. It also offers some suggestions to improve conflict management within cooperative organizations.

Phase I monitoring of social network with baseline periods using poisson regression

Social network analysis is an important analytic tool to forecast social trends by modeling and monitoring the interactions between network members. This paper proposes an extension of a statistical process control method to monitor social networks by determining the baseline periods when the reference network set is collected. We consider probability density profile (PDP) to identify baseline periods using Poisson regression to model the communications between members. Also, Hotelling T² and likelihood ratio test (LRT) statistics are developed to monitor the network in Phase I. The results based on signal probability indicate a satisfactory performance for the proposed method.     

A statistical approach to social network monitoring

Social network monitoring consists of monitoring changes in networks with the aim of detecting significant ones and attempting to identify assignable cause(s) contributing to the occurrence of a change. This paper proposes a method that helps to overcome some of the weaknesses of the existing methods. A Poisson regression model for the probability of the number of communications between network members as a function of vertex attributes is constructed. Multivariate exponentially weighted moving average (MEWMA) and multivariate cumulative sum (MCUSUM) control charts are used to monitor the network formation process. The results indicate more efficient performance for the MEWMA chart in identifying significant changes.     

Bayesian Statistical Inference for Weighted Exponential Distribution

Exponential distribution has an extensive application in reliability. Introducing shape parameter to this distribution have produced various distribution functions. In their study in 2009, Gupta and Kundu brought another distribution function using Azzalini's method, which is applicable in reliability and named as weighted exponential (WE) distribution. The parameters of this distribution function have been recently estimated by the above two authors in classical statistics. In this paper, Bayesian estimates of the parameters are derived. To achieve this purpose we use Lindley's approximation method for the integrals that cannot be solved in closed form. Furthermore, a Gibbs sampling procedure is used to draw Markov chain Monte Carlo samples from the posterior distribution indirectly and then the Bayes estimates of parameters are derived. The estimation of reliability and hazard functions are also discussed. At the end of the paper, some comparisons between classical and Bayesian estimation methods are studied by using Monte Carlo simulation study. The simulation study incorporates complete and Type-II censored samples.     

A comparison of using weighted distribution and joint modeling for analyzing non-ignorable missing responses

In this study, we reconsider weighted distribution from the perspective of missing mechanism since weighted distribution instead of being the distribution of the whole population of interest is only the distribution of respondents (sub-population). After defining some weighted distributions by different mechanisms for indicator of response, we show, by some simulation studies, that using weighted distributions may lead to biased estimates of parameters under the non-ignorable missing mechanism. On the other hand, joint modeling of the response and selection mechanism could result in more efficient and valid estimates of parameters. The lower root of mean squared errors of estimates from the joint modeling approach than those of the weighted distribution is a warranty to the statement that the joint modeling method is more efficient than weighted distribution; this is proved by diverse simulation studies along the article. However, these two methods of the weighted approach and joint modeling give similar results if the selection mechanism is at random. Finally, the methods are applied and compared in the analysis of one well-used real dataset.     

Reversible jump MCMC to identify dropout mechanism in longitudinal data

Existence of missing values is an inseparable part of longitudinal studies in epidemiology, medical and clinical studies. Usually researchers, for simplicity, ignore the missingness mechanism while, ignoring a not at random mechanism may lead to misleading results. In this paper, we use a Bayesian paradigm for fitting selection model of Heckman, which allows the non-ignorable missingness for longitudinal data. Also, we use reversible-jump Markov chain Monte Carlo to allow the model to choose between non-ignorable and ignorable structures for missingness mechanism, and show how the selection can be incorporated. Some simulation studies are performed for illustration of the proposed approach. The approach is also used for analyzing two real data sets.     

Longitudinal data analysis in the presence of informative sampling: weighted distribution or joint modelling

ABSTRACT

Weighted distributions, as an example of informative sampling, work appropriately under the missing at random mechanism since they neglect missing values and only completely observed subjects are used in the study plan. However, length-biased distributions, as a special case of weighted distributions, remove the subjects with short length deliberately, which surely meet the missing not at random mechanism. Accordingly, applying length-biased distributions jeopardizes the results by producing biased estimates. Hence, an alternate method has to be used such that the results are improved by means of valid inferences. We propose methods that are based on weighted distributions and joint modelling procedure and compare them in analysing longitudinal data. After introducing three methods in use, a set of simulation studies and analysis of two real longitudinal datasets affirm our claim.     

Sexual and Reproductive Health Knowledge Among Men and Women Aged 15 to 49 Years in Metropolitan Tehran

Sexual and reproductive health (SRH) knowledge is an important predictor of an individual’s sexual behavior; however, little is known regarding this issue among Iranians. This study assessed sexual and reproductive health knowledge among men and women aged 15 to 49 years in Tehran. A total of 755 men and women aged 15 to 49 years were recruited using multistage, random cluster sampling in June 2014. An illustrative anonymous questionnaire endorsed by the World Health Organization (WHO) was used to collect the data. Bivariate and multivariate analyses were performed. The results showed that men and women were moderately knowledgeable about sexual and reproductive health (mean score of SRH knowledge = 39, range: 26 to 52); however, myths and misperceptions prevailed in different aspects of SRH. For example, only 45% of women and 38.7% of men accurately knew that a person infected with human immunodeficiency virus (HIV) can look healthy. Friends were identified as the primary or secondary source of knowledge about puberty and sex-related issues. A multivariate analysis showed that being female (coefficient = 0.139, p < 0.001) and being married (coefficient = 0.180, p < 0.001) were significant predictors of better SRH knowledge, while being young (age group 15 to 24) was a significant determinant of poor SRH knowledge (coefficient = ?0.161, p < 0.001) when other influencing factors were controlled. Culturally appropriate and age-specific comprehensive education is recommended, particularly for men, the unmarried, and the younger generation in Iran.