TEACHING PROFESSIONAL WRITING TO FAMILY THERAPISTS: THREE APPROACHES

The authors present three educational approaches designed to demystify the writing process and to support better writing skills among family therapists. One framework is an academic course in professional writing, another is a writing seminar for family therapy and physician trainees within a medical school setting, and the third is a 2-hour continuing education writing workshop for family therapists. Each is replicable within a variety of settings.     

计算机与学术写作(英文)

深大外语系把若干计算机软件应用于英语专业本科学生的写作课教材系列。这不仅有助于学生自修写作技巧,且能为教师节省大量时间,使教师有可能对学生作更多的个别辅导。同时,学生在上写作课的过程中还能掌握一种使用计算机的实际技能。近十年来,中国的飞速发展是史无前例的。西方的技术发展史对中国虽然有启迪作用,但其实际要求与中国相去甚远。在这个意义上,深大的尝试对全中国均具有示范意义。     

Design optimization using ANOVA

This paper describes the design optimization of a robot sensor used for locating 3-D objects employing the Taguchi method in a computer simulation scenario. The location information from the sensor is to be utilized to control the movements of an industrial robot in a 'pick-and-place' or assembly operation. The Taguchi method, which is based on the Analysis-of-Variance (ANOVA) approach, is utilized to improve the performance of the sensor over a wider operating range. A review of the Taguchi method is presented along with step-by-step implementation details to identify and optimize the design parameters of the sensor. The method allows us to gauge the impact of various interactions present in the sensor system exclusively and permits us to single out those factors that have a dominant influence on the overall performance of the sensor. The investigation suggests that the Taguchi method is a more structured and efficient approach for achieving a robust design compared with the classical full factorial design approach.     

Measuring agreement between two statistics with applications to age standardization

Summary.  We detail a general method for measuring agreement between two statistics. An application is two ratios of directly standardized rates which differ only by the choice of the standard. If the statistics have a high value for the coefficient of agreement then the expected squared difference between the statistics is small relative to the variance of the average of the two statistics, and inferences vary little by changing statistics. The estimation of a coefficient of agreement between two statistics is not straightforward because there is only one pair of observed values, each statistic calculated from the data. We introduce estimators of the coefficient of agreement for two statistics and discuss their use, especially as applied to functions of standardized rates.     

A social identity model of prosocial behaviors within nonprofit organizations

This study draws on social identity theory, while developing and testing the Social Identification Model of Prosocial Behavior among voluntary participants in nonprofit organizations. This model posits relationships between organization identification, commitment, satisfaction, and prosocial behavior among nonprofit volunteers. Path analysis results indicate successful identification of the model. Specifically, when volunteers identified with their nonprofit, they had higher levels of prosocial behaviors, commitment, and satisfaction. This study provides several important extensions to social identity theory and nonprofit literatures while highlighting the need for further research examining motivations behind prosocial behaviors within nonprofits.     

LOG-PERIODOGRAM ESTIMATION OF LONG MEMORY VOLATILITY DEPENDENCIES WITH CONDITIONALLY HEAVY TAILED RETURNS

Many recent papers have used semiparametric methods, especially the log-periodogram regression, to detect and estimate long memory in the volatility of asset returns. In these papers, the volatility is proxied by measures such as squared, log-squared, and absolute returns. While the evidence for the existence of long memory is strong using any of these measures, the actual long memory parameter estimates can be sensitive to which measure is used. In Monte-Carlo simulations, I find that if the data is conditionally leptokurtic, the log-periodogram regression estimator using squared returns has a large downward bias, which is avoided by using other volatility measures. In United States stock return data, I find that squared returns give much lower estimates of the long memory parameter than the alternative volatility measures, which is consistent with the simulation results. I conclude that researchers should avoid using the squared returns in the semiparametric estimation of long memory volatility dependencies.