Browsing by Author "Wang, Yanping"
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- Children's Religious Coping Following Residential Fires: An Exploratory StudyWang, Yanping (Virginia Tech, 2004-04-22)Recent advancements in the general child disaster literature underscore the important role of coping in children's postdisaster adjustment. Religious coping in children, a potentially important category of coping strategies, has received little attention until recent years. Moreover, its role in the context of post fire adjustment has not been studied. The present study examined the psychometric soundness of the Religious Coping Activities Scale (RCAS; Pargament et al., 1990) in children and adolescents and explored its utility in predicting children's religious coping over time: moreover, the study evaluated its role in predicting PTSD symptomatology over an extended period of time. This investigation included 140 children and adolescents (ages 8-18). Factor analyses of the RCAS revealed a 6-factor solution very similar to the factor structure in the original study. This finding suggests that the RCAS is a promising instrument to measure children's religious coping efforts. Hypotheses concerning the prediction of children's religious coping were only partially supported. Regression analyses indicated mixed findings in terms of the contributions of selected variables to the prediction of children's Spiritually Based Coping and Religious Discontent. Overall, the regression model predicted Religious Discontent better than Spiritually Based Coping. A mixed-effects regression model and hierarchical regression analyses were both employed to examine the role of children's religious coping in predicting short-term and long-term PTSD symptomatology following the residential fires. Results from the mixed-effects regression indicated that loss, time since the fire, child's age, race, and race by age interaction significantly predicted children's PTSD symptoms over time. However, time specific regression analyses revealed different predictive power of the variables across the three assessment waves. Specifically, analyses with Time 1 data revealed the same findings as did the mixed-effects model, except that time since the fire was not a significant predictor in this analysis. General coping strategies appeared to be the only salient predictors for PTSD at Time 2. Finally, Religious Discontent appeared to be negatively related to PTSD at a later time.
- Optimal Experimental Designs for the Poisson Regression Model in Toxicity StudiesWang, Yanping (Virginia Tech, 2002-07-23)Optimal experimental designs for generalized linear models have received increasing attention in recent years. Yet, most of the current research focuses on binary data models especially the one-variable first-order logistic regression model. This research extends this topic to count data models. The primary goal of this research is to develop efficient and robust experimental designs for the Poisson regression model in toxicity studies. D-optimal designs for both the one-toxicant second-order model and the two-toxicant interaction model are developed and their dependence upon the model parameters is investigated. Application of the D-optimal designs is very limited due to the fact that these optimal designs, in terms of ED levels, depend upon the unknown parameters. Thus, some practical designs like equally spaced designs and conditional D-optimal designs, which, in terms of ED levels, are independent of the parameters, are studied. It turns out that these practical designs are quite efficient when the design space is restricted. Designs found in terms of ED levels like D-optimal designs are not robust to parameters misspecification. To deal with this problem, sequential designs are proposed for Poisson regression models. Both fully sequential designs and two-stage designs are studied and they are found to be efficient and robust to parameter misspecification. For experiments that involve two or more toxicants, restrictions on the survival proportion lead to restricted design regions dependent on the unknown parameters. It is found that sequential designs perform very well under such restrictions. In most of this research, the log link is assumed to be the true link function for the model. However, in some applications, more than one link functions fit the data very well. To help identify the link function that generates the data, experimental designs for discrimination between two competing link functions are investigated. T-optimal designs for discrimination between the log link and other link functions such as the square root link and the identity link are developed. To relax the dependence of T-optimal designs on the model truth, sequential designs are studied, which are found to converge to T-optimal designs for large experiments.