A Social Network Analysis of Friendship and Spiritual Support in a Religious Congregation

Religious congregations are social settings where people gather together in community to pursue the sacred (Pargament, 2008). Such settings are important to understand as they provide a context for individuals to develop relationships, share ideas and resources, and connect individuals to larger society (Todd, 2017a). Yet, research to date has not deeply examined the inherently relational nature of religious congregations. Thus, in this study, we used social settings theory (Seidman, 2012; Tseng & Seidman, 2007) to develop and test hypotheses about relationships within one Christian religious congregation. In particular, we used social network analysis to test hypotheses about relational activity, popularity, and homophily for friendship and spiritual support types of relational links. Our findings demonstrate how relational patterns may be linked to participation in congregational activities, occupying a leadership role, a sense of community and spiritual satisfaction, stratification, socialization, and spiritual support. Overall, this advances theory and research on the relational aspects of religious congregations, and more broadly to the literature on social settings. Limitations, directions for future research, and implications for theory and religious congregations also are discussed.     

Random effects diagonal metric multidimensional scaling models

By assuming a distribution for the subject weights in a diagonal metric (INDSCAL) multidimensional scaling model, the subject weights become random effects. Including random effects in multidimensional scaling models offers several advantages over traditional diagonal metric models such as those fitted by the INDSCAL, ALSCAL, and other multidimensional scaling programs. Unlike traditional models, the number of parameters does not increase with the number of subjects, and, because the distribution of the subject weights is modeled, the construction of linear models of the subject weights and the testing of those models is immediate. Here we define a random effects diagonal metric multidimensional scaling model, give computational algorithms, describe our experiences with these algorithms, and provide an example illustrating the use of the model and algorithms.We would like to thank J. Douglas Carroll for early consultation of this research, and Robert I. Jennrich for commenting on an earlier draft of this paper and for help on the computational algorithms. James O. Ramsay and Forrest W. Young were instrumental in providing the example data. This work was supported in part by National Institute of Mental Health grant 1 R43 MH57559-01. We would also like to thank the anonymous referees for comments that helped to clarify our work.     

贵州省技区卫生服务机构准入抽查报告

深入了解我省社区卫生服务发展中存在的问题,随机抽样,现场调查。调查分析发现,社区卫生工作在我省呈现出蓬勃发展的趋势,但也暴露出了许多新的问题。应继续加强人才队伍建设,加强对社区卫生服务机构的监管,依法严格社区卫生服务机构、从业人员和技术的准入管理,制定细化的技术规范、操作规程,建立系统的考核标准、评价体系。     

随机棋局存在专家记忆优势效应吗?

专家在其擅长领域中 ,对有意义刺激所表现出的记忆优势效应 ,已为诸多研究所证实。然而 ,对于随机刺激 ,专家是否存在记忆优势 ,尚未有明确的研究结果。文章以中国象棋为实验材料探讨这一问题 ,在一定程度上克服了先前研究中实验材料的局限性。实验结果表明 ,随机刺激不存在专家记忆优势效应。作者还比较了模板理论和限制调节理论 ,认为前者优于后者     

Semi‐parametric proportional hazards models with crossed random effects for psychometric response times

The semi‐parametric proportional hazards model with crossed random effects has two important characteristics: it avoids explicit specification of the response time distribution by using semi‐parametric models, and it captures heterogeneity that is due to subjects and items. The proposed model has a proportionality parameter for the speed of each test taker, for the time intensity of each item, and for subject or item characteristics of interest. It is shown how all these parameters can be estimated by Markov chain Monte Carlo methods (Gibbs sampling). The performance of the estimation procedure is assessed with simulations and the model is further illustrated with the analysis of response times from a visual recognition task.     

Latent variable models for analyzing children's processing ability

In this paper, we develop a latent processing ability model to analyze the speed of processing ability data. Our approach can not only effectively evaluate the effects of covariates on the latent processing ability, but also estimate the latent trait of each child by calculating its posterior mean. In addition, we derive the correlations structure of latent traits among different age groups. Simulations are conducted to evaluate the performance of our proposed model. The results indicated that the estimation of model parameters is satisfactory overall. The method is evaluated using real data from children aged 4–7 years in Changchun, China.     

等级反应多水平评分者漂移模型的构建

对于评定耗时较长的测验来说,时间因素对评分精确性的影响不容忽视,因此,评分者漂移方面的研究备受关注。研究基于康春花,孙小坚和曾平飞(2016)提出的等级反应多水平侧面模型建构出可用于检测评分者漂移的等级反应多水平评分者漂移模型,并通过模拟研究对模型性能进行验证。结果表明：模型能够精确估计项目和能力参数;且与固定效应模型相比,评分者随机效应模型能更有效地检测出评分者漂移效应,随机效应模型的有效性和稳定性更佳。     

Random effects and extended generalized partial credit models

In this paper it is shown that under the random effects generalized partial credit model for the measurement of a single latent variable by a set of polytomously scored items, the joint marginal probability distribution of the item scores has a closed-form expression in terms of item category location parameters, parameters that characterize the distribution of the latent variable in the subpopulation of examinees with a zero score on all items, and item-scaling parameters. Due to this closed-form expression, all parameters of the random effects generalized partial credit model can be estimated using marginal maximum likelihood estimation without assuming a particular distribution of the latent variable in the population of examinees and without using numerical integration. Also due to this closed-form expression, new special cases of the random effects generalized partial credit model can be identified. In addition to these new special cases, a slightly more general model than the random effects generalized partial credit model is presented. This slightly more general model is called the extended generalized partial credit model. Attention is paid to maximum likelihood estimation of the parameters of the extended generalized partial credit model and to assessing the goodness of fit of the model using generalized likelihood ratio tests. Attention is also paid to person parameter estimation under the random effects generalized partial credit model. It is shown that expected a posteriori estimates can be obtained for all possible score patterns. A simulation study is carried out to show the usefulness of the proposed models compared to the standard models that assume normality of the latent variable in the population of examinees. In an empirical example, some of the procedures proposed are demonstrated.     

Sample size determination for a matched-pairs study with incomplete data using exact approach

This research was motivated by a clinical trial design for a cognitive study. The pilot study was a matched-pairs design where some data are missing, specifically the missing data coming at the end of the study. Existing approaches to determine sample size are all based on asymptotic approaches (e.g., the generalized estimating equation (GEE) approach). When the sample size in a clinical trial is small to medium, these asymptotic approaches may not be appropriate for use due to the unsatisfactory Type I and II error rates. For this reason, we consider the exact unconditional approach to compute the sample size for a matched-pairs study with incomplete data. Recommendations are made for each possible missingness pattern by comparing the exact sample sizes based on three commonly used test statistics, with the existing sample size calculation based on the GEE approach. An example from a real surgeon-reviewers study is used to illustrate the application of the exact sample size calculation in study designs.     

汉字识别的时间进程：来自字谜任务的ERP证据

通过“学习-测试-判断”的三阶段实验范型, 采用事件相关电位（ERP）技术探讨了猜谜任务中对谜底答案（单个汉字）识别的脑内时程动态变化。结果发现：语义识别与字形识别下,汉字刺激均诱发了明显的N1、P2和N2成分,其中语义识别下的P2波幅表现为更加正性,可能与汉字语义的早期加工有关;随后,300-400ms内,语义识别比字形识别诱发了一个更加正性的ERP成分,该成分可能属于P300成分,反映了对汉字语义的进一步整合加工;最后,500-600ms内,语义识别比字形识别同样诱发了一个更加正性的ERP成分,可能反