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。     

高中会考等值方法的比较研究

本研究采用随机等组设计与铆测验相结合的方案。首先验证了两随机等组的平均数、方差和分布状态无显著差异,再用随机等组的等值分作为等值效标来检验其他等值方法的误差,然后比较了在铆测验设计中三种线性等值方法（在不同总体权重下）的误差值,以选出适合高中合考的等值方法及总体权重。经研究发现：会考等值宜采用Tucker观察分数线性等值方法,并宜选择总体权重W1＝1。     

Approximate interval estimation for a certain intraclass correlation coefficient

When the raters participating in a reliability study are a random sample from a larger population of raters, inferences about the intraclass correlation coefficient must be based on the three mean squares from the analysis of variance table summarizing the results: between subjects, between raters, and error. An approximate confidence interval for the parameter is presented as a function of these three mean squares.Dr. Fleiss is also with the Biometrics Research Unit of the New York State Psychiatric Institute. This work was supported in part by grant DE 04068 from the National Institute of Dental Research.     

Grading distractor-identification tests

On distractor-identification tests students mark as many distractors as possible on each test item. A grading scale is developed for this type testing. The scale is optimal in that it is the unique scale giving an unbiased estimate of the student's true score, i.e., the score that would result if no guessing occurred. If the test is administered as a usual multiple choice test and graded using the usual correction for guessing scale, the expected item score is the same as for the distractor-identification testing using the optimal grading scale. However, the variance of the item score is shown to be less for distractor-identification testing than for usual multiple choice testing under certain conditions.     

Multilevel SEM with random slopes in discrete data using the pairwise maximum likelihood

Pairwise maximum likelihood (PML) estimation is a promising method for multilevel models with discrete responses. Multilevel models take into account that units within a cluster tend to be more alike than units from different clusters. The pairwise likelihood is then obtained as the product of bivariate likelihoods for all within-cluster pairs of units and items. In this study, we investigate the PML estimation method with computationally intensive multilevel random intercept and random slope structural equation models (SEM) in discrete data. In pursuing this, we first reconsidered the general ‘wide format’ (WF) approach for SEM models and then extend the WF approach with random slopes. In a small simulation study we the determine accuracy and efficiency of the PML estimation method by varying the sample size (250, 500, 1000, 2000), response scales (two-point, four-point), and data-generating model (mediation model with three random slopes, factor model with one and two random slopes). Overall, results show that the PML estimation method is capable of estimating computationally intensive random intercept and random slopes multilevel models in the SEM framework with discrete data and many (six or more) latent variables with satisfactory accuracy and efficiency. However, the condition with 250 clusters combined with a two-point response scale shows more bias.     

Initial excitatory conditioning with the truly random control procedure in rats: The effects of density of the conditioned stimulus

The present experiments were designed to test whether initial excitatory conditioning to a stimulus was obtained, using a within-subject design of the truly random control (TRC) procedure, and to examine the effects of the density of the conditioned stimulus on it. In Experiment 1, the effect of the three levels of CS density (50%, 25%, 20%) was assessed at six different stages of training, using the conditioned suppression of licking in rats. Although all of the groups given the TRC procedure showed evidence of initial excitatory conditioning, it was more marked in the group with the highest CS density than in the other groups. Experiment 2 confirmed the results of Experiment 1, using a between-subject design. The findings are in line with predictions from the Rescorla-Wagner model, and are discussed with reference to other contemporary theories of associative learning.     

A multistage ranking model

In this paper, we propose a (n–1)² parameter, multistage ranking model, which represents a generalization of Luce's model. We propose then×n item-rank relative frequency matrix (p-matrix) as a device for summarizing a set of rankings. As an alternative to the traditional maximum likelihood estimation, for the proposed model we suggest a method which estimates the parameters from thep-matrix. An illustrative numerical example is given. The proposed model and its differences from Luce's model are briefly discussed. We also show some specialp-matrix patterns possessed by the Thurstonian models and distance-based models.     

A hierarchical latent response model for inferences about examinee engagement in terms of guessing and item-level non-response

In low-stakes assessments, test performance has few or no consequences for examinees themselves, so that examinees may not be fully engaged when answering the items. Instead of engaging in solution behaviour, disengaged examinees might randomly guess or generate no response at all. When ignored, examinee disengagement poses a severe threat to the validity of results obtained from low-stakes assessments. Statistical modelling approaches in educational measurement have been proposed that account for non-response or for guessing, but do not consider both types of disengaged behaviour simultaneously. We bring together research on modelling examinee engagement and research on missing values and present a hierarchical latent response model for identifying and modelling the processes associated with examinee disengagement jointly with the processes associated with engaged responses. To that end, we employ a mixture model that identifies disengagement at the item-by-examinee level by assuming different data-generating processes underlying item responses and omissions, respectively, as well as response times associated with engaged and disengaged behaviour. By modelling examinee engagement with a latent response framework, the model allows assessing how examinee engagement relates to ability and speed as well as to identify items that are likely to evoke disengaged test-taking behaviour. An illustration of the model by means of an application to real data is presented.     

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.