A multidimensional examination of marital conflict and subjective health over 16 years

Guided by stress process perspectives, this study conceptualizes marital conflict as a multidimensional stressor to assess how three aspects of conflict—frequency of disagreements, breadth of disagreements, and cumulative disagreements—impact subjective health. Longitudinal data of married couples spanning 16 years (n = 373 couples) were analyzed using multilevel modeling. For husbands, more frequent disagreements than usual within a given year were associated with poorer subjective health. For wives, the greater cumulative effects of disagreements over 16 years were harmful for subjective health. We discuss how gendered self‐representations and relationship power issues help explain the findings. This research demonstrated the importance of examining multiple aspects of marital conflict to reveal that their subjective health consequences function differently for wives and husbands.     

Kernel principal component analysis combining rotation forest method for linearly inseparable data

Rotation forest (RoF) is an ensemble classifier combining linear analysis theories and decision tree algorithms. In recent existing works, RoF was widely applied to various fields with outstanding performance compared to traditional machine learning techniques, given that a reasonable number of base classifiers is provided. However, the conventional RoF algorithm suffers from classifying linearly inseparable datasets. In this study, a hybrid algorithm integrating kernel principal component analysis (KPCA) and the conventional RoF algorithm is proposed to overcome the classification difficulty for linearly inseparable datasets. The radial basis function (RBF) is selected as the kernel for the KPCA method to establish the nonlinear mapping for linearly inseparable data. Moreover, we evaluate various kernel parameters for better performance. Experimental results show that our algorithm improves the performance of RoF with linearly inseparable datasets, and therefore provides higher classification accuracy rates compared with other ensemble machine learning methods.     

Clustering preference data in the presence of response-style bias

Preference data, such as Likert scale data, are often obtained in questionnaire-based surveys. Clustering respondents based on survey items is useful for discovering latent structures. However, cluster analysis of preference data may be affected by response styles, that is, a respondent's systematic response tendencies irrespective of the item content. For example, some respondents may tend to select ratings at the ends of the scale, which is called an ‘extreme response style’. A cluster of respondents with an extreme response style can be mistakenly identified as a content-based cluster. To address this problem, we propose a novel method of clustering respondents based on their indicated preferences for a set of items while correcting for response-style bias. We first introduce a new framework to detect, and correct for, response styles by generalizing the definition of response styles used in constrained dual scaling. We then simultaneously correct for response styles and perform a cluster analysis based on the corrected preference data. A simulation study shows that the proposed method yields better clustering accuracy than the existing methods do. We apply the method to empirical data from four different countries concerning social values.     

Measuring Long‐Term Patterns of Political Secularization and Desecularization: Did They Happen or Not?

Political secularization theories have predicted religion's decline in public and political life, and desecularization theories have predicted the reverse trend. However, there is little agreement on the timing of either phenomenon or even their existence. Until now, deep empirical tests of any of these were hampered by lack of historical country‐level data on religious preferences of governments (previously used data sets go back only to 1990). However, the new Government Religious Preference data set (GRP) measures state religion from 2015 back to the 1800s. Using GRP data, this article offers the first long‐term quantitative measurement of political secularization and in doing so, weighs in on competing claims regarding its timing. This article finds strong support that political secularization happened gradually over the long 19th century, accelerated after World War II, and peaked in the 1970s or 1980s. In contrast, the article finds only tepid support for the existence of political desecularization overall.     

Trifactor Models for Multiple-Ratings Data

In this study we extend and assess the trifactor model for multiple-ratings data in which two different raters give independent scores for the same responses (e.g., in the GRE essay or to subset of PISA constructed-responses). The trifactor model was extended to incorporate a cross-classified data structure (e.g., items and raters) instead of a strictly hierarchical structure. we present a set of simulations to reflect the incompleteness and imbalance in real-world assessments. The effects of the rate of missingness in the data and of ignoring differences among raters are investigated using two sets of simulations. The use of the trifactor model is also illustrated with empirical data analysis using a well-known international large-scale assessment.     

让自适应测验更知人善选——基于推荐系统的选题策略

基于推荐系统中协同过滤推荐的思想, 提出两种可以利用已有答题者数据的CAT选题策略：直接基于答题者推荐(DEBR)和间接基于答题者推荐(IEBR)。通过两个模拟研究, 在不同题库和不同长度的测验中, 比较了两种推荐选题策略与两种传统选题策略(FMI和BAS)在测量精度和对题目曝光率控制上的表现, 以及影响推荐选题策略表现的因素。结果发现：两种推荐选题策略对题目曝光率的控制优于两种传统选题策略, 测量精度不亚于BAS方法, 其中DEBR侧重选题精度, IEBR对题目曝光率控制最好。已有答题者数据的特点和质量是影响推荐选题策略表现的主要因素。     

Impact of a comprehensive safety program on bicycle helmet use among middle-school children

A bicycle helmet program was evaluated in three middle schools using a multiple baseline across schools design. Two of the three schools had histories of enforcement of helmet use. During baseline many students riding their bikes to and from school did not wear their helmets or wore them incorrectly. A program that consisted of peer data collection of correct helmet use, education on how to wear a bicycle helmet correctly, peer goal setting, public posting of the percentage of correct helmet use, and shared reinforcers, all of which were implemented by the school resource officer, increased afternoon helmet use and afternoon correct helmet use in all three schools. Probe data collected a distance from all three schools indicated that students did not remove their helmets once they were no longer in close proximity to the school, and probe data collected in the morning at two of the schools showed that the behavior change transferred to the morning.     

Games with Zero-knowledge Signaling

We observe that in certain two-player repeated games of incomplete information, where information may be incomplete on both sides, it is possible for an informed player to signal his status as an informed player to the other without revealing any information about the choice of chance. The key to obtaining such a class of games is to relax the assumption that the players’ moves are observable. We show that in such cases players can achieve a kind of signaling that is “zero-knowledge”, in the sense that the other player becomes convinced that her opponent is informed without ever learning the choice of chance. Moreover, such “zero-knowledge signaling” has all of the statistical properties associated with zero-knowledge proofs in intereactive protocols. In particular, under the general assumption that moves are unobservable, such signaling leads to a class of equilibria in repeated games that are separatingin regard to the status of player 1–informed or uninformed–but only for player 2; any other player in a network, being unable to observe the moves of player 2, remains uncertain as to the status of player 1. Special Issue Formal Epistemology II. Edited by Branden Fitelson     

A variable neighborhood search method for generalized blockmodeling of two-mode binary matrices

The clustering of two-mode proximity matrices is a challenging combinatorial optimization problem that has important applications in the quantitative social sciences. We focus on one particular type of problem related to the clustering of a two-mode binary matrix, which is relevant to the establishment of generalized blockmodels for social networks. In this context, clusters for the rows of the two-mode matrix intersect with clusters of the columns to form blocks, which should ideally be either complete (all 1s) or null (all 0s). A new procedure based on variable neighborhood search is presented and compared to an existing two-mode K-means clustering algorithm. The new procedure generally provided slightly greater explained variation; however, both methods yielded exceptional recovery of cluster structure.     

A Measure for the Reliability of a Rating Scale Based on Longitudinal Clinical Trial Data

A new measure for reliability of a rating scale is introduced, based on the classical definition of reliability, as the ratio of the true score variance and the total variance. Clinical trial data can be employed to estimate the reliability of the scale in use, whenever repeated measurements are taken. The reliability is estimated from the covariance parameters obtained from a linear mixed model. The method provides a single number to express the reliability of the scale, but allows for the study of the reliability’s time evolution. The method is illustrated using a case study in schizophrenia. The authors are grateful to J&J PRD for kind permission to use their data. We gratefully acknowledge support from the Belgian IUAP/PAI network “Statistical Techniques and Modeling for Complex Substantive Questions with Complex Data.”