规则适应视角下辱虐管理的成因机制

基于规则适应及资源依赖理论的解释视角,本研究考察了下属工作绩效与政治技能对上级辱虐的影响。通过对采集自两家企业的243对领导-下属的配对问卷进行分析,本研究的多项式回归与响应面分析表明,当下属的工作绩效(遵循做事显规则)与政治技能(遵循做人潜规则)在较高水平上保持一致时,其遭受的上级辱虐最少。但这种较高水平的一致只能维持在特定范围内,当两者过高时,反而易遭致上级的辱虐行为。同时,在工作绩效与政治技能存在差异的情境下,相比于"高工作绩效,低政治技能"的下属,"低工作绩效,高政治技能"的下属遭受的上级辱虐要更少。文章分析并证明了遵循不同规则的下属会导致不同程度的上级辱虐。     

大学生情绪智力与感恩的交叉滞后分析

以修订后的情绪智力量表(EIS)中文版、青少年感恩量表（AGS）为研究工具,对1所普通高校的 406名大一至大三学生进行为期5个月的追踪,考察大学生情绪智力和感恩的发展变化,以及情绪智力与感恩之间的关系。结果发现：（1）大学生的情绪智力相对稳定,感恩存在一定的发展变化;（2）情绪智力与感恩之间呈显著正相关;（3）交叉滞后回归分析结果表明,前测的情绪智力可以显著预测后测的感恩,而前测的感恩不能显著预测后测的情绪智力。     

Do Raven's Colored Progressive Matrices function in the same way in typical and clinical populations? Insights from the intellectual disability field

Standardized tests are used widely in comparative studies of clinical populations, either as dependent or control variables. Yet, one cannot always be sure that the test items measure the same constructs in the groups under study. In the present work, 460 participants with intellectual disability of undifferentiated etiology and 488 typical children were tested using Raven's Colored Progressive Matrices (RCPM). Data were analyzed using binomial logistic regression modeling designed to detect differential item functioning (DIF). Results showed that 12 items out of 36 function differentially between the two groups, but only 2 items exhibit at least moderate DIF. Thus, a very large majority of the items have identical discriminative power and difficulty levels across the two groups. It is concluded that RCPM can be used with confidence in studies comparing participants with and without intellectual disability. In addition, it is suggested that methods for investigating internal bias of tests used in cross-cultural, cross-linguistic or cross-gender comparisons should also be regularly employed in studies of clinical populations, particularly in the field of developmental disability, to show the absence of systematic measurement error (i.e. DIF) affecting item responses.     

Father regression Clinical narratives and theoretical reflections1

The author deals with love‐hate enthrallment and submission to a primitive paternal object. This is a father‐son relationship that extends through increasing degrees of ‘primitiveness’ or extremeness, and is illustrated through three different constellations that constitute a continuum. One pole of the continuum encompasses certain male patients who show a loving, de‐individuated connection to a father experienced as trustworthy, soft, and in need of protection. Further along the continuum is the case of a transsexual patient whose analysis revealed an intense ‘God‐transference’, a bondage to an idealized, feared, and ostensibly protective father‐God introject. A great part of this patient's analysis consisted in a fi erce struggle to liberate himself from this fi gure. The other end of the continuum is occupied by religious terrorists, who exemplify the most radical thralldom to a persecutory, godly object, a regressive submission that banishes woman and enthrones a cruel superego, and that ends in destruction and self‐destruction. Psychoanalytic thinking has traditionally dealt with the oedipal father and recently with the nurturing father, but there is a gap in thinking about the phallic, archaic father, and his relations with his son(s). The author aims at fi lling this gap, at the same time as she also raises the very question of ‘What is a father?’ linking it with literary and religious themes.     

优势分析方法及其应用

优势分析是近年来由Budescu等人新发展起来的一种确定多元回归方程中各预测变量相对重要性的方法。与传统方法相比,优势分析突出的特点是,全面比较了在由全模型所衍生出来的所有子模型情况下,各预测变量(X1,X…XP)在解释或预测标准变量у时,它们之间的相对重要性。本文从基本原理以及具体操作过程对这一新的统计分析方法进行了详细介绍。     

用层级相合性指标探测反应数据中噪音大小

不同的认知诊断模型（CDM）对反应数据中噪音的抗干扰能力不同,在评估CDM性能的模拟实验中,反应数据中所含噪音的大小是十分重要的实验条件。由于噪音的内隐性,为应用中选用相应的CDM带来困难。本文在认知模型已知条件下,欲使用MHCI和NHCI指标评估认知诊断测验的反应数据（0, 1评分）中噪音的大小。模拟实验表明,两指标与噪音存在明显的统计规律。尤其是以NHCI为主要自变量对噪音进行预测的回归方程中,回归模型解释率均接近90%;以此实现对噪音的有效预测,从而为选择CDM提供一个参考。     

The WIG (weighted individual and group) shrinkage estimator

Regularization, or shrinkage estimation, refers to a class of statistical methods that constrain the variability of parameter estimates when fitting models to data. These constraints move parameters toward a group mean or toward a fixed point (e.g., 0). Regularization has gained popularity across many fields for its ability to increase predictive power over classical techniques. However, articles published in JEAB and other behavioral journals have yet to adopt these methods. This paper reviews some common regularization schemes and speculates as to why articles published in JEAB do not use them. In response, we propose our own shrinkage estimator that avoids some of the possible objections associated with the reviewed regularization methods. Our estimator works by mixing weighted individual and group (WIG) data rather than by constraining parameters. We test this method on a problem of model selection. Specifically, we conduct a simulation study on the selection of matching‐law‐based punishment models, comparing WIG with ordinary least squares (OLS) regression, and find that, on average, WIG outperforms OLS in this context.     

Robust inference with binary data

In this paper robustness properties of the maximum likelihood estimator (MLE) and several robust estimators for the logistic regression model when the responses are binary are analysed. It is found that the MLE and the classical Rao's score test can be misleading in the presence of model misspecification which in the context of logistic regression means either misclassification's errors in the responses, or extreme data points in the design space. A general framework for robust estimation and testing is presented and a robust estimator as well as a robust testing procedure are presented. It is shown that they are less influenced by model misspecifications than their classical counterparts. They are finally applied to the analysis of binary data from a study on breastfeeding.The author is partially supported by the Swiss National Science Foundation. She would like to thank Rand Wilcox, Eva Cantoni and Elvezio Ronchetti for their helpful comments on earlier versions of the paper, as well as Stephane Heritier for providing the routine to compute the OBRE.     

Prediction and classification in nonlinear data analysis: Something old,something new,something borrowed,something blue

Prediction and classification are two very active areas in modern data analysis. In this paper, prediction with nonlinear optimal scaling transformations of the variables is reviewed, and extended to the use of multiple additive components, much in the spirit of statistical learning techniques that are currently popular, among other areas, in data mining. Also, a classification/clustering method is described that is particularly suitable for analyzing attribute-value data from systems biology (genomics, proteomics, and metabolomics), and which is able to detect groups of objects that have similar values on small subsets of the attributes.This article is based on the Presidential Address Jacqueline Meulman gave on July 9, 2003 at the 68th Annual Meeting of the Psychometric Society held near Cagliari, Italy on the island of Sardinia.—Editor     

On the use of heterogeneous thresholds ordinal regression models to account for individual differences in response style

This paper proposes a general approach to accounting for individual differences in the extreme response style in statistical models for ordered response categories. This approach uses a hierarchical ordinal regression modeling framework with heterogeneous thresholds structures to account for individual differences in the response style. Markov chain Monte Carlo algorithms for Bayesian inference for models with heterogeneous thresholds structures are discussed in detail. A simulation and two examples based on ordinal probit models are given to illustrate the proposed methodology. The simulation and examples also demonstrate that failing to account for individual differences in the extreme response style can have adverse consequences for statistical inferences.The author is grateful to Ulf Böckenholt, an associate editor, and three anonymous reviewers for helpful comments, and Kristine Kuhn and Kshiti Joshi for providing the data.