A cautionary note on using internal cross validation to select the number of clusters

A highly popular method for examining the stability of a data clustering is to split the data into two parts, cluster the observations in Part A, assign the objects in Part B to their nearest centroid in Part A, and then independently cluster the Part B objects. One then examines how close the two partitions are (say, by the Rand measure). Another proposal is to split the data into k parts, and see how their centroids cluster. By means of synthetic data analyses, we demonstrate that these approaches fail to identify the appropriate number of clusters, particularly as sample size becomes large and the variables exhibit higher correlations.The authors express their thanks to the Sol C. Snider Entrepreneurial Center, Wharton School, for support of this project.     

A Swedish adaptation of the Emotional Contagion Scale: factor structure and psychometric properties

The Emotional Contagion Scale (ECS) is a self-report scale used to measure individual differences in susceptibility to converge towards the emotions expressed by others. The original American ECS (Doherty, 1997), translated into Swedish, was completed by 665 undergraduate students in two independent samples (N = 233 and N = 432, respectively). To investigate the factor structure of the ECS, confirmatory factor analyses of alternative models derived from previous research in emotion and emotional contagion were conducted. The results showed that the proposed one-dimensional structure of the ECS was not tenable. Instead a multi-facet model based on a differential emotions model and a hierarchal valence/differential emotions model was supported. Cross-validation on the second independent sample demonstrated and confirmed the multi-faceted property of the ECS and the equality of the factor structure across samples and genders. With regard to homogeneity and test-retest reliability, the Swedish version showed acceptable results and was in concordance with the original version.     

Predicting Academic Dishonesty on National Examinations: The Roles of Gender,Previous Performance,Examination Center Change,City Change,and Region Change

The purpose of the present studies was to evaluate and predict academic cheating with regard to a national examination in a Middle East country. In Study 1, 4,024 students took part and potential cheaters were classified as those having discrepant scores in multiple administrations that exceeded 1 SD in absolute terms. A latent class mixture analysis suggested two pathways for potential cheating: (a) The first path involved students—most male—who changed city or region of examination during test taking, and (b) the second path described students—most male—who did not change city, region, or center of administration. Study 2 profiled cheaters using a sample of examinees who were actually caught cheating. Participants were 545 students, 253 of whom were caught cheating between 2002 and 2012. Both samples were selected from a pool of 319,219 testees using random sampling procedures. Results indicated that a 4-class solution best fitted the data as in Study 1. Furthermore, a predictive model was tested with an independent cross-validation sample of 112 examinees (56 cheaters, 56 noncheaters). Results indicated that the model classified correctly 78.57 of the new cheating cases (sensitivity) and 94.64% of noncheaters (specificity).     

External analyses of preference models

Using Carroll's external analysis, several studies have found that unfolding models account for more, although seldom significantly more, variance in preferences than Tucker's vector model. In studies of sociometric ratings and political preferences, the unfolding model again rarely outpredicted the vector model by a significant amount. Yet on cross-validation, the unfolding model consistently accounted for more variance. Results suggest that sometimes significance tests are less sensitive than cross-validation procedures to the small but consistent superiority of the unfolding model. Future researchers may wish to use significance tests and cross-validation techniques in comparing models.     

变革型领导与员工工作态度：心理授权的中介作用

研究的主要目的是考察变革型领导与员工满意度、组织承诺之间的关系,以及变革型领导的作用机制,即变革型领导是否会通过心理授权影响员工工作态度。利用14家企业744份调查问卷的结果,采用结构方程模型技术对变革型领导、心理授权、员工满意度、组织承诺之间的关系进行了交叉验证分析。结果表明,愿景激励与德行垂范对组织承诺与员工满意度有显著的影响,而领导魅力与个性化关怀只对员工满意度有显著的影响;心理授权对变革型领导与员工工作态度的关系具有一定的中介作用,愿景激励与德行垂范通过工作意义影响员工满意度与组织承诺;愿景激励通过自我效能影响组织承诺     

企业员工工作伦理的结构

采用自行研制的工作伦理问卷, 通过对全国8个城市955名员工、6所高校508名在校大学生以及两大军区461名现役军人进行问卷调查, 对企业员工工作伦理的因素结构进行了探讨。探索性因素分析(n=478)表明, 企业员工工作伦理由职业操守、工作意义、勤奋奉献、积极进取和人际和谐5个维度构成。验证性因素分析(n=477)结果表明五因素模型是比较理想的模型。复核效化检验结果表明, 基于企业员工样本得到的工作伦理五因素结构在大学生样本(n=508)和军人样本(n=461)中具有一定程度的稳定性与预测力。文章最后对企业员工工作伦理维度的构成和内容进行了分析讨论。     

Shrinkage estimation of the three-parameter logistic model

The three-parameter logistic model is widely used to model the responses to a proficiency test when the examinees can guess the correct response, as is the case for multiple-choice items. However, the weak identifiability of the parameters of the model results in large variability of the estimates and in convergence difficulties in the numerical maximization of the likelihood function. To overcome these issues, in this paper we explore various shrinkage estimation methods, following two main approaches. First, a ridge-type penalty on the guessing parameters is introduced in the likelihood function. The tuning parameter is then selected through various approaches: cross-validation, information criteria or using an empirical Bayes method. The second approach explored is based on the methodology developed to reduce the bias of the maximum likelihood estimator through an adjusted score equation. The performance of the methods is investigated through simulation studies and a real data example.     

A Computational Evaluation of Two Models of Retrieval Processes in Sentence Processing in Aphasia

Can sentence comprehension impairments in aphasia be explained by difficulties arising from dependency completion processes in parsing? Two distinct models of dependency completion difficulty are investigated, the Lewis and Vasishth (2005) activation-based model and the direct-access model (DA; McElree, 2000). These models' predictive performance is compared using data from individuals with aphasia (IWAs) and control participants. The data are from a self-paced listening task involving subject and object relative clauses. The relative predictive performance of the models is evaluated using k-fold cross-validation. For both IWAs and controls, the activation-based model furnishes a somewhat better quantitative fit to the data than the DA. Model comparisons using Bayes factors show that, assuming an activation-based model, intermittent deficiencies may be the best explanation for the cause of impairments in IWAs, although slowed syntax and lexical delayed access may also play a role. This is the first computational evaluation of different models of dependency completion using data from impaired and unimpaired individuals. This evaluation develops a systematic approach that can be used to quantitatively compare the predictions of competing models of language processing.     

Assessment of generalised Bayesian structural equation models for continuous and binary data

The paper proposes a novel model assessment paradigm aiming to address shortcoming of posterior predictive $p$-values, which provide the default metric of fit for Bayesian structural equation modelling (BSEM). The model framework presented in the paper focuses on the approximate zero approach (Psychological Methods, 17 , 2012, 313), which involves formulating certain parameters (such as factor loadings) to be approximately zero through the use of informative priors, instead of explicitly setting them to zero. The introduced model assessment procedure monitors the out-of-sample predictive performance of the fitted model, and together with a list of guidelines we provide, one can investigate whether the hypothesised model is supported by the data. We incorporate scoring rules and cross-validation to supplement existing model assessment metrics for BSEM. The proposed tools can be applied to models for both continuous and binary data. The modelling of categorical and non-normally distributed continuous data is facilitated with the introduction of an item-individual random effect. We study the performance of the proposed methodology via simulation experiments as well as real data on the ‘Big-5’ personality scale and the Fagerstrom test for nicotine dependence.