The mitigating role of leader‒member exchange when perceiving psychological contract violation: a diary survey study among volunteers

Several scholars in the field of volunteering emphasized the pivotal role of psychological contract (PC) violation in explaining maladaptive behavioural reactions—such as counterproductive work behaviour (CWB)—of volunteers. Reactions to violation feelings are, however, interrelated and may intensify over time. Extending this dynamic perspective, we introduce momentary leader–member exchange (LMX) as a buffering social resource in the relationship between violation feelings and (1) CWB and (2) the likelihood to perceive a PC breach. Using weekly diary survey data from 247 volunteers (827 observations), we conducted a moderated multilevel zero-inflated Poisson regression analysis. As hypothesized, experiencing feelings of violation during one week related positively to CWB towards the organization (CWB-O), but not to CWB towards individuals (CWB-I) during the next week. Moreover, experiencing violation feelings during one week increased the likelihood to perceive a PC breach during the subsequent week. Finally, experiencing a high-quality LMX relationship effectively mitigated the positive relationship between violation feelings during one week and (1) CWB-O, and (2) the likelihood to perceive a PC breach during the next week. Our study highlights momentary LMX as an effective redressing mechanism in the relationship between violation feelings and undesirable employee outcomes.     

Approximations to the distribution of a test statistic in covariance structure analysis: A comprehensive study

In structural equation modelling (SEM), a robust adjustment to the test statistic or to its reference distribution is needed when its null distribution deviates from a χ² distribution, which usually arises when data do not follow a multivariate normal distribution. Unfortunately, existing studies on this issue typically focus on only a few methods and neglect the majority of alternative methods in statistics. Existing simulation studies typically consider only non-normal distributions of data that either satisfy asymptotic robustness or lead to an asymptotic scaled χ² distribution. In this work we conduct a comprehensive study that involves both typical methods in SEM and less well-known methods from the statistics literature. We also propose the use of several novel non-normal data distributions that are qualitatively different from the non-normal distributions widely used in existing studies. We found that several under-studied methods give the best performance under specific conditions, but the Satorra–Bentler method remains the most viable method for most situations.     

On measurement properties of continuation ratio models

Three classes of polytomous IRT models are distinguished. These classes are the adjacent category models, the cumulative probability models, and the continuation ratio models. So far, the latter class has received relatively little attention. The class of continuation ratio models includes logistic models, such as the sequential model (Tutz, 1990), and nonlogistic models, such as the acceleration model (Samejima, 1995) and the nonparametric sequential model (Hemker, 1996). Four measurement properties are discussed. These are monotone likelihood ratio of the total score, stochastic ordering of the latent trait by the total score, stochastic ordering of the total score by the latent trait, and invariant item ordering. These properties have been investigated previously for the adjacent category models and the cumulative probability models, and for the continuation ratio models this is done here. It is shown that stochastic ordering of the total score by the latent trait is implied by all continuation ratio models, while monotone likelihood ratio of the total score and stochastic ordering on the latent trait by the total score are not implied by any of the continuation ratio models. Only the sequential rating scale model implies the property of invariant item ordering. Also, we present a Venn-diagram showing the relationships between all known polytomous IRT models from all three classes.     

Factor analysis with (mixed) observed and latent variables in the exponential family

We develop a general approach to factor analysis that involves observed and latent variables that are assumed to be distributed in the exponential family. This gives rise to a number of factor models not considered previously and enables the study of latent variables in an integrated methodological framework, rather than as a collection of seemingly unrelated special cases. The framework accommodates a great variety of different measurement scales and accommodates cases where different latent variables have different distributions. The models are estimated with the method of simulated likelihood, which allows for higher dimensional factor solutions to be estimated than heretofore. The models are illustrated on synthetic data. We investigate their performance when the distribution of the latent variables is mis-specified and when part of the observations are missing. We study the properties of the simulation estimators relative to maximum likelihood estimation with numerical integration. We provide an empirical application to the analysis of attitudes.     

Marginal maximum likelihood estimation of item response theory (IRT) equating coefficients for the common-examinee design

A method of estimating item response theory (IRT) equating coefficients by the common-examinee design with the assumption of the two-parameter logistic model is provided. The method uses the marginal maximum likelihood estimation, in which individual ability parameters in a common-examinee group are numerically integrated out. The abilities of the common examinees are assumed to follow a normal distribution but with an unknown mean and standard deviation on one of the two tests to be equated. The distribution parameters are jointly estimated with the equating coefficients. Further, the asymptotic standard errors of the estimates of the equating coefficients and the parameters for the ability distribution are given. Numerical examples are provided to show the accuracy of the method.     

Measuring emotional management abilities: Further evidence of the importance to distinguish between typical and maximum performance

Freudenthaler and Neubauer (2005) have recently developed and validated performance measures of emotional management abilities (EMA), requiring subjects to indicate their typical behaviour in emotional situations (typical-performance instruction, TP) instead of assessing the effectiveness of different behavioural alternatives or indicating the most adequate response (maximum-performance instruction, MP). In the present study (n = 176), we examined the effects of these two instruction forms (TP vs. MP) on the EMA-scores in a between-subject design. In addition, the relations of typical and maximum EMA to cognitive intelligence components and personality traits were tested. Instruction-related comparisons of the EMA-scores yielded not only lower means but also higher standard deviations and higher reliabilities in the TP-condition. As expected, maximum EMA were significantly correlated with cognitive intelligence components but not with personality traits. In contrast, the typical EMA were moderately associated with personality traits but not related to cognitive abilities, providing further evidence of the importance to distinguish between typical and maximum performance.     

Is perceptual space inherently non-Euclidean?

It is often assumed that the space we perceive is Euclidean, although this idea has been challenged by many authors. Here we show that if spatial cues are combined as described by Maximum Likelihood Estimation, Bayesian, or equivalent models, as appears to be the case, then Euclidean geometry cannot describe our perceptual experience. Rather, our perceptual spatial structure would be better described as belonging to an arbitrarily curved Riemannian space.     

认知元反应理论--IRT直接应用于多值记分题

0-1记分测验的项目反应理论已经得到广泛的研究和应用.但是,许多测验都含有多值记分题,所以需要将IRT推广到此类情况.从认知理论的观点看,每个0-1记分题(项目)和多值记分题的每个测试点都可同样地看成一个由若干知识点构成的集合,称之为认知元;根据认知元之间存在的关系可以确定各受测者对各试题作出特定答案的概率,从而不需要引用任何其它假设就可将IRT的方法直接应用于含多值记分题的测验.本文应用这一理论分析了某些测验样本,结果表明是可行的.     

Limited Information Goodness-of-fit Testing in Multidimensional Contingency Tables

We introduce a family of goodness-of-fit statistics for testing composite null hypotheses in multidimensional contingency tables. These statistics are quadratic forms in marginal residuals up to order r. They are asymptotically chi-square under the null hypothesis when parameters are estimated using any asymptotically normal consistent estimator. For a widely used item response model, when r is small and multidimensional tables are sparse, the proposed statistics have accurate empirical Type I errors, unlike Pearson's X ². For this model in nonsparse situations, the proposed statistics are also more powerful than X ². In addition, the proposed statistics are asymptotically chi-square when applied to subtables, and can be used for a piecewise goodness-of-fit assessment to determine the source of misfit in poorly fitting models. This research has been supported by the Department of Universities, Research, and Information Society (DURSI) of the Catalan Government, by grant BSO2003-08507 of the Spanish Ministry of Science and Technology, and an NSERC Canada grant. We are grateful to the referees for comments leading to improvements.