多源评价的特点与内在机制

多源评价在国外企业中的运用已日益成熟, 但在我国还停留在探索与发展阶段。基于已有的研究发现, 围绕评价过程、评价源及被评价者三方面对多源评价的特点及内在机制进行了探讨与分析。从评价过程看, 其评价目的具有多重性, 评价形式注重匿名性, 且评价结果的合理应用非常重要; 从评价源看, 不同评价源间的评价一致性较低, 且易造成晕轮效应和宽大效应; 从被评价者来看, 个体对多源评价结果的反应, 受到个性特征、反馈信号及自我-他人评价间差距等因素影响。研究也发现, 多源评价所带来的绩效改进结果具有不稳定性。基于此, 如何提高多源评价过程的有效性与准确性, 改善评价者对评价结果的反应, 以及如何对多源评价结果进行有效汇总等是未来值得研究的重要内容。     

Ordinal state-trait regression for intensive longitudinal data

In many psychological studies, in particular those conducted by experience sampling, mental states are measured repeatedly for each participant. Such a design allows for regression models that separate between- from within-person, or trait-like from state-like, components of association between two variables. But these models are typically designed for continuous variables, whereas mental state variables are most often measured on an ordinal scale. In this paper we develop a model for disaggregating between- from within-person effects of one ordinal variable on another. As in standard ordinal regression, our model posits a continuous latent response whose value determines the observed response. We allow the latent response to depend nonlinearly on the trait and state variables, but impose a novel penalty that shrinks the fit towards a linear model on the latent scale. A simulation study shows that this penalization approach is effective at finding a middle ground between an overly restrictive linear model and an overfitted nonlinear model. The proposed method is illustrated with an application to data from the experience sampling study of Baumeister et al. (2020, Personality and Social Psychology Bulletin, 46, 1631).     

ERP Evidence for the Rapid Assignment of an (Appropriate) Antecedent to PRO

Event-related brain potentials were recorded while subjects listened to sentences containing a controlled infinitival complement. Subject and object control items were used, both with 2 potential antecedents in the upper clause. Half of the sentences had a gender agreement violation between the null subject of the infinitival complement and an adjective predicated of it. The rapid detection of this anomaly would indicate that the parser had established the coreference relation between the null subject and an antecedent, and that the processor had rapidly consulted verb control information to select the proper antecedent of the null subject. The results showed that for both subject and object control items ungrammatical adjectives elicited a P600 effect. These data imply that the processor has coindexed the null subject with an antecedent, and that the antecedent has been selected on the basis of control information. These results are compatible with parsing models that emphasize the rapid influence of verb-specific information on sentence processing.     

Modelling monotonic effects of ordinal predictors in Bayesian regression models

Ordinal predictors are commonly used in regression models. They are often incorrectly treated as either nominal or metric, thus under- or overestimating the information contained. Such practices may lead to worse inference and predictions compared to methods which are specifically designed for this purpose. We propose a new method for modelling ordinal predictors that applies in situations in which it is reasonable to assume their effects to be monotonic. The parameterization of such monotonic effects is realized in terms of a scale parameter b representing the direction and size of the effect and a simplex parameter modelling the normalized differences between categories. This ensures that predictions increase or decrease monotonically, while changes between adjacent categories may vary across categories. This formulation generalizes to interaction terms as well as multilevel structures. Monotonic effects may be applied not only to ordinal predictors, but also to other discrete variables for which a monotonic relationship is plausible. In simulation studies we show that the model is well calibrated and, if there is monotonicity present, exhibits predictive performance similar to or even better than other approaches designed to handle ordinal predictors. Using Stan, we developed a Bayesian estimation method for monotonic effects which allows us to incorporate prior information and to check the assumption of monotonicity. We have implemented this method in the R package brms, so that fitting monotonic effects in a fully Bayesian framework is now straightforward.     

Relative importance of criteria: a quantitative approach

A mathematical definition for the assertion ‘a group of criteria A is more important than a group of criteria B’ is introduced. It is shown how quantitative information on relative importance of criteria allows us to obtain a more precise upper estimate for a set of all non-dominated solutions than the well-known Pareto set. © 1997 John Wiley & Sons, Ltd.