组织文化、组织文化吻合度与员工的组织公民行为

以中国普通员工为被试,考察了组织文化、组织文化吻合度与员工组织公民行为(OCB)之间的关系.结果发现: (1)不同年龄、不同类型企业员工的OCB存在显著差异,但在性别、教育水平方面没有差异; (2)不同取向的组织文化对员工不同层面的OCB具有不同的预测作用.具体而言.组织文化对社会层面OCB的预测作用较强;就单一取向的组织文化来看,人本与创新取向的组织文化对OCB的预测作用最强; (3)在组织文化影响员工组织公民行为时,组织文化吻合度具有一定的调节作用,这种调节作用在人本与创新取向以及规范与安定取向的组织文化影响OCB时表现更加明显.     

Psychopathy and aggression: Examining the role of psychopathy factors in predicting laboratory aggression under hostile and instrumental conditions

Psychopathy is a problematic configuration of traits and behaviors that is consistently correlated with aggressive, criminal behavior. Studies have suggested that psychopathy is composed of related but distinct factors that manifest divergent relations with a host of constructs including aggression. In the current study, we used a sample of 126 men to examine whether these psychopathy factors are differentially related to aggression manifested in two conditions (instrumental and hostile/reactive aggression) of a laboratory aggression paradigm. Traits related to an antagonistic interpersonal style and emotional detachment (i.e., Factor 1) were related to aggression in both conditions whereas traits related to negative emotionality, impulsivity, and an antagonistic style (i.e., Factor 2) were related to aggression only in the hostile/reactive condition. Potential explanations for these findings are put forth.     

Associations in human instrumental conditioning

Four experiments were conducted to study the contents of human instrumental conditioning. Experiment 1 found positive transfer between a discriminative stimulus (S^D) and an instrumental response (R) that shared the outcome (O) with the response that was originally trained with the S^D, showing the formation of an S^D → O association. Experiment 2 found that post-acquisition devaluation of an outcome selectively reduced the response trained with that outcome, showing the formation of a R → O association. In Experiment 3, changing the outcome did not prevent participants from giving the response learned with each S^D, even though none of the responses was appropriate for the new outcome, showing evidence of the formation of S^D → R associations in instrumental learning. The three binary associations were shown within the same basic experimental situation. Finally, Experiment 4 found evidence of the formation of the higher order association S^D(R → O) in human instrumental conditioning.     

情绪智力:多维度构思背后的信息

情绪智力作为影响个人发展与领导能力的关键变量已经得到许多关注。虽然许多研究都认为情绪智力更适合从一个多维度变量角度来探讨,但更多研究主要关注情绪智力的整体效应和差异,比较少从情绪智力的单一维度方面来进行研究。本研究以公务员群体来进行分析,运用多元方差分析和结构方程技术来分析人口统计特征对情绪智力多维度构思的影响,并特别关注情绪智力及各维度对个体绩效的影响。最后根据研究结果对与情绪智力相关的理论研究和管理实践提供一些建议。     

目击证人辨认研究

目击证人辨认对司法审判具有重要的意义,这使其成为心理学研究领域的热点之一。本文重点介绍了影响目击证人辨认准确性的因素,即估计者变量和系统变量,并分析了目击证人辨认研究的现存问题。     

Empirically Corrected Rescaled Statistics for SEM with Small N and Large p

Survey data often contain many variables. Structural equation modeling (SEM) is commonly used in analyzing such data. With typical nonnormally distributed data in practice, a rescaled statistic T_rml proposed by Satorra and Bentler was recommended in the literature of SEM. However, T_rml has been shown to be problematic when the sample size N is small and/or the number of variables p is large. There does not exist a reliable test statistic for SEM with small N or large p, especially with nonnormally distributed data. Following the principle of Bartlett correction, this article develops empirical corrections to T_rml so that the mean of the empirically corrected statistics approximately equals the degrees of freedom of the nominal chi-square distribution. Results show that empirically corrected statistics control type I errors reasonably well even when N is smaller than 2p, where T_rml may reject the correct model 100% even for normally distributed data. The application of the empirically corrected statistics is illustrated via a real data example.     

A Comparison of Methods for Creating Multiple Imputations of Nominal Variables

Many variables that are analyzed by social scientists are nominal in nature. When missing data occur on these variables, optimal recovery of the analysis model's parameters is a challenging endeavor. One of the most popular methods to deal with missing nominal data is multiple imputation (MI). This study evaluated the capabilities of five MI methods that can be used to treat incomplete nominal variables: multiple imputation with chained equations (MICE) using polytomous regression as the elementary imputation method; MICE based on classification and regression trees (CART); MICE based on nested logistic regressions; the ranking procedure described by Allison (2002 Allison, P. D. (2002). Missing data. Thousand Oaks, CA: Sage Publications. https://doi.org/10.4135/9780857020994.n4[Crossref] , [Google Scholar]); and a joint modeling approach based on the general location model. We first motivate our inquiry with an applied example and then present the results of a Monte Carlo simulation study that compared the performance of the five imputation methods under conditions of varying sample size, percentage of missing data, and number of nominal response categories. We found that MICE with polytomous regression was the strongest performer while the Allison (2002 Allison, P. D. (2002). Missing data. Thousand Oaks, CA: Sage Publications. https://doi.org/10.4135/9780857020994.n4[Crossref] , [Google Scholar]) ranking procedure and MICE with CART performed poorly in most conditions.     

People with intellectual disability who are ageing: Perceived needs assessment

Background/Objective: Ageing and progressive increase in life expectancy in people with intellectual disability present significant challenges. The present study is to examine the needs that arise in the aging process of this population. To detect these needs is fundamental to ensure personal wellbeing; almost no psychometrically sound instruments have been developed. Method: A structured interview was developed and evaluated by a panel of 20 experts to obtain evidence of content validity. The interview was then applied to 1,173 people with intellectual disability. The ages ranged from 35 to 80 (M = 52.27; SD = 7.5). Results: The interview consisted of 93 items evaluating four areas: perceived needs, personal problems, future perspectives, and solutions required. The results demonstrated the proximity and relationship of the various needs evaluated. In addition it was observed that those with intellectual disability who were older, male and users of residential services tended to indicate more needs, problems, and required more solutions. Conclusions: The instrument has adequate evidence of validity, allowing the evaluation of specific needs of people with intellectual disability. The results advocate the design of transversal intervention policies in order to effectively meet the needs and requirements of this population.     

Generalized multilevel structural equation modeling

A unifying framework for generalized multilevel structural equation modeling is introduced. The models in the framework, called generalized linear latent and mixed models (GLLAMM), combine features of generalized linear mixed models (GLMM) and structural equation models (SEM) and consist of a response model and a structural model for the latent variables. The response model generalizes GLMMs to incorporate factor structures in addition to random intercepts and coefficients. As in GLMMs, the data can have an arbitrary number of levels and can be highly unbalanced with different numbers of lower-level units in the higher-level units and missing data. A wide range of response processes can be modeled including ordered and unordered categorical responses, counts, and responses of mixed types. The structural model is similar to the structural part of a SEM except that it may include latent and observed variables varying at different levels. For example, unit-level latent variables (factors or random coefficients) can be regressed on cluster-level latent variables. Special cases of this framework are explored and data from the British Social Attitudes Survey are used for illustration. Maximum likelihood estimation and empirical Bayes latent score prediction within the GLLAMM framework can be performed using adaptive quadrature in gllamm, a freely available program running in Stata.gllamm can be downloaded from http://www.gllamm.org. The paper was written while Sophia Rabe-Hesketh was employed at and Anders Skrondal was visiting the Department of Biostatistics and Computing, Institute of Psychiatry, King's College London.