Developing multiple identifications through different social interactions at work

The question of how employees’ identifications with various foci at work (e.g., team, supervisor, or profession) develop and how they can be managed actively still remains largely unanswered. This is surprising, given the previously demonstrated benefits of employees’ identifications for organizational outcomes. Building on the social validation framework, we propose that changes in different social interactions (social support and effective feedback from supervisors and co-workers) over time are apt to socially validate different provisional selves and thereby differentially foster identification with the supervisor, the team, and the profession. In a longitudinal study of 212 apprentices, who are newcomers to both the organization and the profession, we test this idea using latent change scores in a structural equation modelling approach. Supporting our hypotheses, we show that changes in social support from supervisors and co-workers are related to changes in supervisor and team identification. Increased feedback from co-workers, but not from supervisors, predicted increased professional identification.     

应聘者非言语信息对面试官评分的影响:解释、测量和控制

应聘者非言语信息是影响结构化面试中面试官准确评分的一个重要因素。社会互动理论和拟剧论从面试官角度,社会影响理论和相互依赖理论从应聘者角度分别解释了非言语信息对面试官评分的影响。对非言语信息的测量,除了传统的自陈式问卷,一些新兴的计算机识别技术也被使用。为了消除非言语信息对面试评分准确性的影响,通常会从面试前、面试中和面试后三个阶段对其进行控制。未来可以基于机器学习和大数据分析,探索非言语信息对结构化面试评分的影响,建构新的理论以解释影响过程。     

Contributions to Estimation of Polychoric Correlations

This research concerns the estimation of polychoric correlations in the context of fitting structural equation models to observed ordinal variables by multistage estimation. The first main contribution of this research is to propose and evaluate a Monte Carlo estimator for the asymptotic covariance matrix (ACM) of the polychoric correlation estimates. In multistage estimation, the ACM plays a prominent role, as overall test statistics, derived fit indices, and parameter standard errors all depend on this quantity. The ACM, however, must itself be estimated. Established approaches to estimating the ACM use a sample-based version, which can yield poor estimates with small samples. A simulation study demonstrates that the proposed Monte Carlo estimator can be more efficient than its sample-based counterpart. This leads to better calibration for established test statistics, in particular with small samples. The second main contribution of this research is a further exploration of the consequences of violating the normality assumption for the underlying response variables. We show the consequences depend on the type of nonnormality, and the number and location of thresholds. The simulation study also demonstrates that overall test statistics have little power to detect the studied forms of nonnormality, regardless of the ACM estimator.     

认知加工速度研究中常用的实验和统计方法

认知加工速度理论是解释年龄与认知老年化现象的理论之一,也是近年来心理学研究中的热点之一.该文从理论要点、实验设计、指标选择和统计分析手段四个方面对认知加工速度的研究进行了回顾.在阐述理论要点的基础上,介绍了常用的实验指标和选择指标的标准,以及一些常用的统计分析手段.作者认为除了深入理解理论之外,谨慎地设计实验方案、选择适当的实验指标,以及采用恰当的统计手段都是研究得以顺利进行的必要保障.     

Regression among factor scores

Structural equation models with latent variables are sometimes estimated using an intuitive three-step approach, here denoted factor score regression. Consider a structural equation model composed of an explanatory latent variable and a response latent variable related by a structural parameter of scientific interest. In this simple example estimation of the structural parameter proceeds as follows: First, common factor models areseparately estimated for each latent variable. Second, factor scores areseparately assigned to each latent variable, based on the estimates. Third, ordinary linear regression analysis is performed among the factor scores producing an estimate for the structural parameter. We investigate the asymptotic and finite sample performance of different factor score regression methods for structural equation models with latent variables. It is demonstrated that the conventional approach to factor score regression performs very badly. Revised factor score regression, using Regression factor scores for the explanatory latent variables and Bartlett scores for the response latent variables, produces consistent estimators for all parameters.     

New measures of political attitudes in Northern Ireland: a social identity perspective

Twenty‐eight measures of political attitudes were validated on a sample of 388 undergraduate students from Northern Ireland. Confirmatory factor analysis showed the scales to be unidimensional, discriminantly valid, with generally excellent reliabilities. The pattern of intergroup differentiation between Catholics and Protestants conformed to Social Identity Theory, with maximum differentiation on important issues, Catholics adopting a social change ideology and Protestants defending the status quo. Catholics and Protestants resolved their respective group associations with violence by condemning both it and terrorism, and also reported interdenominational friendships. The utility of these new measures of political attitudes in terms of measuring changes due to political initiatives, cross‐community reconciliation programmes and in assessing changes in attitudes as a result of integrated or segregated denominational schooling within the Province is outlined. Copyright © 2000 John Wiley & Sons, Ltd.     

类似性比较对3～5岁儿童内隐推理的影响

本研究以三个实验探讨了3～5岁儿童词汇学习的内隐推理过程。实验1首先让被试者看一个熟悉物体的标准图片,分别以熟悉的或者不熟悉的名词命名,然后要求从两个选项中选择一个图片与标准图片匹配。结果表明3～4岁儿童可以不熟悉名词完成词汇扩展作业,但是只有5岁儿童可以熟悉名词完成词汇扩展作业,这表明5岁儿童可能有一个内隐推理加工过程。实验2以不熟悉物体作为标准图片,以熟悉名词与指代词分别作为指导语进一步测试5岁儿童的内隐推理过程,结果表明这个年龄阶段儿童的类概念形成有一种自上而下加工的内隐推理过程。实验3呈现基本概念的图片检验什么因素影响3～5岁儿童词汇学习的内隐推理过程,结果表明在类似性比较条件下与3～4岁儿童相比,5岁儿童更加明显地表现了内隐推理的加工过程。最后以类似性理论讨论了本研究的结果。     

态度结构一致性及其对态度和行为的影响

态度结构一致性指态度的情感或认知成分与总体态度的一致程度以及态度的情感与认知成分两者间的一致程度。本文介绍了结构一致性的界定和操作方法,回顾了结构一致性对态度和行为的影响,包括其在态度形成中的作用以及对态度可获得性和态度-行为关系的影响。同时指出未来研究应关注各种结构一致性的交互,将结构一致性研究推广到群体水平,并进一步应用于社会预警研究。     

算术应用题二重变异样例学习的迁移效果

设计并运用了三种表面特征变异的二重样例和三种结构特征变异的二重样例,对二年级小学生解决算术应用题的迁移效果进行了实验研究。结果表明：一个原样例的学习就能明显促进了二年级小学生解决与原样例表面特征不同而结构特征相同的近迁移问题;学习三种表面特征变异的二重样例后没有产生明显的远迁移效果;而学习了三种结构特征变异的二重样例后产生了不同程度的远迁移效果,其中,规则平行组合二重样例学习的远迁移效果最好,其次是规则变异二重样例学习的远迁移效果,规则镶嵌组合变异二重样例学习的远迁移效果最差     

Career maturity: A latent means and covariance structure analysis of Daidalos

A latent means and covariance structure analysis was conducted to assess the construct validity and construct comparability in the measuring of career maturity across boys and girls. The career maturity inventory has been coined Daidalos. The sample consisted of 2,443 high school students recruited from one county in Norway. Of these, 1,132 were males, and 1,311 were females. The mean age of the participants was 17 years of age. Goodness-of-fit statistics provided support for a five-factor first-order model in which the factor loadings, factor covariances and item intercepts were invariant across groups. Additionally, ΔS-B χ² was shown to be non-significant for the testing of invariance of the measurement model. Three significant differences in latent means were detected, with boys scoring higher on negative attitudes toward school or dropout intentions, and girls scoring higher on career uncertainty and need for world-of-work information.