当事人眼里的工作同盟：质的分析

为了解中国当事人如何看待咨询中的工作同盟, 对工作同盟做初步的本土概念化, 采用协商一致的质的研究方法对来自1所大学心理咨询中心的20名当事人的访谈结果进行了分析。结果发现, 与工作同盟相关的域有6个, 分别是情感联结、任务、投入、合作模式、发展变化、影响因素。前三个域可以看作是工作同盟的构成要素, 而后三个域则是对其外部特征的描述。对每个域的含义及其与现有理论和研究的关系进行了讨论。     

Bicriteria cost versus service analysis of a distribution network—a case study

In order to improve the distribution system for the Nordic countries the BASF AG considered 13 alternative scenarios to the existing system. These involved the construction of warehouses at various locations. For every scenario the transportation, storage, and handling cost incurred was to be as low as possible, where restrictions on the delivery time were given. The scenarios were evaluated according to (minimal) total cost and weighted average delivery time. The results led to a restriction to only three cases involving only one new warehouse each. For these, a more accurate model for the cost was developed and evaluated, yielding results similar to a simple linear model. Since there were no clear preferences between cost and delivery time, the final decision was chosen to represent a compromise between the two criteria. Copyright © 1999 John Wiley & Sons, Ltd.     

个体解决三项系列问题的心理模型

该研究设计了2种实验条件,对360名6-19岁个体解决三项系列问题的能力发展进行了研究,旨在讨论个体解决三项系列问题的心理模型的发展问题,结果表明：(1)6-19岁个体对三项系列问题中有必然逻辑答案的题目的判断能力的发展速度比较平稳;(2)个体对三项系列问题中有必然逻辑答案的题目的判断能力先于对没有逻辑答案的题目,6-9岁个体对三项系列问题中没有必然逻辑答案的题目的判断能力的发展速度低于10岁以后的个体;(3)空间模型、语义模型、语义一空间混合模型和枢纽项比较模型是相互补充的,是在不同认知发展阶段依一定次序,按一定的阶段逐渐形成的,经历了5个不同的发展阶段,最终形成以枢纽项比较模型为核心、四种心理模型并存于个体的心理结构之中的复合模型。     

疼痛恐惧对疼痛的影响及其认知机制

疼痛恐惧是把将要出现的疼痛刺激视为主要威胁而产生的一种恐惧,它会影响与疼痛相关的感知觉、注意、回避行为以及安慰剂止痛的效果.疼痛恐惧的认知模型主要有活动回避模型、恐惧-回避模型以及内感受性恐惧条件模型.未来的研究要进一步探究疼痛恐惧的认知机制,完善疼痛恐惧的认知模型并扩展疼痛恐惧的研究方法.     

消费行为研究中的联合分析法

联合分析(Conjoint Analysis)是一种定量的研究消费者选择偏好的方法。虽然从它诞生到现在只有40年的历史,但它对消费者行为学的影响很大,它的发展更是日新月异。文章首先介绍了联合分析的基本概念,然后重点阐述了联合分析的原理、步骤和发展历程,最后对联合分析的发展方向作了一个简要概括。     

影响儿童至老年人“位置法”记忆训练效果的因素探析

１１８名儿童、少年、青年、中年和老年被试参加＂位置法＂记忆训练,在训练前后对被试进行了一些认知功能的测查,以探讨被试的年龄及认知能力诸因素对记忆训练效果的影响及其预测性。结果表明：①训练前字词和图形记忆成绩、＂数字符号＂测验成绩与训练后记忆成绩相关极其显著,＂词汇＂测验成绩和想象力与训练后记忆成绩的相关也达到显著性水平;②年龄与训练后记忆效果的关系是非线性的;③除＂词汇＂成绩外,各项认知成绩均可预测训练后字词记忆成绩,训练后的图形记忆迁移效果可由＂数字符号＂测验成绩预测。     

Optimal designs for the generalized partial credit model

Analysing ordinal data is becoming increasingly important in psychology, especially in the context of item response theory. The generalized partial credit model (GPCM) is probably the most widely used ordinal model and has found application in many large-scale educational assessment studies such as PISA. In the present paper, optimal test designs are investigated for estimating persons’ abilities with the GPCM for calibrated tests when item parameters are known from previous studies. We find that local optimality may be achieved by assigning non-zero probability only to the first and last categories independently of a person's ability. That is, when using such a design, the GPCM reduces to the dichotomous two-parameter logistic (2PL) model. Since locally optimal designs require the true ability to be known, we consider alternative Bayesian design criteria using weight distributions over the ability parameter space. For symmetric weight distributions, we derive necessary conditions for the optimal one-point design of two response categories to be Bayes optimal. Furthermore, we discuss examples of common symmetric weight distributions and investigate under what circumstances the necessary conditions are also sufficient. Since the 2PL model is a special case of the GPCM, all of these results hold for the 2PL model as well.     

Analysing multisource feedback with multilevel structural equation models: Pitfalls and recommendations from a simulation study

When multisource feedback instruments, for example, 360-degree feedback tools, are validated, multilevel structural equation models are the method of choice to quantify the amount of reliability as well as convergent and discriminant validity. A non-standard multilevel structural equation model that incorporates self-ratings (level-2 variables) and others’ ratings from different additional perspectives (level-1 variables), for example, peers and subordinates, has recently been presented. In a Monte Carlo simulation study, we determine the minimal required sample sizes for this model. Model parameters are accurately estimated even with the smallest simulated sample size of 100 self-ratings and two ratings of peers and of subordinates. The precise estimation of standard errors necessitates sample sizes of 400 self-ratings or at least four ratings of peers and subordinates. However, if sample sizes are smaller, mainly standard errors concerning common method factors are biased. Interestingly, there are trade-off effects between the sample sizes of self-ratings and others’ ratings in their effect on estimation bias. The degree of convergent and discriminant validity has no effect on the accuracy of model estimates. The χ² test statistic does not follow the expected distribution. Therefore, we suggest using a corrected level-specific standardized root mean square residual to analyse model fit and conclude with further recommendations for applied organizational research.     

A caveat on the Savage–Dickey density ratio: The case of computing Bayes factors for regression parameters

The Savage–Dickey density ratio is a simple method for computing the Bayes factor for an equality constraint on one or more parameters of a statistical model. In regression analysis, this includes the important scenario of testing whether one or more of the covariates have an effect on the dependent variable. However, the Savage–Dickey ratio only provides the correct Bayes factor if the prior distribution of the nuisance parameters under the nested model is identical to the conditional prior under the full model given the equality constraint. This condition is violated for multiple regression models with a Jeffreys–Zellner–Siow prior, which is often used as a default prior in psychology. Besides linear regression models, the limitation of the Savage–Dickey ratio is especially relevant when analytical solutions for the Bayes factor are not available. This is the case for generalized linear models, non-linear models, or cognitive process models with regression extensions. As a remedy, the correct Bayes factor can be computed using a generalized version of the Savage–Dickey density ratio.