排名列表项目展示方式对于项目评价的极化效应

排名列表是帮助消费者快速识别品牌或产品优劣的手段。研究基于空间隐喻理论, 探究排名列表的不同展示方式(垂直vs水平)对消费者列表项目评价的极化影响及作用机制。通过5个实验发现: 消费者对于垂直排名列表相比水平排名列表中的项目具有更大的评价极化效应, 并且验证了项目感知差异在上述关系中的中介作用, 即垂直排名列表的评价极化效应是由个体较高的排名列表项目感知差异所驱动的。此外, 当属性可评估性较低时, 才会出现不同列表项目的评价极化效应; 相反, 当排名列表项目属性可评估性较高时, 该评价极化效应消失。     

The use of metaphors in understanding atypical gender identity development and its psychosocial impact

Abstract

Over the years I have found a number of metaphors which have helped me to deal with particular dynamics in therapeutic work or with group and institutional conflicts involving the Gender Identity Development Service at the Tavistock Centre. This nationwide service, which is for young people with atypical gender identity development and their families, was established in 1989. In this paper I describe some of these metaphors in relation to the particular problems or conflicts which had stimulated their appearance in my mind. The emergence of these metaphors links the vicissitudes of atypical gender identity development to issues regarding symbolisation or symbolic thinking. Metaphors such as ‘working at the edge’ or ‘navigating between Scylla and Charybdis’ allow the professional to hold on to multiple perspectives and to maintain a certain degree of ambiguity in situations in which the interpersonal dynamics can be experienced as rigid and deterministic. The emergence of metaphors can then be perceived by the professional with a sense of relief and freedom of thinking. In this paper, metaphors are linked to: the model of care developed; the therapeutic stance; and the aims, risks and pressures experienced by the professional in this area of work. The association between gender dysphoria in some young people and autistic spectrum features is explored. The paper emphasises the importance of responding flexibly to individual differences and of recognising complexity.     

非正态分布下概化理论方差分量变异量估计

方差分量估计是概化理论的必用技术,但受限于抽样,需要对其变异量进行探讨。采用Monte Carlo数据模拟技术,探讨非正态数据分布对四种方法估计概化理论方差分量变异量的影响。结果表明：（1）不同非正态数据分布下,各种估计方法的“性能”表现出差异性;（2）数据分布对方差分量变异量估计有影响,适合于非正态分布数据的方差分量变异量估计方法不一定适合于正态分布数据。     

概化理论缺失数据方差分量估计

各种心理调查、心理实验中, 数据的缺失随处可见。由于数据缺失, 给概化理论分析非平衡数据的方差分量带来一系列问题。基于概化理论框架下, 运用Matlab 7.0软件, 自编程序模拟产生随机双面交叉设计p×i×r缺失数据, 比较和探讨公式法、REML法、拆分法和MCMC法在估计各个方差分量上的性能优劣。结果表明：(1) MCMC方法估计随机双面交叉设计p×i×r缺失数据方差分量, 较其它3种方法表现出更强的优势; (2) 题目和评分者是缺失数据方差分量估计重要的影响因素。     

概化理论不同评分方案缺失数据方差分量估计

包含评分者侧面的测验通常不符合任意一种概化理论设计,因此从概化理论的角度来看这类测验下的数据应属于缺失数据,而决定缺失结构的就是测验的评分方案。用R软件模拟出三种评分方案下的数据,并比较传统法、评价法和拆分法在各评分方案下的估计效果,结果表明：（1）传统法估计准确性较差;（2）评分者一致性较高时,适宜用评价法进行估计;（3）拆分法的估计结果最准确,仅在固定评分者评分方案下需注意评分者与考生数量之比,该比值小于等于0.0047 时估计结果较为准确。     

Psychodynamic Psychotherapy Process Research: A quick ride through what you should know about process (research)

This paper gives an overview of psychoanalytic process research, which brings to light the complexity of psychotherapy sessions. This complexity is so rich that many instruments intended to make the process measurable failed in the past because they initially used a strategy of complexity reduction. This method however did not help to further our understanding of the complexity involved. Three former presidents of the Society for Psychotherapy Research (Stiles, Hill, Elliot 2015) decided to solve the following equivalence paradox: many therapies work successfully though they all follow different theories, produce a heterogeneity of processes and often enough a misfit between what theories maintain to be a good process and their realization in the treatment room. As theories are often compared to maps, a driver would wonder which route to take. But therapists‐as‐drivers go undaunted ‐ and nevertheless achieve their goals together with their patients. Norcross and Wampold (2018) found that good therapists invent new therapies with every new patient. They hypothesize that it is because of conversation. This is in accord with the 3 former presidents’ proposal to return to detailed single case analyses including the micro‐analytic power of conversation analysis of which a few insights are outlined in this article.     

Analysis of Variance Models with Stochastic Group Weights

The analysis of variance (ANOVA) is still one of the most widely used statistical methods in the social sciences. This article is about stochastic group weights in ANOVA models – a neglected aspect in the literature. Stochastic group weights are present whenever the experimenter does not determine the exact group sizes before conducting the experiment. We show that classic ANOVA tests based on estimated marginal means can have an inflated type I error rate when stochastic group weights are not taken into account, even in randomized experiments. We propose two new ways to incorporate stochastic group weights in the tests of average effects – one based on the general linear model and one based on multigroup structural equation models (SEMs). We show in simulation studies that our methods have nominal type I error rates in experiments with stochastic group weights while classic approaches show an inflated type I error rate. The SEM approach can additionally deal with heteroscedastic residual variances and latent variables. An easy-to-use software package with graphical user interface is provided.     

The effects on the predictive variance of a new subject’s score for a new test

Abstract: It is often required to predict the scores or their variations under interest. Ishii and Watanabe (2001) investigated, in the context of psychological measurement, the Bayesian predictive distribution of a new subject’s scores for tests and subjects’ scores for a new test. In this paper, the Bayesian posterior predictive distribution of a new subject’s scores for a new parallel test were considered. And the effects of the number of subjects, the number of the tests, and the test reliability were investigated. Then, it was found that, under assumptions that (co)variance parameters are known, the predictive variance of a new subject’s score for a new test was equal to the predictive variances of the new subject’s scores for the existent tests. It was also found that the effect of the number of subjects was relatively large and the effect of the number of tests was relatively small, when a new subject’s scores for existent tests were not observed.     

Statistical inference of minimum rank factor analysis

For any given number of factors, Minimum Rank Factor Analysis yields optimal communalities for an observed covariance matrix in the sense that the unexplained common variance with that number of factors is minimized, subject to the constraint that both the diagonal matrix of unique variances and the observed covariance matrix minus that diagonal matrix are positive semidefinite. As a result, it becomes possible to distinguish the explained common variance from the total common variance. The percentage of explained common variance is similar in meaning to the percentage of explained observed variance in Principal Component Analysis, but typically the former is much closer to 100 than the latter. So far, no statistical theory of MRFA has been developed. The present paper is a first start. It yields closed-form expressions for the asymptotic bias of the explained common variance, or, more precisely, of the unexplained common variance, under the assumption of multivariate normality. Also, the asymptotic variance of this bias is derived, and also the asymptotic covariance matrix of the unique variances that define a MRFA solution. The presented asymptotic statistical inference is based on a recently developed perturbation theory of semidefinite programming. A numerical example is also offered to demonstrate the accuracy of the expressions.This work was supported, in part, by grant DMS-0073770 from the National Science Foundation.     

The Distance from Psychological Research to Real Life

Considering our experiences in everyday life, the result of research based on sample assumed to be derived from a “population” cannot afford individual predictions. That is because the variabilities within individuals which play an important role in actual psychological processes are erased from the data in samples-based studies. In this article, we begin the discussion with how the intra-individual variance contributes to individual prediction. As case study works on the basis of variability within each individual, it explores the intrinsic mechanism of development and emphasize the connection between person and environment. The question of how to generalize from case studies requires that the researcher to selectively consider some of the variance and construction of a hypothetical model that shows how different features of oppositions (Yin and Yang) are integrated into an abstract whole that is applicable to all individual cases.