祖辈协同教养与幼儿错误信念理解能力的关系:亲子依恋与祖辈同住的作用

为考察祖辈协同教养与幼儿错误信念理解之间的关系以及幼儿亲子依恋的中介作用,采用方便抽样的方法,调查了某城市三所幼儿园345名小班至大班的祖辈协同教养幼儿及其家庭教养人。对幼儿使用操作任务考察错误信念理解能力,对其家庭教养人利用自陈式问卷收集家庭教养方式和亲子依恋等数据。结果显示：(1)祖辈协同教养类型显著影响幼儿的错误信念理解水平和亲子依恋,“有祖辈同住”协同教养幼儿的错误信念理解水平以及依恋的安全性均显著低于“无祖辈同住”协同教养幼儿;(2)亲子依恋在祖辈协同教养的民主信任和教养冲突两个维度与幼儿错误信念理解能力之间分别存在显著的中介作用;(3)祖辈协同教养类型在教养冲突、亲子依恋、错误信念理解能力的中介模型中起调节作用,在“有祖辈同住”条件下,协同教养冲突显著负向预测幼儿的亲子依恋安全感。     

Avoidance Responses to Alternative Alcohol Warning Labels1

A preference-based measure of responses to warning labels was used to study alternative alcohol warning labels that differed in: (a) length, (b) presence of qualifier words, (c) alternative content, and (d) specific beverage. The specific risks mentioned were more important than the overall label length, qualifier words reduced avoidance responses, various alternative warnings elicited more avoidance responses than the existing alcohol warning label, and more avoidance responses were made to labels on a whiskey bottle than on a beer bottle. Warnings with the words “poison,”“cancer,” or “health problems” were the most powerful. The results for college students in Study 1 were replicated in a high-school sample in Study 2.     

Contrasting strategic and Milan therapies

Three related models of therapy are often grouped together as the strategic therapies. These are the brief therapy model associated with the Mental Reseach Institute, approaches developed by Jay Haley and Cloë Madanes, and the model developed by the Milan associates. Controversy exists, however, as to whether the Milan model should be included as a strategic therapy. It appears that the similarities among the three models can mask deeper differences, thus confounding the confusion. This paper contrasts the models in their development, theory, and practice.     

从个体到集体：心理学视角下的集体记忆

基于拓展思维的社会互动主义视角, 采用流行病学研究思路, 记忆的形成与维护构成了集体记忆心理学研究的核心问题。相关研究一方面以“对话”为研究语境, 系统阐释了集体记忆形成中的社会传染、检索诱发遗忘、共享现实和网络聚合等心理机制; 另一方面, 聚焦于既存的记忆, 阐释了集体记忆有效性的心理原则, 同时, 记忆的身份关联性得到了确证。集体记忆心理学研究仍需拓展视野, 加强对文化记忆的有效性研究, 密切关注媒介技术变革引发的记忆变化, 积极推进本土化研究。     

Commentary on “Mediation analysis and categorical variables: The final frontier” by Dawn Iacobucci

The commentary discusses several topics in Dr. Iacobucci's article on mediation analysis with categorical variables including the lack of equivalency of mediated effect measures, ab, and c–c′, that are algebraically equivalent in ordinary least squares regression models. The commentary raises the issue of correlated mediation coefficients and presents formulas to include the correlation in the test of significance. References to new developments in causal mediation analysis for categorical variables are given and the importance of programs of research to establish a mediation theory is emphasized.     

Countertheses: Communication,material conditions,and future sociocultural development

Chaos in brain function. Edited by Erol Basar Springer‐Verlag, 1990, Berlin. Softcover, 176 pp., 66 figures, index.     

Explanation of Two Anomalous Results in Statistical Mediation Analysis

Previous studies of different methods of testing mediation models have consistently found two anomalous results. The first result is elevated Type I error rates for the bias-corrected and accelerated bias-corrected bootstrap tests not found in nonresampling tests or in resampling tests that did not include a bias correction. This is of special concern as the bias-corrected bootstrap is often recommended and used due to its higher statistical power compared with other tests. The second result is statistical power reaching an asymptote far below 1.0 and in some conditions even declining slightly as the size of the relationship between X and M, a, increased. Two computer simulations were conducted to examine these findings in greater detail. Results from the first simulation found that the increased Type I error rates for the bias-corrected and accelerated bias-corrected bootstrap are a function of an interaction between the size of the individual paths making up the mediated effect and the sample size, such that elevated Type I error rates occur when the sample size is small and the effect size of the nonzero path is medium or larger. Results from the second simulation found that stagnation and decreases in statistical power as a function of the effect size of the a path occurred primarily when the path between M and Y, b, was small. Two empirical mediation examples are provided using data from a steroid prevention and health promotion program aimed at high school football players (Athletes Training and Learning to Avoid Steroids; Goldberg et al., 1996), one to illustrate a possible Type I error for the bias-corrected bootstrap test and a second to illustrate a loss in power related to the size of a. Implications of these findings are discussed.