Comments on “the measurement of factorial indeterminacy”

Guttman's index of indeterminacy (2² – 1) measures the potential amount of uncertainty in picking the right alternative interpretation for a factor. When alternative solutions for a factor are equally likely to be correct, then the squared multiple correlation ² for predicting the factor from the observed variables is the average correlation _AB between independently selected alternative solutionsA andB, while var ( _AB )=(1 – ²)²/s, wheres is the dimensionality of the space in which unpredicted components of alternative solutions are to be found. When alternative solutions for the factor are not equally likely to be chosen, ² is the lower bound for E( _AB ); however, E( _AB ) need not be a modal value in the distribution of _AB . Guttman's index and E( _AB ) measure different aspects of the same indeterminacy problem.     

A look,by simulation,at the validity of some asymptotic distribution results for rotated loadings

In the last few years, a number of asymptotic results for the distribution of unrotated and rotated factor loadings have been given. This paper investigates the validity of some of these results based on simulation techniques. In particular, it looks at principal component extraction and quartimax rotation on a problem with 13 variables. The indication is that the asymptotic results are quite good.     

儿童学校适应的分类及判定

以6579名小学三、五年级学生为被试,采用聚类分析和判别分析方法,结果表明:(1)根据儿童学校适应的指标将儿童的学校适应状况分为良好型、闲适型、紧张型、高危型四种类型.(2)良好型的儿童,在学业自我评估、学业动机和心理健康水平三个水平上的得分为正向,在问题行为上的得分为负;高危型儿童的结果与适应良好型的儿童完全相反;而在闲适型的儿童中,学业动机为负、心理健康水平为正;在紧张型的儿童中,学业动机为正向、而心理健康水平是负向的.(3)建立儿童学校适应的判别函数,为儿童在教育和心理问题进行指导提供了初步诊断依据,同时也对有关学校适应的研究提供新的视角.     

大学生学习适应量表的编制

对当代大学生学习适应问题进行了深入分析,在有关研究的基础上,探讨了当代大学生学习适应的主要因素,并编制一套适合当代大学生学习适应的测验。对四类院校918名大学生进行了施测,对调查数据进行了探索性因素分析和验证性因素分析。探索性因素分析结果表明,大学生学习适应的主要因素包括学习动机、教学模式、学习能力、学习态度、环境因素等5个方面;验证性因素分析结果表明,所提取的5个因素与构想模型拟合较好,测验具有较好的信、效度     

情绪智力的基础：情绪觉察的研究现状与展望

情绪觉察作为情绪智力的重要基础,指的是识别与描述自己和他人情绪的能力,对个体的心理健康,人际交往具有重要意义。为系统考察情绪觉察的研究现状,采用定性和定量相结合的方式回顾以往研究,即先采用文献计量学的方法定量分析该领域研究的概况,然后在此基础上,对研究的具体内容从概念界定、测量工具、神经基础、相关因素和干预研究五个方面作进一步系统梳理。最后,对以往研究进行了总结和展望,以期为今后研究提供借鉴。     

A cautionary note on using internal cross validation to select the number of clusters

A highly popular method for examining the stability of a data clustering is to split the data into two parts, cluster the observations in Part A, assign the objects in Part B to their nearest centroid in Part A, and then independently cluster the Part B objects. One then examines how close the two partitions are (say, by the Rand measure). Another proposal is to split the data into k parts, and see how their centroids cluster. By means of synthetic data analyses, we demonstrate that these approaches fail to identify the appropriate number of clusters, particularly as sample size becomes large and the variables exhibit higher correlations.The authors express their thanks to the Sol C. Snider Entrepreneurial Center, Wharton School, for support of this project.     

Sensitive Topics,Missing Data,and Refusal in Social Network Studies: An Ethical Examination

We describe our ethics‐driven process of addressing missing data within a social network study about accountability for racism, classism, sexism, heterosexism, cis‐sexism, ableism, and other forms of oppression among social justice union organizers. During data collection, some would‐be participants did not return emails and others explicitly refused to engage in the research. All refusals came from women of color. We faced an ethical dilemma: Should we continue to seek participation from those who had not yet responded, with the hopes of recruiting more women of color from within the network so their perspectives would not be tokenized? Or, should we stop asking those who had been contacted multiple times, which would compromise the social network data and analysis? We delineate ways in which current discussions of the ethics of social network studies fell short, given our framework and our community psychology (CP ) values. We outline literature that was helpful in thinking through this challenge; we looked outside of CP to the decolonization literature on refusal. Lessons learned include listening for the possible meanings of refusals and considering the level of engagement and the labor required of participants when designing research studies.