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.     

Caveats of non-ipsatization of basic values: A review of issues and a simulation study

Ipsatization, or a correction of variables by their common component, is routinely applied to measures of basic values. Although ipsatization has been criticized, the consequences of non-ipsatization are rarely discussed. We show that the ipsatization of values is intertwined with their definition. A common factor involved in ipsatization was suggested to represent a nuisance variable, a response style, social desirability, or other constructs. A simulation study illustrated that within-individual centering revealed more accurate value scores when the common factor was in the data, with exception of the situation when values were consistently and positively correlated with each other. We conclude that in different conditions both applying and failing to apply ipsatization can cause bias. Therefore, no general advice in regard to ipsatization can be provided.     

心理因素与学业表现：机器学习分类预测模型

随着高等教育规模的扩大,学业表现不良逐渐成为一个不容忽视的现象,对学业表现不良的学生进行预测并提早给予干预可降低退学率并减少教育资源的损失。由于导致学业表现不良的因素众多且关系复杂,传统的基于相关分析的研究方法很难建立早期预测模型并进行应用。本研究旨在利用机器学习算法,对数据进行挖掘,并建立学业表现预测模型。研究对653名大一新生的心理健康状况、应对方式、人格、内外控倾向和相关人口统计学信息进行了收集,并在一年后采集了其学业成绩,利用随机森林(RF)、K邻近（KNN）、支持向量机（SVM）、决策树（DT）、朴素贝叶斯（NB）等机器学习算法建立了分类模型。结果显示,随机森林算法在识别学业表现不良学生时有最好的表现,其中准确率95.86%, 召回率91.83%,f1分数为93.80%。特征重要性分析显示,前10个对模型有最高贡献度的特征包括：年龄、性别、是否为独生子、内外控倾向、神经质倾向、积极应对倾向、宜人性倾向、一般症状指数、开放性倾向和焦虑水平。为避免过度拟合问题,本研究在一年后收集的166名新生样本中进行了模型验证,结果显示模型在新数据集上有较好的泛化表现,其中f1分数90.90％,准确率92.60％,召回率89.26％。研究提示基于人口统计学和心理测评信息,机器学习算法有助于及早识别学业表现不良学生并为开展早期干预提供启示。     

The Natural Selection of Prediction Heuristics: Anchoring and Adjustment versus Representativeness

There are several heuristics which people use in making numerical predictions and these heuristics compete for the determination of prediction output. Some of them (e.g. representativeness) lead to excessively extreme predictions while others (e.g. anchoring and adjustment) lead to regressive (and even over-regressive) predictions. In this paper we study the competition between these two heuristics by varying the representation of predictor and outcome. The results indicate that factors which facilitate reliance on representativeness (e.g. compatibility between predictor and outcome) indeed lead to an increase in extremity, while factors that facilitate reliance on anchoring and adjustment (e.g. increased salience of a potential anchor) lead to a decrease in extremity.     

Agreeableness and the common core of dark traits are functionally different constructs

The Dark Factor of Personality (D) has been suggested as the basic disposition underlying dark traits, thereby representing their common core. However, it has also been argued that such commonalities reflect the low pole of Agreeableness. The present study (N = 729) employed five established inventories to model the Agreeableness construct and considered seven theoretically derived criterion variables, including one behavioral outcome. Results indicate that Agreeableness and D exhibit a substantial, but far from perfect, association of r = −.64. Further, D incrementally improved the prediction of all but one criterion measure. These results speak against the notion that the commonalities of dark traits can be reduced to low Agreeableness and rather support the contention to consider Agreeableness and D as functionally distinct constructs.     

A Validation of a Multicultural Competency Measure Among South Korean Counselors

The authors translated the California Brief Multicultural Competence Scale (CBMCS; Gamst et al., 2004), a measure of multicultural competence, into Korean for cross-cultural validation. An exploratory factor analysis followed by a confirmatory factor analysis on a sample of Korean counselors (N = 365) supported a 3-factor model: Multicultural Ability, Multicultural Knowledge, and Multicultural Awareness. The Korean version was deemed to possess sound psychometric properties, such as high test-retest reliability and criterion-related validity. Los autores tradujeron al idioma coreano la Escala Breve de Competencia Multicultural de California (CBMCS, por sus siglas en inglés; Gamst et al., 2004), un instrumento de medida de competencia multicultural, para su validación intercultural. Un análisis factorial exploratorio seguido de un análisis factorial confirmatorio sobre una muestra de consejeros coreanos (N = 365) respaldó un modelo de 3 factores: Habilidad Multicultural, Conocimiento Multicultural y Conciencia Multicultural. Se concluyó que la versión en coreano tenía propiedades psicométricas sólidas, como una alta fiabilidad de la repetición de las pruebas y una alta validez de criterio.     

Lasso回归：从解释到预测

传统的最小二乘回归法关注于对当前数据集的准确估计, 容易导致模型的过拟合, 影响模型结论的可重复性。随着方法学领域的发展, 涌现出的新兴统计工具可以弥补传统方法的局限, 从过度关注回归系数值的解释转向提升研究结果的预测能力也愈加成为心理学领域重要的发展趋势。Lasso方法通过在模型估计中引入惩罚项的方式, 可以获得更高的预测准确度和模型概化能力, 同时也可以有效地处理过拟合和多重共线性问题, 有助于心理学理论的构建和完善。     

“己之所欲”还是“成人之美”？解释水平视角下个体赠送-接受礼物的偏好不对称性

解释水平理论被广泛应用于消费研究领域。本研究以解释水平理论为理论基础,采用情境问卷调查方法,通过5个实验探讨个体赠送与接受礼物时是否存在性别和角色偏好的不对称性。结果发现,一般情境下,受性别因素影响,男生偏好赠送和接受工具性礼物,女生偏好赠送和接受表达性礼物。但随着心理距离（社会距离和空间距离）的拉近,个体的解释水平降低,更关心他人的喜好和需求,赠送者会更基于接收者的偏好进行礼物选择。