On the Measurement of Implicative Dilemmas

The implicative dilemma (ID) is gradually receiving attention of clinical psychologists and researchers. Although research on its clinical relevance is promising, there are still some issues regarding its measurement that need further consideration. This article explores the adequacy of the different thresholds used to determine construct association, the efficacy of the different indices commonly used to measure IDs, and a verification of the ID not being a mere subproduct of the self-ideal distance. It also introduces a new index. Finally, guidelines are provided for practitioners on how to interpret the indices' numerical results in relation to major depression.     

论动态稳定

目前,人类认识在两方面取得了共识：一方面,认识到了可持续发展的意义．另一方面,认识到了可持续发展与稳定的关系,并提出稳定是压倒一切的主题。现在的问题是,对稳定主要是从平衡方面去理解：经济稳定就是经济平衡,自然稳定就是生态平衡等。实际上这仍然是一种静态的稳定,这种平衡的稳定是没有活力的,因而是同进化和发展的目标正相矛盾的。本文则指出,具有活力的稳定是一种非平衡的稳定,其在本质上是一种动态稳定,其同进化和发展的目标正相一致。     

如何规范城市居民最低生活保障标准的测算

目前,我国城市居民最低生活保障标准的制定和调整比较随意,整体水平偏低,难以有效满足城市贫困人口的基本生活需求,并且没有根据不同家庭的情况执行多元化的弹性标准。为此,需要进一步规范低保标准的测算,其关键是要深入理解《城市居民最低生活保障条例》的有关规定,掌握测算低保标准的科学方法,合理区分不同类型家庭的基本生活需求,坚持共享发展成果,不断调整保障标准的原则,特别是要科学确定低保标准测算的指标体系和测算程序。     

从婴儿期到成年期的依恋稳定性

Bowlby依恋理论的核心假设是婴儿时期的依恋模型影响着个体成年时的依恋表征,这涉及依恋稳定性的问题.围绕这一问题,很多心理学家开展了实证研究.随着成人依恋访谈的出现,人们开始进行长期的追踪研究.人们对于早期依恋表征是如何影响成人依恋模型的内在机制,形成了修正论和原型论两种观点.本文主要介绍依恋稳定性的研究进展和内在机制的理论探讨,并就当前依恋稳定性研究的不足进行展望.     

On the Interaction of Theory and Data in Concept Learning

Standard models of concept learning generally focus on deriving statistical properties of a category based on data (i.e., category members and the features that describe them) but fail to give appropriate weight to the contact between people's intuitive theories and these data. Two experiments explored the role of people's prior knowledge or intuitive theories on category learning by manipulating the labels associated with the category. Learning differed dramatically when categories of children's drawings were meaningfully labeled (e.g., “done by creative children”) compared to when they were labeled in a neutral manner. When categories are meaningfully labeled, people bring intuitive theories to the learning context. Learning then involves a process in which people search for evidence in the data that supports abstract features or hypotheses that have been activated by the intuitive theories. In contrast, when categories are labeled in a neutral manner, people search for simple features that distinguish one category from another. Importantly, the final study suggests that learning involves an interaction of people's intuitive theories with data, in which theories and data mutually influence each other. The results strongly suggest that straight-forward, relatively modular ways of incorporating prior knowledge into models of category learning are inadequate. More telling, the results suggest that standard models may have fundamental limitations. We outline a speculative model of learning in which the interaction of theory and data is tightly coupled. The article concludes by comparing the results to recent artificial intelligence systems that use prior knowledge during learning.     

Measurement of Deliberate Self-Harm: Preliminary Data on the Deliberate Self-Harm Inventory

Deliberate self-harm has recently begun to receive more systematic attention from clinical researchers. However, there remains a general lack of consensus as to how to define and measure this important clinical construct. There is still no standardized, empirically validated measure of deliberate self-harm, making it more difficult for research in this area to advance. The present paper provides an integrative, conceptual definition of deliberate self-harm as well as preliminary psychometric data on a newly developed measure of self-harm, the Deliberate Self-Harm Inventory (DSHI). One hundred and fifty participants from undergraduate psychology courses completed research packets consisting of the DSHI and other measures, and 93 of these participants completed the DSHI again after an interval of 2–4 weeks (M = 3.3 weeks). Preliminary findings indicate that the DSHI has high internal consistency; adequate construct, convergent, and discriminant validity; and adequate test-retest reliability.     

On the Treatment of Outliners in Cognitive and Psychomotor Test Data

Authors of many statistical texts and review articles have pointed to the possi- ble adverse effects that outliers can have on the calculation of sample statistics and have suggested several methods for detecting and treating outliers. We investigated two different methods-data censoring and transformation-for treating outliers in aptitude test data at the item level and total-score level and their effects on the internal consistency and predictive validity of six computer- ized tests being evaluated by the U.S. Air Force. Results from our sample of more than 2,000 pilot training candidates indicated that neither outlier treat- ment method at either level of analysis had significant effects on the tests' internal consistencies or predictive validities. Possible reasons for these findings include the frequency with which outliers occur and the robustness of linear modeling methods.     

On the Stability of Psychological Hardiness: A Three-Year Longitudinal Study

More than 30 years of research has established psychological hardiness as an important individual resiliency resource. One important question still remaining is whether psychological hardiness can be trained. The present study explored this question longitudinally within the context of a 3-year military academy training program. Cadets from 3 different Norwegian military academies (N = 293) completed hardiness questionnaires during the first week of their training, and then again at the end of each year, resulting in a total of 4 waves of data. Using hierarchical linear modeling, no statistically significant effect of time on hardiness scores was found. The nonsignificant growth parameter was examined further using Bayesian statistics as an indicator of the relative evidence for the null hypothesis of no change over time versus the alternative hypothesis of change. The resulting Bayes factor provided substantial support in our data for the null hypothesis of no hardiness development during the 3-year officer training programs.     

Establishing Measurement Equivalence and Invariance in Longitudinal Data With Item Response Theory

If measurement invariance does not hold over 2 or more measurement occasions, differences in observed scores are not directly interpretable. Golembiewski, Billingsley, and Yeager (1976) identified 2 types of psychometric differences over time as beta change and gamma change. Gamma change is a fundamental change in thinking about the nature of a construct over time. Beta change can be described as respondents' change in calibration of the response scale over time. Recently, researchers have had considerable success establishing measurement invariance using confirmatory factor analytic (CFA) techniques. However, the use of item response theory (IRT) techniques for assessing item parameter drift can provide additional useful information regarding the psychometric equivalence of a measure over time that is not attainable with traditional CFA techniques. This article marries the terminology commonly used in CFA and IRT techniques and illustrates real advantages for identifying beta change over time with IRT methods rather than typical CFA methods, utilizing a longitudinal assessment of job satisfaction as an example.     

Precision of Curriculum-Based Measurement Reading Data: Considerations for Multiple-Baseline Designs

Single-case designs provide an established technology for evaluating the effects of academic interventions. Researchers interested in studying the long-term effects of reading interventions often use curriculum-based measures of reading (CBM-R) as they possess many of the desirable characteristics for use in a time-series design. The reliability of CBM-R scores is often supported by research from group designs, but making idiographic interpretations regarding the change in a student’s oral reading rate requires attention to the precision of static scores and growth estimates. The purpose of this paper is twofold. First, we discuss how recent empirical work on the technical adequacy of CBM-R scores has revealed multiple threats to the data-evaluation validity when CBM-R passages are used to measure oral reading rate. Second, we identify pertinent considerations for conducting a visual analysis of intervention effects based on CBM-R data. We conclude with a brief discussion of implications for researchers considering the use of CBM-R within multiple-baseline designs.     

论数据的本质及其与信息的关系

大数据技术的进步,给传统科学哲学带来了诸多挑战。科学哲学家们需要重新思考数据的本质,进而深入思考数据的特性以及与信息的内在关联。对数据本质的理解主要有两种:一是关系论,二是表征论。以大数据技术的发展为依据,可知关系论更加合理,同时可将信息解释为数据加意义。数据和现象的区分,可以进一步为我们提供理解数据之本质的途径。从大数据的发展看数据和现象的区分,可以得知在不同语境中对数据的定义是不同的,因此数据的两种定义其实并没有冲突。通过对香农的信息定义的解读,可知信息和数据之间有着内在的关联,二者是关系实体的不同表述方式。解决问题的关键在于该定义所使用的语境,以及是否赋予意义在此关系之上,因此数据就是信息,万物皆可信息化或数据化。     

Using Big Data and Machine Learning in Personality Measurement: Opportunities and Challenges

This conceptual paper examines the promises and critical challenges posed by contemporary personality measurement using big data. More specifically, the paper provides (i) an introduction to the type of technologies that give rise to big data, (ii) an overview of how big data is used in personality research and how it might be used in the future, (iii) a framework for approaching big data in personality science, (iv) an exploration of ideas that connect psychometric reliability and validity, as well as principles of fairness and privacy, to measures of personality that use big data, (v) a discussion emphasizing the importance of collaboration with other disciplines for personality psychologists seeking to adopt big data methods, and finally, (vi) a list of practical considerations for researchers seeking to move forward with big data personality measurement and research. It is expected that this paper will provide insights, guidance, and inspiration that helps personality researchers navigate the challenges and opportunities posed by using big data methods in personality measurement. © 2020 European Association of Personality Psychology     

基于GT和多面Rasch模型的结构化面试分析

该研究应用GT和多面Rasch模型对结构化面试数据进行分析,并提出一些建议针对某辅导员招聘面试数据,运用GT从宏观上分析应聘者、考官和项目所带来的总体误差大小,在此基础上,运用多面Rasch模型从微观上进一步探查考官严厉度、应聘者能力差异、项目难易度及侧面偏差．结果表明：1）GT分析表明应聘者产生的变异较大（90．65％）,说明面试可靠性较高,且当考官数为2时可靠性已较好.2）多面Rasch模型分析出了各侧面效应中的非拟合因素及交互效应中的偏差因素,表明面试误差主要来自考官间严厉度的差异及其自身一致性的不稳定。将GT与多面Rasch模型相结合分析面试数据不仅能测查出评价过程各方面的问题因素,并能更好地作整体把握。     

大学生债务容忍度的结构特征及其测量

基于国内外已有研究,采用自编问卷,探讨了助学贷款受信学生债务容忍度的结构特征.探索性和验证性因素分析的结果表明,受信者债务容忍度可基于债务认知、负债压力和债务监控三个维度进行评价;由此形成的助学贷款受信学生债务容忍度问卷具有较理想的项目区分度,较高的同质性,良好的区分效度和可接受的构想效度;男性受信者对债务相关行为具有更高的监控意识和能力,且初入学的大学生和即将毕业的大学生对债务具有更高的容忍度.     

数据权属与规制探讨

数据可分为原生数据与衍生数据。原生数据视来源不同分属国家、集体或个人。数据处理而形成的衍生数据往往涉及多方利益,形成“利益光谱”,对应多方权利主体。基于价值优先次序、权利位阶规则等,数据利益的“两头强化,多方平衡”,是我国法律数据确权和规制的指导思想。“两头强化”是指法律强化对位于“利益光谱”两端的国家利益与个人权益的保护,“多方平衡”指的是多方主体数据权益的平衡。数据规制的“硬法”进路为：明确法律概念,分析“利益光谱”,确定利益归属,权衡优先次序,平衡多方利益,搭建规制框架,制定具体规则。

     

病理性使用互联网的界定与测量

病理性使用互联网（Pathological Internet Use,PIU）是人在互联网使用过程中出现的一种新型心理疾病。很多研究者认为互联网使用过度或不当是病理性使用互联网的主要原因。该文对病理性使用互联网已有研究的概念的界定与测量问题进行了分析与整合,讨论了在中国社会文化背景下如何界定与测量病理性使用互联网。     

非正态分布测量数据对克隆巴赫信度α系数的影响

对“增大被试群体异质性,能提高测验信度”观点提出质疑。讨论非正态分布测量数据的偏度对克隆巴赫信度α系数的影响,以模拟方法验证Box—COX正态化变换对信度的高估现象,进而给出对克隆巴赫信度α系数估计的改进方法。