非词复述测验的编制和初步分析

本研究编制了一种用于测量儿童语音工作记忆的测验———非词复述测验,并在48名四年级小学生中初步进行信度、效度检验和项目分析。结果表明:(1)该测验有较高的重测信度;(2)该测验具有较高的结构效度和效标效度;(3)分测验二的项目难度分布比较合理,多数项目鉴别力较高,而分测验一的项目难度分布和项目鉴别力则有待于在今后的研究中进一步提高。     

The analysis of bridging constructs with hierarchical clustering methods: An application to identity

When analyzing psychometric surveys, some design and sample size limitations challenge existing approaches. Hierarchical clustering, with its graphics (heat maps, dendrograms, means plots), provides a nonparametric method for analyzing factorially-designed survey data, and small samples data. In the present study, we demonstrated the advantages of using hierarchical clustering (HC) for the analysis of non-higher-order measures, comparing the results of HC against those of exploratory factor analysis. As a factorially-designed survey, we used the Identity Labels and Life Contexts Questionnaire (ILLCQ), a novel measure to assess identity as a bridging construct for the intersection of identity domains and life contexts. Results suggest that, when used to validate factorially-designed measures, HC and its graphics are more stable and consistent compared to EFA.     

Production planning of Pakistan Tobacco Company (PTC) using quantitative and multiple‐criteria decision analysis—A case in‐point

The main purpose of this paper is to improve the production planning of Pakistan Tobacco Company by selecting the most preferred brand and subsequently generating maximum profit from it. As the company produces a variety of products, the technique of multi criteria decision making is used to select the most preferred brand. To generate the maximum output from the preferred brand, different methods of qualitative managerial analysis are used, which include decision analysis to decide “why and where” the manufacturing should be carried out, transportation model to minimize the logistics cost while meeting the demand, and linear programming technique to maximize the profit generated in 2014–2015. The result obtained from analytical hierarchy process shows that the most preferred brand of the company with respect to price, quality, and comfort is John Player Gold Leaf. The decision analysis explains that this brand should be manufactured in the Jhelum factory of the company as it is more cost‐effective to produce and there is a high availability of resources. Transportation model minimizes the logistics cost of this brand from the 2 factories while meeting the demand at each provinces, central warehouse. Linear programming contributes in generating a profit of 32.738 billion PKR with an amount of 0.35 million PKR, which is more than that of the current profit of the company in the year. These results will allow the top management of the company to take corrective decisions well in time, gain a core competency in cost reduction, and make the supply chain process more efficient.     

Are Perfectionism Dimensions Vulnerability Factors for Depressive Symptoms After Controlling for Neuroticism? A Meta‐analysis of 10 Longitudinal Studies

Extensive evidence suggests neuroticism is a higher‐order personality trait that overlaps substantially with perfectionism dimensions and depressive symptoms. Such evidence raises an important question: Which perfectionism dimensions are vulnerability factors for depressive symptoms after controlling for neuroticism? To address this, a meta‐analysis of research testing whether socially prescribed perfectionism, concern over mistakes, doubts about actions, personal standards, perfectionistic attitudes, self‐criticism and self‐oriented perfectionism predict change in depressive symptoms, after controlling for baseline depression and neuroticism, was conducted. A literature search yielded 10 relevant studies (N = 1,758). Meta‐analysis using random‐effects models revealed that all seven perfectionism dimensions had small positive relationships with follow‐up depressive symptoms beyond baseline depression and neuroticism. Perfectionism dimensions appear neither redundant with nor captured by neuroticism. Results lend credence and coherence to theoretical accounts and empirical studies suggesting perfectionism dimensions are part of the premorbid personality of people vulnerable to depressive symptoms. Copyright © 2016 European Association of Personality Psychology     

在押毒品犯人格类型的聚类分析

目的 :探讨毒品犯的人格分类模型。方法 :以 378名在押毒品犯接受CPI测验数据为基础 ,在 2 0个变量组成的 2 0维空间内对毒品犯进行Q型聚类。结果 :采用分层聚类中的Q型聚类方法分析在押毒品犯的人格类型 ,得到 3种基本的人格类型 ,经F检验和LSD检验 ,表明 3种人格类型在各分量表上的得分具有十分显著的差异。结论 :怯弱 -不成熟型、独立 -不成熟型和可控制 -不成熟型 3种人格类型是一个有效的分类模型 ;这一分类模型有助于监狱开展对在押毒品犯的分类矫治工作     

儿童期逆境与基因对男性服刑人员攻击性的影响

暴力再犯危险性评估是当今再犯危险性评估工作中的重点, 其中, 攻击性是服刑人员暴力再犯行为稳定的个体因素。对攻击性进行研究, 有助于预防和降低服刑人员在假释或出狱后的暴力再犯行为风险, 关系社会的长治久安。研究表明, 遭受儿童期逆境和携带易感基因(如MAOA-uVNTR低活性等位基因)是导致攻击行为的重要因素。但在现有的研究中, 儿童期逆境的计分方式局限于简单的线性加总, 或所依据的统计模型忽略逆境各维度之间的交互作用和非线性关系; 在服刑人员攻击性的评估中未考虑攻击性的亚类, 而且多使用自报告的量表测评, 这些问题制约了评估的有效预测力。本研究拟通过建立潜在类别模型, 分析男性服刑人员和普通成年人群在儿童期逆境上的亚类; 以实验与问卷测量结果、司法行为记录作为攻击性指标, 揭示儿童期逆境如何影响个体的主动性攻击、反应性攻击及暴力犯罪行为, 重点探讨儿童期逆境潜在类别对主动性攻击和反应性攻击的影响, 以及MAOA-uVNTR、COMT Val158Met、5-HTTLPR基因多态性在其中的调节作用。研究结果有助于找出高攻击性个体的生物遗传指标, 从而发现受儿童期逆境经历影响的易感人群, 为暴力行为的风险预测以及针对暴力攻击行为的行为矫正和相关药物设计提供理论和实证参考, 提高相关工作的效率。     

MCDM methods in strategic planning of forestry on state‐owned lands in Finland: applications and experiences

The forests in Finland have been under intensive planning for decades. Currently, mathematical programming is widely used in planning of wood production. Today's multi‐functional forestry, however, calls for more flexible decision support methods. MCDM tools have been used in responding to fresh planning challenges. For example, the Finnish Forest and Park Service, entrusted with the care of the vast majority of state‐owned natural resources in Finland, endeavours to produce large‐scale natural resource plans satisfying the needs of both economic, social, and ecological sustainability. Participatory approach is applied in the process. Several forestry applications of MCDM methods, particularly those making use of the AHP or the HIPRE program, have been presented. Also, the outranking methods ELECTRE and PROMETHEE have been tested. Due to the nature of forestry applications, statistical techniques for analysing uncertainties in pairwise comparisons and for utilizing interval judgement data have been developed to improve the usability of the AHP. Recently, a hybrid method called A'WOT, making use of the AHP and SWOT, was also introduced into strategic forest planning. This paper summarizes the experiences gained in applying a MAVT and two outranking methods in connection with a participatory natural resource planning process in Finland. In addition, some results of the method development work related to application needs are briefly presented. The details of the planning cases reviewed here have previously been presented in forestry journals. The purpose of this paper is not only to show how MCDM methods have been applied in forestry, but also to discuss the usability and usefulness of MCDM methods from the viewpoint of supporting forestry decision making—and how they might further be improved. Also, some perspectives for the future development work of MCDM applications in the field of natural resource management are focused on. As a conclusion, the use of more than just one MCDM method in a single planning process is seen usually recommendable. In addition, developing hybrid MCDM methods is regarded as a potential direction for future research. Also, closer co‐operation between method developers and appliers is called for to produce more useful applications. Copyright © 2002 John Wiley & Sons, Ltd.     

Factor Retention Using Machine Learning With Ordinal Data

Determining the number of factors in exploratory factor analysis is probably the most crucial decision when conducting the analysis as it clearly influences the meaningfulness of the results (i.e., factorial validity). A new method called the Factor Forest that combines data simulation and machine learning has been developed recently. This method based on simulated data reached very high accuracy for multivariate normal data, but it has not yet been tested with ordinal data. Hence, in this simulation study, we evaluated the Factor Forest with ordinal data based on different numbers of categories (2–6 categories) and compared it to common factor retention criteria. It showed higher overall accuracy for all types of ordinal data than all common factor retention criteria that were used for comparison (Parallel Analysis, Comparison Data, the Empirical Kaiser Criterion and the Kaiser Guttman Rule). The results indicate that the Factor Forest is applicable to ordinal data with at least five categories (typical scale in questionnaire research) in the majority of conditions and to binary or ordinal data based on items with less categories when the sample size is large.