《青少年心理健康素质调查表》自我分量表的编制

理论上建构了心理健康素质自我结构模型,依此模型编制了心理健康素质自我量表。以武汉市的青少年为样本(N=390),初步获得信度与效度指标。自我认知、自我评价和自我调控3个二级结构的α系数分别为0.81、0.76和0.59。验证性因素分析的结果初步支持本研究所提出的自我的三级结构模型。     

A diagnostic tree model for polytomous responses with multiple strategies

Constructed-response items have been shown to be appropriate for cognitively diagnostic assessments because students’ problem-solving procedures can be observed, providing direct evidence for making inferences about their proficiency. However, multiple strategies used by students make item scoring and psychometric analyses challenging. This study introduces the so-called two-digit scoring scheme into diagnostic assessments to record both students’ partial credits and their strategies. This study also proposes a diagnostic tree model (DTM) by integrating the cognitive diagnosis models with the tree model to analyse the items scored using the two-digit rubrics. Both convergent and divergent tree structures are considered to accommodate various scoring rules. The MMLE/EM algorithm is used for item parameter estimation of the DTM, and has been shown to provide good parameter recovery under varied conditions in a simulation study. A set of data from TIMSS 2007 mathematics assessment is analysed to illustrate the use of the two-digit scoring scheme and the DTM.     

Geometric representation of association between categories

Categories can be counted, rated, or ranked, but they cannot be measured. Likewise, persons or individuals can be counted, rated, or ranked, but they cannot be measured either. Nevertheless, psychology has realized early on that it can take an indirect road to measurement: What can be measured is the strength of association between categories in samples or populations, and what can be quantitatively compared are counts, ratings, or rankings made under different circumstances, or originating from different persons. The strong demand for quantitative analysis of categorical data has thus created a variety of statistical methods, with substantial contributions from psychometrics and sociometrics. What is the common basis of these methods dealing with categories? The basic element they share is that the sample space has a special geometry, in which categories (or persons) are point masses forming a simplex, while distributions of counts or profiles of ratings are centers of gravity, which are also point masses. Rankings form a discrete subset in the interior of the simplex, known as the permutation polytope, and paired comparisons form another subset on the edges of the simplex. Distances between point masses form the basic tool of analysis. The paper gives some history of major concepts, which naturally leads to a new concept: the shadow point. It is then shown how loglinear models, Luce and Rasch models, unfolding models, correspondence analysis and homogeneity analysis, forced classification and classification trees, as well as other models and methods, fit into this particular geometrical framework.     

The conjunctive model of hierarchical classes

This paper describes the conjunctive counterpart of De Boeck and Rosenberg's hierarchical classes model. Both the original model and its conjunctive counterpart represent the set-theoretical structure of a two-way two-mode binary matrix. However, unlike the original model, the new model represents the row-column association as a conjunctive function of a set of hypothetical binary variables. The conjunctive nature of the new model further implies that it may represent some conjunctive higher order dependencies among rows and columns. The substantive significance of the conjunctive model is illustrated with empirical applications. Finally, it is shown how conjunctive and disjunctive hierarchical classes models relate to Galois lattices, and how hierarchical classes analysis can be useful to construct lattice models of empirical data.The research reported in this paper was supported by NATO (Grant CRG.921321 to Iven Van Mechelen and Seymour Rosenberg) and by the Research Fund of Katholieke Universiteit Leuven (Grants PDM92/19 and POR93/3 to Iven Van Mechelen; Grants OT89/9 and F91/56 to Paul De Boeck).     

多项式加工树模型在再认启发式中的应用

摘 要 再认启发式利用再认线索进行决策。以往研究采用一致率、击中率、虚报率和区分指数来表示再认启发式使用,然而这些方法都存在局限。多项式加工树模型能够分离不同的认知加工过程,为了解决再认使用与知识使用的混淆,研究者提出一种多项式加工树模型 r-model 测量再认启发式的使用。本文将重 点介绍 r-model,具体包括 r-model 的内容、数据分析以及考虑个体差异的分层 r-model。最后,从 r-model 的模型修正和边界条件两个方面提出未来研究方向。 关键词 再认启发式;流畅启发式;多项式加工树;贝叶斯分层模型     

The dissociable influence of social context on judgements of facial attractiveness and trustworthiness

The cheerleader effect occurs when the same face is rated to be more attractive when it is seen in a group compared to when seen alone. We investigated whether this phenomenon also occurs for trustworthiness judgements, and examined how these effects are influenced by the characteristics of the individual being evaluated and those of the group they are seen in. Across three experiments, we reliably replicated the cheerleader effect. Most faces became more attractive in a group. Yet, the size of the cheerleader effect that each face experienced was not related to its own attractiveness, nor to the attractiveness of the group or the group’s digitally averaged face. We discuss the implications of our findings for the hierarchical encoding and contrast mechanisms that have previously been used to explain the cheerleader effect. Surprisingly, judgements of facial trustworthiness did not experience a ‘cheerleader effect’. Instead, we found that untrustworthy faces became significantly more trustworthy in all groups, while there was no change for faces that were already trustworthy alone. Taken together, our results demonstrate that social context can have a dissociable influence on our first impressions, depending on the trait being evaluated.     

多项式加工树模型次序约束的量化分析及其应用

多项式加工树（MPT）模型是一种认知测量模型,能够对潜在认知过程进行测量和检验。已有研究探讨了二链MPT模型次序约束的重新参数化问题,本研究探讨了MPT模型次序约束的量化分析方法并从二链推广到多链,同时归纳出MPT模型参数向量内和参数向量间两参数次序约束量化分析的结论。数据分析结果表明该方法不仅在MPT模型框架下验证了潜在参数次序关系,而且给出了约束的量化指标,为潜在认知测量提供更有意义的解释。     

CAREGIVER SENSITIVITY AND CONSISTENCY AND CHILDREN'S PRIOR FAMILY EXPERIENCE AS CONTEXTS FOR EARLY DEVELOPMENT WITHIN INSTITUTIONS

The current study addressed whether two institution‐wide interventions in St. Petersburg, Russian Federation, that increased caregiver sensitivity (Training Only: TO) or both caregiver sensitivity and consistency (Training plus Structural Changes: T+SC) promoted better socioemotional and cognitive development than did a No Intervention (NoI) institution during the first year of life for children who were placed soon after birth. It also assessed whether having spent less than 9 versus 9 to 36 months with a family prior to institutionalization was related to children's subsequent socioemotional and cognitive development within these three institutions. The Battelle Developmental Inventory (J. Newborg, J.R. Stock, L. Wnek, J. Guidubaldi, & J. Svinicki, 1988) was used to assess the socioemotional and cognitive functioning of children in NoI (n = 95), TO (n = 104), and T+SC (n = 86) at two to three time points during their first 6 to 12 months of residency. Results suggest that improving caregiver sensitivity can improve the cognitive development of infants in the first year of institutionalization whereas improving caregiver consistency in addition to sensitivity is more beneficial for socioemotional development than is sensitivity alone. Similarly, for children in T+SC, longer time with a family prior to institutionalization (consistent caregiver, unknown sensitivity) was associated with better socioemotional, but not cognitive, baseline scores and more rapid cognitive than socioemotional development during institutionalization. These results suggest caregiver sensitivity is more highly related to cognitive development whereas caregiver consistency is more related to socioemotional development in the first years of life.     

Proposal of a driver profile classification in relation to risk level in overtaking maneuvers

Traffic crashes are a worldwide problem, and records have indicated frontal collisions have resulted in the most significant number of fatalities. Such a type of crash is frequently caused by improper overtaking of vehicles, which highlights the interference of human factors. Therefore, investigations on driver's risk perception are necessary. This study proposes a classification of driver's risk level through a decision tree using the Classification and Regression Tree (CART) algorithm from data collected from the overtaking maneuvers in a driving simulator. The model obtained by CART algorithm indicated young male drivers are more likely to take risks in overtaking maneuvers. The results were correlated with governmental records and similar studies. In addition, the results showed the potential of the tool for used as a risk level classifier, as well as the validation of the driving simulator in studies associated with human factor behaviours, accident analysis and investigation.