基于DINA模型的Q矩阵修正方法

本研究开发了一种基于DINA模型的认知诊断测验Q矩阵修正方法—— g 法, 为侦查并修正Q矩阵中的错误提供方法学支持, 从而为保证Q矩阵的合理性提供基础, 并为进一步提高认知诊断的准确率服务。本文采用Monte Carlo模拟及与国外同类研究相比较的方法进行, 研究发现：(1)不论在何种作答失误概率(5%, 10%, 15%)情况下, 当s,g临界值为0.2, 0.25或0.3时, 本研究提出的g 法均能有效地修正错误Q矩阵; 同时, 当Q矩阵无错误时, g 法对该Q矩阵未做任何修改。表明g 法对Q矩阵是否存在错误具有较强的识别能力及修正能力。(2)与国外同类研究相比, 本研究提出的g 法具有较理想的修正率, 且与de la Torre (2008)提出d法的修正效果相当。但相比较而言, g 法较d 法更为简单。(3) g 法不仅能有效地修正错误的Q矩阵, 而且还可以进一步提高认知诊断的正确率, 尤其是对模式判准率(PMR), 诊断正确率的最高增幅高达40%, 大大改善了认知诊断的准确率。     

Personality assessment in the Great Apes: Comparing ecologically valid behavior measures, behavior ratings, and adjective ratings

Three methods of personality assessment (behavior measures, behavior ratings, adjective ratings) were compared in 20 zoo-housed Great Apes: bonobos (Pan paniscus), chimpanzees (Pan troglodytes verus), gorillas (Gorilla gorilla gorilla), and orangutans (Pongo pygmaeus abelii). To test a new bottom-up approach, the studied trait constructs were systematically generated from the species’ behavioral repertoires. The assessments were reliable, temporally stable, and showed substantial cross-method coherence. In most traits, behavior ratings mediated the relations between adjective ratings and behavior measures. Results suggest that high predictability of manifest behavior is best achieved by behavior ratings, not by adjectives. Empirical evidence for trait constructs beyond current personality models points to the necessity of broad and systematic approaches for valid inferences on a species’ personality structure.     

Socio-Ecological Predictor Model of Parental Intention to Participate in Triple P-Positive Parenting Program

We examined variables associated with parental intention to participate in a parenting program viewed within a socio-ecological framework. In two metropolitan areas in Japan, 222 families voluntarily completed a series of questionnaires. A series of multiple regression analyses was used to evaluate the Socio-Ecological Predictor Model that was able to account for 50% of the variance associated with parental intention to participate in a parenting program. The most significant predictor in the model was program features, which accounted for 30% of the variance. It is important to consider program features that make it more likely that parents will engage positively in a parenting program. We discuss parental intention to participate in a parenting program as well as the limitations of the study and areas for future research.     

一种基于Q矩阵理论朴素的认知诊断方法

现有的认知诊断方法均是在复杂的统计测量学知识基础上构建的, 需要经过大量的运算才可实现对被试的诊断分类。这使得相关研究者及一线教师在理解和运用某一认知诊断方法时困难重重。相比之下, 孙佳楠、张淑梅、辛涛和包钰(2011)提出的广义距离判别法(GDD)较其他认知诊断方法更简单易用且分类准确率高。本研究在改进的Q矩阵理论(丁树良, 祝玉芳, 林海菁, 蔡艳, 2009; 丁树良, 杨淑群, 汪文义, 2010)的基础上, 借鉴GDD的思路, 提出一种无需进行参数估计的朴素的认知诊断方法, 即海明距离判别法(HDD)。根据判别方式的不同将其分为R方法和B方法。采用Monte Carlo模拟的研究方法, 以模式判准率(PMR)和属性平均判准率(AAMR)作为衡量被试知识状态分类准确率的指标, 与GDD进行比较。结果表明, HDD具有更简便的操作步骤和更好的分类准确率。     

Two-Way Regularized Fuzzy Clustering of Multiple Correspondence Analysis

Multiple correspondence analysis (MCA) is a useful tool for investigating the interrelationships among dummy-coded categorical variables. MCA has been combined with clustering methods to examine whether there exist heterogeneous subclusters of a population, which exhibit cluster-level heterogeneity. These combined approaches aim to classify either observations only (one-way clustering of MCA) or both observations and variable categories (two-way clustering of MCA). The latter approach is favored because its solutions are easier to interpret by providing explicitly which subgroup of observations is associated with which subset of variable categories. Nonetheless, the two-way approach has been built on hard classification that assumes observations and/or variable categories to belong to only one cluster. To relax this assumption, we propose two-way fuzzy clustering of MCA. Specifically, we combine MCA with fuzzy k-means simultaneously to classify a subgroup of observations and a subset of variable categories into a common cluster, while allowing both observations and variable categories to belong partially to multiple clusters. Importantly, we adopt regularized fuzzy k-means, thereby enabling us to decide the degree of fuzziness in cluster memberships automatically. We evaluate the performance of the proposed approach through the analysis of simulated and real data, in comparison with existing two-way clustering approaches.     

Assessing PTSD in the military: Validation of a scale distributed to Danish soldiers after deployment since 1998

Since 1998, soldiers deployed to war zones with the Danish Defense (≈31,000) have been invited to fill out a questionnaire on post‐mission reactions. This provides a unique data source for studying the psychological toll of war. Here, we validate a measure of PTSD‐symptoms from the questionnaire. Soldiers from two cohorts deployed to Afghanistan with the International Security Assistance Force (ISAF) in 2009 (ISAF7, N = 334) and 2013 (ISAF15, N = 278) filled out a standard questionnaire (Psychological Reactions following International Missions, PRIM) concerning a range of post‐deployment reactions including symptoms of PTSD (PRIM‐PTSD). They also filled out a validated measure of PTSD‐symptoms in DSM‐IV, the PTSD‐checklist (PCL). We tested reliability of PRIM‐PTSD by estimating Cronbach's alpha, and tested validity by correlating items, clusters, and overall scale with corresponding items in the PCL. Furthermore, we conducted two confirmatory factor analytic models to test the factor structure of PRIM‐PTSD, and tested measurement invariance of the selected model. Finally, we established a screening and a clinical cutoff score by application of ROC analysis. We found high internal consistency of the PRIM‐PTSD (Cronbach's alpha = 0.88; both cohorts), strong item‐item (0.48–0.83), item‐cluster (0.43–0.72), cluster‐cluster (0.71–0.82) and full‐scale (0.86–0.88) correlations between PRIM‐PTSD and PCL. The factor analyses showed adequate fit of a one‐factor model, which was also found to display strong measurement invariance across cohorts. ROC curve analysis established cutoff scores for screening (sensitivity = 1, specificity = 0.93) and clinical use (sensitivity = 0.71, specificity = 0.98). In conclusion, we find that PRIM‐PTSD is a valid measure for assessing PTSD‐symptoms in Danish soldiers following deployment.     

The Italian version of the Empathy Questionnaire for 18- to 36-months-old children: psychometric properties and measurement invariance across gender of the EmQue-I13

Empathy is the basic ability to respond affectively to the emotions of others and is observed early in human development. This study (N = 304) tested the Italian version of the Empathy Questionnaire (EmQue), which is a parent scale assessing empathy-related behaviors in toddlers. For this study, it was completed by participants’ mothers only. The EmQue measurement model and its factorial invariance across children’s gender were tested via confirmatory factor analysis. Highly satisfactory goodness-of-fit indexes were found for a three factor-structure (contagion, attention to the feelings of others and prosocial actions) with 13 loading items. Furthermore, the data supported invariance of measurement across gender groups. These results suggest that the EmQue-I13 is a reliable instrument for investigating empathy and prosocial behavior in 18- to 36-month-old children in the Italian context.     

Measuring illness representations in breast cancer survivors (BCS) prescribed tamoxifen: Modification and validation of the Revised Illness Perceptions Questionnaire (IPQ-BCS)

Objective: The Revised Illness Perceptions Questionnaire (IPQ-R), widely used to assess illness perceptions, may fail to measure unique characteristics of different illnesses. This study modified and validated the IPQ-R for breast cancer survivors to provide detailed understanding of the specific illness perceptions held by these patients.

Design: Initial modifications were made following qualitative interviews and were revised in a think-aloud study. The modified scale was tested in 753 breast cancer survivors prescribed tamoxifen. Modifications included adding a tamoxifen consequences scale and adapting the timeline scales to measure beliefs around risk of recurrence and cure. A confirmatory factor analysis was conducted on the modified questionnaire and an exploratory factor analysis on the causal beliefs scale. Test–retest reliability, internal consistency and construct validity were also examined.

Results: The proposed eight-factor structure showed acceptable model fit, with high loadings and good reliability for all subscales. Correlations between subscales were consistent with theory and previous research.

Conclusions: The IPQ-BCS is valid and reliable, and provides unique understanding of specific perceptions held by this population, including beliefs surrounding risk of recurrence and consequences of ongoing hormonal treatment. Identifying these perceptions will aid development of interventions targeting depression, fear of recurrence and medication non-adherence.