Diversity of ideas about diversity measurement

Measurement of diversity is important in several sciences but has evolved mainly within ecology. An ecological community with many species is more diverse than one with few but the way the total number of individuals is distributed among the species also influences the degree of diversity or heterogeneity. Although not always explicitly used, it can be shown that diversity is a concept of some importance in psychology, too. In general terms quantification of diversity is related to the apportionment of some quantity into a number of well-defined classes. The dual-concept type of diversity index reflects both the number of classes and the degree of evenness (homogeneity) of the apportionment. The quantity that is distributed has commonly been a population of elements and apportionment homogeneity measures population heterogeneity. The statistical context has dominated the thinking about the measurement of diversity, heterogeneity, and homogeneity. Some well-known indices of diversity and the ideas behind them are discussed. It is concluded that the general geometric representation of homogeneity (similarity) and heterogeneity (dissimilarity) (Junge, 1978; 1991) offers a more general approach to the measurement of diversity.     

Latent change classes in dichotomous data

Changes in dichotomous data caused by treatments can be analyzed by means of the so-called linear logistic model with relaxed assumptions (LLRA). The LLRA does not require observable criteria representing a single underlying latent trait, but it postulates the generalizability of the treatment effects over criteria and subjects. To test this latter crucial assumption, the mixture LLRA was proposed that allows directly unobservable types of subjects to have different treatment effects. As the earlier methods for estimating the parameters of the mixture LLRA have specific drawbacks, a further method based on the conditional maximum likelihood principle will be presented here. In contrast to the earlier conditional methods, it uses all of the dichotomous change data while having fewer parameters. Further, its goodness-of-fit tests become more sensitive to a falsely specified number of change-types even though the treatment effects are biased. For typically occurring small to moderate sample sizes, however, parametric bootstrapping of the distributions of the fit statistics is recommended for performing hypotheses tests. Finally, three applications of the new method to empirical data are described: first, about the effect of the so-called Trager psychophysical integration, second, about the effect of autogenic therapy on patients with psychosomatic symptoms, and, third, about the effect of religious education on the attitude towards sects. The mixture LLRA is implemented in the menu-driven program MIXLLRA which can be obtained from Ivo Ponocny via e-mail (ivo.ponocny@univie.ac.at).     

不同自尊者在赌博情境下的风险规避行为

为了考察不同自尊水平的个体在赌博情境下的风险规避行为,263名大学生被试同时完成外显Rosenberg量表、内隐联想测验和21点赌博游戏。实验结果发现,(1)随着风险的增加,所有被试都表现出风险规避;(2)在高外显自尊水平的个体中,低内隐自尊水平个体比高内隐自尊水平个体表现出更多的风险规避行为;在低内隐自尊的个体中,高外显自尊个体比低外显自尊个体表现出更多的风险规避行为。风险规避行为间接地反映出个体的防御倾向,结果提示,在风险情境中,高外显低内隐自尊个体表现出较高的自我防御倾向。     

Meta‐CART: A tool to identify interactions between moderators in meta‐analysis

In the framework of meta‐analysis, moderator analysis is usually performed only univariately. When several study characteristics are available that may account for treatment effect, standard meta‐regression has difficulties in identifying interactions between them. To overcome this problem, meta‐CART has been proposed: an approach that applies classification and regression trees (CART) to identify interactions, and then subgroup meta‐analysis to test the significance of moderator effects. The previous version of meta‐CART has its shortcomings: when applying CART, the sample sizes of studies are not taken into account, and the effect sizes are dichotomized around the median value. Therefore, this article proposes new meta‐CART extensions, weighting study effect sizes by their accuracy, and using a regression tree to avoid dichotomization. In addition, new pruning rules are proposed. The performance of all versions of meta‐CART was evaluated via a Monte Carlo simulation study. The simulation results revealed that meta‐regression trees with random‐effects weights and a 0.5‐standard‐error pruning rule perform best. The required sample size for meta‐CART to achieve satisfactory performance depends on the number of study characteristics, the magnitude of the interactions, and the residual heterogeneity.     

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.     

A rating measure of ADHD-related neuropsychological impairment in children and adolescents: Data from the Cognition and Motivation in Everyday Life (CAMEL) Scale from population and clinical samples

A new parent-completed questionnaire, the Cognition and Motivation in Everyday Life (CAMEL) scale, was developed to provide a comprehensive assessment of neuropsychological impairment in children related to attention-deficit/hyperactivity disorder (ADHD) across diverse cognitive, motivational and energetic domains. Its psychometric properties were investigated. A total of 60 items were generated to cover a wide range of putative ADHD-related neuropsychological processes. A clinical (n = 142) and community (n = 810) sample of parents with children between 6 and 16 years of age completed the questionnaire. Data on ADHD symptoms were also collected with a commonly-used, validated parent rating scale to explore the associations between CAMEL scores and dimensional measures of child ADHD and conduct problems. Factor analysis identified six factors which we labeled (i) Cognition, (ii) Self-Direction and Organization, (iii) Effort Engagement, (iv) Arousal Regulation, (v) Motivational Responsiveness, and (vi) Cautiousness. Self-Direction and Organization and Arousal Regulation were the strongest predictors for ADHD symptomatology. Self-Direction and Organization was strongly associated with inattention and Arousal Regulation with hyperactivity-impulsivity symptoms. Parents distinguished between broad neuropsychological domains in reliable and plausible ways, making distinctions between key aspects of functioning. However, the boundaries between these domains did not map directly onto the distinctions drawn within traditional models of ADHD deficits. Further research is required to examine the predictive validity and cost-effectiveness of the CAMEL scale compared to direct objective testing using laboratory measures in predicting prognosis and treatment outcome.