The Role of Independence and Stationarity in Probabilistic Models of Binary Choice

After more then 50 years of probabilistic choice modeling in economics, marketing, political science, psychology, and related disciplines, theoretical and computational advances give scholars access to a sophisticated array of modeling and inference resources. We review some important, but perhaps often overlooked, properties of major classes of probabilistic choice models. For within‐respondent applications, we discuss which models require repeated choices by an individual to be independent and response probabilities to be stationary. We show how some model classes, but not others, are invariant over variable preferences, variable utilities, or variable choice probabilities. These models, but not others, accommodate pooling of responses or averaging of choice proportions within participant when underlying parameters vary across observations. These, but not others, permit pooling/averaging across respondents in the presence of individual differences. We also review the role of independence and stationarity in statistical inference, including for probabilistic choice models that, themselves, do not require those properties. Copyright © 2017 John Wiley & Sons, Ltd.     

Simmering in the Soviet pot: language heterogeneity in early Soviet socio-linguistics

At the beginning of the ’30s—the period of lively debates on the relation between language and society—one of the main issues in linguistics was language heterogeneity. On the example of the texts by Boris Larin, Georgij Danilov and Lev Jakubinskij we shall compare two attitudes about unity and division of a language. If the studies by Larin and Danilov in various ways establish divisions in society and language at the end of the ’20s, in the ’30s there is a marked tendency to recognize language unity and the cohesiveness of the proletarian society, as seen in socio-linguistic analyses by Jakubinskij. The conclusion, suggested at the end of this exposition, claims that the idea of one national language grows in importance in the discourse of the Soviet linguistics at the beginning of 1930s. Disappearance of the contemporary language heterogeneity in the discourse of Soviet linguists of the period corroborates how linguistics adapts to the political conceptions of society.

团队多样性与组织支持对团队创造力的影响

采用实验法在专业异质性与群体断层两个水平上操纵团队多样性, 通过两个实验考察团队多样性与组织支持的交互作用对团队创造力的影响。研究结果表明：(1)在独创性维度上, 专业异质团队在工具支持条件下的独创性显著高于专业同质团队, 情感和物质支持条件下二者差异不显著。当团队出现群体断层时, 情感支持与工具支持具有同样的促进作用, 即在这两种组织支持下, 强断层团队的独创性显著高于弱断层团队; 物质支持条件下二者差异不显著。(2)在适宜性维度上, 组织支持的主效应显著, 两个实验一致地发现物质支持条件下的适宜性显著高于情感支持和工具支持。本研究从团队多样性与组织支持交互作用的角度考察不同复杂程度、不同组织目标的多样性团队所需要的组织支持条件, 对促进团队创造力具有一定的理论与实践价值。     

Heterogeneity Coefficients for Mahalanobis' D as a Multivariate Effect Size

The Mahalanobis distance D is the multivariate generalization of Cohen's d and can be used as a standardized effect size for multivariate differences between groups. An important issue in the interpretation of D is heterogeneity, that is, the extent to which contributions to the overall effect size are concentrated in a small subset of variables rather than evenly distributed across the whole set. Here I present two heterogeneity coefficients for D based on the Gini coefficient, a well-known index of inequality among values of a distribution. I discuss the properties and limitations of the two coefficients and illustrate their use by reanalyzing some published findings from studies of gender differences.     

高自尊的异质性研究述评

自尊不只有高低之分,很多研究者分别从实证研究的结果矛盾性、对自尊的概念及其测量方法的理论思考等方面论述了高自尊异质性存在的可能性,并分别从不同角度提出了高自尊的不同类型及其测量方法,如,不稳定的高自尊与稳定的高自尊、防御的高自尊与真诚的高自尊、相倚的高自尊与真正的高自尊、高外显低内隐自尊与高外显高内隐自尊、脆弱的高自尊与安全的高自尊等,其测量方法也因不同类型而不同。这些研究均说明高自尊存在异质性,需在未来的自尊研究中加以注意     

自尊与攻击行为的关系述评

传统观点认为低自尊容易导致攻击,但最近此观点遭到置疑。一些实验证实了低自尊者容易陷入被欺负的境地;Salmivalli等认为某些高自尊的人更具攻击性,而另一些却没有;Bushman和Baumeister等认为自尊与攻击之间没什么联系,但自恋与攻击有高相关。总之,目前自尊与攻击的关系还很难明了,但很多结论都证实了自我中心被威胁学说和高自尊异质性学说。     

Territorial sense of community,ethnic prejudice and political orientation

This research focused on territorial sense of community and ethnic prejudice. In particular, we related these two constructs and investigated their relationship to individual characteristics (personal political orientation, contact with immigrants, etc.) and community‐level ones (such as ethnic composition and the political orientation of the local government). Two studies were conducted in four small towns in central and southern Italy. For each study, 160 people were contacted. The instruments used were a questionnaire, the Italian Sense of Community Scale and the Blatant Prejudice Scale. The results showed that territorial sense of community is not related to ethnic prejudice or to the ethnic heterogeneity of the territory. It was also found that people with right‐wing political sympathies revealed higher levels of prejudice than people with left‐wing sympathies. Finally, in a town with a stable and consolidated left‐wing orientation of the local government we found, when the influence of personal political orientation was controlled for, lower levels of prejudice than in a town with a right‐wing orientation. Copyright © 2008 John Wiley & Sons, Ltd.     

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.     

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.