Sensitivity of MRQAP Tests to Collinearity and Autocorrelation Conditions

Multiple regression quadratic assignment procedures (MRQAP) tests are permutation tests for multiple linear regression model coefficients for data organized in square matrices of relatedness among n objects. Such a data structure is typical in social network studies, where variables indicate some type of relation between a given set of actors. We present a new permutation method (called “double semi-partialing”, or DSP) that complements the family of extant approaches to MRQAP tests. We assess the statistical bias (type I error rate) and statistical power of the set of five methods, including DSP, across a variety of conditions of network autocorrelation, of spuriousness (size of confounder effect), and of skewness in the data. These conditions are explored across three assumed data distributions: normal, gamma, and negative binomial. We find that the Freedman–Lane method and the DSP method are the most robust against a wide array of these conditions. We also find that all five methods perform better if the test statistic is pivotal. Finally, we find limitations of usefulness for MRQAP tests: All tests degrade under simultaneous conditions of extreme skewness and high spuriousness for gamma and negative binomial distributions. Special thanks go to Cajo Ter Braak, Philip Hans Franses, Patrick Houweling, Pierre Legendre, three anonymous reviewers, the associate editor, and the editor for comments.     

等级效标分数的概率神经网络预测方法研究

针对基于统计学的等级效标分数预测存在的问题,提出了运用概率神经网络进行等级效标分数预测的方法。在20种条件下进行了计算机模拟实验,包括测验分数为单变量和多变量,以及各种水平的白噪声干扰条件,结果表明在测验分数为多变量的情况下,或者在有白噪声干扰的条件下,运用概率神经网络方法可以比统计学方法更好地对等级效标分数进行预测。     

Conceptual Hierarchies in a Flat Attractor Network: Dynamics of Learning and Computations

The structure of people's conceptual knowledge of concrete nouns has traditionally been viewed as hierarchical ( Collins & Quillian, 1969 ). For example, superordinate concepts ( vegetable ) are assumed to reside at a higher level than basic-level concepts ( carrot ). A feature-based attractor network with a single layer of semantic features developed representations of both basic-level and superordinate concepts. No hierarchical structure was built into the network. In Experiment and Simulation 1, the graded structure of categories (typicality ratings) is accounted for by the flat attractor network. Experiment and Simulation 2 show that, as with basic-level concepts, such a network predicts feature verification latencies for superordinate concepts ( vegetable ). In Experiment and Simulation 3, counterintuitive results regarding the temporal dynamics of similarity in semantic priming are explained by the model. By treating both types of concepts the same in terms of representation, learning, and computations, the model provides new insights into semantic memory.     

Joint effects of social networks and information giving on innovative performance after organizational entry

We combine the social network approach and organizational socialization literature in order to examine the influence of social networks and proactive behavior on newcomers' innovative performance. A sample of new employees completed questionnaires on social network and proactive measures, and their supervisors rated their innovative performance. The results suggest that both sparse (low density) social networks and newcomers' information giving were related to innovative performance. The results also indicate that information giving moderated the relation between sparse social networks and innovative performance: when newcomers' information giving was high, the characteristics of their social networks were not related to their innovative performance.     

It is more than personal: Development and validation of a multidimensional privacy orientation scale

This study summarizes the development and validation of a multidimensional privacy orientation scale designed for measuring privacy attitudes of Social Network Site (SNS) users. Findings confirm the existence of four dimensions: (1) belief in the value of “privacy as a right”; (2) “other-contingent privacy”; (3) “concern about own informational privacy” and (4) “concern about privacy of others.” Moreover, a segmentation of SNS users based on these attitude scores reveals three types of users: (1) privacy advocates, who are concerned about both their own and other people’s privacy; (2) privacy individualists, who are concerned mostly about their own privacy, and (3) privacy indifferents, whose score on all dimensions are lower than other segments. The results indicate that the four privacy orientation dimensions and three user segments predict key differences in terms of privacy protective behavior, information disclosure, and viewing personal information of others.     

Integrating the Automatic and the Controlled: Strategies in Semantic Priming in an Attractor Network With Latching Dynamics

Semantic priming has long been recognized to reflect, along with automatic semantic mechanisms, the contribution of controlled strategies. However, previous theories of controlled priming were mostly qualitative, lacking common grounds with modern mathematical models of automatic priming based on neural networks. Recently, we introduced a novel attractor network model of automatic semantic priming with latching dynamics. Here, we extend this work to show how the same model can also account for important findings regarding controlled processes. Assuming the rate of semantic transitions in the network can be adapted using simple reinforcement learning, we show how basic findings attributed to controlled processes in priming can be achieved, including their dependency on stimulus onset asynchrony and relatedness proportion and their unique effect on associative, category‐exemplar, mediated and backward prime‐target relations. We discuss how our mechanism relates to the classic expectancy theory and how it can be further extended in future developments of the model.     

Dynamic Social Networks in Recovery Homes

Acute treatment aftercare in the form of sober living environments—i.e., recovery houses—provide an inexpensive and effective medium-term treatment alternative for many with substance use disorders. Limited evidence suggests that house-situated social relationships and associated social support are critical determinants of how successful these residential experiences are for their members, but little is known about the mechanisms underlying these relationships. This study explored the feasibility of using dynamic social network modeling to understand house-situated longitudinal associations among individual Alcoholics Anonymous related recovery behaviors, length of residence, dyadic interpersonal trust, and dyadic confidant relationship formation processes. Trust and confidant relationships were measured 3 months apart in U.S. urban-area recovery houses, all of which were part of a network of substance use recovery homes. A stochastic actor-based model was successfully estimated from this data set. Results suggest that confidant relationships are predicted by trust, while trust is affected by recovery behaviors and length of residence. Conceptualizing recovery houses as a set of independent, evolving social networks that can be modeled jointly appears to be a promising direction for research.     

The Individualised Patient‐Progress System: A decade of international collaborative networking

Background: The backstage of research projects is seldom presented to the general audience, in particular, how the research questions emerged, how the team interacted and how the methods and design were decided upon. Aim: In this paper we will recount the story behind an international practice‐based research network for personalising health assessment, the joint journey of researchers and therapists from three different countries in creating the Individualised Patient‐Progress System (IPPS). Results: We first describe how the idea of the IPPS emerged; then we show how IPPS is being piloted and integrated into clinical practice. Next, we present the views of therapists and researchers who have been part of this project. Conclusions: We discuss some lessons learned from this 14‐year collaborative research programme.     

How school-based dancing classes change affective and collaborative networks of adolescents

ObjectiveThe present research investigated the effects of adolescents' co-participation in a federally funded dance intervention project on students' affective and collaborative networks. In the intervention, students instructed by professional dancers collaboratively developed a dance-choreography during regular class hours in student groups. We expected that the number of reciprocated affective and collaborative ties should increase in classrooms participating in the intervention, but that boys should particularly benefit from the intervention.DesignWe used a quasi-experimental untreated control group design with dependent pretest and posttest samples. Participants were 421 youths (48% boys) in 23 classrooms of primary and secondary schools in Berlin, Germany. Of these, 226 (54%) participated in the intervention. Classrooms from the same grade and school were recruited as comparison groups. We assessed both networks using sociometric questionnaires.Method/ResultsWe found a significant Treatment × Gender interaction showing that reciprocated collaborative relations increased only for boys in the intervention group. Analyses probing potential mechanisms showed this was due to their choosing more collaboration partners, in particular more girls.ConclusionFindings suggest that school-based dancing programs encouraging coordinated physical activity in student groups may be particularly beneficial for boys, encouraging them to consider girls as academic cooperation partners and to proactively develop their collaborative networks.