The application of network meta-analysis is becoming increasingly widespread, and for a successful implementation, it requires that the direct comparison result and the indirect comparison result should be consistent. Because of this, a proper detection of inconsistency is often a key issue in network meta-analysis as whether the results can be…

An important factor for First Generation High School students (FGS) in higher education is social capital. To highlight differences in social capital between FGS and their Non-FGS peers (NFGS) by analysing the structure of their ego-centred social networks and its' effect on their career planning, we conducted two cross-sectional studies: on high…

The current study compares estimates of peer influence from an analytic approach that explicitly address network processes with those from traditional approaches that do not. Using longitudinal network data from the PROmoting School-community-university Partnerships to Enhance Resilience peers project, we obtain estimates of social influence on…

Like learning analytics, physics education research is a relatively young field that draws on perspectives from multiple disciplines. Network analysis has an even more heterodox perspective, with roots in mathematics, sociology, and, more recently, computer science and physics. This paper reviews how network analysis has been used in physics…

Social comparisons with peers are important sources of self-development during adolescence. Many previous studies showed that students' academic self-concepts (ASC) form by contrasting one's own achievement with the average of one's class or school (the Big-Fish-Little-Pond Effect [BFLPE]). Based on social comparison theory, however, we would…

Traditional visualization in meta-analysis uses forest plots to illustrate the combined treatment effect, along with the respective results from primary trials. While the purpose of visualization is clear in the pairwise setting, additional treatments broaden the focus and extend the results to be illustrated in network meta-analysis. The…

The recent popularity of models that capture the dynamic coevolution of both network structure and behavior has driven the need for summary indices to assess the adequacy of these models to reproduce dynamic properties of scientific or practical importance. Whereas there are several existing indices for assessing the ability of the model to…

The purpose of this study is to determine the variables that affect career transition levels of Turkish and U.S. participants. For this purpose, 336 (n[subscript U.S.] = 151; n[subscript Turkey] = 185) participants who reported their socio-economic level as middle experiencing career transition at least for 2-3 months are included in the study.…

This study shows that residual variation can cause problems related to scaling in exponential random graph models (ERGM). Residual variation is likely to exist when there are unmeasured variables in a model--even those uncorrelated with other predictors--or when the logistic form of the model is inappropriate. As a consequence, coefficients cannot…

The current study investigated whether the difficulty in finding group differences in prosody between speakers with autism spectrum disorder (ASD) and neurotypical (NT) speakers might be explained by identifying different acoustic profiles of speakers which, while still perceived as atypical, might be characterized by different acoustic qualities.…

Component network meta-analysis (CNMA) compares treatments comprising multiple components and estimates the effects of individual components. For network meta-analysis, a standard network plot with nodes for treatments and edges for direct comparisons between treatments is drawn to visualize the evidence structure and the connections between…

Increased levels of internationalization have led to individuals working in multicultural organizations, a trend that is likely to continue for the foreseeable future. To navigate these environments successfully, more emphasis is being placed on the importance of higher education in preparing and arming the future workforce with the international…

Understanding student cognitive states is essential for assessing human learning. The deep neural networks (DNN)-inspired cognitive state prediction method improved prediction performance significantly; however, the lack of explainability with DNNs and the unitary scoring approach fail to reveal the factors influencing human learning. Identifying…

Diffusion can contribute to the spread of preventive intervention effects from participants to non-participants, but best practices for randomized trials prevent contamination of conditions. These practices conflict with cultural values of community benefit, which are salient among American Indians. This study embedded social network measures…

Motivation helps drive students to success in general chemistry, and active learning environments with social interactions has consistently shown to improve motivation. However, analyzing student outcomes in an interactive environment is best done by considering students not as isolated units but as working together and influencing each other.…