Structural and Contextual Factors Regarding the Accessibility of Elective Office for Women of Color at the Local Level

Different from the majority of past research on gender gaps in political officeholding, we adopt an intersectionality framework and move beyond traditional individual factors to explore what implications certain elements in the political opportunity structure may have for women and men of color elected officials serving in county, municipal, and local school board offices. We argue that structural and contextual factors, such as type of institutions, election systems, and jurisdictional racial makeup, may influence the calculations women of color make concerning the accessibility of elective office. Using data from the Gender and Multiracial Leadership (GMCL) project, we find county offices to be the least, and seats on school boards the most, accessible to women of color. Of the types of electoral arrangements, we find multimember districts (MMD) to be the most significant variable predicting the likelihood of women of color in office. Although both Black and Latina women benefit from having a significant share of coethnics or nonwhites in jurisdictions, the two groups of women have an opposite relationship to their racial constituent makeup than that held by their male counterparts.     

A Note on High-Dimensional Linear Regression With Interactions

The problem of interaction selection in high-dimensional data analysis has recently received much attention. This note aims to address and clarify several fundamental issues in interaction selection for linear regression models, especially when the input dimension p is much larger than the sample size n. We first discuss how to give a formal definition of “importance” for main and interaction effects. Then we focus on two-stage methods, which are computationally attractive for high-dimensional data analysis but thus far have been regarded as heuristic. We revisit the counterexample of Turlach and provide new insight to justify two-stage methods from the theoretical perspective. In the end, we suggest new strategies for interaction selection under the marginality principle and provide some simulation results.     

Back propagation neural networks and multiple regressions in the case of heteroskedasticity

This paper compares the performance between regression analysis and a clustering based neural network approach when the data deviates from the homoscedasticity assumption of regression. Heteroskedasticity is a problem that arises in linear regression due to the unequal error variances. One of the methods to deal heteroskedasticity in classical regression theory is weighted least-square regression (WLS). In order to deal the problem of heteroskedasticity, backpropagation neural network is applied. In this context, an algorithm is proposed which is based on robust estimates of location and dispersion matrix that helps in preserving the error assumption of the linear regression. Analysis is carried out with appropriate designs using simulated data and the results are presented.     

Exponentially weighted moving average charts for correlated multivariate Poisson processes

In this article, we study exponentially weighted moving average (EWMA) control schemes to monitor the multivariate Poisson distribution with a general covariance structure, so that the practitioner can simultaneously monitor multiple correlated attribute processes more effectively. The statistical performance of the charts is assessed in terms of the run length properties and compared against other mainstream attribute control schemes. The application of the proposed methods to real-life and simulated datasets is demonstrated.     

Relatives in Residence: Relatedness of Household Members Drives Schooling Differentials in Mozambique

Children typically receive investments from their fathers, but absent fathers often invest at low levels. In a father's absence, what types of nonfathers invest heavily in children? This article investigates educational participation as a reflection of childhood investments on Ibo Island, Mozambique, where only one third of school‐aged children live with their biological fathers. Father‐present children generally attended school at the highest rates. Stepchildren and father‐absent relatives (e.g., grandchildren, nieces) attended school at comparably high rates if any coresiding children were father‐present. This may signal high altruism among present fathers toward some nonoffspring. Consistent with this result, a fixed effects model indicates that, within the same household, adult males invested equally in their own children, relatives, and stepchildren. Prejudicially lower investments were made in children who were unrelated to the household's adult males, however; this result has strong negative implications for the well‐being of African children fostered by nonrelatives.     

Beyond interviewer effects in the standardized measurement of ego-centric networks

The effects which interviewers exert on the collection of ego-centric networks have recently come into the focus of methodological considerations. Studies consistently show that the size of networks varies depending on the interviewer. We would like to expand on this research strand by pointing to different aspects which have so far gone unremarked in the discussion. First, size is mainly analysed as a network measure which is influenced during data collection, while other common measures such as network density or composition have not received sufficient consideration. Second, large-scale surveys using face-to-face interviews usually allocate interviewers to a single sampling point. Differences between sampling points (locality effects) are attributed to interviewer effects. Hence, we disentangle the effects of the locality and interviewer. Third, the discussion on interviewer effects often follows an “actor-oriented” consideration of how data collection situations are structured by interviewers. Expanding this approach from a relational perspective, we consider the relationship between the interviewers and respondents and whether this relationship influences the collection of network data. To test our hypotheses about the influence of interviewers, the locality and the interviewer-respondent relationship on different network measures, we use data from the 2010 German General Social Survey (n = 2827 respondents, n = 220 interviewers). The multilevel analyses show that the relationship between the interviewer and the respondent is not very relevant. Furthermore, the analyses show that interviewers have an influence on the network size but not on measures of their composition. However, evidence on the prevalence of locality or interviewer effects is mixed. Finally, homophilous interviewer-respondent relationships have very little effect on network characteristics. We find evidence of training and fatigue effects on network size. However, much of the variation in network size caused by the interviewer still remains unexplained. We draw conclusions on how to organize interview situations in surveys.     

Investor commitment to serial entrepreneurs: A multilayer network analysis

Social networks are complex systems composed of interdependent organizations and people with diverse network structures. Understanding network dynamics, such as exchange commitment, requires a methodological toolkit that does not assume away complexity. In this study, we extend a technique for analyzing longitudinal, multilayer network data called network alignment. We introduce a novel metric – intersect proportions – for analyzing similarity between divergent graphs. We demonstrate the application of network alignment and intersect proportions to the context of investor commitment to startups and entrepreneurs. Using this technique, we are able to disentangle exchange commitment across complex networks.